Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
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Updated
Dec 4, 2024
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
🐟 🍣 🍱 Highly-accurate & wicked fast transcript-level quantification from RNA-seq reads using selective alignment
NicheNet: predict active ligand-target links between interacting cells
Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch
Spatial alignment of single cell transcriptomic data.
One-step to Cluster and Visualize Gene Expression Matrix
R/shiny interface for interactive visualization of data in SummarizedExperiment objects
Training and evaluating a variational autoencoder for pan-cancer gene expression data
Deep learning for gene expression inference
Python3 binding to mRMR Feature Selection algorithm (currently not maintained)
R package to access DoRothEA's regulons
😎 A curated list of software and resources for exploring and visualizing (browsing) expression data 😎
Building classifiers using cancer transcriptomes across 33 different cancer-types
CodonTransformer: The ultimate tool for codon optimization, optimizing DNA sequences for heterologous protein expression across 164 species.
Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses.
BASiCS: Bayesian Analysis of Single-Cell Sequencing Data. This is an unstable experimental version. Please see http://bioconductor.org/packages/BASiCS/ for the official release version
integrated RNA-seq Analysis Pipeline
A repository with exploration into using transformers to predict DNA ↔ transcription factor binding
Repository for the R package EPIC, to Estimate the Proportion of Immune and Cancer cells from bulk gene expression data.
Enjoy your transcriptomic data and analysis responsibly - like sipping a cocktail
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