Overview

OmixHub is a platform that interfaces with GDC using python to help users to apply ML based analysis on different sequencing data. Currently we support only for RNA-Seq based datasets from genomic data commons (GDC)

1. Cohort Creation of Bulk RNA Seq Tumor and Normal Samples from GDC.

2. Bioinformatics analysis:

   1. Application of PyDESeq2 and GSEA in a single pipeline.

3. Classical ML analysis:

   1. Applying clustering, supervised ML and outlier sum statistics.

4. Custom API Connections:

   1. Search and retrieval of Cancer Data cohorts from GDC using complex json filters (Methods in src.Connectors for GDC API search and retrieval using custom queries)
   2. Interacting with MongoDB database in a pythonic manner (DOCS coming soon).
   3. Interacting with Google cloud BigQuery in a pythonic manner (DOCS coming soon).

API DOCUMENTATION LINK

https://omixhub.readthedocs.io/en/latest/getting_started.html

GETTING STARTED:

1. Clone the repository git clone https://github.com/adhal007/OmixHub.git
2. Create the correct conda enviroment for OmixHub: conda env create -f environment.yaml

INSTALLATION

Please follow the instructions on

ADDITIONAL CODE DOCS:

• Application Examples
  • Outlier sum statistics
  • Supervised ML using Ensemble models on kidney cancer data
  • Clustering using bayesian optimized parameters of kidney cancer sub-types from TCGA
  • Analysis of differentially expressed genes functionally
• Roadmap for future developments
• Methods in src.Connectors for GDC API search and retrieval using custom queries

References:

1. Characterizing tumor toxicity in Gene therapy targets from Bulk RNA-Sequencing
2. Bayesian Framework for identifying gene expression outliers in individual sample of RNA-Seq data