Pipeline to analyze small RNAs from single-cells

DANCE: a deep learning library and benchmark platform for single-cell analysis

SOMA PyTorch loaders

Investigate, understand, and interpret single cell data in minutes, not days by leveraging RAPIDS-singlecell, powered by NVIDIA RAPIDS

DOLPHIN: Advances Single-cell RNA-seq Analysis Beyond Gene-Level by Integrating Exon-Level Quantification and Junction Reads with Deep Neural Networks

A customizable and painless way to generate neat-looking heatmap.

Shiny app for the analysis of single cell data

🎨🎨🎨 Collection of most color palettes in a single R package

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

Gene Ontology Gene Sets from BioMart

The Subread software package is a tool kit for processing next-gen sequencing data. It includes Subread aligner, Subjunc exon-exon junction detector and featureCounts read summarization program.

Cross Modality Optimal Transport for multimodal inference

Codes used in pan-T cell analysis

Deciphering tumor ecosystems at super-resolution from spatial transcriptomics with TESLA

Framework for sensitive DE testing (using neighbourhoods)

This repository contains scripts to identify healthy and malignant cells from scRNAseq with CloneTracer and process data from Optimized 10x libraries

resources for faculty

Demultiplexing and debarcoding tool designed for LR-Split-seq data.

CellRank: dynamics from multi-view single-cell data

trajectory inference

A library for creating complex UpSet plots with ggplot2 geoms

Pandas API for multiple Gene Set Enrichment Analysis implementations in Python (GSEApy, cudaGSEA, GSEA)

This repository contains R code, with which you can create 3D UMAP and tSNE plots of Seurat analyzed scRNAseq data

Single-cell analysis in Python. Scales to >100M cells.