Alexander V. Favorov

replicateFest uses negative binomial models for replicated and Fisher's exact tests for unreplicated FEST TCR-seq to call antigen-expanded clonotypes, recovering all nine published HIV-1 responders.

STAPLE automates spatial transcriptomics analysis by integrating multiple tools into a modular framework and provides AI-driven interpretation of results.

This paper introduces SpaceMarkers, a method to infer molecular changes from cell-cell interactions in spatial transcriptomics data, applied to tumor-immune interactions in metastasis and immunotherapy.

Hobotnica estimates molecular signature quality by assessing their ability to stratify data and assign significance scores for comparing different groups.

The paper identifies allele-specific transcription factor binding events across the human genome using ChIP-Seq data, revealing regulatory variants linked to disease and molecular mechanisms.

nearBynding characterizes RBP binding preferences relative to RNA structure using CLIP-seq data without peak-calling.

The paper functionally annotates human long non-coding RNAs by systematically knocking down 285 lncRNAs and analyzing molecular phenotypes using CAGE sequencing.

The paper presents FC-R2, an expanded transcriptome atlas of over 109,000 genes, including lncRNAs, and demonstrates its utility in analyzing gene expression across normal and diseased human samples.

This study integrates multiple scATAC-seq datasets using matrix factorization and transfer learning to identify common regulatory patterns across datasets.

This paper introduces EVAsc, a method to analyze expression variation in scRNA-seq data to quantify tumor heterogeneity across cell types, subtypes, and patients.

This paper reviews matrix factorization applications for systems-level omics analysis, highlighting their ability to uncover biological knowledge from high-dimensional data.

The paper introduces REVA, a nonparametric correlation measure for multivariate data, showing it is robust to noise, high dimensions, and outliers, and applies it to cancer biology.

The paper uses integrated time course omics analysis to distinguish immediate therapeutic response from acquired resistance in head and neck cancer.

SEVA identifies differential heterogeneity in gene isoform usage between cancer subtypes using RNA-seq data.

StereoGene estimates genomewide correlation of continuous genomic features, revealing spatial relationships between epigenomic features and splicing sites.

The paper introduces PatternMarkers and GWCoGAPS to identify data-driven biomarkers from whole-genome data using NMF, demonstrating their application on GTex data.