This repository proposes a computational toolbox to complement the Python package stabilized-ica for the analysis of omics data.

• sicaomics.singlecell proposes an adaptation of stabilized-ica for the special case of AnnData format. It is modeled after the scanpy package that deals with single-cell gene expression data.
• sicaomics.annotate proposes tools to annotate the extracted stabilized ICA sources with functionnal enrichment analysis (using Reactome or ToppGene knowledge databases).

Install sica-omics package from source with the following command:

pip install git+https://github.com/ncaptier/sica-omics

import scanpy
from sicaomics.singlecell import ica

adata = scanpy.read_h5ad('GSE90860_3.h5ad')
adata.X -= adata.X.mean(axis =0)

ica(adata , observations = 'genes' , n_components = 30 , n_runs = 100)

#### Perform stabilized-ica decomposition ####
import pandas as pd
from sica.base import StabilizedICA

df = pd.read_csv("data.csv", index_col=0)
sICA = StabilizedICA(n_components=45, n_runs=30 ,plot=True, n_jobs = -1)
sICA.fit(df)
Metagenes = pd.DataFrame(
    sICA.S_, 
    columns = df.columns,
    index = ['metagene ' + str(i) for i in range(sICA.S_.shape[0])]
)

#### Annotate metagenes with Reactome ####
from sicaomics.annotate import reactome

Rannot = reactome.ReactomeAnalysis(
    data = Metagenes,
    threshold = 3,
    method = 'std',
    tail = 'heaviest',
    convert_ids = False
)
Rannot.get_analysis(metagene = 'metagene 0')

Note: For more detailed examples please refer to this jupyter notebook

This package was created as a part of the PhD project of Nicolas Captier in the Computational Systems Biology of Cancer group of Institut Curie.

[1] Chen,J. et al. (2009) ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res., 37 (Suppl. 2), W305--W311
[2] Gillespie,M. et al. (2021) The reactome pathway knowledgebase 2022, Nucleic Acids Research, 2021; gkab1028, https://doi.org/10.1093/nar/gkab1028
[3] Wolf,F. et al. (2018) SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19, 15. https://doi.org/10.1186/s13059-017-1382-0