Name		Name	Last commit message	Last commit date
parent directory ..
qc		qc
170210_naba_matrisome.txt		170210_naba_matrisome.txt
CAFgroups.Rdata		CAFgroups.Rdata
README.md		README.md
ROTS_summary_pop.Rdata		ROTS_summary_pop.Rdata
RPKM.full.Rdata		RPKM.full.Rdata
expr_raw_counts.Rdata		expr_raw_counts.Rdata
for_paper.Rproj		for_paper.Rproj
last-COUNTS.Rdata		last-COUNTS.Rdata
last-RPKM.Rdata		last-RPKM.Rdata
step0-install.R		step0-install.R
step1-qc.Rmd		step1-qc.Rmd
step1-qc.html		step1-qc.html
step2-get-matrix.Rmd		step2-get-matrix.Rmd
step2-get-matrix.html		step2-get-matrix.html
step3-dbscan.Rmd		step3-dbscan.Rmd
step3-dbscan.html		step3-dbscan.html
step4-DEG.Rmd		step4-DEG.Rmd
step4-DEG.html		step4-DEG.html
step5-run_seurat.R		step5-run_seurat.R
step6-SC3.R		step6-SC3.R
time-for-ROTS.png		time-for-ROTS.png
top18-genes-in-4-subgroup.txt		top18-genes-in-4-subgroup.txt
tsne_out.Rdata		tsne_out.Rdata

README.md

完全复现文章图表

首先，文章作者已经上传了全部数据和代码在GitHub，所以复现是必然的： www.github.com/KPLab/SCS_CAF

当然，我们需要理解作者的代码，并且做出自己的分析流程。

首先是QC步骤，见：step1-qc.html

然后是筛选表达矩阵，见：step2-get-matrix.html

接着是tSNE+dbscan进行降维聚类，见：step3-dbscan.html

最后是差异基因分析并且可视化，见：step4-DEG.html