[](https://zenodo.org/badge/latestdoi/174428856)
[](https://badge.fury.io/py/bioinfokit)
[](https://pepy.tech/project/bioinfokit)
[](https://travis-ci.org/reneshbedre/bioinfokit)
[](https://anaconda.org/bioconda/bioinfokit)
<!--
[](https://www.buymeacoffee.com/renesh)
-->
The bioinfokit toolkit aimed to provide various easy-to-use functionalities to analyze,
visualize, and interpret the biological data generated from genome-scale omics experiments.
**<span style="color:#33a8ff">How to install:</span>**
bioinfokit requires
- Python 3
- NumPy
- scikit-learn
- seaborn
- pandas
- matplotlib
- SciPy
- matplotlib_venn
bioinfokit can be installed using pip, easy_install and git.
latest bioinfokit version: [](https://badge.fury.io/py/bioinfokit)
Install using <a href="https://pip.pypa.io/en/stable/installing/" target="_blank">pip</a> for Python 3 (easiest way)
```python
# install
pip install bioinfokit
# upgrade to latest version
pip install bioinfokit --upgrade
# uninstall
pip uninstall bioinfokit
```
Install using <a href="https://setuptools.readthedocs.io/en/latest/easy_install.html" target="_blank">easy_install</a> for Python 3 (easiest way)
```python
# install latest version
easy_install bioinfokit
# specific version
easy_install bioinfokit==0.3
# uninstall
pip uninstall bioinfokit
```
Install using <a href="https://docs.conda.io/en/latest/" target="_blank">conda</a>
```python
conda install -c bioconda bioinfokit
```
Install using <a href="https://git-scm.com/book/en/v2/Getting-Started-Installing-Git" target="_blank">git</a>
```python
# download and install bioinfokit (Tested on Linux, Mac, Windows)
git clone https://github.com/reneshbedre/bioinfokit.git
cd bioinfokit
python setup.py install
```
### Check the version of bioinfokit
```python
>>> import bioinfokit
>>> bioinfokit.__version__
'0.4'
```
## How to cite bioinfokit?
- Renesh Bedre. (2020, March 5). reneshbedre/bioinfokit: Bioinformatics data analysis and visualization toolkit. Zenodo. http://doi.org/10.5281/zenodo.3698145.
- Additionally check <a href='https://zenodo.org/record/3841708#.XyCfi-dOmUk' target='_blank'>Zenodo</a> to cite specific version of bioinfokit
## Support
If you enjoy bioinfokit, consider supporting me,
<a href="https://www.buymeacoffee.com/renesh" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a>
# Getting Started
## Gene expression analysis
### Volcano plot
latest update v2.0.8
`bioinfokit.visuz.GeneExpression.volcano(df, lfc, pv, lfc_thr, pv_thr, color, valpha, geneid, genenames, gfont, dim, r, ar,
dotsize, markerdot, sign_line, gstyle, show, figtype, axtickfontsize, axtickfontname, axlabelfontsize,
axlabelfontname, axxlabel, axylabel, xlm, ylm, plotlegend, legendpos, figname, legendanchor, legendlabels, theme)`
Parameters | Description
------------ | -------------
`df` |Pandas dataframe table having atleast gene IDs, log fold change, P-values or adjusted P-values columns
`lfc` | Name of a column having log or absolute fold change values [string][default:logFC]
`pv` | Name of a column having P-values or adjusted P-values [string][default:p_values]
`lfc_thr` | Log fold change cutoff for up and downregulated genes [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list][default:(1.0, 1.0)]
`pv_thr` | p value or adjusted p value cutoff for up and downregulated genes [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list][default:(0.05, 0.05)]
`color` | [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of three colors [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list][default: color=("green", "grey", "red")]
`valpha` | Transparency of points on volcano plot [float (between 0 and 1)][default: 1.0]
`geneid` | Name of a column having gene Ids. This is necessary for plotting gene label on the points [string][default: None]
`genenames` | [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of gene Ids to label the points. The gene Ids must be present in the geneid column. If this option set to "deg" it will label all genes defined by lfc_thr and pv_thr [string, tuple, dict][default: None]
`gfont` | Font size for genenames [float][default: 10.0]. gfont not compatible with gstyle=2.
`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (5, 5)]
`r` | Figure resolution in dpi [int][default: 300]. Not compatible with `show`= True
`ar` | Rotation of X and Y-axis ticks labels [float][default: 90]
`dotsize`| The size of the dots in the plot [float][default: 8]
`markerdot` | Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: "o"]
`sign_line` | Show grid lines on plot with defined log fold change (`lfc_thr`) and P-value (`pv_thr`) threshold value [True or False][default:False]
`gstyle` | Style of the text for genenames. 1 for default text and 2 for box text [int][default: 1]
`show` | Show the figure on console instead of saving in current folder [True or False][default:False]
`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
`axtickfontsize` | Font size for axis ticks [float][default: 9]
`axtickfontname` | Font name for axis ticks [string][default: 'Arial']
`axlabelfontsize` | Font size for axis labels [float][default: 9]
`axlabelfontname` | Font name for axis labels [string][default: 'Arial']
`axxlabel` | Label for X-axis. If you provide this option, default label will be replaced [string][default: None]
`axylabel` | Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]
`xlm` | Range of ticks to plot on X-axis [float (left, right, interval)][default: None]
`ylm` | Range of ticks to plot on Y-axis [float (bottom, top, interval)][default: None]
`plotlegend` | plot legend on volcano plot [True or False][default:False]
`legendpos` | position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:"best"]
`figname` | name of figure [string ][default:"volcano"]
`legendanchor` | position of the legend outside of the plot. For more options see bbox_to_anchor parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [list][default:None]
`legendlabels` | legend label names. If you provide custom label names keep the same order of label names as default [list][default:['significant up', 'not significant', 'significant down']]
`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']
Returns:
Volcano plot image in same directory (volcano.png)
<a href="https://reneshbedre.com/blog/volcano.html" target="_blank">Working example</a>
### Inverted Volcano plot
latest update v2.0.8
`bioinfokit.visuz.GeneExpression.involcano(table, lfc, pv, lfc_thr, pv_thr, color, valpha, geneid, genenames, gfont, gstyle,
dotsize, markerdot, r, dim, show, figtype, axxlabel, axylabel, axlabelfontsize, axtickfontsize,
axtickfontname, plotlegend, legendpos, legendanchor, figname, legendlabels, ar, theme)`
Parameters | Description
------------ | -------------
`table` |Pandas dataframe table having atleast gene IDs, log fold change, P-values or adjusted P-values
`lfc` | Name of a column having log fold change values [default:logFC]
`pv` | Name of a column having P-values or adjusted P-values [default:p_values]
`lfc_thr` | Log fold change cutoff for up and downregulated genes [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list] [default:(1.0, 1.0)]
`pv_thr` | p value or adjusted p value cutoff for up and downregulated genes [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list] [default:(0.05, 0.05)]
`color` | [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of three colors [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list][default: color=("green", "grey", "red")]
`valpha` | Transparency of points on volcano plot [float (between 0 and 1)][default: 1.0]
`geneid` | Name of a column having gene Ids. This is necessary for plotting gene label on the points [string][default: None]
`genenames` | [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of gene Ids to label the points. The gene Ids must be present in the geneid column. If this option set to "deg" it will label all genes defined by lfc_thr and pv_thr [string, [Tuple](https://www.reneshbedre.com/blog/python-tuples.html), dict][default: None]
`gfont` | Font size for genenames [float][default: 10.0]
`gstyle` | Style of the text for genenames. 1 for default text and 2 for box text [int][default: 1]
`dotsize`| The size of the dots in the plot [float][default: 8]
`markerdot` | Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: "o"]
`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (5, 5)]
`r` | Figure resolution in dpi [int][default: 300]. Not compatible with `show`= True
`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
`show` | Show the figure on console instead of saving in current folder [True or False][default:False]
`axxlabel` | Label for X-axis. If you provide this option, default label will be replaced [string][default: None]
`axylabel` | Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]
`axlabelfontsize` | Font size for axis labels [float][default: 9]
`axtickfontsize` | Font size for axis ticks [float][default: 9]
`axtickfontname` | Font name for axis ticks [string][default: 'Arial']
`plotlegend` | plot legend on inverted volcano plot [True or False][default:False]
`legendpos` | position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:"best"]
`legendanchor` | position of the legend outside of the plot. For more options see bbox_to_anchor parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [list][default:None]
`figname` | name of figure [string ][default:"involcano"]
`legendlabels` | legend label names. If you provide custom label names keep the same order of label names as default [list][default:['significant up', 'not significant', 'significant down']]
`ar` | Rotation of X and Y-axis ticks labels [float][default: 90]
`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']
Returns:
Inverted volcano plot image in same directory (involcano.png)
<a href="https://reneshbedre.github.io/blog/volcano.html" target="_blank">Working example</a>
### MA plot
latest update v2.0.7
`bioinfokit.visuz.GeneExpression.ma(df, lfc, ct_count, st_count, pv, basemean, lfc_thr, color, dim, dotsize, show, r, valpha, figtype, axxlabel,
axylabel, axlabelfontsize, axtickfontsize, axtickfontname, xlm, ylm, fclines, fclinescolor, legendpos, legendanchor,
figname, legendlabels, plotlegend, ar, theme, geneid, genenames, gfont, gstyle, title)`
Parameters | Description
------------ | -------------
`df` | Pandas dataframe table having atleast gene IDs, log fold change, and normalized counts (control and treatment) columns
`lfc` | Name of a column having log fold change values [default:"logFC"]
`ct_count` | Name of a column having count values for control sample.Ignored if basemean provided [default:"value1"]
`st_count` | Name of a column having count values for treatment sample. Ignored if basemean provided [default:"value2"]
`pv` | Name of a column having _p_ values or adjusted _p_ values
`basemean` | Basemean (mean of normalized counts) from DESeq2 results
`lfc_thr` | Log fold change cutoff for up and downregulated genes [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list][default:(1.0, 1.0)]
`color` | [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of three colors [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list][default: ("green", "grey", "red")]
`dotsize`| The size of the dots in the plot [float][default: 8]
`markerdot` | Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: "o"]
`valpha` | Transparency of points on plot [float (between 0 and 1)][default: 1.0]
`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (5, 5)]
`r` | Figure resolution in dpi [int][default: 300]. Not compatible with `show`= True
`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
`show` | Show the figure on console instead of saving in current folder [True or False][default:False]
`axxlabel` | Label for X-axis. If you provide this option, default label will be replaced [string][default: None]
`axylabel` | Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]
`axlabelfontsize` | Font size for axis labels [float][default: 9]
`axtickfontsize` | Font size for axis ticks [float][default: 9]
`axtickfontname` | Font name for axis ticks [string][default: 'Arial']
`xlm` | Range of ticks to plot on X-axis [float (left, right, interval)][default: None]
`ylm` | Range of ticks to plot on Y-axis [float (bottom, top, interval)][default: None]
`fclines` | draw log fold change threshold lines as defines by `lfc` [True or False][default:False]
`fclinescolor` | color of fclines [string][default: '#2660a4']
`plotlegend` | plot legend on MA plot [True or False][default:False]
`legendpos` | position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:"best"]
`legendanchor` | position of the legend outside of the plot. For more options see bbox_to_anchor parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [list][default:None]
`figname` | name of figure [string ][default:"ma"]
`legendlabels` | legend label names. If you provide custom label names keep the same order of label names as default [list][default:['significant up', 'not significant', 'significant down']]
`ar` | Rotation of X and Y-axis ticks labels [float][default: 90]
`theme` | Change background theme. If theme set to `dark_background`, the dark background will be produced instead of default white. See more themes [here](https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html) [string][default:'None']
`geneid` | Name of a column having gene Ids. This is necessary for plotting gene label on the points [string][default: None]
`genenames` | [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of gene Ids to label the points. The gene Ids must be present in the geneid column. If this option set to "deg" it will label all genes defined by lfc_thr and pv_thr [string, [Tuple](https://www.reneshbedre.com/blog/python-tuples.html), dict][default: None]
`gfont` | Font size for genenames [float][default: 10.0]
`gstyle` | Style of the text for genenames. 1 for default text and 2 for box text [int][default: 1]
`title` | Add main title to the plot [string][default: None]
Returns:
MA plot image in same directory (ma.png)
<a href="https://www.reneshbedre.com/blog/ma.html" target="_blank">Working example</a>
### Heatmap
`latest update v2.0.1`
`bioinfokit.visuz.gene_exp.hmap(table, cmap='seismic', scale=True, dim=(6, 8), rowclus=True, colclus=True, zscore=None,
xlabel=True, ylabel=True, tickfont=(12, 12), show, r, figtype, figname, theme)`
Parameters | Description
------------ | -------------
`file` | CSV delimited data file. It should not have NA or missing values
`cmap` | Color Palette for heatmap [string][default: 'seismic']
`scale` | Draw a color key with heatmap [boolean (True or False)][default: True]
`dim` | heatmap figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 8)]
`rowclus` | Draw hierarchical clustering for rows [boolean (True or False)][default: True]
`colclus` | Draw hierarchical clustering for columns [boolean (True or False)][default: True]
`zscore` | Z-score standardization of row (0) or column (1). It works when clus is True. [None, 0, 1][default: None]
`xlabel` | Plot X-label [boolean (True or False)][default: True]
`ylabel` | Plot Y-label [boolean (True or False)][default: True]
`tickfont` | Fontsize for X and Y-axis tick labels [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats][default: (14, 14)]
`show` | Show the figure on console instead of saving in current folder [True or False][default:False]
`r` | Figure resolution in dpi [int][default: 300]. Not compatible with `show`= True
`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
`figname` | name of figure [string ][default:"heatmap"]
`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']
Returns:
heatmap plot (heatmap.png, heatmap_clus.png)
<a href="https://www.reneshbedre.com/blog/heatmap-python.html" target="_blank">Working example</a>
## Clustering analysis
### Scree plot
`latest update v2.0.1`
`bioinfokit.visuz.cluster.screeplot(obj, axlabelfontsize, axlabelfontname, axxlabel, axylabel,
figtype, r, show, dim, theme)`
Parameters | Description
------------ | -------------
`obj` | list of component name and component variance
`axlabelfontsize` | Font size for axis labels [float][default: 9]
`axlabelfontname` | Font name for axis labels [string][default: 'Arial']
`axxlabel` | Label for X-axis. If you provide this option, default label will be replaced [string][default: None]
`axylabel` | Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]
`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
`r` | Figure resolution in dpi [int][default: 300]
`show` | Show the figure on console instead of saving in current folder [True or False][default:False]
`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]
`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']
Returns:
Scree plot image (screeplot.png will be saved in same directory)
<a href="https://www.reneshbedre.com/blog/principal-component-analysis.html" target="_blank">Working Example</a>
### Principal component analysis (PCA) loadings plots
`latest update v2.0.1`
`bioinfokit.visuz.cluster.pcaplot(x, y, z, labels, var1, var2, var3, axlabelfontsize, axlabelfontname,
figtype, r, show, plotlabels, dim, theme)`
Parameters | Description
------------ | -------------
`x` | loadings (correlation coefficient) for principal component 1 (PC1)
`y` | loadings (correlation coefficient) for principal component 2 (PC2)
`z` | loadings (correlation coefficient) for principal component 3 (PC2)
`labels` | original variables labels from dataframe used for PCA
`var1` | Proportion of PC1 variance [float (0 to 1)]
`var2` | Proportion of PC2 variance [float (0 to 1)]
`var3` | Proportion of PC3 variance [float (0 to 1)]
`axlabelfontsize` | Font size for axis labels [float][default: 9]
`axlabelfontname` | Font name for axis labels [string][default: 'Arial']
`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
`r` | Figure resolution in dpi [int][default: 300]
`show` | Show the figure on console instead of saving in current folder [True or False][default:False]
`plotlabels` | Plot labels as defined by labels parameter [True or False][default:True]
`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]
`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']
Returns:
PCA loadings plot 2D and 3D image (pcaplot_2d.png and pcaplot_3d.png will be saved in same directory)
<a href="https://www.reneshbedre.com/blog/principal-component-analysis.html" target="_blank">Working Example</a>
### Principal component analysis (PCA) biplots
`latest update v2.0.2`
`bioinfokit.visuz.cluster.biplot(cscore, loadings, labels, var1, var2, var3, axlabelfontsize, axlabelfontname,
figtype, r, show, markerdot, dotsize, valphadot, colordot, arrowcolor, valphaarrow, arrowlinestyle, arrowlinewidth,
centerlines, colorlist, legendpos, datapoints, dim, theme)`
Parameters | Description
------------ | -------------
`cscore` | principal component scores (obtained from PCA().fit_transfrom() function in sklearn.decomposition)
`loadings` | loadings (correlation coefficient) for principal components
`labels` | original variables labels from dataframe used for PCA
`var1` | Proportion of PC1 variance [float (0 to 1)]
`var2` | Proportion of PC2 variance [float (0 to 1)]
`var3` | Proportion of PC3 variance [float (0 to 1)]
`axlabelfontsize` | Font size for axis labels [float][default: 9]
`axlabelfontname` | Font name for axis labels [string][default: 'Arial']
`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
`r` | Figure resolution in dpi [int][default: 300]
`show` | Show the figure on console instead of saving in current folder [True or False][default:False]
`markerdot` | Shape of the dot on plot. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: "o"]
`dotsize`| The size of the dots in the plot [float][default: 6]
`valphadot` | Transparency of dots on plot [float (between 0 and 1)][default: 1]
`colordot` | Color of dots on plot [string or list ][default:"#4a4e4d"]
`arrowcolor` | Color of the arrow [string ][default:"#fe8a71"]
`valphaarrow` | Transparency of the arrow [float (between 0 and 1)][default: 1]
`arrowlinestyle` | line style of the arrow. check more styles at https://matplotlib.org/3.1.0/gallery/lines_bars_and_markers/linestyles.html [string][default: '-']
`arrowlinewidth`| line width of the arrow [float][default: 1.0]
`centerlines`| draw center lines at x=0 and y=0 for 2D plot [bool (True or False)][default: True]
`colorlist` | list of the categories to assign the color [list][default:None]
`legendpos` | position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:"best"]
`datapoints`| plot data points on graph [bool (True or False)][default: True]
`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]
`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']
Returns:
PCA biplot 2D and 3D image (biplot_2d.png and biplot_3d.png will be saved in same directory)
<a href="https://www.reneshbedre.com/blog/principal-component-analysis.html" target="_blank">Working Example</a>
### t-SNE plot
`latest update v2.0.1`
`bioinfokit.visuz.cluster.tsneplot(score, colorlist, axlabelfontsize, axlabelfontname,
figtype, r, show, markerdot, dotsize, valphadot, colordot, dim, figname, legendpos,
legendanchor, theme)`
Parameters | Description
------------ | -------------
`score` | t-SNE component embeddings (obtained from TSNE().fit_transfrom() function in sklearn.manifold)
`colorlist` | list of the categories to assign the color [list][default:None]
`axlabelfontsize` | Font size for axis labels [float][default: 9]
`axlabelfontname` | Font name for axis labels [string][default: 'Arial']
`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
`r` | Figure resolution in dpi [int][default: 300]
`show` | Show the figure on console instead of saving in current folder [True or False][default:False]
`markerdot` | Shape of the dot on plot. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: "o"]
`dotsize`| The size of the dots in the plot [float][default: 6]
`valphadot` | Transparency of dots on plot [float (between 0 and 1)][default: 1]
`colordot` | Color of dots on plot [string or list ][default:"#4a4e4d"]
`legendpos` | position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:"best"]
`legendanchor` | position of the legend outside of the plot. For more options see bbox_to_anchor parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [list][default:None]
`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]
`figname` | name of figure [string ][default:"tsne_2d"]
`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']
Returns:
t-SNE 2D image (tsne_2d.png will be saved in same directory)
<a href="https://www.reneshbedre.com/blog/tsne.html" target="_blank">Working Example</a>
## Normalization
### RPM or CPM normalization
`latest update v0.8.9`
Normalize raw gene expression counts into Reads per million mapped reads (RPM) or Counts per million mapped reads (CPM)
`bioinfokit.analys.norm.cpm(df)`
Parameters | Description
------------ | -------------
`df` | Pandas dataframe containing raw gene expression values. Genes with missing expression values (NA) will be dropped.
