Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
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Updated
Mar 27, 2024
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
AltAnalyze is a multi-functional and easy-to-use software package for automated single-cell and bulk gene and splicing analyses. Easy-to-use precompiled graphical user-interface versions available from our website.
Cell2Sentence turns scRNA-seq data into text for LLM training.
Coarse-graining of large single-cell RNA-seq data into metacells
🦠 Regularly updated list of publicly available datasets with single-cell (scRNAseq) and T-cell/antibody immune repertoire (AIRR / RepSeq / immunosequencing) data of COVID-19 patients with SARS-CoV-2.
Using Bulk Gene Expression to Estimate Cell-Type-Specific Gene Expression via Deconvolution
The Cornell Single-Cell Muscle Project (scMuscle) aims to collect, analyze and provide to the research community skeletal muscle transcriptomic data
Single cell transcriptomic data from Tasic, et al. (2016)
V-SVA: An R Shiny application for detecting and annotating hidden sources of variation in single cell RNA-seq data
Extracellular vesicle-derived miRNA-mediated cell-cell communication inference for single-cell transcriptomic data
PicturedRocks Single Cell Analysis Tool
Gene expression on C. elegans single cells
Repo linked to our recent publication in Science Advances
V-SVA: An R Shiny application for detecting and annotating hidden sources of variation in single cell RNA-seq data
Large-scale Benchmarking of Single-cell Differential Expression Analysis Methods
Benchmarking records for scRNA-seq CNV detection.
scRNA-seq datasets to support Jung et al, Sci Adv, 7(6): eabe5735, 2021. DOI: 10.1126/sciadv.abe5735
ScRNA-seq Analysis Copilot serves as an interactive agent to learn single cell sequencing, answering questions and helping new learners to practice analysis.
A package that performs cell type annotations on single cell resolution of spatial transcriptomics data, find the niche interactions and covariation patterns between interacted cell types.
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