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.

MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data

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.

Simulating single-cell data using gene regulatory networks 📠

SNV calling from single cell sequencing

A machine learning method for the discovery of the minimum marker gene combinations for cell type identification from single-cell RNA sequencing

Identifying tumor cells at the single-cell level using machine learning

VoxHunt: Resolving human brain organoid heterogeneity through single-cell genomic comparison to spatial brain maps

NASQAR: A web-based platform for High-throughput sequencing data analysis and visualization

netSmooth: A Network smoothing based method for Single Cell RNA-seq imputation

R based workflow for multiplexed imaging data

A fast and accurate deconvolution algorithm based on regularized matrix completion algorithm (ENIGMA)

scGEAToolbox: Matlab toolbox for single-cell gene expression analyses

An modular and extensible app for visualization of mass spectrometry data and optimization of data acquisition.

An R package to download and merge labeled single-cell RNA-seq data from the PanglaoDB database into a Seurat object.

Rmarkdown / compiled html files for human reprogramming dataset analysis

A highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning.

A highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning.