R package for pathway analysis in scRNA-seq data
Deep exponential families for single-cell data.
Profile RNA-seq data using published TB gene signatures
🧬 Toolkit for Evaluating Gene Sets as Phenotype Markers in R
Tool for group biology estimation in single-cell RNAseq data
This repository contains code used to build and interpret a deep learning model. It is a DNN classifier trained using gene expression data (TCGA). Then is interpreted to identify cancer specific gene expression signatures.
Using biological constraints to improve the performance of transcriptomic gene signatures
This repository is an attempt to deduce a gene signature to predict NRF2 levels in Lung Cancer patients using multiomics data from public datasets
Master's Thesis: multi-omics analysis of cancer stemness and immune evasion in breast cancer using gene signatures, WGCNA and machine learning.
Signed multi-module composite activity scoring for CosMx SMI spatial transcriptomics, with per-cell scores registered onto tissue images
Interpretable spatial transcriptomics analysis of breast tumors and lymph node metastases. Mapping of tumor architecture and regional signaling using 10x Visium data
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