# Overview of SCDE
The `scde` package implements a set of statistical methods for analyzing single-cell RNA-seq data. `scde` fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The `scde` package also contains the `pagoda` framework which applies pathway and gene set overdispersion analysis to identify aspects of transcriptional heterogeneity among single cells.
The overall approach to the differential expression analysis is detailed in the following publication:
["Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi:10.1038/nmeth.2967)](http://www.nature.com/nmeth/journal/v11/n7/abs/nmeth.2967.html)
The overall approach to pathways and gene set overdispersion analysis is detailed in the following publication:
["Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734)](http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3734.html)
**For additional installation information, tutorials, and more, please visit [the SCDE website ☞](http://hms-dbmi.github.io/scde/)**
# Sample analyses and images
## Single cell error modeling
<table>
<tr>
<td width=400px>
<img src="vignettes/figures/pagoda-cell.model.fits-0.png" width="400px">
</td>
<td>
<code>scde</code> fits individual error models for single cells using counts derived from single-cell RNA-seq data to estimate drop-out and amplification biases on gene expression magnitude.
</td>
</tr>
</table>
## Differential expression analysis
<table>
<tr>
<td width=250px>
<img src="vignettes/figures/scde-diffexp3-1.png" width="250px">
</td>
<td>
<pre>
lb mle ub ce Z cZ
Dppa5a 8.075 9.965 11.541 8.075 7.160 5.968
Pou5f1 5.357 7.208 9.178 5.357 7.160 5.968
Gm13242 5.672 7.681 9.768 5.672 7.159 5.968
Tdh 5.829 8.075 10.281 5.829 7.159 5.968
Ift46 5.435 7.366 9.217 5.435 7.150 5.968</pre>
<br>
<code>scde</code> compares groups of single cells and tests for differential expression, taking into account variability in the single cell RNA-seq data due to drop-out and amplification biases in order to identify more robustly differentially expressed genes.
</td>
</tr>
</table>
## Pathway and gene set overdispersion analysis
<table>
<tr>
<td width=400px>
<img src="vignettes/figures/PAGODA.gif" width="400px">
</td>
<td>
<code>scde</code> contains <code>pagoda</code> routines that characterize aspects of transcriptional heterogeneity in populations of single cells using pre-defined gene sets as well as 'de novo' gene sets derived from the data. Significant aspects are used to cluster cells into subpopulations. A graphical user interface can be deployed to interactively explore results. See examples from the PAGODA publication <a href="http://pklab.med.harvard.edu/scde/pagoda.links.html">here</a>. See analysis of the PBMC data from 10x Genomics <a href="http://pklab.med.harvard.edu/cgi-bin/R/rook/10x.pbmc/index.html">here</a>.
</td>
</tr>
</table>
---
`scde` is maintained by [Jean Fan](https://github.com/jefworks) of the [Kharchenko Lab](http://pklab.med.harvard.edu/) at the [Department of Biomedical Informatics at Harvard Medical School](https://github.com/hms-dbmi).
---
# Contributing
We welcome any bug reports, enhancement requests, and other contributions. To submit a bug report or enhancement request, please use the [`scde` GitHub issues tracker](https://github.com/hms-dbmi/scde/issues). For more substantial contributions, please fork this repo, push your changes to your fork, and submit a pull request with a good commit message. For more general discussions or troubleshooting, please consult the [`scde` Google Group](http://hms-dbmi.github.io/scde/help.html).