Returns:
RPM or CPM normalized Pandas dataframe as class attributes (cpm_norm)
<a href="https://www.reneshbedre.com/blog/expression_units.html#rpm-or-cpm-reads-per-million-mapped-reads-or-counts-per-million-mapped-reads-" target="_blank">Working Example</a>
### RPKM or FPKM normalization
`latest update v0.9`
Normalize raw gene expression counts into Reads per kilo base per million mapped reads (RPKM) or
Fragments per kilo base per million mapped reads (FPKM)
`bioinfokit.analys.norm.rpkm(df, gl)`
Parameters | Description
------------ | -------------
`df` | Pandas dataframe containing raw gene expression values. Genes with missing expression or gene length values (NA) will be dropped.
`gl` | Name of a column having gene length in bp [string][default: None]
Returns:
RPKM or FPKM normalized Pandas dataframe as class attributes (rpkm_norm)
<a href="https://www.reneshbedre.com/blog/expression_units.html#rpkm-reads-per-kilo-base-per-million-mapped-reads" target="_blank">Working Example</a>
### TPM normalization
`latest update v0.9.1`
Normalize raw gene expression counts into Transcript per million (TPM)
`bioinfokit.analys.norm.tpm(df, gl)`
Parameters | Description
------------ | -------------
`df` | Pandas dataframe containing raw gene expression values. Genes with missing expression or gene length values (NA) will be dropped.
`gl` | Name of a column having gene length in bp [string][default: None]
Returns:
TPM normalized Pandas dataframe as class attributes (tpm_norm)
<a href="https://www.reneshbedre.com/blog/expression_units.html#tpm-transcript-per-million" target="_blank">Working Example</a>
## Variant analysis
### Manhattan plot
`latest update v2.0.1`
`bioinfokit.visuz.marker.mhat(df, chr, pv, log_scale, color, dim, r, ar, gwas_sign_line, gwasp, dotsize, markeridcol,
markernames, gfont, valpha, show, figtype, axxlabel, axylabel, axlabelfontsize, ylm, gstyle, figname, theme)`
Parameters | Description
------------ | -------------
`df` |Pandas dataframe object with atleast SNP, chromosome, and P-values columns
`chr` | Name of a column having chromosome numbers [string][default:None]
`pv` | Name of a column having P-values. Must be numeric column [string][default:None]
`log_scale` | Change the values provided in `pv` column to minus log10 scale. If set to `False`, the original values in `pv` will be used. This is useful in case of Fst values. [Boolean (True or False)][default:True]
`color` | List the name of the colors to be plotted. It can accept two alternate colors or the number colors equal to chromosome number. If nothing (None) provided, it will randomly assign the color to each chromosome [list][default:None]
`gwas_sign_line` |Plot statistical significant threshold line defined by option `gwasp` [Boolean (True or False)][default: False]
`gwasp` | Statistical significant threshold to identify significant SNPs [float][default: 5E-08]
`dotsize`| The size of the dots in the plot [float][default: 8]
`markeridcol` | Name of a column having SNPs. This is necessary for plotting SNP names on the plot [string][default: None]
`markernames` | The list of the SNPs to display on the plot. These SNP should be present in SNP column. Additionally, it also accepts the dict of SNPs and its associated gene name. If this option set to True, it will label all SNPs with P-value significant score defined by `gwasp` [string, list, [Tuple](https://www.reneshbedre.com/blog/python-tuples.html), dict][default: True]
`gfont` | Font size for SNP names to display on the plot [float][default: 8]. gfont not compatible with gstyle=2.
`valpha` | Transparency of points on plot [float (between 0 and 1)][default: 1.0]
`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]
`r` | Figure resolution in dpi [int][default: 300]
`ar` | Rotation of X-axis labels [float][default: 90]
`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
`show` | Show the figure on console instead of saving in current folder [Boolean (True or False)][default:False]
`axxlabel` | Label for X-axis. If you provide this option, default label will be replaced [string][default: None]
`axylabel` | Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]
`axlabelfontsize` | Font size for axis labels [float][default: 9]
`ylm` | Range of ticks to plot on Y-axis [float [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) (bottom, top, interval)][default: None]
`gstyle` | Style of the text for markernames. 1 for default text and 2 for box text [int][default: 1]
`figname` | name of figure [string][default:"manhattan"]
`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']
Returns:
Manhattan plot image in same directory (Manhattan.png)
<a href="https://www.reneshbedre.com/blog/manhattan-plot.html" target="_blank">Working example</a>
### Variant annotation
latest update v0.9.3
Assign genetic features and function to the variants in VCF file
`bioinfokit.analys.marker.vcf_anot(file, id, gff_file, anot_attr)`
Parameters | Description
------------ | -------------
`file` | VCF file
`id` | chromosome id column in VCF file [string][default='#CHROM']
`gff_file` | GFF3 genome annotation file
`anot_attr` | Gene function tag in attributes field of GFF3 file
Returns:
Tab-delimited text file with annotation (annotated text file will be saved in same directory)
<a href="https://reneshbedre.com/blog/vcfanot.html" target="_blank">Working Example</a>
### Concatenate VCF files
latest update v0.9.4
Concatenate multiple VCF files into single VCF file (for example, VCF files for each chromosome)
`bioinfokit.analys.marker.concatvcf(file)`
Parameters | Description
------------ | -------------
`file` | Multiple vcf files separated by comma
Returns:
Concatenated VCF file (concat_vcf.vcf)
<a href="https://www.reneshbedre.com/blog/mergevcf.html" target="_blank">Working example</a>
### Split VCF file
`bioinfokit.analys.marker.splitvcf(file)`
Split single VCF file containing variants for all chromosomes into individual file containing variants for each chromosome
Parameters | Description
------------ | -------------
`file` | VCF file to split
`id` | chromosome id column in VCF file [string][default='#CHROM']
Returns:
VCF files for each chromosome
<a href="https://www.reneshbedre.com/blog/mergevcf.html" target="_blank">Working example</a>
## High-throughput sequence analysis
### FASTQ batch downloads from SRA database
latest update v0.9.7
`bioinfokit.analys.fastq.sra_bd(file, t, other_opts)`
FASTQ files will be downloaded using `fasterq-dump`. Make sure you have the latest version of the NCBI SRA toolkit
(version 2.10.8) is installed and binaries are added to the system path
Parameters | Description
------------ | -------------
`file` | List of SRA accessions for batch download. All accession must be separated by a newline in the file.
`t` | Number of threads for parallel run [int][default=4]
`other_opts` | Provide other relevant options for `fasterq-dump` [str][default=None] <br> Provide the options as a space-separated string. You can get a detailed option for `fasterq-dump` using the `-help` option.
Returns:
FASTQ files for each SRA accession in the current directory unless specified by `other_opts`
<a href="https://www.reneshbedre.com/blog/ncbi_sra_toolkit.html" target="_blank">Description and working example</a>
### FASTQ quality format detection
`bioinfokit.analys.format.fq_qual_var(file)`
Parameters | Description
------------ | -------------
`file` |FASTQ file to detect quality format [deafult: None]
Returns:
Quality format encoding name for FASTQ file (Supports only Sanger, Illumina 1.8+ and Illumina 1.3/1.4)
<a href="https://www.reneshbedre.com/blog/fqqualfmt.html" target="_blank">Working Example</a>
### Sequencing coverage
latest update v0.9.7
`bioinfokit.analys.fastq.seqcov(file, gs)`
Parameters | Description
------------ | -------------
`file` | FASTQ file
`gs` | Genome size in Mbp
Returns:
Sequencing coverage of the given FASTQ file
<a href="https://www.reneshbedre.com/blog/sequencing-coverage.html" target="_blank">Description and Working example</a>
### Split the sequence into smaller subsequences
latest update v2.0.6
`bioinfokit.analys.Fasta.split_seq(seq, seq_size, seq_overlap, any_cond, outfmt)`
Parameters | Description
------------ | -------------
`seq` | Input sequence [string]
`seq_size` | subsequence size [int][default: 3]
`seq_overlap` | Split the sequence in overlap mode [bool][default: True]
`any_cond` | Split sequence based on a condition. Note yet defined.
`outfmt` | Output format for the subsequences. If parameter set to 'fasta', the file will be saved in same folder with name `output_chunks.fasta` ['list' or 'fasta'][default: 'list']
Returns:
Subsequences in list or fasta file (output_chunks.fasta) format
<a href="https://reneshbedre.com/blog/split-seq.html" target="_blank">Description and Working example</a>
### Reverse complement of DNA sequence
latest update v2.1.1
`bioinfokit.analys.Fasta.rev_com(sequence)`
Parameters | Description
------------ | -------------
`seq` | DNA sequence to perform reverse complement
`file` | DNA sequence in a fasta file
Returns:
Reverse complement of original DNA sequence
<a href="https://www.reneshbedre.com/blog/reverse-complement-dna-python.html" target="_blank">Working example</a>
### File format conversions
`bioinfokit.analys.format`
Function | Parameters | Description
------------|---------- | -------------
`bioinfokit.analys.format.fqtofa(file)` | `FASTQ file` | Convert FASTQ file into FASTA format
`bioinfokit.analys.format.hmmtocsv(file)` | `HMM file` | Convert HMM text output (from HMMER tool) to CSV format
`bioinfokit.analys.format.tabtocsv(file)` | `TAB file` | Convert TAB file to CSV format
`bioinfokit.analys.format.csvtotab(file)` | `CSV file` | Convert CSV file to TAB format
Returns:
Output will be saved in same directory
<a href="https://www.reneshbedre.com/blog/format.html" target="_blank">Working example</a>
### GFF3 to GTF file format conversion
`latest update v1.0.1`
`bioinfokit.analys.gff.gff_to_gtf(file, trn_feature_name)`
Parameters | Description
------------ | -------------
`file` | GFF3 genome annotation file
`trn_feature_name` | Name of the feature (column 3 of GFF3 file) of RNA transcripts if other than 'mRNA' or 'transcript'
Returns:
GTF format genome annotation file (file.gtf will be saved in same directory)
<a href="https://www.reneshbedre.com/blog/gffgtf.html" target="_blank">Working Example</a>
### Bioinformatics file readers and processing (FASTA, FASTQ, and VCF)
latest update v2.0.4
Function | Parameters | Description
------------|---------- | -------------
`bioinfokit.analys.Fasta.fasta_reader(file)` | `FASTA file` | FASTA file reader
`bioinfokit.analys.fastq.fastq_reader(file)` | `FASTQ file` | FASTQ file reader
`bioinfokit.analys.marker.vcfreader(file)` | `VCF file` | VCF file reader
Returns:
File generator object (can be iterated only once) that can be parsed for the record
<a href="https://www.reneshbedre.com/blog/filereaders.html" target="_blank">Description and working example</a>
### Extract subsequence from FASTA files
latest update v2.0.4
`bioinfokit.analys.Fasta.ext_subseq(file, id, st, end, strand)`
Extract the subsequence of specified region from FASTA file. If the target subsequence region is on minus strand. the
reverse complementary of subsequence will be printed.
Parameters | Description
------------ | -------------
`file` | FASTA file [file]
`id` | The ID of sequence from FASTA file to extract the subsequence [string]
`st` | Start integer coordinate of subsequnece [int]
`end` | End integer coordinate of subsequnece [int]
`strand` | Strand of the subsequence ['plus' or 'minus'][default: 'plus']
<!--
`out_file` | Write subsequence to file instead of stdout [file][default: None]
-->
Returns:
Subsequence to stdout
### Extract sequences from FASTA file
latest update v2.1.3
`bioinfokit.analys.Fasta.extract_seq(file, id)`
Extract the sequences from FASTA file based on the list of sequence IDs provided from other file
Parameters | Description
------------ | -------------
`file` | FASTA file [file]
`id` | List of sequence IDs separated by new line. This file can also contain the ID, start and end coordinates separated by TAB [file]
Returns:
Sequences extracted from FASTA file based on the given IDs provided in id file. Output FASTA file will be saved as
output.fasta in current working directory.
