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The complete docs are available at [screenpro2.rtfd.io](https://screenpro2.readthedocs.io).

## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
* [Load Data](#load-data)
* [Perform Screen Processing Analysis](#perform-screen-processing-analysis)
+ [Drug Screen Workflow: calculate `gamma`, `rho`, and `tau` scores](#drug-screen-workflow-calculate--gamma----rho----and--tau---scores)
+ [Flow cytometry based screen workflow: calculate phenotype score to compare high and low bins](#flow-cytometry-based-screen-workflow-calculate-phenotype-score-to-compare-high-and-low-bins)
- [Supported CRISPR Screen Platforms](#supported-crispr-screen-platforms)
+ [CRISPRi-dual-sgRNA-screens](#crispri-dual-sgrna-screens)
- [License](#license)
- [Citation](#citation)

## Introduction
This package is conceptually similar to the [**ScreenProcessing**](https://github.com/mhorlbeck/ScreenProcessing)
pipeline but **ScreenPro2** is designed to be more modular, flexible, and extensible as the field of Functional
Genomics evolves and newer CRISPR screen platforms are developed. Common CRISPR screen methods that we have
implemented here are illustrated in a recent review paper:

> From: [A new era in functional genomics screens](https://www.nature.com/articles/s41576-021-00409-w)
> Fig. 1: Common types of CRISPR screening modalities indicating advances in CRISPR methods.
> <img width="1000" alt="image" src="https://github.com/GilbertLabUCSF/ScreenPro2/assets/53412130/a39400ad-b24f-4859-b6e7-b4d5f269119c">
## Installation
ScreenPro2 is available on PyPI and can be installed with pip:
```bash
pip install ScreenPro2
```
___
For the latest version (development version) install from GitHub:
```bash
pip install git+https://github.com/ArcInstitute/ScreenPro2.git
```

## Usage
Note that ScreenPro2 starts with a counts matrix of oligo counts (samples x oligos) so you will need to process your
raw sequencing data into a counts matrix before using ScreenPro2.

### Load Data
First, load your data into an `AnnData` object (see [anndata](https://anndata.readthedocs.io/en/latest/index.html) for
more information).

The `AnnData` object should have the following structure:
- `adata.X` should be a pandas dataframe of counts (samples x oligos)
- `adata.obs` should be a pandas dataframe of sample metadata including "condition" and "replicate" columns
- `adata.var` should be a pandas dataframe of oligo metadata including "target" and "targetType" columns
- "target" column should be the gene name or other identifier for the reference oligo
- "targetType" column should be the type of reference oligo. Currently, negative control oligos should have
`"targetType" == "negCtrl"`

Then you need create a `ScreenPro` object. Here is an example code making a `ScreenPro` object from an `AnnData` object:

```python
import pandas as pd
import anndata as ad
import screenpro as scp

adata = ad.AnnData(
X = counts_df, # pandas dataframe of counts (samples x oligos)
obs = meta_df, # pandas dataframe of sample metadata including "condition" and "replicate" columns
var = target_df # pandas dataframe of oligo metadata including "target" and "targetType" columns
)

screen = scp.ScreenPro(adata)
```
<img width="600" alt="image" src="https://github.com/abearab/ScreenPro2/assets/53412130/d1c8c3ad-3668-4390-8b1d-bf72b591a927">

### Perform Screen Processing Analysis
Once the `ScreenPro` object is created, you can use several available workflows to calculate the enrichment of each oligo
between screen arms.

#### Drug Screen Workflow: calculate `gamma`, `rho`, and `tau` scores
`.calculateDrugScreen` method can be used to calculate the enrichment of each gene between screen arms for a drug
screen experiment. This method calculates `gamma`, `rho`, and `tau` scores for each gene and adds them to the
`.phenotypes` attribute of the `ScreenPro` object.

Here is an example for running the workflow on a [CRISPRi-dual-sgRNA-screens](#crispri-dual-sgrna-screens) dataset:

```python
# Run the ScreenPro2 workflow for CRISPRi-dual-sgRNA-screens
screen.calculateDrugScreen(
t0='T0',
untreated='DMSO', # replace with the label for untreated condition
treated='Drug', # replace with the label for treated condition
db_untreated=1, # replace with doubling rate of untreated condition
db_treated=1, # replace with doubling rate of treated condition
score_level='compare_reps'
)
```
___
For example, in a Decitabine CRISPRi drug screen (see Figure 1B-C in [this bioRxiv paper](https://www.biorxiv.org/content/10.1101/2022.12.14.518457v2.full)), each phenotype score represents a comparison between different arms of the screen and `rho` scores shows the main drug phenotype as illustrated here:
<img width="800" alt="image" src="https://github.com/abearab/ScreenPro2/assets/53412130/b84b3e1f-e049-4da6-b63d-d4c72bc97cda">

#### Flow cytometry based screen workflow: calculate phenotype score to compare high and low bins
`.calculateFlowBasedScreen` method can be used to calculate the enrichment of each target between high bin vs. low bin
of a flow cytometry-based screen experiment. This method calculates `PhenoScore` for each target and adds them to the
`.phenotypes` attribute of the `ScreenPro` object.

```python
# Run the ScreenPro2 workflow for CRISPRi-dual-sgRNA-screens
screen.calculateFlowBasedScreen(
low_bin='low_bin', high_bin='high_bin',
score_level='compare_reps'
)
```
## Supported CRISPR Screen Platforms
One of the main goals of ScreenPro2 is to make it easy to process data from commonly used CRISPR screen platforms.
Also, it is designed to be modular to enable easy extension to custom CRISPR screen platforms or other commonly used
platforms in addition to the ones currently implemented.

___
Currently, ScreenPro2 has easy-to-use workflows for the following CRISPR screen platforms:
#### CRISPRi-dual-sgRNA-screens
[Replogle et al., _eLife_ (2022)](https://elifesciences.org/articles/81856)

Replogle et al. developed a CRISPRi screening platform that uses two sgRNAs per gene within a single plasmid, and it has
been used to perform genome-scale CRISPRi screens. If you follow the codes in the provided [GitHub repository](https://github.com/josephreplogle/CRISPRi-dual-sgRNA-screens), you
will end up with oligo counts and once you make `ScreenPro` object, you can use the ScreenPro2 workflow for this
platform to calculate the enrichment of each gene between screen arms.

## License
ScreenPro2 is licensed under the terms of the MIT license (see [LICENSE](LICENSE) for more information) and developed
by Abolfazl (Abe) Arab ([@abearab](https://github.com/abearab)), a Research Associate in the Gilbert lab at UCSF and Arc Institute.

## Citation
If you use ScreenPro2 in your research, please cite the following paper.
__[This is an archived repository, see the current version in Arc Institute GitHub]__
https://github.com/ArcInstitute/ScreenPro2

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