semvec/pypi-packages huggingface dataset:
scEasyMode 1.0.1
Wrappers for automating single cell workflows in python
Requires Python: >=3.6

# ScEasyMode: Wrappers for automating single cell workflows in Python

## Features
- Multiseq correction in python using a within-barcode zscore correction
- Plotting for stacked barplots in your dataset
- Mouse cell filtering/separation from mixed dataset
- Scanpy wrapper that simplifies the workflow

## Installation
<b> Install using Pip </b>
```sh
pip3 install scEasyMode
```

<b> Install using a Conda Environment </b>
- You may also use Conda to start an environment with ScEasyMode installed inside it. You can install conda from [here.](https://docs.anaconda.com/anaconda/install/)
- Firstly, clone the repository and create the environment as shown below. Then, activate the environment.

```sh
git clone https://github.com/johnnyUCSF/scEasyMode
cd scEasyMode
conda env create -f environment.yml
conda activate sceasymode_env
```

- Now start your Jupyter Notebook or Python shell inside the conda environment

## Usage
### Load the modules

```python
from scEasyMode import mousefilter
from scEasyMode import clusterplot
from scEasyMode import pymulti
from scEasyMode import sceasy
```

### Demultiplex your samples

```python
import pandas as pd
from scEasyMode import pymulti

# Define parameters

len_10x=16 # Number of bases in cell barcodes
len_umi=12 # Length of UMI
len_multi=15, # Number of bases in the HTO barcodes / HashTag O

fastq_r1 = 'path/to/file'
fastq_r2 = 'path/to/file'
sample_name = 'test_demultiplexing'

cell_BC_file = 'path/to/cell_barcodes' # Counts Matrix after alignment and pre-processing
cell_bcs = pd.read_csv(cell_BC_file, sep='\t', header=None)[0].tolist()

multi_BC_file = 'path/to/barcodes' # Barcodes TSV file from 10x or Illumina
bcsmulti = pd.read_csv(multi_BC_file,sep=',',index_col=1,header=None)
bcsmulti.columns = ['multi']
bcsmulti = bcsmulti['multi'].tolist()

pymulti.pymulti(fastq_r1, fastq_r2, bcsmulti=bcsmulti, bcs10x=cell_bcs,
                len_10x=len_10x, len_multi=len_multi, len_umi=len_umi, split=True,
                hamming=True, median_only=True, sampname= sample_name,  filter_unmapped_reads=True)

# This function will output multiple graphs
# It will also store a matrix of the assigned barcodes in the 'pymulti' directory inside the working directory.
# Note that some reads are unmapped. If you want to retain them, you can do so by specifying filter_unmapped_reads=False.
```
ScEasyMode 1.0.0
Wrappers for automating single cell workflows in python
Requires Python: >=3.6

# ScEasyMode: Wrappers for automating single cell workflows in Python

## Features
- Multiseq correction in python using a within-barcode zscore correction
- Plotting for stacked barplots in your dataset
- Mouse cell filtering/separation from mixed dataset
- Scanpy wrapper that simplifies the workflow

## Installation
<b> Install using Pip </b>
```sh
pip3 install git+https://github.com/johnnyUCSF/scEasyMode#egg='scEasyMode'
```

<b> Install using a Conda Environment </b>
- You may also use Conda to start an environment with ScEasyMode installed inside it. You can install conda from [here.](https://docs.anaconda.com/anaconda/install/)
- Firstly, clone the repository and create the environment as shown below. Then, activate the environment.

```sh
git clone https://github.com/johnnyUCSF/scEasyMode
cd scEasyMode
conda env create -f environment.yml
conda activate sceasymode_env
```

- Now start your Jupyter Notebook or Python shell inside the conda environment

## Usage
### Load the modules

```python
from scEasyMode import mousefilter
from scEasyMode import clusterplot
from scEasyMode import pymulti
from scEasyMode import sceasy
```

### Demultiplex your samples

```python
import pandas as pd
from scEasyMode import pymulti

# Define parameters

len_10x=16 # Number of bases in cell barcodes
len_umi=12 # Length of UMI
len_multi=15, # Number of bases in the HTO barcodes / HashTag O

fastq_r1 = 'path/to/file'
fastq_r2 = 'path/to/file'
sample_name = 'test_demultiplexing'

cell_BC_file = 'path/to/cell_barcodes' # Counts Matrix after alignment and pre-processing
cell_bcs = pd.read_csv(cell_BC_file, sep='\t', header=None)[0].tolist()

multi_BC_file = 'path/to/barcodes' # Barcodes TSV file from 10x or Illumina
bcsmulti = pd.read_csv(multi_BC_file,sep=',',index_col=1,header=None)
bcsmulti.columns = ['multi']
bcsmulti = bcsmulti['multi'].tolist()

pymulti.pymulti(fastq_r1, fastq_r2, bcsmulti=bcsmulti, bcs10x=cell_bcs,
                len_10x=len_10x, len_multi=len_multi, len_umi=len_umi, split=True,
                hamming=True, median_only=True, sampname= sample_name,  filter_unmapped_reads=True)

# This function will output multiple graphs
# It will also store a matrix of the assigned barcodes in the 'pymulti' directory inside the working directory.
# Note that some reads are unmapped. If you want to retain them, you can do so by specifying filter_unmapped_reads=False.
```