<a href="https://www.reneshbedre.com/blog/extract-seq-fasta-python.html" target="_blank">Description and working example</a>
### Split FASTA file into multiple FASTA files
latest update v2.0.4
`bioinfokit.analys.Fasta.split_fasta(file, n, bases_per_line)`
Split one big FASTA file into multiple smaller FASTA files
Parameters | Description
------------ | -------------
`file` | FASTA file [file]
`n` | Number of FASTA files to split the big FASTA file [int][default: 2]
`bases_per_line` | Number of bases per line for ouput FASTA files [int][default: 60]
Returns:
Number of smaller FASTA files with prefix output (output_0.fasta, output_1.fasta and so on)
### Convert multi-line FASTA into single-line FASTA
latest update v2.1.2
`bioinfokit.analys.Fasta.multi_to_single_line(file)`
Convert multi-line FASTA (where sequences are on multi lines) into single-line FASTA (where sequences are in single line)
Parameters | Description
------------ | -------------
`file` | FASTA file [file]
Returns:
Single line FASTA (output.fasta). Output FASTA file will be saved as output.fasta in current working directory.
<a href="https://www.reneshbedre.com/blog/convert-multi-single-line-fasta.html" target="_blank">Description and working example</a>
### Merge counts files from featureCounts
latest update v2.0.5
`bioinfokit.analys.HtsAna.merge_featureCount(pattern, gene_column_name)`
Merge counts files generated from featureCounts when it runs individually on large samples. The count files must be in
same folder and should end with .txt file extension.
Parameters | Description
------------ | -------------
`pattern` | file name pattern for each count file [default: '*.txt']
`gene_column_name` | gene id column name for feature and meta-features [default: 'Geneid']
Returns:
Merge count file (gene_matrix_count.csv) in same folder
### Split BED file by chromosome
latest update v2.0.9
`bioinfokit.analys.HtsAna.split_bed(bed)`
Split the BED file by chromosome names
Parameters | Description
------------ | -------------
`bed` | Input BED file [default: None]
Returns:
BED file for each chromosome (files will be saved in same directory)
[Working example](https://www.reneshbedre.com/blog/bedtools-genomics.html#38-split-bed-file-by-chromosome)
### Max and Min sequence lengths from Fasta
latest update v2.1.4
`bioinfokit.analys.Fasta.max_min_len(fasta)`
Find Max and Min sequence lengths from Fasta
Parameters | Description
------------ | -------------
`fasta` | Input Fasta file [default: None]
Returns:
Max and Min sequence lengths from Fasta file
[Working example](https://www.reneshbedre.com/blog/bedtools-genomics.html#38-split-bed-file-by-chromosome)
## Functional enrichment analysis
### Gene family enrichment analysis (GenFam)
latest update v1.0.0
`bioinfokit.analys.genfam.fam_enrich(id_file, species, id_type, stat_sign_test, multi_test_corr, min_map_ids, alpha)`
GenFam is a comprehensive classification and enrichment analysis tool for plant genomes. It provides a unique way to
characterize the large-scale gene datasets such as those from transcriptome analysis (read <a href='https://onlinelibrary.wiley.com/doi/full/10.1002/pld3.191'>GenFam</a> paper for more details)
Parameters | Description
------------ | -------------
`id_file` | Text file containing the list of gene IDs to analyze using GenFam. IDs must be separated by newline.
`species` | Plant species ID for GenFam analysis. All plant species ID provided [here](https://github.com/reneshbedre/reneshbedre.github.io/blob/master/assets/posts/genfam/genfam_species_id.md)
`id_type` | Plant species ID type <br> <strong><em>1</em></strong>: Phytozome locus ID <br> <strong><em>2</em></strong>: Phytozome transcript ID <br> <strong><em>3</em></strong>: Phytozome PAC ID <br>
`stat_sign_test` | Statistical significance test for enrichment analysis [default=1]. <br> <strong><em>1</em></strong>: Fisher exact test <br> <strong><em>2</em></strong>: Hypergeometric distribution <br> <strong><em>3</em></strong>: Binomial distribution <br> <strong><em>4</em></strong>: Chi-squared distribution <br>
`multi_test_corr` | Multiple testing correction test [default=3]. <br> <strong><em>1</em></strong>: Bonferroni <br> <strong><em>2</em></strong>: Bonferroni-Holm <br> <strong><em>3</em></strong>: Benjamini-Hochberg <br>
`min_map_ids` | Minimum number of gene IDs from the user list (`id_file`) must be mapped to the background database for performing GenFam analysis [default=5]
`alpha` | Significance level [float][default: 0.05]
Returns:
Attribute | Description
------------ | -------------
df_enrich | Enriched gene families with p < 0.05
genfam_info | GenFam run information
Output files | Output figures and files from GenFam analysis <br> <strong><em>genfam_enrich.png</em></strong>: GenFam figure for enriched gene families <br> <strong><em>fam_enrich_out.txt</em></strong>: List of enriched gene families with mapped gene IDs, GO annotation, and detailed statistics <br> <strong><em>fam_all_out.txt</em></strong>: List of all gene families with mapped gene IDs, GO annotation, and detailed statistics
<a href="https://reneshbedre.github.io/blog/genfam.html" target="_blank">Description and working example</a>
### Check allowed ID types for plant species for GenFam
latest update v1.0.0
`bioinfokit.analys.genfam.check_allowed_ids(species)`
Parameters | Description
------------ | -------------
`species` | Plant species ID to check for allowed ID type. All plant species ID provided [here](https://github.com/reneshbedre/reneshbedre.github.io/blob/master/assets/posts/genfam/genfam_species_id.md)
Returns:
Allowed ID types for GenFam
<a href="https://reneshbedre.github.io/blog/genfam.html" target="_blank">Description and working example</a>
## Biostatistical analysis
### Correlation matrix plot
latest update v2.0.1
`bioinfokit.visuz.stat.corr_mat(table, corm, cmap, r, dim, show, figtype, axtickfontsize, axtickfontname, theme)`
Parameters | Description
------------ | -------------
`table` | Dataframe object with numerical variables (columns) to find correlation. Ideally, you should have three or more variables. Dataframe should not have identifier column.
`corm` | Correlation method [pearson,kendall,spearman] [default:pearson]
`cmap` | Color Palette for heatmap [string][default: 'seismic']. More colormaps are available at https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html
`r` | Figure resolution in dpi [int][default: 300]. Not compatible with `show`= True
`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 5)]
`show` | Show the figure on console instead of saving in current folder [True or False][default:False]
`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
`axtickfontsize` | Font size for axis ticks [float][default: 7]
`axtickfontname` | Font name for axis ticks [string][default: 'Arial']
`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']
Returns:
Correlation matrix plot image in same directory (corr_mat.png)
<a href="https://www.reneshbedre.com/blog/correlation-analysis.html" target="_blank">Working example</a>
### Bar-dot plot
`latest update v0.8.5`
`bioinfokit.visuz.stat.bardot(df, colorbar, colordot, bw, dim, r, ar, hbsize, errorbar, dotsize, markerdot, valphabar,
valphadot, show, figtype, axxlabel, axylabel, axlabelfontsize, axlabelfontname, ylm, axtickfontsize, axtickfontname,
yerrlw, yerrcw)`
Parameters | Description
------------ | -------------
`df` |Pandas dataframe object
`colorbar` | Color of bar graph [string or list][default:"#bbcfff"]
`colordot` | Color of dots on bar [string or list][default:"#ee8972"]
`bw` |Width of bar [float][default: 0.4]
`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]
`r` | Figure resolution in dpi [int][default: 300]
`ar` | Rotation of X-axis labels [float][default: 0]
`hbsize` | Horizontal bar size for standard error bars [float][default: 4]
`errorbar` | Draw standard error bars [bool (True or False)][default: True]
`dotsize`| The size of the dots in the plot [float][default: 6]
`markerdot` | Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: "o"]
`valphabar` | Transparency of bars on plot [float (between 0 and 1)][default: 1]
`valphadot` | Transparency of dots on plot [float (between 0 and 1)][default: 1]
`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
`show` | Show the figure on console instead of saving in current folder [True or False][default:False]
`axxlabel` | Label for X-axis. If you provide this option, default label will be replaced [string][default: None]
`axylabel` | Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]
`axlabelfontsize` | Font size for axis labels [float][default: 9]
`axlabelfontname` | Font name for axis labels [string][default: 'Arial']
`ylm` | Range of ticks to plot on Y-axis [float [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) (bottom, top, interval)][default: None]
`axtickfontsize` | Font size for axis ticks [float][default: 9]
`axtickfontname` | Font name for axis ticks [string][default: 'Arial']
`yerrlw` | Error bar line width [float][default: None]
`yerrcw` | Error bar cap width [float][default: None]
Returns:
Bar-dot plot image in same directory (bardot.png)
<a href="https://reneshbedre.github.io/blog/bardot.html" target="_blank">Working Example</a>
### One sample and two sample Z-tests
`latest update v2.1.0`
`bioinfokit.analys.stat.ztest(df, x, y, mu, x_std, y_std, alpha, test_type)`
Parameters | Description
------------ | -------------
`df` | Pandas dataframe for appropriate Z-test. <br> <b>One sample</b>: It should have atleast one variable <br> <b>Two sample independent</b>: It should have atleast two variables
`x` | column name for x group [string][default: None]
`y` | column name for x group [string][default: None]
`mu` | Population or known mean for the one sample Z-test [float][default: None]
`x_std` | Population standard deviation for x group [float][default: None]
`y_std` | Population standard deviation for y group [float][default: None]
`alpha` | Significance level for confidence interval (CI). If alpha=0.05, then 95% CI will be calculated [float][default: 0.05]
`test_type` | Type of Z-test [int (1,2)][default: None]. <br> <strong><em>1</em></strong>: One sample Z-test <br> <strong><em>2</em></strong>: Two sample Z-test
Returns:
Summary output as class attribute (summary and result)
<a href="https://reneshbedre.com/blog/ttest.html" target="_blank">Description and Working example</a>
### One sample and two sample (independent and paired) t-tests
`latest update v2.1.0`
`bioinfokit.analys.stat.ttest(df, xfac, res, evar, alpha, test_type, mu)`
Parameters | Description
------------ | -------------
`df` | Pandas dataframe for appropriate t-test. <br> <b>One sample</b>: It should have atleast dependent (res) variable <br> <b>Two sample independent</b>: It should have independent (xfac) and dependent (res) variables <br> <b>Two sample paired</b>: It should have two dependent (res) variables
`xfac` | Independent group column name with two levels [string][default: None]
`res` | Dependent variable column name [string or list or [Tuple](https://www.reneshbedre.com/blog/python-tuples.html)][default: None]
`evar` | t-test with equal variance [bool (True or False)][default: True]
`alpha` | Significance level for confidence interval (CI). If alpha=0.05, then 95% CI will be calculated [float][default: 0.05]
`test_type` | Type of t-test [int (1,2,3)][default: None]. <br> <strong><em>1</em></strong>: One sample t-test <br> <strong><em>2</em></strong>: Two sample independent t-test <br> <strong><em>3</em></strong>: Two sample paired t-test
`mu` | Population or known mean for the one sample t-test [float][default: None]
Returns:
Summary output as class attribute (summary and result)
<a href="https://reneshbedre.com/blog/ttest.html" target="_blank">Description and Working example</a>
### Chi-square test
`latest update v0.9.5`
`bioinfokit.analys.stat.chisq(df, p)`
Parameters | Description
------------ | -------------
`df` | Pandas dataframe. It should be one or two-dimensional contingency table.
`p` | Theoretical expected probabilities for each group. It must be non-negative and sum to 1. If p is provide Goodness of Fit test will be performed [list or [Tuple](https://www.reneshbedre.com/blog/python-tuples.html)][default: None]
Returns:
Summary and expected counts as class attributes (summary and expected_df)
<a href="https://www.reneshbedre.com/blog/chi-square-test.html" target="_blank">Working example</a>
<!--
### One-way ANOVA
`bioinfokit.stat.oanova(table, res, xfac, ph, phalpha)`
Parameters | Description
------------ | -------------
`table` | Pandas dataframe in stacked table format
`res` | Response variable (dependent variable) [string][default: None]
`xfac` | Treatments or groups or factors (independent variable) [string][default: None]
`ph` | perform pairwise comparisons with Tukey HSD test [bool (True or False)] [default: False]
`phalpha` |significance level Tukey HSD test [float (0 to 1)][default: 0.05]
Returns:
ANOVA summary, multiple pairwise comparisons, and assumption tests statistics
<a href="https://reneshbedre.github.io/blog/oanova.html" target="_blank">Working example</a>
-->
### Linear regression analysis
`bioinfokit.visuz.stat.lin_reg(df, x, y)`
Parameters | Description
------------ | -------------
`df` |Pandas dataframe object
`x` | Name of column having independent X variables [list][default:None]
`y` | Name of column having dependent Y variables [list][default:None]
Returns:
Regression analysis summary
<a href="https://www.reneshbedre.com/blog/linear-regression.html" target="_blank">Working Example</a>
### Regression plot
latest update v2.0.1
`bioinfokit.visuz.stat.regplot(df, x, y, yhat, dim, colordot, colorline, r, ar, dotsize, markerdot, linewidth,
valphaline, valphadot, show, figtype, axxlabel, axylabel, axlabelfontsize, axlabelfontname, xlm, ylm, axtickfontsize,
axtickfontname, theme)`
Parameters | Description
------------ | -------------
`df` | Pandas dataframe object
`x` | Name of column having independent X variables [string][default:None]
`y` | Name of column having dependent Y variables [string][default:None]
`yhat` |Name of column having predicted response of Y variable (y_hat) from regression [string][default:None]
`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]
`r` | Figure resolution in dpi [int][default: 300]
`ar` | Rotation of X-axis labels [float][default: 0]
`dotsize`| The size of the dots in the plot [float][default: 6]
`markerdot` | Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: "o"]
`valphaline` | Transparency of regression line on plot [float (between 0 and 1)][default: 1]
`valphadot` | Transparency of dots on plot [float (between 0 and 1)][default: 1]
`linewidth` | Width of regression line [float][default: 1]
`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
`show` | Show the figure on console instead of saving in current folder [True or False][default:False]
`axxlabel` | Label for X-axis. If you provide this option, default label will be replaced [string][default: None]
`axylabel` | Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]
`axlabelfontsize` | Font size for axis labels [float][default: 9]
`axlabelfontname` | Font name for axis labels [string][default: 'Arial']
`xlm` | Range of ticks to plot on X-axis [float [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) (bottom, top, interval)][default: None]
`ylm` | Range of ticks to plot on Y-axis [float [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) (bottom, top, interval)][default: None]
`axtickfontsize` | Font size for axis ticks [float][default: 9]
`axtickfontname` | Font name for axis ticks [string][default: 'Arial']
`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']
Returns:
Regression plot image in same directory (reg_plot.png)
<a href="https://www.reneshbedre.com/blog/linear-regression.html" target="_blank">Working Example</a>
### Tukey HSD test
`latest update v1.0.3`
`bioinfokit.analys.stat.tukey_hsd(df, res_var, xfac_var, anova_model, phalpha, ss_typ)`
It performs multiple pairwise comparisons of treatment groups using Tukey's HSD (Honestly Significant Difference) test
to check if group means are significantly different from each other. It uses the Tukey-Kramer approach if the sample sizes
are unequal among the groups.
Parameters | Description
------------ | -------------
`df` | Pandas dataframe with the variables mentioned in the `res_var`, `xfac_var` and `anova_model` options. It should not have missing data. The missing data will be omitted.
