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ngs-data-analysis-long-course

ngs-pipeline

By study this, it won't be costly or time-consuming to customize a NGS data analysis pipeline

onlyoffice.github.io

organoid

pancanstem_web

phenograph

Subpopulation detection in high-dimensional single-cell data

proxyee-down

http下载工具，基于http代理，支持多连接分块下载

python

All Algorithms implemented in Python

pythonspidernotes

Python入门网络爬虫之精华版

r4ds

R for data science

rememberu

ribocode

release version

rnabio.org

website for the rnaseq course

rnacocktail

rnamodificationseqprocess

RNA Modification seq data process

rnaseq-workflow

rnaseq_tutorial

Informatics for RNA-seq: A web resource for analysis on the cloud. Educational tutorials and working pipelines for RNA-seq analysis including an introduction to: cloud computing, critical file formats, reference genomes, gene annotation, expression, differential expression, alternative splicing, data visualization, and interpretation.

rscriptscollection

A small collection for R scripts forked from jmzeng. Maybe learn, modify and add content.

sc3-paper-figures

Scripts to recreate figures from the SC3 paper

scde

R package for analyzing single-cell RNA-seq data

scrna-seq-tcc-prep

Preprocessing of single-cell RNA-Seq data for input to kallisto

scseqr

scSeqR (Single Cell Sequencing R package) is an interactive R package to works with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq and CITE-seq). As some research studies require a more attuned forms of normalization or spike-in normalization in some cases, scSeqR allows the users to chose from multiple normalization methods and correcting for dropouts (nonzero events counted as zero). Because some of the cell types are more challenging to work with, scSeqR also allows the users to choose from different clustering algorithms (i.e. ward.D, kmeans, ward.D2, hierarchical, etc.) and indexing methods (i.e. silhouette, ccc, kl, gap-stats, etc.) to adjust for sensitivity and stringency in order to find less or more subpopulations of cell types to design both unsupervised and supervised models to best suit your research. scSeqR provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells and genes, cell helth and cell cycle, merging, normalizing for dropouts and batch differences, pathway analysis, cell type prediction and tools to find marker genes for clusters and conditions. scSeqR inputs single cell data in 10X format, large numeric matrix files and data frames.

ssgsea2.0

Single sample Gene Set Enrichment analysis (ssGSEA) and PTM Enrichment Analysis (PTM-SEA)

survminer

Survival Analysis and Visualization

tcga_example

tcgamutations

R data package for pre-compiled somatic mutations from TCGA cohorts (from Broad Firehose and TCGA MC3 Project)

tidyr

Easily tidy data with spread and gather functions.

tourism-dashboard-public

Source code the New Zealand Tourism Dashboard which is deployed to https://mbienz.shinyapps.io/tourism_dashboard_prod/

tumortype-wgs

Classifying tumor types based on Whole Genome Sequencing (WGS) data

upsetr

An R implementation of the UpSet set visualization technique published by Lex, Gehlenborg, et al..