Explore @powellgenomicslab Open Source projects

Single Cell Computational Genomics (@powellgenomicslab)

powellgenomicslab

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scPred

scPred package for cell type prediction from scRNA-seq data

Nebulosa

R package to visualize gene expression data based on weighted kernel density estimation

DropletQC

Nuclear fraction QC metric to detect empty droplets and damaged cells in scRNA-seq data

onek1k_phase1

Contains code to analyze the OneK1K data and perform eQTL mapping of 14 cell types

ascend

Analysis of Single Cell Expression, Normalization and Differential expression

BGI_vs_Illumina_Benchmark

This repository contains code related to the publication "Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing" by Senabouth et al.

CROP-seq

Repository for the processing and analysis of CROP-seq data

PEER_factors

Contains analysis code to generate PEER factors from pseudo-bulk data and perform eQTL association analysis

SingleCell_Prediction

This repo is for predicting cell types using gene expression features

Nebulosa_Rv3

Compatible package with older R versions

onek1k_dea

Characterization of transcriptional heterogeneity in OneK1K project

sc-veQTL

Analysis code for sc-veQTL study

CellRankJupyter

Dockerfile for Cell Rank Jupyter notebooks - includes scanpy, velocyto, scvelo and CellRank

SingularityBaseImages

Singularity base images that will be build on singularity hub and can be used to build other images

iPSC_Village_scripts

scripts used to analyze the iPSC village project

Powell_lab_resources

A public GitHub repo to share resources for common computational challenges

DropletQC_paper

Analysis code and figures for the DropletQC manuscript

scExplore

R package for single-cell data visualization

OneK1K-Web-browser

Braque

Braque provides a collection of methods to perform pseudo-bulk analysis from single-cell data.

RPE_scRNA_AgedStudy

Public repository for code related to the generation and analysis of iPSC-derived retina pigmented epithelium cells by Lidgerwood et al.