`res_var` | Name of a column having response variable [string][default: None]
`xfac_var` | Name of a column having factor or group for pairwise comparison [string][default: None]
`anova_model` | ANOVA model (calculated using statsmodels `ols` function) [string][default: None]
`phalpha` | Significance level [float][default: 0.05]
`ss_typ` | Type of sum of square to perform ANOVA [int][default: 2]
Returns:
Attribute | Description
------------ | -------------
`tukey_summary` | Pairwise comparisons for main and interaction effects by Tukey HSD test
<a href="https://www.reneshbedre.com/blog/anova.html" target="_blank">Description and Working example</a>
### Bartlett's test
`latest update v1.0.3`
`bioinfokit.analys.stat.bartlett(df, xfac_var, res_var)`
It performs Bartlett's test to check the homogeneity of variances among the treatment groups. It accepts the input
table in a stacked format. More details https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.bartlett.html
Parameters | Description
------------ | -------------
`df` | Pandas dataframe containing response (`res_var`) and independent variables (`xfac_var`) in a stacked format. It should not have missing data. The missing data will be omitted.
`res_var` | Name of a column having response variable [string][default: `None`]
`xfac_var` | Name of a column having treatment groups (independent variables) [string or list][default: `None`]
Returns:
Attribute | Description
------------ | -------------
`bartlett_summary` | Pandas dataframe containing Bartlett's test statistics, degree of freedom, and <i>p</i> value
<a href="https://www.reneshbedre.com/blog/anova.html#test-anova-assumptions" target="_blank">Description and Working example</a>
### Levene's test
`latest update v1.0.3`
`bioinfokit.analys.stat.levene(df, xfac_var, res_var)`
It performs Levene's test to check the homogeneity of variances among the treatment groups. It accepts the input
table in a stacked format. More details https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.levene.html
Parameters | Description
------------ | -------------
`df` | Pandas dataframe containing response (`res_var`) and independent variables (`xfac_var`) in a stacked format. It should not have missing data. The missing data will be omitted.
`res_var` | Name of a column having response variable [string][default: `None`]
`xfac_var` | Name of a column having treatment groups (independent variables) [string or list][default: `None`]
`center` | Choice for the Levene's test [string (`median`, `mean`, `trimmed`)] [default: `median`] <br> <strong><em>median</em></strong>: Brown-Forsythe Levene-type test <br> <strong><em>mean</em></strong>: original Levene's test <br> <strong><em>trimmed</em></strong>: Brown-Forsythe Levene-type test
Returns:
Attribute | Description
------------ | -------------
`levene_summary` | Pandas dataframe containing Levene's test statistics, degree of freedom, and <i>p</i> value
<a href="https://www.reneshbedre.com/blog/anova.html#test-anova-assumptions" target="_blank">Description and Working example</a>
### ROC plot
`latest update v2.0.1`
`bioinfokit.visuz.stat.roc(fpr, tpr, c_line_style, c_line_color, c_line_width, diag_line, diag_line_style,
diag_line_width, diag_line_color, auc, shade_auc, shade_auc_color, axxlabel, axylabel, axtickfontsize,
axtickfontname, axlabelfontsize, axlabelfontname, plotlegend, legendpos, legendanchor, legendcols, legendfontsize,
legendlabelframe, legend_columnspacing, dim, show, figtype, figname, r, ylm, theme)`
Receiver operating characteristic (ROC) curve for visualizing classification performance
Parameters | Description
------------ | -------------
`fpr` | Increasing false positive rates obtained from `sklearn.metrics.roc_curve` [list][default:None]
`tpr` | Increasing true positive rates obtained from `sklearn.metrics.roc_curve` [list][default:None]
`c_line_style` | Line style for ROC curve [string][default:'-']
`c_line_color` | Line color for ROC curve [string][default:'#f05f21']
`c_line_width` | Line width for ROC curve [float][default:1]
`diag_line` | Plot reference line [True or False][default: True]
`diag_line_style` | Line style for reference line [string][default:'--']
`diag_line_width` | Line width for reference line [float][default:1]
`diag_line_color` | Line color for reference line [string][default:'b']
`auc` | Area under ROC. It can be obtained from `sklearn.metrics.roc_auc_score` [float][default: None]
`shade_auc`| Shade are for AUC [True or False][default: False]
`shade_auc_color` | Shade color for AUC [string][default: '#f48d60']
`axxlabel` | Label for X-axis [string][default: 'False Positive Rate (1 - Specificity)']
`axylabel` | Label for Y-axis [string][default: 'True Positive Rate (Sensitivity)']
`axtickfontsize` | Font size for axis ticks [float][default: 9]
`axtickfontname` | Font name for axis ticks [string][default: 'Arial']
`axlabelfontsize` | Font size for axis labels [float][default: 9]
`axlabelfontname` | Font name for axis labels [string][default: 'Arial']
`plotlegend` | plot legend [True or False][default:True]
`legendpos` | position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:'lower right']
`legendanchor` | position of the legend outside of the plot. For more options see bbox_to_anchor parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [list][default:None]
`legendcols` | Number of columns for legends [int][default: 1]
`legendfontsize` | Font size for the legends [float][default:8]
`legendlabelframe` | Box frame for the legend [True or False][default: False]
`legend_columnspacing` | Spacing between the legends [float][default: None]
`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (5, 4)]
`show` | Show the figure on console instead of saving in current folder [True or False][default:False]
`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']
`figname` | name of figure [string ][default:'roc']
`r` | Figure resolution in dpi [int][default: 300]. Not compatible with `show`= True
`ylm` | Range of ticks to plot on Y-axis [float (bottom, top, interval)][default: None]
`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']
Returns:
ROC plot image in same directory (roc.png)
<a href="https://www.reneshbedre.com/blog/logistic-regression.html#prediction-of-test-dataset-using-fitted-model" target="_blank">Working example</a>
### Regression metrics
Calculate Root Mean Square Error (RMSE), Mean squared error (MSE), Mean absolute error (MAE), and Mean absolute percent
error (MAPE) from regression fit
`latest update v1.0.8`
`bioinfokit.analys.stat.reg_metric(y, yhat, resid)`
Parameters | Description
------------ | -------------
`y` | Original values for dependent variable [numpy array] [default: None]
`yhat` | Predicted values from regression [numpy array] [default: None]
`resid` | Regression residuals [numpy array][default: None]
Returns:
Pandas dataframe with values for RMSE, MSE, MAE, and MAPE
<a href="https://www.reneshbedre.com/blog/linear-regression.html" target="_blank">Working example</a>
<b>Venn Diagram</b>
`bioinfokit.visuz.venn(vennset, venncolor, vennalpha, vennlabel)`
Parameters | Description
------------ | -------------
`vennset` | Venn dataset for 3 and 2-way venn. Data should be in the format of (100,010,110,001,101,011,111) for 3-way venn and 2-way venn (10, 01, 11) [default: (1,1,1,1,1,1,1)]
`venncolor` | Color Palette for Venn [color code][default: ('#00909e', '#f67280', '#ff971d')]
`vennalpha` | Transparency of Venn [float (0 to 1)][default: 0.5]
`vennlabel` | Labels to Venn [string][default: ('A', 'B', 'C')]
Returns:
Venn plot (venn3.png, venn2.png)
<a href="https://www.reneshbedre.com/blog/venn.html" target="_blank">Working example</a>
## References:
- Travis E. Oliphant. A guide to NumPy, USA: Trelgol Publishing, (2006).
- John D. Hunter. Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, 9, 90-95 (2007),
DOI:10.1109/MCSE.2007.55 (publisher link)
- Fernando Pérez and Brian E. Granger. IPython: A System for Interactive Scientific Computing, Computing in Science &
Engineering, 9, 21-29 (2007), DOI:10.1109/MCSE.2007.53 (publisher link)
- Michael Waskom, Olga Botvinnik, Joel Ostblom, Saulius Lukauskas, Paul Hobson, MaozGelbart, … Constantine Evans.
(2020, January 24). mwaskom/seaborn: v0.10.0 (January 2020) (Version v0.10.0). Zenodo. http://doi.org/10.5281/zenodo.3629446
- Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu
Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau,
Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay. Scikit-learn: Machine Learning in Python, Journal of Machine
Learning Research, 12, 2825-2830 (2011)
- Wes McKinney. Data Structures for Statistical Computing in Python, Proceedings of the 9th Python in Science
Conference, 51-56 (2010)
- Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski,
Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod
Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, Ä°lhan Polat, Yu
Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E.A. Quintero,
Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0
Contributors. (2020) SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17(3),
261-272.
- David C. Howell. Multiple Comparisons With Unequal Sample Sizes. https://www.uvm.edu/~statdhtx/StatPages/MultipleComparisons/unequal_ns_and_mult_comp.html
<!--
## bioinfokit cited by 11 research articles:
- Karstensen KT, Schein A, Petri A, Bøgsted M, Dybkær K, Uchida S, Kauppinen S. Long Non-Coding RNAs in Diffuse Large B-Cell Lymphoma. Non-coding RNA. 2021 Mar;7(1):1.
- de Rezende Rodovalho V, da Luz BS, Nicolas A, do Carmo FL, Jardin J, Briard-Bion V, Jan G, Le Loir Y, de Carvalho Azevedo VA, Guedon E. Environmental conditions modulate the protein content and immunomodulatory activity of extracellular vesicles produced by the probiotic Propionibacterium freudenreichii. Applied and Environmental Microbiology. 2020 Dec 11.
- Jarvis L, Rainbow D, Coppard V, Howlett S, Davies J, Mullay H, Hester J, Ashmore T, Van Den Bosch A, Grist J, Coles A. Therapeutically expanded human regulatory T-cells are super-suppressive due to HIF1A induced expression of CD73.
- Al-Bakhat L, Al-Serhani N. LncRNAs and Protein-coding Genes Expression Analysis for Myelodysplastic Syndromes Diagnoses. In2020 International Conference on Artificial Intelligence & Modern Assistive Technology (ICAIMAT) 2020 Nov 24 (pp. 1-6). IEEE.
- Aishwarya S, Gunasekaran K, Margret AA. Computational gene expression profiling in the exploration of biomarkers, non-coding functional RNAs and drug perturbagens for COVID-19. Journal of Biomolecular Structure and Dynamics. 2020 Nov 17:1-6.
- Liang L, Darbandi SF, Pochareddy S, Gulden FO, Gilson MC, Sheppard BK, Sahagun A, An JY, Werling DM, Rubenstein JL, Sestan N. Developmental dynamics of voltage-gated sodium channel isoform expression in the human and mouse neocortex. bioRxiv. 2020 Jan 1.
- Irigoyen S, Ramasamy M, Pant S, Niraula P, Bedre R, Gurung M, Rossi D, Laughlin C, Gorman Z, Achor D, Levy A. Plant hairy roots enable high throughput identification of antimicrobials against Candidatus Liberibacter spp. Nature Communications. 2020 Nov 16;11(1):1-4.
- Lu J, Wilfred P, Korbie D, Trau M. Regulation of Canonical Oncogenic Signaling Pathways in Cancer via DNA Methylation. Cancers. 2020 Nov;12(11):3199.
- Gribble J, Pruijssers AJ, Agostini ML, Anderson-Daniels J, Chappell JD, Lu X, Stevens LJ, Routh AL, Denison MR. The coronavirus proofreading exoribonuclease mediates extensive viral recombination. BioRxiv. 2020 Jan 1.
- Aarts J, Bijma P, Xia S, Textor J, de Vries A. A GWAS about the wingsize of Nasonia Vitripennis.
- Greaney AM, Adams TS, Raredon MS, Gubbins E, Schupp JC, Engler AJ, Ghaedi M, Yuan Y, Kaminski N, Niklason LE. Platform
Effects on Regeneration by Pulmonary Basal Cells as Evaluated by Single-Cell RNA Sequencing. Cell Reports. 2020 Mar
24;30(12):4250-65.
