# Welcome to the miTEA-HiRes package!
miTEA-HiRes is an open-source package, designed to easily compute high resolution microRNA activity
maps. This package is used in our paper "Inferring single-cell and spatial microRNA
activity from transcriptomics data" by Herbst et al [1].
If you use miTEA-HiRes in your research, please cite [1].
*** add link and publication
## Installation
You will need Python 3.8 or above.
```
pip install mitea_hires
```
## Support and bug report:
This package is provided by Yakhini research group.
If you encounter any issues, please contact efiherbst through gmail.
# Example 1 - Single-cell data with 'Total' activity mode
In this case, miTEA-HiRes computes the activity for each cell and microRNA, and produces activity
maps on a UMAP layout for the most active microRNAs.
### Input format:
One or more raw counts matrices should be available in the data path, in the form of zipped or
unzipped 'txt', 'tsv', 'mtx' or 'pkl' files.
```
YOUR_DATA_FOLDER
| counts_1.txt
│ counts_2.txt
| ...
```
If 10X files (i.e. 'mtx') are used for single-cell data, your data folder should look as follows:
```
YOUR_DATA_FOLDER
| *_barcodes.tsv.gz
│ *_genes.tsv.gz
│ *.mtx.gz
```
### Usage:
You may use the package via command-line tool:
```
mitea-hires --data_path='PATH_TO_YOUR_DATA' --dataset_name='DATASET_NAME'
```
Or by importing the library into your code:
```
import mitea_hires
data_path = 'PATH_TO_YOUR_DATA'
dataset_name = 'NAME_OF_YOUR_DATASET'
mitea_hires.compute(data_path=data_path, dataset_name=dataset_name)
```
# Example 2 - Single-cell data with 'Comaprative' activity mode
In this case, miTEA-HiRes computes microRNA differential activity for two populations of interest.
microRNA activity for each population is presented on histogram and UMAP layouts for miroRNAs of
potential interest.
### Input format:
Same as in Example 1, with the following required preprocessing:
A unique population string should be included withing each cell id string.
Taking for example one cell from 'CONTROL' population and one from 'DISEASE':
1. Cell string 'AACAATGTGCTCCGAG' should be transformed to 'AACAATGTGCTCCGAG_CONTROL'.
2. Cell string 'AACAGCCTCCTGACTA' should be transformed to 'AACAGCCTCCTGACTA_DISEASE'.
### Usage:
Example using command line:
```
mitea-hires --data_path='PATH_TO_YOUR_DATA' --dataset_name='DATASET_NAME' --populations='DISEASE','CONTROL'
```
# Example 3 - Spatial trascriptomics data
In this case, miTEA-HiRes computes the activity for each spot and microRNA, and produces spatial
activity maps for microRNAs which are most abundantly active throughout the spots.
### Input format:
Make sure you have obtained the following files for your Visium data (these can be downloaded
from Visium website):
> filtered_feature_bc_matrix
>
> spatial
Your data path should then look as follows:
```
YOUR_DATA_FOLDER
└───filtered_feature_bc_matrix
│ │ barcodes.tsv.gz
│ │ features.tsv.gz
│ │ matrix.mtx.gz
└───spatial
│ tissue_positions_list.csv
│ ...
```
### Usage:
```
mitea-hires --data_path='PATH_TO_YOUR_DATA' --dataset_name='DATASET_NAME'
```
# Outputs
'results' folder is generated under the provided data_path unless specified otherwise and contains:
1. csv files with the activity p-values (and other related statistics) for every cell/spot and
every microRNA.
2. html file with sorted microRNAs according to their potential interest, including links to
activity maps.
3. folder with activity map plots.
# Recommended setup
mitea-hires is desinged to utilize all CPUs available for parallel computing, hence in order to
speed up the processing time, you may want to consider using resources with more CPUs.
For example, an input of spatial trascriptomics data including 2,264 spots, ~6,000 genes per spot,
computing activity for 706 microRNAs using a cloud instance with 16 cores takes 22 minutes.
# Advanced usage
run ```mitea-hires --helpfull``` in command line to see additional supported flags.
mitea-hires can also be imported within your python script and then, end-to-end compuation can be
executed calling the 'compute' function. Alternatively, in order to use parts of the computation,
other functions can be called.
## Supported species
miTEA-HiRes currently supports mouse and human.
