Developer guild¶

Below we describe main components of the framework, and how to extend the existing implementations.

Framework¶

The omicverse code is stored in the omicverse folder in the github repository, with the __init__.py file taking care of the import of the library functions.

A omicverse framework is primarily composed of 5 components.

• utils: Functions, including data, plotting, etc.
• pp: preprocess, including quantity control, normalize, etc.
• bulk: to analysis the bulk omic-seq like RNA-seq or Proper-seq.
• single: to analysis the single cell omic-seq like scRNA-seq or scATAC-seq
• space: to analysis the spatial RNA-seq
• bulk2single: to integrate the bulk RNA-seq and single cell RNA-seq

The __init__.py file is responsible for importing function entries within each folder, and all function functions use a file starting with _*.py for function writing.

For Developer¶

If you want to provide pull request for omicverse, you need to be clear about which module the functionality you are developing is subordinate to, e.g. TOSICA belongs to the algorithms of the single-cell domain, i.e., you need to add the _tosica.py file inside the single folder of omicverse and _init__.py inside the from . _tosica import pyTOSICA to make the omicverse add the new functionality

.
├── omicverse               
├───── single
├──────── __init__.py 
├──────── _tosica.py 

All functions require parameter descriptions in the following format:

def preprocess(adata:anndata.AnnData, mode:str='scanpy', target_sum:int=50*1e4, n_HVGs:int=2000,
    organism:str='human', no_cc:bool=False)->anndata.AnnData:
    """
    Preprocesses the AnnData object adata using either a scanpy or a pearson residuals workflow for size normalization
    and highly variable genes (HVGs) selection, and calculates signature scores if necessary. 

    Arguments:
        adata: The data matrix.
        mode: The mode for size normalization and HVGs selection. It can be either 'scanpy' or 'pearson'. If 'scanpy', performs size normalization using scanpy's normalize_total() function and selects HVGs 
            using pegasus' highly_variable_features() function with batch correction. If 'pearson', selects HVGs 
            using scanpy's experimental.pp.highly_variable_genes() function with pearson residuals method and performs 
            size normalization using scanpy's experimental.pp.normalize_pearson_residuals() function. 
        target_sum: The target total count after normalization.
        n_HVGs: the number of HVGs to select.
        organism: The organism of the data. It can be either 'human' or 'mouse'. 
        no_cc: Whether to remove cc-correlated genes from HVGs.

    Returns:
        adata: The preprocessed data matrix. 
    """

Pull request¶

1. You need to fork omicverse at first, and git clone your fork from your repository.
2. When you updated the related function development, open a pull request and waited reviewed and merged.