Source: https://scholar.google.com/scholar?start=0&q=bioinfokit&hl=en&as_sdt=0,44
-->
Last updated: November 20, 2021
Raw data
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"author": "Renesh Bedre",
"author_email": "reneshbe@gmail.com",
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"description": "\r\n[](https://zenodo.org/badge/latestdoi/174428856)\r\n[](https://badge.fury.io/py/bioinfokit)\r\n[](https://pepy.tech/project/bioinfokit)\r\n[](https://travis-ci.org/reneshbedre/bioinfokit)\r\n[](https://anaconda.org/bioconda/bioinfokit)\r\n<!--\r\n[](https://www.buymeacoffee.com/renesh)\r\n-->\r\n\r\nThe bioinfokit toolkit aimed to provide various easy-to-use functionalities to analyze, \r\nvisualize, and interpret the biological data generated from genome-scale omics experiments.\r\n\r\n**<span style=\"color:#33a8ff\">How to install:</span>**\r\n\r\nbioinfokit requires\r\n- Python 3\r\n- NumPy \r\n- scikit-learn\r\n- seaborn\r\n- pandas\r\n- matplotlib\r\n- SciPy \r\n- matplotlib_venn\r\n\r\nbioinfokit can be installed using pip, easy_install and git.\r\n\r\nlatest bioinfokit version: [](https://badge.fury.io/py/bioinfokit)\r\n\r\nInstall using <a href=\"https://pip.pypa.io/en/stable/installing/\" target=\"_blank\">pip</a> for Python 3 (easiest way)\r\n\r\n```python\r\n# install\r\npip install bioinfokit\r\n\r\n# upgrade to latest version\r\npip install bioinfokit --upgrade\r\n\r\n# uninstall \r\npip uninstall bioinfokit\r\n```\r\n\r\nInstall using <a href=\"https://setuptools.readthedocs.io/en/latest/easy_install.html\" target=\"_blank\">easy_install</a> for Python 3 (easiest way)\r\n```python\r\n# install latest version\r\neasy_install bioinfokit\r\n\r\n# specific version\r\neasy_install bioinfokit==0.3\r\n\r\n# uninstall \r\npip uninstall bioinfokit\r\n```\r\n\r\nInstall using <a href=\"https://docs.conda.io/en/latest/\" target=\"_blank\">conda</a> \r\n\r\n```python\r\nconda install -c bioconda bioinfokit\r\n```\r\n\r\nInstall using <a href=\"https://git-scm.com/book/en/v2/Getting-Started-Installing-Git\" target=\"_blank\">git</a>\r\n\r\n```python\r\n# download and install bioinfokit (Tested on Linux, Mac, Windows) \r\ngit clone https://github.com/reneshbedre/bioinfokit.git\r\ncd bioinfokit\r\npython setup.py install\r\n```\r\n\r\n### Check the version of bioinfokit\r\n```python\r\n>>> import bioinfokit\r\n>>> bioinfokit.__version__\r\n'0.4'\r\n```\r\n\r\n## How to cite bioinfokit?\r\n- Renesh Bedre. (2020, March 5). reneshbedre/bioinfokit: Bioinformatics data analysis and visualization toolkit. Zenodo. http://doi.org/10.5281/zenodo.3698145.\r\n- Additionally check <a href='https://zenodo.org/record/3841708#.XyCfi-dOmUk' target='_blank'>Zenodo</a> to cite specific version of bioinfokit\r\n\r\n## Support\r\n\r\nIf you enjoy bioinfokit, consider supporting me,\r\n\r\n<a href=\"https://www.buymeacoffee.com/renesh\" target=\"_blank\"><img src=\"https://cdn.buymeacoffee.com/buttons/default-orange.png\" alt=\"Buy Me A Coffee\" height=\"41\" width=\"174\"></a>\r\n\r\n\r\n# Getting Started\r\n\r\n## Gene expression analysis\r\n\r\n### Volcano plot\r\n\r\nlatest update v2.0.8\r\n\r\n`bioinfokit.visuz.GeneExpression.volcano(df, lfc, pv, lfc_thr, pv_thr, color, valpha, geneid, genenames, gfont, dim, r, ar, \r\n dotsize, markerdot, sign_line, gstyle, show, figtype, axtickfontsize, axtickfontname, axlabelfontsize, \r\n axlabelfontname, axxlabel, axylabel, xlm, ylm, plotlegend, legendpos, figname, legendanchor, legendlabels, theme)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`df` |Pandas dataframe table having atleast gene IDs, log fold change, P-values or adjusted P-values columns\r\n`lfc` | Name of a column having log or absolute fold change values [string][default:logFC]\r\n`pv` | Name of a column having P-values or adjusted P-values [string][default:p_values]\r\n`lfc_thr` | Log fold change cutoff for up and downregulated genes [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list][default:(1.0, 1.0)]\r\n`pv_thr` | p value or adjusted p value cutoff for up and downregulated genes [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list][default:(0.05, 0.05)]\r\n`color` | [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of three colors [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list][default: color=(\"green\", \"grey\", \"red\")]\r\n`valpha` | Transparency of points on volcano plot [float (between 0 and 1)][default: 1.0]\r\n`geneid` | Name of a column having gene Ids. This is necessary for plotting gene label on the points [string][default: None]\r\n`genenames` | [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of gene Ids to label the points. The gene Ids must be present in the geneid column. If this option set to \"deg\" it will label all genes defined by lfc_thr and pv_thr [string, tuple, dict][default: None]\r\n`gfont` | Font size for genenames [float][default: 10.0]. gfont not compatible with gstyle=2.\r\n`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (5, 5)]\r\n`r` | Figure resolution in dpi [int][default: 300]. Not compatible with `show`= True\r\n`ar` | Rotation of X and Y-axis ticks labels [float][default: 90]\r\n`dotsize`| The size of the dots in the plot [float][default: 8]\r\n`markerdot` | Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: \"o\"]\r\n`sign_line` | Show grid lines on plot with defined log fold change (`lfc_thr`) and P-value (`pv_thr`) threshold value [True or False][default:False]\r\n`gstyle` | Style of the text for genenames. 1 for default text and 2 for box text [int][default: 1]\r\n`show` | Show the figure on console instead of saving in current folder [True or False][default:False]\r\n`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']\r\n`axtickfontsize` | Font size for axis ticks [float][default: 9]\r\n`axtickfontname` | Font name for axis ticks [string][default: 'Arial']\r\n`axlabelfontsize` | Font size for axis labels [float][default: 9]\r\n`axlabelfontname` | Font name for axis labels [string][default: 'Arial']\r\n`axxlabel` | Label for X-axis. If you provide this option, default label will be replaced [string][default: None]\r\n`axylabel` | Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]\r\n`xlm` | Range of ticks to plot on X-axis [float (left, right, interval)][default: None]\r\n`ylm` | Range of ticks to plot on Y-axis [float (bottom, top, interval)][default: None]\r\n`plotlegend` | plot legend on volcano plot [True or False][default:False]\r\n`legendpos` | position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:\"best\"]\r\n`figname` | name of figure [string ][default:\"volcano\"]\r\n`legendanchor` | position of the legend outside of the plot. For more options see bbox_to_anchor parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [list][default:None]\r\n`legendlabels` | legend label names. If you provide custom label names keep the same order of label names as default [list][default:['significant up', 'not significant', 'significant down']]\r\n`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']\r\n\r\nReturns:\r\n\r\nVolcano plot image in same directory (volcano.png)\r\n<a href=\"https://reneshbedre.com/blog/volcano.html\" target=\"_blank\">Working example</a>\r\n\r\n### Inverted Volcano plot\r\n\r\nlatest update v2.0.8\r\n\r\n`bioinfokit.visuz.GeneExpression.involcano(table, lfc, pv, lfc_thr, pv_thr, color, valpha, geneid, genenames, gfont, gstyle,\r\n dotsize, markerdot, r, dim, show, figtype, axxlabel, axylabel, axlabelfontsize, axtickfontsize, \r\n axtickfontname, plotlegend, legendpos, legendanchor, figname, legendlabels, ar, theme)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`table` |Pandas dataframe table having atleast gene IDs, log fold change, P-values or adjusted P-values\r\n`lfc` | Name of a column having log fold change values [default:logFC]\r\n`pv` | Name of a column having P-values or adjusted P-values [default:p_values]\r\n`lfc_thr` | Log fold change cutoff for up and downregulated genes [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list] [default:(1.0, 1.0)]\r\n`pv_thr` | p value or adjusted p value cutoff for up and downregulated genes [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list] [default:(0.05, 0.05)]\r\n`color` | [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of three colors [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list][default: color=(\"green\", \"grey\", \"red\")]\r\n`valpha` | Transparency of points on volcano plot [float (between 0 and 1)][default: 1.0]\r\n`geneid` | Name of a column having gene Ids. This is necessary for plotting gene label on the points [string][default: None]\r\n`genenames` | [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of gene Ids to label the points. The gene Ids must be present in the geneid column. If this option set to \"deg\" it will label all genes defined by lfc_thr and pv_thr [string, [Tuple](https://www.reneshbedre.com/blog/python-tuples.html), dict][default: None]\r\n`gfont` | Font size for genenames [float][default: 10.0]\r\n`gstyle` | Style of the text for genenames. 1 for default text and 2 for box text [int][default: 1]\r\n`dotsize`| The size of the dots in the plot [float][default: 8]\r\n`markerdot` | Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: \"o\"]\r\n`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (5, 5)]\r\n`r` | Figure resolution in dpi [int][default: 300]. Not compatible with `show`= True\r\n`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']\r\n`show` | Show the figure on console instead of saving in current folder [True or False][default:False]\r\n`axxlabel` | Label for X-axis. If you provide this option, default label will be replaced [string][default: None]\r\n`axylabel` | Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]\r\n`axlabelfontsize` | Font size for axis labels [float][default: 9]\r\n`axtickfontsize` | Font size for axis ticks [float][default: 9]\r\n`axtickfontname` | Font name for axis ticks [string][default: 'Arial']\r\n`plotlegend` | plot legend on inverted volcano plot [True or False][default:False]\r\n`legendpos` | position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:\"best\"]\r\n`legendanchor` | position of the legend outside of the plot. For more options see bbox_to_anchor parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [list][default:None]\r\n`figname` | name of figure [string ][default:\"involcano\"]\r\n`legendlabels` | legend label names. If you provide custom label names keep the same order of label names as default [list][default:['significant up', 'not significant', 'significant down']]\r\n`ar` | Rotation of X and Y-axis ticks labels [float][default: 90]\r\n`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']\r\n\r\n\r\nReturns:\r\n\r\nInverted volcano plot image in same directory (involcano.png)\r\n<a href=\"https://reneshbedre.github.io/blog/volcano.html\" target=\"_blank\">Working example</a>\r\n\r\n### MA plot\r\n\r\nlatest update v2.0.7\r\n\r\n`bioinfokit.visuz.GeneExpression.ma(df, lfc, ct_count, st_count, pv, basemean, lfc_thr, color, dim, dotsize, show, r, valpha, figtype, axxlabel,\r\n axylabel, axlabelfontsize, axtickfontsize, axtickfontname, xlm, ylm, fclines, fclinescolor, legendpos, legendanchor,\r\n figname, legendlabels, plotlegend, ar, theme, geneid, genenames, gfont, gstyle, title)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`df` | Pandas dataframe table having atleast gene IDs, log fold change, and normalized counts (control and treatment) columns\r\n`lfc` | Name of a column having log fold change values [default:\"logFC\"]\r\n`ct_count` | Name of a column having count values for control sample.Ignored if basemean provided [default:\"value1\"]\r\n`st_count` | Name of a column having count values for treatment sample. Ignored if basemean provided [default:\"value2\"]\r\n`pv` | Name of a column having _p_ values or adjusted _p_ values \r\n`basemean` | Basemean (mean of normalized counts) from DESeq2 results \r\n`lfc_thr` | Log fold change cutoff for up and downregulated genes [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list][default:(1.0, 1.0)]\r\n`color` | [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of three colors [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) or list][default: (\"green\", \"grey\", \"red\")]\r\n`dotsize`| The size of the dots in the plot [float][default: 8]\r\n`markerdot` | Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: \"o\"]\r\n`valpha` | Transparency of points on plot [float (between 0 and 1)][default: 1.0]\r\n`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (5, 5)]\r\n`r` | Figure resolution in dpi [int][default: 300]. Not compatible with `show`= True\r\n`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']\r\n`show` | Show the figure on console instead of saving in current folder [True or False][default:False]\r\n`axxlabel` | Label for X-axis. If you provide this option, default label will be replaced [string][default: None]\r\n`axylabel` | Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]\r\n`axlabelfontsize` | Font size for axis labels [float][default: 9]\r\n`axtickfontsize` | Font size for axis ticks [float][default: 9]\r\n`axtickfontname` | Font name for axis ticks [string][default: 'Arial']\r\n`xlm` | Range of ticks to plot on X-axis [float (left, right, interval)][default: None]\r\n`ylm` | Range of ticks to plot on Y-axis [float (bottom, top, interval)][default: None]\r\n`fclines` | draw log fold change threshold lines as defines by `lfc` [True or False][default:False]\r\n`fclinescolor` | color of fclines [string][default: '#2660a4']\r\n`plotlegend` | plot legend on MA plot [True or False][default:False]\r\n`legendpos` | position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:\"best\"]\r\n`legendanchor` | position of the legend outside of the plot. For more options see bbox_to_anchor parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [list][default:None]\r\n`figname` | name of figure [string ][default:\"ma\"]\r\n`legendlabels` | legend label names. If you provide custom label names keep the same order of label names as default [list][default:['significant up', 'not significant', 'significant down']]\r\n`ar` | Rotation of X and Y-axis ticks labels [float][default: 90]\r\n`theme` | Change background theme. If theme set to `dark_background`, the dark background will be produced instead of default white. See more themes [here](https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html) [string][default:'None']\r\n`geneid` | Name of a column having gene Ids. This is necessary for plotting gene label on the points [string][default: None]\r\n`genenames` | [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of gene Ids to label the points. The gene Ids must be present in the geneid column. If this option set to \"deg\" it will label all genes defined by lfc_thr and pv_thr [string, [Tuple](https://www.reneshbedre.com/blog/python-tuples.html), dict][default: None]\r\n`gfont` | Font size for genenames [float][default: 10.0]\r\n`gstyle` | Style of the text for genenames. 