Enables cellxgene to generate violin, stacked violin, stacked bar, heatmap, volcano, embedding, dot, track, density, 2D density, sankey and dual-gene plot in high-resolution SVG/PNG format. It also performs differential gene expression analysis and provides a Command Line Interface (CLI) for advanced users to perform analysis using python and R.

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.

scGEAToolbox: Matlab toolbox for single-cell gene expression analyses

an integrative algorithm to distinguish spatially variable cell subclusters by reconstructing cells onto a pseudo space with spatial transcriptome references

A guide to using the Python scRNA-seq analysis package Scanpy from R

single cell and bulk RNASeq analysis scripts

Data analysis for the paper "Single cell transcriptomics of human epidermis identifies basal stem cell transition states" in Nature Communications.

CHARGE Consortium: collaboration for mapping targets from 1000G GWAS on coronary artery calcification (CAC) with Maryam Kavousi, Maxime Bos, Clint L. Miller, and Pat Peyser.

Collection of walkthrough tutorials on how to use Tweedieverse for differential analysis of omics data.

Gene expression on C. elegans single cells

Single cell data analysis tutorials

Taller de la CDSB 2021 sobre análisis de datos de scRNA-seq con Bioconductor

Repo linked to our recent publication in Science Advances

Distance Metrics for scRNA

2021-05-03 presentation for the Human Cell Atlas - Latin America workshop on snRNA_seq

Prioritise cell-type-specific gene targets from the Rare Disease Celltyping project.

Code and access to data related to the SIFT-seq publication by Chandran et al., Nature Medicine (2022)

Programming in practice based on RNA-seq, scRNA-seq, ATAC-seq, scATAC-seq or ChIP-seq.