InMoose

InMoose implements Python versions of ComBat and ComBat-Seq to correct batch effects and support integration of multi-omic datasets with emphasis on transcriptomic (microarray and RNA-Seq) data.

Key Features:

• Batch Effect Correction: Provides Python implementations of ComBat and ComBat-Seq for correcting batch effects in microarray and RNA-Seq expression data.
• Transcriptomic and Multi-omic Focus: Targets integration and harmonization of multi-omic datasets with a particular emphasis on transcriptomic measurements.
• Performance and Efficiency: Preserves the mathematical integrity of the R ComBat and ComBat-Seq implementations while improving computational efficiency and speed for large-scale genomic analyses.
• Open-Source License: Distributed under the GPL-3.0 license to enable transparent, community-driven development.

Scientific Applications:

• Data Integration across Omic Layers: Enables integration of genomics and transcriptomics datasets by harmonizing expression values across batches.
• Differential Expression Analysis: Improves reliability of differential expression analyses by removing non-biological batch-associated variation.
• Pathway Enrichment: Reduces technical bias in expression data to support more accurate pathway enrichment results.
• Integrative Multi-omic Studies: Facilitates combined analyses across omic layers by providing batch-corrected transcriptomic inputs.

Methodology:

Implements the ComBat and ComBat-Seq algorithms in Python using statistical modeling based on a parametric empirical Bayes framework to estimate parameters that adjust for known batch variables while preserving biological variability; implementations retain the mathematical formulations of the R originals and target computational efficiency.