1 for default text and 2 for box text [int][default: 1]\r\n`title` | Add main title to the plot [string][default: None]\r\n\r\nReturns:\r\n\r\nMA plot image in same directory (ma.png)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/ma.html\" target=\"_blank\">Working example</a>\r\n\r\n\r\n### Heatmap\r\n\r\n`latest update v2.0.1`\r\n\r\n`bioinfokit.visuz.gene_exp.hmap(table, cmap='seismic', scale=True, dim=(6, 8), rowclus=True, colclus=True, zscore=None, \r\n xlabel=True, ylabel=True, tickfont=(12, 12), show, r, figtype, figname, theme)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`file` | CSV delimited data file. It should not have NA or missing values\r\n`cmap` | Color Palette for heatmap [string][default: 'seismic']\r\n`scale` | Draw a color key with heatmap [boolean (True or False)][default: True]\r\n`dim` | heatmap figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 8)]\r\n`rowclus` | Draw hierarchical clustering for rows [boolean (True or False)][default: True]\r\n`colclus` | Draw hierarchical clustering for columns [boolean (True or False)][default: True]\r\n`zscore` | Z-score standardization of row (0) or column (1). It works when clus is True. [None, 0, 1][default: None]\r\n`xlabel` | Plot X-label [boolean (True or False)][default: True]\r\n`ylabel` | Plot Y-label [boolean (True or False)][default: True]\r\n`tickfont` | Fontsize for X and Y-axis tick labels [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats][default: (14, 14)]\r\n`show` | Show the figure on console instead of saving in current folder [True or False][default:False]\r\n`r` | Figure resolution in dpi [int][default: 300]. Not compatible with `show`= True\r\n`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']\r\n`figname` | name of figure [string ][default:\"heatmap\"]\r\n`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']\r\n\r\nReturns:\r\n\r\nheatmap plot (heatmap.png, heatmap_clus.png)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/heatmap-python.html\" target=\"_blank\">Working example</a>\r\n\r\n## Clustering analysis\r\n### Scree plot\r\n\r\n`latest update v2.0.1`\r\n\r\n`bioinfokit.visuz.cluster.screeplot(obj, axlabelfontsize, axlabelfontname, axxlabel, axylabel,\r\n figtype, r, show, dim, theme)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`obj` | list of component name and component variance\r\n`axlabelfontsize` | Font size for axis labels [float][default: 9]\r\n`axlabelfontname` | Font name for axis labels [string][default: 'Arial']\r\n`axxlabel` | Label for X-axis. If you provide this option, default label will be replaced [string][default: None]\r\n`axylabel` | Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]\r\n`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']\r\n`r` | Figure resolution in dpi [int][default: 300]\r\n`show` | Show the figure on console instead of saving in current folder [True or False][default:False]\r\n`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]\r\n`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']\r\n\r\nReturns:\r\n\r\nScree plot image (screeplot.png will be saved in same directory)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/principal-component-analysis.html\" target=\"_blank\">Working Example</a>\r\n\r\n### Principal component analysis (PCA) loadings plots\r\n\r\n`latest update v2.0.1`\r\n\r\n`bioinfokit.visuz.cluster.pcaplot(x, y, z, labels, var1, var2, var3, axlabelfontsize, axlabelfontname,\r\n figtype, r, show, plotlabels, dim, theme)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`x` | loadings (correlation coefficient) for principal component 1 (PC1)\r\n`y` | loadings (correlation coefficient) for principal component 2 (PC2)\r\n`z` | loadings (correlation coefficient) for principal component 3 (PC2)\r\n`labels` | original variables labels from dataframe used for PCA\r\n`var1` | Proportion of PC1 variance [float (0 to 1)]\r\n`var2` | Proportion of PC2 variance [float (0 to 1)]\r\n`var3` | Proportion of PC3 variance [float (0 to 1)]\r\n`axlabelfontsize` | Font size for axis labels [float][default: 9]\r\n`axlabelfontname` | Font name for axis labels [string][default: 'Arial']\r\n`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']\r\n`r` | Figure resolution in dpi [int][default: 300]\r\n`show` | Show the figure on console instead of saving in current folder [True or False][default:False]\r\n`plotlabels` | Plot labels as defined by labels parameter [True or False][default:True]\r\n`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]\r\n`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']\r\n\r\nReturns:\r\n\r\nPCA loadings plot 2D and 3D image (pcaplot_2d.png and pcaplot_3d.png will be saved in same directory)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/principal-component-analysis.html\" target=\"_blank\">Working Example</a>\r\n\r\n### Principal component analysis (PCA) biplots\r\n\r\n`latest update v2.0.2`\r\n\r\n`bioinfokit.visuz.cluster.biplot(cscore, loadings, labels, var1, var2, var3, axlabelfontsize, axlabelfontname,\r\n figtype, r, show, markerdot, dotsize, valphadot, colordot, arrowcolor, valphaarrow, arrowlinestyle, arrowlinewidth,\r\n centerlines, colorlist, legendpos, datapoints, dim, theme)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`cscore` | principal component scores (obtained from PCA().fit_transfrom() function in sklearn.decomposition)\r\n`loadings` | loadings (correlation coefficient) for principal components\r\n`labels` | original variables labels from dataframe used for PCA\r\n`var1` | Proportion of PC1 variance [float (0 to 1)]\r\n`var2` | Proportion of PC2 variance [float (0 to 1)]\r\n`var3` | Proportion of PC3 variance [float (0 to 1)]\r\n`axlabelfontsize` | Font size for axis labels [float][default: 9]\r\n`axlabelfontname` | Font name for axis labels [string][default: 'Arial']\r\n`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']\r\n`r` | Figure resolution in dpi [int][default: 300]\r\n`show` | Show the figure on console instead of saving in current folder [True or False][default:False]\r\n`markerdot` | Shape of the dot on plot. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: \"o\"]\r\n`dotsize`| The size of the dots in the plot [float][default: 6]\r\n`valphadot` | Transparency of dots on plot [float (between 0 and 1)][default: 1]\r\n`colordot` | Color of dots on plot [string or list ][default:\"#4a4e4d\"]\r\n`arrowcolor` | Color of the arrow [string ][default:\"#fe8a71\"]\r\n`valphaarrow` | Transparency of the arrow [float (between 0 and 1)][default: 1]\r\n`arrowlinestyle` | line style of the arrow. check more styles at https://matplotlib.org/3.1.0/gallery/lines_bars_and_markers/linestyles.html [string][default: '-']\r\n`arrowlinewidth`| line width of the arrow [float][default: 1.0]\r\n`centerlines`| draw center lines at x=0 and y=0 for 2D plot [bool (True or False)][default: True]\r\n`colorlist` | list of the categories to assign the color [list][default:None]\r\n`legendpos` | position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:\"best\"]\r\n`datapoints`| plot data points on graph [bool (True or False)][default: True]\r\n`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]\r\n`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']\r\n\r\nReturns:\r\n\r\nPCA biplot 2D and 3D image (biplot_2d.png and biplot_3d.png will be saved in same directory)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/principal-component-analysis.html\" target=\"_blank\">Working Example</a>\r\n\r\n### t-SNE plot\r\n\r\n`latest update v2.0.1`\r\n\r\n`bioinfokit.visuz.cluster.tsneplot(score, colorlist, axlabelfontsize, axlabelfontname,\r\n figtype, r, show, markerdot, dotsize, valphadot, colordot, dim, figname, legendpos,\r\n legendanchor, theme)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`score` | t-SNE component embeddings (obtained from TSNE().fit_transfrom() function in sklearn.manifold)\r\n`colorlist` | list of the categories to assign the color [list][default:None]\r\n`axlabelfontsize` | Font size for axis labels [float][default: 9]\r\n`axlabelfontname` | Font name for axis labels [string][default: 'Arial']\r\n`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']\r\n`r` | Figure resolution in dpi [int][default: 300]\r\n`show` | Show the figure on console instead of saving in current folder [True or False][default:False]\r\n`markerdot` | Shape of the dot on plot. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: \"o\"]\r\n`dotsize`| The size of the dots in the plot [float][default: 6]\r\n`valphadot` | Transparency of dots on plot [float (between 0 and 1)][default: 1]\r\n`colordot` | Color of dots on plot [string or list ][default:\"#4a4e4d\"]\r\n`legendpos` | position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:\"best\"]\r\n`legendanchor` | position of the legend outside of the plot. For more options see bbox_to_anchor parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [list][default:None]\r\n`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]\r\n`figname` | name of figure [string ][default:\"tsne_2d\"]\r\n`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']\r\n\r\nReturns:\r\n\r\nt-SNE 2D image (tsne_2d.png will be saved in same directory)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/tsne.html\" target=\"_blank\">Working Example</a>\r\n\r\n## Normalization\r\n\r\n### RPM or CPM normalization\r\n\r\n`latest update v0.8.9`\r\n\r\nNormalize raw gene expression counts into Reads per million mapped reads (RPM) or Counts per million mapped reads (CPM)\r\n\r\n`bioinfokit.analys.norm.cpm(df)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`df` | Pandas dataframe containing raw gene expression values. Genes with missing expression values (NA) will be dropped.\r\n\r\nReturns:\r\n\r\nRPM or CPM normalized Pandas dataframe as class attributes (cpm_norm)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/expression_units.html#rpm-or-cpm-reads-per-million-mapped-reads-or-counts-per-million-mapped-reads-\" target=\"_blank\">Working Example</a>\r\n\r\n\r\n### RPKM or FPKM normalization\r\n\r\n`latest update v0.9`\r\n\r\nNormalize raw gene expression counts into Reads per kilo base per million mapped reads (RPKM) or \r\nFragments per kilo base per million mapped reads (FPKM)\r\n\r\n`bioinfokit.analys.norm.rpkm(df, gl)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`df` | Pandas dataframe containing raw gene expression values. Genes with missing expression or gene length values (NA) will be dropped.\r\n`gl` | Name of a column having gene length in bp [string][default: None]\r\n\r\nReturns:\r\n\r\nRPKM or FPKM normalized Pandas dataframe as class attributes (rpkm_norm)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/expression_units.html#rpkm-reads-per-kilo-base-per-million-mapped-reads\" target=\"_blank\">Working Example</a>\r\n\r\n### TPM normalization\r\n\r\n`latest update v0.9.1`\r\n\r\nNormalize raw gene expression counts into Transcript per million (TPM) \r\n\r\n`bioinfokit.analys.norm.tpm(df, gl)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`df` | Pandas dataframe containing raw gene expression values. Genes with missing expression or gene length values (NA) will be dropped.\r\n`gl` | Name of a column having gene length in bp [string][default: None]\r\n\r\nReturns:\r\n\r\nTPM normalized Pandas dataframe as class attributes (tpm_norm)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/expression_units.html#tpm-transcript-per-million\" target=\"_blank\">Working Example</a>\r\n\r\n\r\n## Variant analysis\r\n\r\n### Manhattan plot\r\n\r\n`latest update v2.0.1`\r\n\r\n`bioinfokit.visuz.marker.mhat(df, chr, pv, log_scale, color, dim, r, ar, gwas_sign_line, gwasp, dotsize, markeridcol, \r\n markernames, gfont, valpha, show, figtype, axxlabel, axylabel, axlabelfontsize, ylm, gstyle, figname, theme)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`df` |Pandas dataframe object with atleast SNP, chromosome, and P-values columns\r\n`chr` | Name of a column having chromosome numbers [string][default:None]\r\n`pv` | Name of a column having P-values. Must be numeric column [string][default:None]\r\n`log_scale` | Change the values provided in `pv` column to minus log10 scale. If set to `False`, the original values in `pv` will be used. This is useful in case of Fst values. [Boolean (True or False)][default:True]\r\n`color` | List the name of the colors to be plotted. It can accept two alternate colors or the number colors equal to chromosome number. If nothing (None) provided, it will randomly assign the color to each chromosome [list][default:None]\r\n`gwas_sign_line` |Plot statistical significant threshold line defined by option `gwasp` [Boolean (True or False)][default: False]\r\n`gwasp` | Statistical significant threshold to identify significant SNPs [float][default: 5E-08]\r\n`dotsize`| The size of the dots in the plot [float][default: 8]\r\n`markeridcol` | Name of a column having SNPs. This is necessary for plotting SNP names on the plot [string][default: None]\r\n`markernames` | The list of the SNPs to display on the plot. These SNP should be present in SNP column. Additionally, it also accepts the dict of SNPs and its associated gene name. If this option set to True, it will label all SNPs with P-value significant score defined by `gwasp` [string, list, [Tuple](https://www.reneshbedre.com/blog/python-tuples.html), dict][default: True]\r\n`gfont` | Font size for SNP names to display on the plot [float][default: 8]. gfont not compatible with gstyle=2.\r\n`valpha` | Transparency of points on plot [float (between 0 and 1)][default: 1.0]\r\n`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]\r\n`r` | Figure resolution in dpi [int][default: 300]\r\n`ar` | Rotation of X-axis labels [float][default: 90]\r\n`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']\r\n`show` | Show the figure on console instead of saving in current folder [Boolean (True or False)][default:False]\r\n`axxlabel` | Label for X-axis. If you provide this option, default label will be replaced [string][default: None]\r\n`axylabel` | Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]\r\n`axlabelfontsize` | Font size for axis labels [float][default: 9]\r\n`ylm` | Range of ticks to plot on Y-axis [float [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) (bottom, top, interval)][default: None]\r\n`gstyle` | Style of the text for markernames. 1 for default text and 2 for box text [int][default: 1]\r\n`figname` | name of figure [string][default:\"manhattan\"]\r\n`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']\r\n\r\nReturns:\r\n\r\nManhattan plot image in same directory (Manhattan.png)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/manhattan-plot.html\" target=\"_blank\">Working example</a>\r\n\r\n### Variant annotation \r\n\r\nlatest update v0.9.3\r\n\r\nAssign genetic features and function to the variants in VCF file\r\n\r\n`bioinfokit.analys.marker.vcf_anot(file, id, gff_file, anot_attr)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`file` | VCF file\r\n`id` | chromosome id column in VCF file [string][default='#CHROM']\r\n`gff_file` | GFF3 genome annotation file\r\n`anot_attr` | Gene function tag in attributes field of GFF3 file\r\n\r\nReturns:\r\n\r\nTab-delimited text file with annotation (annotated text file will be saved in same directory)\r\n\r\n<a href=\"https://reneshbedre.com/blog/vcfanot.html\" target=\"_blank\">Working Example</a>\r\n\r\n### Concatenate VCF files\r\n\r\nlatest update v0.9.4\r\n\r\nConcatenate multiple VCF files into single VCF file (for example, VCF files for each chromosome)\r\n\r\n`bioinfokit.analys.marker.concatvcf(file)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`file` | Multiple vcf files separated by comma\r\n\r\nReturns:\r\n\r\nConcatenated VCF file (concat_vcf.vcf)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/mergevcf.html\" target=\"_blank\">Working example</a>\r\n\r\n### Split VCF file\r\n\r\n`bioinfokit.analys.marker.splitvcf(file)`\r\n\r\nSplit single VCF file containing variants for all chromosomes into individual file containing variants for each chromosome\r\n\r\nParameters | Description\r\n------------ | -------------\r\n `file` | VCF file to split\r\n `id` | chromosome id column in VCF file [string][default='#CHROM']\r\n\r\n\r\nReturns:\r\n\r\nVCF files for each chromosome\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/mergevcf.html\" target=\"_blank\">Working example</a>\r\n\r\n\r\n## High-throughput sequence analysis\r\n\r\n### FASTQ batch downloads from SRA database\r\n\r\nlatest update v0.9.7\r\n\r\n`bioinfokit.analys.fastq.sra_bd(file, t, other_opts)`\r\n\r\nFASTQ files will be downloaded using `fasterq-dump`. Make sure you have the latest version of the NCBI SRA toolkit \r\n(version 2.10.8) is installed and binaries are added to the system path\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`file` | List of SRA accessions for batch download. All accession must be separated by a newline in the file. \r\n`t` | Number of threads for parallel run [int][default=4]\r\n`other_opts` | Provide other relevant options for `fasterq-dump` [str][default=None] <br> Provide the options as a space-separated string. You can get a detailed option for `fasterq-dump` using the `-help` option. \r\n\r\nReturns:\r\n\r\nFASTQ files for each SRA accession in the current directory unless specified by `other_opts`\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/ncbi_sra_toolkit.html\" target=\"_blank\">Description and working example</a>\r\n\r\n\r\n\r\n### FASTQ quality format detection\r\n\r\n`bioinfokit.analys.format.fq_qual_var(file)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`file` |FASTQ file to detect quality format [deafult: None]\r\n\r\nReturns:\r\n\r\nQuality format encoding name for FASTQ file (Supports only Sanger, Illumina 1.8+ and Illumina 1.3/1.4)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/fqqualfmt.html\" target=\"_blank\">Working Example</a>\r\n\r\n### Sequencing coverage\r\n\r\nlatest update v0.9.7\r\n\r\n`bioinfokit.analys.fastq.seqcov(file, gs)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`file` | FASTQ file\r\n`gs` | Genome size in Mbp\r\n\r\nReturns:\r\n\r\nSequencing coverage of the given FASTQ file\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/sequencing-coverage.html\" target=\"_blank\">Description and Working example</a>\r\n\r\n\r\n### Split the sequence into smaller subsequences\r\n\r\nlatest update v2.0.6\r\n\r\n`bioinfokit.analys.Fasta.split_seq(seq, seq_size, seq_overlap, any_cond, outfmt)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`seq` | Input sequence [string]\r\n`seq_size` | subsequence size [int][default: 3]\r\n`seq_overlap` | Split the sequence in overlap mode [bool][default: True]\r\n`any_cond` | Split sequence based on a condition. Note yet defined. \r\n`outfmt` | Output format for the subsequences. If parameter set to 'fasta', the file will be saved in same folder with name `output_chunks.fasta` ['list' or 'fasta'][default: 'list']\r\n\r\nReturns:\r\n\r\nSubsequences in list or fasta file (output_chunks.fasta) format\r\n\r\n<a href=\"https://reneshbedre.com/blog/split-seq.html\" target=\"_blank\">Description and Working example</a>\r\n\r\n\r\n### Reverse complement of DNA sequence\r\n\r\nlatest update v2.1.1\r\n\r\n`bioinfokit.analys.Fasta.rev_com(sequence)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`seq` | DNA sequence to perform reverse complement\r\n`file` | DNA sequence in a fasta file\r\n\r\nReturns:\r\n\r\nReverse complement of original DNA sequence\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/reverse-complement-dna-python.html\" target=\"_blank\">Working example</a>\r\n\r\n### File format conversions\r\n\r\n`bioinfokit.analys.format`\r\n\r\nFunction | Parameters | Description\r\n------------|---------- | -------------\r\n`bioinfokit.analys.format.fqtofa(file)` | `FASTQ file` | Convert FASTQ file into FASTA format\r\n`bioinfokit.analys.format.hmmtocsv(file)` | `HMM file` | Convert HMM text output (from HMMER tool) to CSV format\r\n`bioinfokit.analys.format.tabtocsv(file)` | `TAB file` | Convert TAB file to CSV format\r\n`bioinfokit.analys.format.csvtotab(file)` | `CSV file` | Convert CSV file to TAB format\r\n\r\n\r\nReturns:\r\n\r\nOutput will be saved in same directory\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/format.html\" target=\"_blank\">Working example</a>\r\n\r\n### GFF3 to GTF file format conversion\r\n\r\n`latest update v1.0.1`\r\n\r\n`bioinfokit.analys.gff.gff_to_gtf(file, trn_feature_name)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`file` | GFF3 genome annotation file\r\n`trn_feature_name` | Name of the feature (column 3 of GFF3 file) of RNA transcripts if other than 'mRNA' or 'transcript'\r\n\r\nReturns:\r\n\r\nGTF format genome annotation file (file.gtf will be saved in same directory)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/gffgtf.html\" target=\"_blank\">Working Example</a>\r\n\r\n### Bioinformatics file readers and processing (FASTA, FASTQ, and VCF)\r\n\r\nlatest update v2.0.4\r\n\r\nFunction | Parameters | Description\r\n------------|---------- | -------------\r\n`bioinfokit.analys.Fasta.fasta_reader(file)` | `FASTA file` | FASTA file reader\r\n`bioinfokit.analys.fastq.fastq_reader(file)` | `FASTQ file` | FASTQ file reader\r\n`bioinfokit.analys.marker.vcfreader(file)` | `VCF file` | VCF file reader\r\n\r\nReturns:\r\n\r\nFile generator object (can be iterated only once) that can be parsed for the record\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/filereaders.html\" target=\"_blank\">Description and working example</a>\r\n\r\n### Extract subsequence from FASTA files\r\n\r\nlatest update v2.0.4\r\n\r\n`bioinfokit.analys.Fasta.ext_subseq(file, id, st, end, strand)`\r\n\r\nExtract the subsequence of specified region from FASTA file. If the target subsequence region is on minus strand. the\r\nreverse complementary of subsequence will be printed.