[1] Inferring single-cell and spatial microRNA activity from transcriptomics data. *Herbst et al.*
Raw data
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"description": "# Welcome to the miTEA-HiRes package!\n\nmiTEA-HiRes is an open-source package, designed to easily compute high resolution microRNA activity \nmaps. This package is used in our paper \"Inferring single-cell and spatial microRNA \nactivity from transcriptomics data\" by Herbst et al [1].\n\nIf you use miTEA-HiRes in your research, please cite [1]. \n*** add link and publication \n\n## Installation\nYou will need Python 3.8 or above.\n```\npip install mitea_hires\n```\n\n## Support and bug report:\nThis package is provided by Yakhini research group.\nIf you encounter any issues, please contact efiherbst through gmail.\n\n# Example 1 - Single-cell data with 'Total' activity mode\nIn this case, miTEA-HiRes computes the activity for each cell and microRNA, and produces activity \nmaps on a UMAP layout for the most active microRNAs.\n### Input format:\nOne or more raw counts matrices should be available in the data path, in the form of zipped or \nunzipped 'txt', 'tsv', 'mtx' or 'pkl' files. \n \n ```\nYOUR_DATA_FOLDER\n| counts_1.txt\n\u2502 counts_2.txt\n| ...\n```\n\nIf 10X files (i.e. 'mtx') are used for single-cell data, your data folder should look as follows: \n ```\nYOUR_DATA_FOLDER\n| *_barcodes.tsv.gz \n\u2502 *_genes.tsv.gz\n\u2502 *.mtx.gz\n```\n### Usage:\nYou may use the package via command-line tool:\n```\nmitea-hires --data_path='PATH_TO_YOUR_DATA' --dataset_name='DATASET_NAME'\n```\n\nOr by importing the library into your code:\n```\nimport mitea_hires\n\ndata_path = 'PATH_TO_YOUR_DATA'\ndataset_name = 'NAME_OF_YOUR_DATASET' \n\nmitea_hires.compute(data_path=data_path, dataset_name=dataset_name)\n```\n\n# Example 2 - Single-cell data with 'Comaprative' activity mode\nIn this case, miTEA-HiRes computes microRNA differential activity for two populations of interest. \nmicroRNA activity for each population is presented on histogram and UMAP layouts for miroRNAs of \npotential interest.\n### Input format:\nSame as in Example 1, with the following required preprocessing:\nA unique population string should be included withing each cell id string.\nTaking for example one cell from 'CONTROL' population and one from 'DISEASE':\n1. Cell string 'AACAATGTGCTCCGAG' should be transformed to 'AACAATGTGCTCCGAG_CONTROL'.\n2. Cell string 'AACAGCCTCCTGACTA' should be transformed to 'AACAGCCTCCTGACTA_DISEASE'.\n### Usage:\nExample using command line:\n```\nmitea-hires --data_path='PATH_TO_YOUR_DATA' --dataset_name='DATASET_NAME' --populations='DISEASE','CONTROL'\n```\n# Example 3 - Spatial trascriptomics data\nIn this case, miTEA-HiRes computes the activity for each spot and microRNA, and produces spatial \nactivity maps for microRNAs which are most abundantly active throughout the spots.\n### Input format:\nMake sure you have obtained the following files for your Visium data (these can be downloaded \nfrom Visium website):\n> filtered_feature_bc_matrix\n>\n> spatial\n\nYour data path should then look as follows:\n ```\nYOUR_DATA_FOLDER\n\u2514\u2500\u2500\u2500filtered_feature_bc_matrix\n\u2502 \u2502 barcodes.tsv.gz\n\u2502 \u2502 features.tsv.gz\n\u2502 \u2502 matrix.mtx.gz\n\u2514\u2500\u2500\u2500spatial\n\t\u2502 tissue_positions_list.csv\n\t\u2502 ...\n```\n### Usage:\n```\nmitea-hires --data_path='PATH_TO_YOUR_DATA' --dataset_name='DATASET_NAME'\n```\n\n# Outputs\n'results' folder is generated under the provided data_path unless specified otherwise and contains:\n1. csv files with the activity p-values (and other related statistics) for every cell/spot and \n every microRNA.\n2. html file with sorted microRNAs according to their potential interest, including links to \n activity maps.\n3. folder with activity map plots.\n\n# Recommended setup\nmitea-hires is desinged to utilize all CPUs available for parallel computing, hence in order to \nspeed up the processing time, you may want to consider using resources with more CPUs.\nFor example, an input of spatial trascriptomics data including 2,264 spots, ~6,000 genes per spot, \ncomputing activity for 706 microRNAs using a cloud instance with 16 cores takes 22 minutes.\n\n# Advanced usage\nrun ```mitea-hires --helpfull``` in command line to see additional supported flags.\n\nmitea-hires can also be imported within your python script and then, end-to-end compuation can be \nexecuted calling the 'compute' function. Alternatively, in order to use parts of the computation, \nother functions can be called.\n\n## Supported species\nmiTEA-HiRes currently supports mouse and human. \n\n[1] Inferring single-cell and spatial microRNA activity from transcriptomics data. *Herbst et al.* \n",
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