Copy number calling and variant classification using targeted short read sequencing

Code for paper: DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification [ECCV 2024]

Analysis of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH) in cancer

R package that automatically classifies the cells in the scRNA data by segregating non-malignant cells of tumor microenviroment from the malignant cells. It also infers the copy number profile of malignant cells, identifies subclonal structures and analyses the specific and shared alterations of each subpopulation.

An R package for studying mutational signatures and structural variant signatures along clonal evolution in cancer.

Genetic Heterogeneity Profiling by Single Cell RNA Sequencing

Texomer: Integrating Analysis of Cancer Genome and Transcriptome Sequencing Data

Robust and Accurate Deconvolution

A Fast Branch and Bound Algorithm for the Perfect Tumor Phylogeny Reconstruction Problem

Analysis of treatment naive and neo-adjuvant chemotherapy treated high-grade serous ovarian cancer samples

Clustering tumor cells based on SNVs from single-cell sequencing data

SIFA: identify tumor subclones and infer phylogenetic tree from WGS data

Phylogenies of Breast Cancer Brain Metastases

Reconstructing phylogenetic tree from noisy mutation profile of single cells

Official repository of "Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models"

This project provides an Spectre's implementation of Data Mining Group's algorithms for processing mass spectrometry imaging (MSI) datasets.

Reproducibility materials for "Bayesian Hierarchical Varying-sparsity Regression Models with Application to Cancer Proteogenomics" by Yang Ni, Francesco C. Stingo, Min Jin Ha, Rehan Akbani & Veerabhadran Baladandayuthapani

A tool to find reads supporting/opposing structural variant breakpoints

scMuffin - A MUlti-Features INtegrative approach for SC data analysis

Experiments for evaluating SECEDO, clustering tumor cells based on single cell sequencing data