| Name | proteomicruler JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | https://github.com/noatgnu/proteomicRuler |
| Summary | Estimate copy number from deep profile MS experiment using the Proteomic Ruler algorithm from Wiśniewski, J. R., Hein, M. Y., Cox, J. and Mann, M. (2014) A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards. Mol Cell Proteomics 13, 3497–3506. |
| upload_time | 2023-03-22 17:49:49 |
| maintainer | |
| docs_url | None |
| author | Toan K. Phung |
| requires_python | >=3.9,<3.12 |
| license | MIT |
| keywords |
proteomic
ruler
histone
mass spectrometry
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
Proteomic Ruler
--
An implementation of the same algorithm from Perseus `Wiśniewski, J. R., Hein, M. Y., Cox, J. and Mann, M. (2014) A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards. Mol Cell Proteomics 13, 3497–3506.` used for estimation of protein copy number from deep profile experiment.
Requirements
--
Python >= 3.9
Installation
--
```bash
pip install proteomicruler
```
Usage
--
In order to use the package, it is required that the input data is loaded into a `pandas.DataFrame` object. The following
basic parameters are also required:
- `accession_id_col` - column name that contains protein accession ids
- `mw_col` - column name that contains molecular weight of proteins
- `ploidy` - ploidy number
- `total_cellular_protein_concentration` - total cellular protein concentration used for calculation of total volume
- `intensity_columns` - list of column names that contain sample intensities
```python
import pandas as pd
accession_id_col = "Protein IDs"
# used as unique index and to directly fetch mw data from UniProt
mw_col = "Mass"
# molecular weight column name
ploidy = 2
# ploidy number
total_cellular_protein_concentration = 200
# cellular protein concentration used for calculation of total volume
filename = r"example_data\example_data.tsv" # example data from Perseus
df = pd.read_csv(filename, sep="\t")
# selecting intensity columns
intensity_columns = df.columns[57:57+16] # select 16 columns starting from column 57th that contain sample intensity
```
If the data does not contain molecular weight information, it is required to fetch it from UniProt.
```python
from proteomicRuler.ruler import add_mw
df = add_mw(df, accession_id_col)
df = df[pd.notnull(df[mw_col])]
df[mw_col] = df[mw_col].astype(float)
```
The RuleR object can be created by passing the `DataFrame` object and the required parameters.
```python
from proteomicRuler.ruler import Ruler
ruler = Ruler(df, intensity_columns, mw_col, accession_id_col, ploidy, total_cellular_protein_concentration) #
ruler.df.to_csv("output.txt", sep="\t", index=False)
```
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"description": "Proteomic Ruler\n--\n\nAn implementation of the same algorithm from Perseus `Wi\u015bniewski, J. R., Hein, M. Y., Cox, J. and Mann, M. (2014) A \u201cProteomic Ruler\u201d for Protein Copy Number and Concentration Estimation without Spike-in Standards. Mol Cell Proteomics 13, 3497\u20133506.` used for estimation of protein copy number from deep profile experiment.\n\nRequirements\n--\n\nPython >= 3.9\n\nInstallation\n--\n```bash\npip install proteomicruler\n```\n\nUsage\n--\n\nIn order to use the package, it is required that the input data is loaded into a `pandas.DataFrame` object. The following\nbasic parameters are also required:\n- `accession_id_col` - column name that contains protein accession ids\n- `mw_col` - column name that contains molecular weight of proteins\n- `ploidy` - ploidy number\n- `total_cellular_protein_concentration` - total cellular protein concentration used for calculation of total volume\n- `intensity_columns` - list of column names that contain sample intensities\n\n```python\nimport pandas as pd\n\naccession_id_col = \"Protein IDs\"\n# used as unique index and to directly fetch mw data from UniProt\n\nmw_col = \"Mass\"\n# molecular weight column name\n\nploidy = 2\n# ploidy number\n\ntotal_cellular_protein_concentration = 200\n# cellular protein concentration used for calculation of total volume\n\nfilename = r\"example_data\\example_data.tsv\" # example data from Perseus\ndf = pd.read_csv(filename, sep=\"\\t\")\n\n# selecting intensity columns\nintensity_columns = df.columns[57:57+16] # select 16 columns starting from column 57th that contain sample intensity\n\n\n\n```\n\nIf the data does not contain molecular weight information, it is required to fetch it from UniProt.\n\n```python\nfrom proteomicRuler.ruler import add_mw\n\ndf = add_mw(df, accession_id_col)\ndf = df[pd.notnull(df[mw_col])]\ndf[mw_col] = df[mw_col].astype(float)\n```\n\nThe RuleR object can be created by passing the `DataFrame` object and the required parameters.\n\n```python\nfrom proteomicRuler.ruler import Ruler\n\nruler = Ruler(df, intensity_columns, mw_col, accession_id_col, ploidy, total_cellular_protein_concentration) #\nruler.df.to_csv(\"output.txt\", sep=\"\\t\", index=False)\n```\n",
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