MOSS - Multi-Omic Integration via Sparse Singular Value Decomposition

High dimensionality, noise and heterogeneity among samples and features challenge the omic integration task. Here we present an omic integration method based on sparse singular value decomposition (SVD) to deal with these limitations, by: a. obtaining the main axes of variation of the combined omics, b. imposing sparsity constraints at both subjects (rows) and features (columns) levels using Elastic Net type of shrinkage, and c. allowing both linear and non-linear projections (via t-Stochastic Neighbor Embedding) of the omic data to detect clusters in very convoluted data (Gonzalez-Reymundez et. al, 2022) <doi:10.1093/bioinformatics/btac179>.

Last updated 9 months ago

elastic-netmulti-omic-integrationsparse-singular-value-decompositionvariable-selection

5.55 score 7 stars 17 scripts 642 downloads

eCV - Enhanced Coefficient of Variation and IDR Extensions for Reproducibility Assessment

Reproducibility assessment is essential in extracting reliable scientific insights from high-throughput experiments. While the Irreproducibility Discovery Rate (IDR) method has been instrumental in assessing reproducibility, its standard implementation is constrained to handling only two replicates. Package 'eCV' introduces an enhanced Coefficient of Variation (eCV) metric to assess the likelihood of omic features being reproducible. Additionally, it offers alternatives to the Irreproducible Discovery Rate (IDR) calculations for multi-replicate experiments. These tools are valuable for analyzing high-throughput data in genomics and other omics fields. The methods implemented in 'eCV' are described in Gonzalez-Reymundez et al., (2023) <doi:10.1101/2023.12.18.572208>.

Last updated 10 months ago

3.00 score 1 scripts 146 downloads