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By study this, it won't be costly or time-consuming to customize a NGS data analysis pipeline
Subpopulation detection in high-dimensional single-cell data
http下载工具,基于http代理,支持多连接分块下载
All Algorithms implemented in Python
Python入门网络爬虫之精华版
R for data science
release version
website for the rnaseq course
RNA Modification seq data process
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.
A small collection for R scripts forked from jmzeng. Maybe learn, modify and add content.
Scripts to recreate figures from the SC3 paper
R package for analyzing single-cell RNA-seq data
Preprocessing of single-cell RNA-Seq data for input to kallisto
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.
Single sample Gene Set Enrichment analysis (ssGSEA) and PTM Enrichment Analysis (PTM-SEA)
Survival Analysis and Visualization
R data package for pre-compiled somatic mutations from TCGA cohorts (from Broad Firehose and TCGA MC3 Project)
Easily tidy data with spread and gather functions.
Source code the New Zealand Tourism Dashboard which is deployed to https://mbienz.shinyapps.io/tourism_dashboard_prod/
Classifying tumor types based on Whole Genome Sequencing (WGS) data
An R implementation of the UpSet set visualization technique published by Lex, Gehlenborg, et al..
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.