\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`file` | FASTA file [file]\r\n`id` | The ID of sequence from FASTA file to extract the subsequence [string]\r\n`st` | Start integer coordinate of subsequnece [int] \r\n`end` | End integer coordinate of subsequnece [int]\r\n`strand` | Strand of the subsequence ['plus' or 'minus'][default: 'plus'] \r\n\r\n<!--\r\n`out_file` | Write subsequence to file instead of stdout [file][default: None] \r\n-->\r\n\r\nReturns:\r\n\r\nSubsequence to stdout \r\n\r\n### Extract sequences from FASTA file\r\n\r\nlatest update v2.1.3\r\n\r\n`bioinfokit.analys.Fasta.extract_seq(file, id)`\r\n\r\nExtract the sequences from FASTA file based on the list of sequence IDs provided from other file\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`file` | FASTA file [file] \r\n`id` | List of sequence IDs separated by new line. This file can also contain the ID, start and end coordinates separated by TAB [file]\r\n\r\nReturns:\r\n\r\nSequences extracted from FASTA file based on the given IDs provided in id file. Output FASTA file will be saved as \r\noutput.fasta in current working directory.\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/extract-seq-fasta-python.html\" target=\"_blank\">Description and working example</a>\r\n\r\n\r\n### Split FASTA file into multiple FASTA files\r\n\r\nlatest update v2.0.4\r\n\r\n`bioinfokit.analys.Fasta.split_fasta(file, n, bases_per_line)`\r\n\r\nSplit one big FASTA file into multiple smaller FASTA files\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`file` | FASTA file [file] \r\n`n` | Number of FASTA files to split the big FASTA file [int][default: 2]\r\n`bases_per_line` | Number of bases per line for ouput FASTA files [int][default: 60]\r\n\r\nReturns:\r\n\r\nNumber of smaller FASTA files with prefix output (output_0.fasta, output_1.fasta and so on)\r\n\r\n### Convert multi-line FASTA into single-line FASTA\r\n\r\nlatest update v2.1.2\r\n\r\n`bioinfokit.analys.Fasta.multi_to_single_line(file)`\r\n\r\nConvert multi-line FASTA (where sequences are on multi lines) into single-line FASTA (where sequences are in single line)\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`file` | FASTA file [file] \r\n\r\nReturns:\r\n\r\nSingle line FASTA (output.fasta). Output FASTA file will be saved as output.fasta in current working directory.\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/convert-multi-single-line-fasta.html\" target=\"_blank\">Description and working example</a>\r\n\r\n\r\n### Merge counts files from featureCounts\r\n\r\nlatest update v2.0.5\r\n\r\n`bioinfokit.analys.HtsAna.merge_featureCount(pattern, gene_column_name)`\r\n\r\nMerge counts files generated from featureCounts when it runs individually on large samples. The count files must be in\r\nsame folder and should end with .txt file extension.\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`pattern` | file name pattern for each count file [default: '*.txt'] \r\n`gene_column_name` | gene id column name for feature and meta-features [default: 'Geneid']\r\n\r\nReturns:\r\n\r\nMerge count file (gene_matrix_count.csv) in same folder\r\n\r\n### Split BED file by chromosome\r\n\r\nlatest update v2.0.9\r\n\r\n`bioinfokit.analys.HtsAna.split_bed(bed)`\r\n\r\nSplit the BED file by chromosome names\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`bed` | Input BED file [default: None] \r\n\r\nReturns:\r\n\r\nBED file for each chromosome (files will be saved in same directory)\r\n\r\n[Working example](https://www.reneshbedre.com/blog/bedtools-genomics.html#38-split-bed-file-by-chromosome)\r\n\r\n### Max and Min sequence lengths from Fasta\r\n\r\nlatest update v2.1.4\r\n\r\n`bioinfokit.analys.Fasta.max_min_len(fasta)`\r\n\r\nFind Max and Min sequence lengths from Fasta\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`fasta` | Input Fasta file [default: None] \r\n\r\nReturns:\r\n\r\nMax and Min sequence lengths from Fasta file\r\n\r\n[Working example](https://www.reneshbedre.com/blog/bedtools-genomics.html#38-split-bed-file-by-chromosome)\r\n\r\n## Functional enrichment analysis\r\n\r\n### Gene family enrichment analysis (GenFam) \r\n\r\nlatest update v1.0.0\r\n\r\n`bioinfokit.analys.genfam.fam_enrich(id_file, species, id_type, stat_sign_test, multi_test_corr, min_map_ids, alpha)`\r\n\r\nGenFam is a comprehensive classification and enrichment analysis tool for plant genomes. It provides a unique way to \r\ncharacterize the large-scale gene datasets such as those from transcriptome analysis (read <a href='https://onlinelibrary.wiley.com/doi/full/10.1002/pld3.191'>GenFam</a> paper for more details)\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`id_file` | Text file containing the list of gene IDs to analyze using GenFam. IDs must be separated by newline.\r\n`species` | Plant species ID for GenFam analysis. All plant species ID provided [here](https://github.com/reneshbedre/reneshbedre.github.io/blob/master/assets/posts/genfam/genfam_species_id.md)\r\n`id_type` | Plant species ID type <br> <strong><em>1</em></strong>: Phytozome locus ID <br> <strong><em>2</em></strong>: Phytozome transcript ID <br> <strong><em>3</em></strong>: Phytozome PAC ID <br> \r\n`stat_sign_test` | Statistical significance test for enrichment analysis [default=1]. <br> <strong><em>1</em></strong>: Fisher exact test <br> <strong><em>2</em></strong>: Hypergeometric distribution <br> <strong><em>3</em></strong>: Binomial distribution <br> <strong><em>4</em></strong>: Chi-squared distribution <br>\r\n`multi_test_corr` | Multiple testing correction test [default=3]. <br> <strong><em>1</em></strong>: Bonferroni <br> <strong><em>2</em></strong>: Bonferroni-Holm <br> <strong><em>3</em></strong>: Benjamini-Hochberg <br> \r\n`min_map_ids` | Minimum number of gene IDs from the user list (`id_file`) must be mapped to the background database for performing GenFam analysis [default=5]\r\n`alpha` | Significance level [float][default: 0.05]\r\n\r\nReturns:\r\n\r\nAttribute | Description\r\n------------ | -------------\r\ndf_enrich | Enriched gene families with p < 0.05\r\ngenfam_info | GenFam run information\r\nOutput files | Output figures and files from GenFam analysis <br> <strong><em>genfam_enrich.png</em></strong>: GenFam figure for enriched gene families <br> <strong><em>fam_enrich_out.txt</em></strong>: List of enriched gene families with mapped gene IDs, GO annotation, and detailed statistics <br> <strong><em>fam_all_out.txt</em></strong>: List of all gene families with mapped gene IDs, GO annotation, and detailed statistics\r\n\r\n<a href=\"https://reneshbedre.github.io/blog/genfam.html\" target=\"_blank\">Description and working example</a>\r\n\r\n### Check allowed ID types for plant species for GenFam\r\n\r\nlatest update v1.0.0\r\n\r\n`bioinfokit.analys.genfam.check_allowed_ids(species)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`species` | Plant species ID to check for allowed ID type. All plant species ID provided [here](https://github.com/reneshbedre/reneshbedre.github.io/blob/master/assets/posts/genfam/genfam_species_id.md)\r\n\r\nReturns:\r\n\r\nAllowed ID types for GenFam\r\n\r\n<a href=\"https://reneshbedre.github.io/blog/genfam.html\" target=\"_blank\">Description and working example</a>\r\n\r\n\r\n\r\n## Biostatistical analysis\r\n\r\n### Correlation matrix plot\r\n\r\nlatest update v2.0.1\r\n\r\n`bioinfokit.visuz.stat.corr_mat(table, corm, cmap, r, dim, show, figtype, axtickfontsize, axtickfontname, theme)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`table` | Dataframe object with numerical variables (columns) to find correlation. Ideally, you should have three or more variables. Dataframe should not have identifier column.\r\n`corm` | Correlation method [pearson,kendall,spearman] [default:pearson]\r\n`cmap` | Color Palette for heatmap [string][default: 'seismic']. More colormaps are available at https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html\r\n`r` | Figure resolution in dpi [int][default: 300]. Not compatible with `show`= True\r\n`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 5)] \r\n`show` | Show the figure on console instead of saving in current folder [True or False][default:False]\r\n`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']\r\n`axtickfontsize` | Font size for axis ticks [float][default: 7]\r\n`axtickfontname` | Font name for axis ticks [string][default: 'Arial']\r\n`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']\r\n\r\n\r\nReturns:\r\n\r\nCorrelation matrix plot image in same directory (corr_mat.png)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/correlation-analysis.html\" target=\"_blank\">Working example</a>\r\n\r\n\r\n### Bar-dot plot\r\n\r\n`latest update v0.8.5`\r\n\r\n`bioinfokit.visuz.stat.bardot(df, colorbar, colordot, bw, dim, r, ar, hbsize, errorbar, dotsize, markerdot, valphabar, \r\n valphadot, show, figtype, axxlabel, axylabel, axlabelfontsize, axlabelfontname, ylm, axtickfontsize, axtickfontname,\r\n yerrlw, yerrcw)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`df` |Pandas dataframe object\r\n`colorbar` | Color of bar graph [string or list][default:\"#bbcfff\"]\r\n`colordot` | Color of dots on bar [string or list][default:\"#ee8972\"]\r\n`bw` |Width of bar [float][default: 0.4]\r\n`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]\r\n`r` | Figure resolution in dpi [int][default: 300]\r\n`ar` | Rotation of X-axis labels [float][default: 0]\r\n`hbsize` | Horizontal bar size for standard error bars [float][default: 4]\r\n`errorbar` | Draw standard error bars [bool (True or False)][default: True]\r\n`dotsize`| The size of the dots in the plot [float][default: 6]\r\n`markerdot` | Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: \"o\"]\r\n`valphabar` | Transparency of bars on plot [float (between 0 and 1)][default: 1]\r\n`valphadot` | Transparency of dots on plot [float (between 0 and 1)][default: 1]\r\n`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']\r\n`show` | Show the figure on console instead of saving in current folder [True or False][default:False]\r\n`axxlabel` | Label for X-axis. If you provide this option, default label will be replaced [string][default: None]\r\n`axylabel` | Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]\r\n`axlabelfontsize` | Font size for axis labels [float][default: 9]\r\n`axlabelfontname` | Font name for axis labels [string][default: 'Arial']\r\n`ylm` | Range of ticks to plot on Y-axis [float [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) (bottom, top, interval)][default: None]\r\n`axtickfontsize` | Font size for axis ticks [float][default: 9]\r\n`axtickfontname` | Font name for axis ticks [string][default: 'Arial']\r\n`yerrlw` | Error bar line width [float][default: None]\r\n`yerrcw` | Error bar cap width [float][default: None]\r\n\r\nReturns:\r\n\r\nBar-dot plot image in same directory (bardot.png)\r\n\r\n<a href=\"https://reneshbedre.github.io/blog/bardot.html\" target=\"_blank\">Working Example</a>\r\n\r\n\r\n### One sample and two sample Z-tests\r\n\r\n`latest update v2.1.0`\r\n\r\n`bioinfokit.analys.stat.ztest(df, x, y, mu, x_std, y_std, alpha, test_type)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`df` | Pandas dataframe for appropriate Z-test. <br> <b>One sample</b>: It should have atleast one variable <br> <b>Two sample independent</b>: It should have atleast two variables \r\n`x` | column name for x group [string][default: None]\r\n`y` | column name for x group [string][default: None]\r\n`mu` | Population or known mean for the one sample Z-test [float][default: None]\r\n`x_std` | Population standard deviation for x group [float][default: None]\r\n`y_std` | Population standard deviation for y group [float][default: None]\r\n`alpha` | Significance level for confidence interval (CI). If alpha=0.05, then 95% CI will be calculated [float][default: 0.05]\r\n`test_type` | Type of Z-test [int (1,2)][default: None]. <br> <strong><em>1</em></strong>: One sample Z-test <br> <strong><em>2</em></strong>: Two sample Z-test \r\n\r\nReturns:\r\n\r\nSummary output as class attribute (summary and result) \r\n\r\n<a href=\"https://reneshbedre.com/blog/ttest.html\" target=\"_blank\">Description and Working example</a>\r\n\r\n\r\n### One sample and two sample (independent and paired) t-tests\r\n\r\n`latest update v2.1.0`\r\n\r\n`bioinfokit.analys.stat.ttest(df, xfac, res, evar, alpha, test_type, mu)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`df` | Pandas dataframe for appropriate t-test. <br> <b>One sample</b>: It should have atleast dependent (res) variable <br> <b>Two sample independent</b>: It should have independent (xfac) and dependent (res) variables <br> <b>Two sample paired</b>: It should have two dependent (res) variables\r\n`xfac` | Independent group column name with two levels [string][default: None]\r\n`res` | Dependent variable column name [string or list or [Tuple](https://www.reneshbedre.com/blog/python-tuples.html)][default: None]\r\n`evar` | t-test with equal variance [bool (True or False)][default: True]\r\n`alpha` | Significance level for confidence interval (CI). If alpha=0.05, then 95% CI will be calculated [float][default: 0.05]\r\n`test_type` | Type of t-test [int (1,2,3)][default: None]. <br> <strong><em>1</em></strong>: One sample t-test <br> <strong><em>2</em></strong>: Two sample independent t-test <br> <strong><em>3</em></strong>: Two sample paired t-test\r\n`mu` | Population or known mean for the one sample t-test [float][default: None]\r\n\r\nReturns:\r\n\r\nSummary output as class attribute (summary and result) \r\n\r\n<a href=\"https://reneshbedre.com/blog/ttest.html\" target=\"_blank\">Description and Working example</a>\r\n\r\n\r\n\r\n### Chi-square test \r\n\r\n`latest update v0.9.5`\r\n\r\n`bioinfokit.analys.stat.chisq(df, p)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`df` | Pandas dataframe. It should be one or two-dimensional contingency table. \r\n`p` | Theoretical expected probabilities for each group. It must be non-negative and sum to 1. If p is provide Goodness of Fit test will be performed [list or [Tuple](https://www.reneshbedre.com/blog/python-tuples.html)][default: None] \r\n\r\n\r\nReturns:\r\n\r\nSummary and expected counts as class attributes (summary and expected_df)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/chi-square-test.html\" target=\"_blank\">Working example</a>\r\n\r\n<!