So you want to do a: Single Cell RNA-seq experiment

Analysis of HCA Preview Data

Iteratively randomly pooling scRNA-seq expressing a given gene from different numbers of cells and running DESeq2 with fdrtools correction to determine how many times which genes come out as enriched with said gene

An autoencoder based imputation of the sparse single cell RNA-seq data.

Plot_ly-based plotting functions for use with Seurat objects

adtseq provides simple Rcpp methods to identify Antibody-Derived-Tags in single cell CITE/REAP-seq data

A short tutorial to help beginners in biostatistics and genomics understand an usual single-cell RNA-sequencing workflow

Work for my MSc thesis - 'Characterising transcriptional variation in tissue-resident macrophage subsets using sc-RNA-seq'

R package - Analysis of Single Cell Expression, Normalisation and Differential expression (ascend)

Examples of methods for scRNAseq that might benefit from BaseSet

R package for Bayesian deconvolution of bulk RNAseq data

Create violin plots of gene expression for multiple genes

Display gene expression along a given reduced dimension on a heatmap

Get a gene expression table from transcript compatibility counts

Binary Factor Analysis: a dimensionality reduction tool for noisy, high throughput single cell genomic data

Reproducible code for the scRNA-seq analyses in the paper 'The neuroendocrine response to stress at single-cell resolution" by Lopez et al. 2020.

R package: {rfca} Random forest-based cell annotation methods for scRNAseq analysis. {rfca} contains methods which identifies cell types using machine learning trained on a diversity of cell types, without the need for a labelled training dataset. It also allows you to train your own cell prediction models with your own labels (cell type, subtyp…

Single-Cell Data Science

I've written a few functions that help me look through scRNA-seq data while working with Seurat.

R Scripts used in scRNA-seq analysis of T2D Islets