--\r\n### One-way ANOVA\r\n\r\n`bioinfokit.stat.oanova(table, res, xfac, ph, phalpha)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`table` | Pandas dataframe in stacked table format\r\n`res` | Response variable (dependent variable) [string][default: None]\r\n`xfac` | Treatments or groups or factors (independent variable) [string][default: None]\r\n`ph` | perform pairwise comparisons with Tukey HSD test [bool (True or False)] [default: False]\r\n`phalpha` |significance level Tukey HSD test [float (0 to 1)][default: 0.05]\r\n\r\n\r\nReturns:\r\n\r\nANOVA summary, multiple pairwise comparisons, and assumption tests statistics\r\n\r\n<a href=\"https://reneshbedre.github.io/blog/oanova.html\" target=\"_blank\">Working example</a>\r\n-->\r\n\r\n### Linear regression analysis\r\n\r\n`bioinfokit.visuz.stat.lin_reg(df, x, y)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`df` |Pandas dataframe object\r\n`x` | Name of column having independent X variables [list][default:None]\r\n`y` | Name of column having dependent Y variables [list][default:None]\r\n\r\nReturns:\r\n\r\nRegression analysis summary\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/linear-regression.html\" target=\"_blank\">Working Example</a>\r\n\r\n### Regression plot\r\n\r\nlatest update v2.0.1\r\n\r\n`bioinfokit.visuz.stat.regplot(df, x, y, yhat, dim, colordot, colorline, r, ar, dotsize, markerdot, linewidth, \r\n valphaline, valphadot, show, figtype, axxlabel, axylabel, axlabelfontsize, axlabelfontname, xlm, ylm, axtickfontsize,\r\n axtickfontname, theme)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`df` | Pandas dataframe object\r\n`x` | Name of column having independent X variables [string][default:None]\r\n`y` | Name of column having dependent Y variables [string][default:None]\r\n`yhat` |Name of column having predicted response of Y variable (y_hat) from regression [string][default:None]\r\n`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (6, 4)]\r\n`r` | Figure resolution in dpi [int][default: 300]\r\n`ar` | Rotation of X-axis labels [float][default: 0]\r\n`dotsize`| The size of the dots in the plot [float][default: 6]\r\n`markerdot` | Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: \"o\"]\r\n`valphaline` | Transparency of regression line on plot [float (between 0 and 1)][default: 1]\r\n`valphadot` | Transparency of dots on plot [float (between 0 and 1)][default: 1]\r\n`linewidth` | Width of regression line [float][default: 1]\r\n`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']\r\n`show` | Show the figure on console instead of saving in current folder [True or False][default:False]\r\n`axxlabel` | Label for X-axis. If you provide this option, default label will be replaced [string][default: None]\r\n`axylabel` | Label for Y-axis. If you provide this option, default label will be replaced [string][default: None]\r\n`axlabelfontsize` | Font size for axis labels [float][default: 9]\r\n`axlabelfontname` | Font name for axis labels [string][default: 'Arial']\r\n`xlm` | Range of ticks to plot on X-axis [float [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) (bottom, top, interval)][default: None]\r\n`ylm` | Range of ticks to plot on Y-axis [float [Tuple](https://www.reneshbedre.com/blog/python-tuples.html) (bottom, top, interval)][default: None]\r\n`axtickfontsize` | Font size for axis ticks [float][default: 9]\r\n`axtickfontname` | Font name for axis ticks [string][default: 'Arial']\r\n`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']\r\n\r\n\r\nReturns:\r\n\r\nRegression plot image in same directory (reg_plot.png)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/linear-regression.html\" target=\"_blank\">Working Example</a>\r\n\r\n\r\n### Tukey HSD test\r\n\r\n`latest update v1.0.3`\r\n\r\n`bioinfokit.analys.stat.tukey_hsd(df, res_var, xfac_var, anova_model, phalpha, ss_typ)`\r\n\r\nIt performs multiple pairwise comparisons of treatment groups using Tukey's HSD (Honestly Significant Difference) test \r\nto check if group means are significantly different from each other. It uses the Tukey-Kramer approach if the sample sizes\r\nare unequal among the groups.\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`df` | Pandas dataframe with the variables mentioned in the `res_var`, `xfac_var` and `anova_model` options. It should not have missing data. The missing data will be omitted.\r\n`res_var` | Name of a column having response variable [string][default: None]\r\n`xfac_var` | Name of a column having factor or group for pairwise comparison [string][default: None]\r\n`anova_model` | ANOVA model (calculated using statsmodels `ols` function) [string][default: None]\r\n`phalpha` | Significance level [float][default: 0.05]\r\n`ss_typ` | Type of sum of square to perform ANOVA [int][default: 2]\r\n\r\nReturns:\r\n\r\nAttribute | Description\r\n------------ | -------------\r\n`tukey_summary` | Pairwise comparisons for main and interaction effects by Tukey HSD test \r\n\r\n<a href=\"https://www.reneshbedre.com/blog/anova.html\" target=\"_blank\">Description and Working example</a>\r\n\r\n### Bartlett's test\r\n\r\n`latest update v1.0.3`\r\n\r\n`bioinfokit.analys.stat.bartlett(df, xfac_var, res_var)`\r\n\r\nIt performs Bartlett's test to check the homogeneity of variances among the treatment groups. It accepts the input \r\ntable in a stacked format. More details https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.bartlett.html\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`df` | Pandas dataframe containing response (`res_var`) and independent variables (`xfac_var`) in a stacked format. It should not have missing data. The missing data will be omitted.\r\n`res_var` | Name of a column having response variable [string][default: `None`]\r\n`xfac_var` | Name of a column having treatment groups (independent variables) [string or list][default: `None`]\r\n\r\nReturns:\r\n\r\nAttribute | Description\r\n------------ | -------------\r\n`bartlett_summary` | Pandas dataframe containing Bartlett's test statistics, degree of freedom, and <i>p</i> value\r\n\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/anova.html#test-anova-assumptions\" target=\"_blank\">Description and Working example</a>\r\n\r\n\r\n### Levene's test\r\n\r\n`latest update v1.0.3`\r\n\r\n`bioinfokit.analys.stat.levene(df, xfac_var, res_var)`\r\n\r\nIt performs Levene's test to check the homogeneity of variances among the treatment groups. It accepts the input \r\ntable in a stacked format. More details https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.levene.html\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`df` | Pandas dataframe containing response (`res_var`) and independent variables (`xfac_var`) in a stacked format. It should not have missing data. The missing data will be omitted.\r\n`res_var` | Name of a column having response variable [string][default: `None`]\r\n`xfac_var` | Name of a column having treatment groups (independent variables) [string or list][default: `None`]\r\n`center` | Choice for the Levene's test [string (`median`, `mean`, `trimmed`)] [default: `median`] <br> <strong><em>median</em></strong>: Brown-Forsythe Levene-type test <br> <strong><em>mean</em></strong>: original Levene's test <br> <strong><em>trimmed</em></strong>: Brown-Forsythe Levene-type test \r\n\r\nReturns:\r\n\r\nAttribute | Description\r\n------------ | -------------\r\n`levene_summary` | Pandas dataframe containing Levene's test statistics, degree of freedom, and <i>p</i> value\r\n\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/anova.html#test-anova-assumptions\" target=\"_blank\">Description and Working example</a>\r\n\r\n### ROC plot\r\n\r\n`latest update v2.0.1`\r\n\r\n`bioinfokit.visuz.stat.roc(fpr, tpr, c_line_style, c_line_color, c_line_width, diag_line, diag_line_style, \r\n diag_line_width, diag_line_color, auc, shade_auc, shade_auc_color, axxlabel, axylabel, axtickfontsize, \r\n axtickfontname, axlabelfontsize, axlabelfontname, plotlegend, legendpos, legendanchor, legendcols, legendfontsize,\r\n legendlabelframe, legend_columnspacing, dim, show, figtype, figname, r, ylm, theme)`\r\n\r\nReceiver operating characteristic (ROC) curve for visualizing classification performance\r\n\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`fpr` | Increasing false positive rates obtained from `sklearn.metrics.roc_curve` [list][default:None]\r\n`tpr` | Increasing true positive rates obtained from `sklearn.metrics.roc_curve` [list][default:None]\r\n`c_line_style` | Line style for ROC curve [string][default:'-']\r\n`c_line_color` | Line color for ROC curve [string][default:'#f05f21']\r\n`c_line_width` | Line width for ROC curve [float][default:1]\r\n`diag_line` | Plot reference line [True or False][default: True]\r\n`diag_line_style` | Line style for reference line [string][default:'--']\r\n`diag_line_width` | Line width for reference line [float][default:1]\r\n`diag_line_color` | Line color for reference line [string][default:'b']\r\n`auc` | Area under ROC. It can be obtained from `sklearn.metrics.roc_auc_score` [float][default: None]\r\n`shade_auc`| Shade are for AUC [True or False][default: False]\r\n`shade_auc_color` | Shade color for AUC [string][default: '#f48d60']\r\n`axxlabel` | Label for X-axis [string][default: 'False Positive Rate (1 - Specificity)']\r\n`axylabel` | Label for Y-axis [string][default: 'True Positive Rate (Sensitivity)']\r\n`axtickfontsize` | Font size for axis ticks [float][default: 9]\r\n`axtickfontname` | Font name for axis ticks [string][default: 'Arial']\r\n`axlabelfontsize` | Font size for axis labels [float][default: 9]\r\n`axlabelfontname` | Font name for axis labels [string][default: 'Arial']\r\n`plotlegend` | plot legend [True or False][default:True]\r\n`legendpos` | position of the legend on plot. For more options see loc parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [string ][default:'lower right']\r\n`legendanchor` | position of the legend outside of the plot. For more options see bbox_to_anchor parameter at https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html [list][default:None]\r\n`legendcols` | Number of columns for legends [int][default: 1]\r\n`legendfontsize` | Font size for the legends [float][default:8]\r\n`legendlabelframe` | Box frame for the legend [True or False][default: False]\r\n`legend_columnspacing` | Spacing between the legends [float][default: None]\r\n`dim` | Figure size [[Tuple](https://www.reneshbedre.com/blog/python-tuples.html) of two floats (width, height) in inches][default: (5, 4)]\r\n`show` | Show the figure on console instead of saving in current folder [True or False][default:False]\r\n`figtype` | Format of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']\r\n`figname` | name of figure [string ][default:'roc']\r\n`r` | Figure resolution in dpi [int][default: 300]. Not compatible with `show`= True\r\n`ylm` | Range of ticks to plot on Y-axis [float (bottom, top, interval)][default: None]\r\n`theme` | Change background theme. If theme set to `dark`, the dark background will be produced instead of white [string][default:'None']\r\n\r\nReturns:\r\n\r\nROC plot image in same directory (roc.png)\r\n<a href=\"https://www.reneshbedre.com/blog/logistic-regression.html#prediction-of-test-dataset-using-fitted-model\" target=\"_blank\">Working example</a>\r\n\r\n\r\n### Regression metrics\r\n\r\nCalculate Root Mean Square Error (RMSE), Mean squared error (MSE), Mean absolute error (MAE), and Mean absolute percent \r\nerror (MAPE) from regression fit\r\n\r\n`latest update v1.0.8`\r\n\r\n`bioinfokit.analys.stat.reg_metric(y, yhat, resid)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`y` | Original values for dependent variable [numpy array] [default: None]\r\n`yhat` | Predicted values from regression [numpy array] [default: None]\r\n`resid` | Regression residuals [numpy array][default: None]\r\n\r\nReturns:\r\n\r\nPandas dataframe with values for RMSE, MSE, MAE, and MAPE\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/linear-regression.html\" target=\"_blank\">Working example</a>\r\n\r\n\r\n\r\n<b>Venn Diagram</b>\r\n\r\n`bioinfokit.visuz.venn(vennset, venncolor, vennalpha, vennlabel)`\r\n\r\nParameters | Description\r\n------------ | -------------\r\n`vennset` | Venn dataset for 3 and 2-way venn. Data should be in the format of (100,010,110,001,101,011,111) for 3-way venn and 2-way venn (10, 01, 11) [default: (1,1,1,1,1,1,1)]\r\n`venncolor` | Color Palette for Venn [color code][default: ('#00909e', '#f67280', '#ff971d')]\r\n`vennalpha` | Transparency of Venn [float (0 to 1)][default: 0.5]\r\n`vennlabel` | Labels to Venn [string][default: ('A', 'B', 'C')]\r\n\r\nReturns:\r\n\r\nVenn plot (venn3.png, venn2.png)\r\n\r\n<a href=\"https://www.reneshbedre.com/blog/venn.html\" target=\"_blank\">Working example</a>\r\n\r\n\r\n\r\n\r\n## References:\r\n- Travis E. Oliphant. A guide to NumPy, USA: Trelgol Publishing, (2006).\r\n- John D. Hunter. Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, 9, 90-95 (2007), \r\n DOI:10.1109/MCSE.2007.55 (publisher link)\r\n- Fernando P\u00c3\u00a9rez and Brian E. Granger. IPython: A System for Interactive Scientific Computing, Computing in Science & \r\n Engineering, 9, 21-29 (2007), DOI:10.1109/MCSE.2007.53 (publisher link)\r\n- Michael Waskom, Olga Botvinnik, Joel Ostblom, Saulius Lukauskas, Paul Hobson, MaozGelbart, \u00e2\u20ac\u00a6 Constantine Evans. \r\n (2020, January 24). mwaskom/seaborn: v0.10.0 (January 2020) (Version v0.10.0). Zenodo. http://doi.org/10.5281/zenodo.3629446\r\n- Fabian Pedregosa, Ga\u00c3\u00abl Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu \r\n Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, \r\n Matthieu Brucher, Matthieu Perrot, \u00c3\u2030douard Duchesnay. Scikit-learn: Machine Learning in Python, Journal of Machine \r\n Learning Research, 12, 2825-2830 (2011)\r\n- Wes McKinney. Data Structures for Statistical Computing in Python, Proceedings of the 9th Python in Science \r\n Conference, 51-56 (2010)\r\n- Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, \r\n Pearu Peterson, Warren Weckesser, Jonathan Bright, St\u00c3\u00a9fan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod \r\n Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, \u00c4\u00b0lhan Polat, Yu \r\n Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E.A. Quintero, \r\n Charles R Harris, Anne M. Archibald, Ant\u00c3\u00b4nio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 \r\n Contributors. (2020) SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17(3), \r\n 261-272. \r\n- David C. Howell. Multiple Comparisons With Unequal Sample Sizes. https://www.uvm.edu/~statdhtx/StatPages/MultipleComparisons/unequal_ns_and_mult_comp.html\r\n\r\n<!--\r\n## bioinfokit cited by 11 research articles:\r\n- Karstensen KT, Schein A, Petri A, B\u00c3\u00b8gsted M, Dybk\u00c3\u00a6r K, Uchida S, Kauppinen S. Long Non-Coding RNAs in Diffuse Large B-Cell Lymphoma. Non-coding RNA. 2021 Mar;7(1):1.\r\n- de Rezende Rodovalho V, da Luz BS, Nicolas A, do Carmo FL, Jardin J, Briard-Bion V, Jan G, Le Loir Y, de Carvalho Azevedo VA, Guedon E. Environmental conditions modulate the protein content and immunomodulatory activity of extracellular vesicles produced by the probiotic Propionibacterium freudenreichii. Applied and Environmental Microbiology. 2020 Dec 11.\r\n- Jarvis L, Rainbow D, Coppard V, Howlett S, Davies J, Mullay H, Hester J, Ashmore T, Van Den Bosch A, Grist J, Coles A. Therapeutically expanded human regulatory T-cells are super-suppressive due to HIF1A induced expression of CD73.\r\n- Al-Bakhat L, Al-Serhani N. LncRNAs and Protein-coding Genes Expression Analysis for Myelodysplastic Syndromes Diagnoses. In2020 International Conference on Artificial Intelligence & Modern Assistive Technology (ICAIMAT) 2020 Nov 24 (pp. 1-6). IEEE.\r\n- Aishwarya S, Gunasekaran K, Margret AA. Computational gene expression profiling in the exploration of biomarkers, non-coding functional RNAs and drug perturbagens for COVID-19. Journal of Biomolecular Structure and Dynamics. 2020 Nov 17:1-6.\r\n- Liang L, Darbandi SF, Pochareddy S, Gulden FO, Gilson MC, Sheppard BK, Sahagun A, An JY, Werling DM, Rubenstein JL, Sestan N. Developmental dynamics of voltage-gated sodium channel isoform expression in the human and mouse neocortex. bioRxiv. 2020 Jan 1.\r\n- Irigoyen S, Ramasamy M, Pant S, Niraula P, Bedre R, Gurung M, Rossi D, Laughlin C, Gorman Z, Achor D, Levy A. Plant hairy roots enable high throughput identification of antimicrobials against Candidatus Liberibacter spp. Nature Communications. 2020 Nov 16;11(1):1-4.\r\n- Lu J, Wilfred P, Korbie D, Trau M. Regulation of Canonical Oncogenic Signaling Pathways in Cancer via DNA Methylation. Cancers. 2020 Nov;12(11):3199.\r\n- Gribble J, Pruijssers AJ, Agostini ML, Anderson-Daniels J, Chappell JD, Lu X, Stevens LJ, Routh AL, Denison MR. The coronavirus proofreading exoribonuclease mediates extensive viral recombination. BioRxiv. 2020 Jan 1. \r\n- Aarts J, Bijma P, Xia S, Textor J, de Vries A. A GWAS about the wingsize of Nasonia Vitripennis.\r\n- Greaney AM, Adams TS, Raredon MS, Gubbins E, Schupp JC, Engler AJ, Ghaedi M, Yuan Y, Kaminski N, Niklason LE. Platform\r\n Effects on Regeneration by Pulmonary Basal Cells as Evaluated by Single-Cell RNA Sequencing. Cell Reports. 2020 Mar\r\n 24;30(12):4250-65.\r\n \r\nSource: https://scholar.google.com/scholar?start=0&q=bioinfokit&hl=en&as_sdt=0,44\r\n-->\r\n\r\nLast updated: November 20, 2021\r\n\r\n",
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