没有合适的资源?快使用搜索试试~ 我知道了~
scde:用于分析单细胞RNA序列数据的R包
共96个文件
rd:35个
png:17个
md:5个
3星 · 超过75%的资源 需积分: 50 4 下载量 167 浏览量
2021-02-05
14:39:52
上传
评论 1
收藏 3.93MB ZIP 举报
温馨提示
SCDE概述 scde程序包实现了一组用于分析单细胞RNA-seq数据的统计方法。 scde适合用于单细胞RNA-seq测量的单个误差模型。 然后可以将这些模型用于评估细胞组之间的差异表达以及其他类型的分析。 scde软件包还包含pagoda框架,该pagoda框架应用途径和基因集过度分散分析来识别单细胞之间转录异质性的各个方面。 以下出版物详细介绍了差异表达分析的总体方法: 在以下出版物中详细介绍了途径和基因组过度分散分析的总体方法: 有关其他安装信息,教程等,请访问 样品分析和图像 单细胞错误建模 scde使用源自单细胞RNA-seq数据的计数scde拟合单细胞的个体误差模型,
资源详情
资源评论
资源推荐
收起资源包目录
scde-master.zip (96个子文件)
scde-master
vignettes
experimental.md 3KB
genesets.md 4KB
figures
experimental-pagoda-1.png 25KB
pagoda-tSNE-1.png 18KB
pagoda-cell.model.fits-0.png 137KB
pagoda-showTopPathwayGenes-1.png 34KB
PAGODA.gif 1.52MB
pagoda-topPathways-1.png 29KB
experimental-data-1.png 144KB
pagoda-Screen_Shot_2015-06-07_at_4.53.46_PM.png 186KB
scde-diffexp3-1.png 144KB
scde-detailed4-1.png 89KB
pagoda-viewAspects-1.png 26KB
pagoda-varnorm-1.png 74KB
pagoda-controlForCellCycle-1.png 36KB
pagoda-clusterPCA-1.png 62KB
pagoda-correlatedCollapse-1.png 21KB
scde-batch-1.png 157KB
pagoda-topPathways2-1.png 42KB
pagoda.md 17KB
diffexp.Rmd 14KB
diffexp.md 15KB
pagoda.Rmd 16KB
experimental.Rmd 4KB
scde-manual.pdf 144KB
genesets.Rmd 3KB
NAMESPACE 1KB
DESCRIPTION 2KB
src
pagoda.cpp 4KB
pagoda.h 269B
dqrdc2.f 6KB
matSlideMult.cpp 554B
matSlideMult.h 148B
dqrsl.f 9KB
Makevars 173B
Makevars.win 256B
dqrls.f 4KB
dqrutl.f 2KB
jpmatLogBoot.cpp 21KB
jpmatLogBoot.h 676B
dqrdc.f 7KB
bwpca.h 564B
bwpca.cpp 11KB
inst
NEWS 370B
R
functions.R 312KB
.travis.yml 1KB
web
pagoda.png 3KB
additional.css 304B
pathcl_canvas.js 39KB
pathcl_canvas_1.1.js 48KB
pathcl.css 2KB
pathcl.js 42KB
README.md 4KB
data
o.ifm.rda 2KB
knn.rda 6KB
scde.edff.rda 84KB
es.mef.small.rda 280KB
pollen.rda 602KB
man
scde.expression.prior.Rd 1KB
pagoda.varnorm.Rd 4KB
view.aspects.Rd 1KB
pagoda.gene.clusters.Rd 3KB
ViewPagodaApp-class.Rd 1KB
pagoda.show.pathways.Rd 2KB
pagoda.effective.cells.Rd 1KB
es.mef.small.Rd 328B
pagoda.subtract.aspect.Rd 2KB
pagoda.reduce.loading.redundancy.Rd 2KB
scde.posteriors.Rd 3KB
make.pagoda.app.Rd 2KB
papply.Rd 503B
scde.failure.probability.Rd 2KB
pagoda.cluster.cells.Rd 2KB
knn.Rd 294B
clean.counts.Rd 926B
pollen.Rd 279B
bwpca.Rd 2KB
scde.fit.models.to.reference.Rd 2KB
scde.test.gene.expression.difference.Rd 3KB
pagoda.pathway.wPCA.Rd 3KB
scde.error.models.Rd 4KB
o.ifm.Rd 353B
winsorize.matrix.Rd 805B
clean.gos.Rd 841B
pagoda.reduce.redundancy.Rd 2KB
scde.expression.difference.Rd 3KB
scde.expression.magnitude.Rd 936B
show.app.Rd 1KB
scde.browse.diffexp.Rd 3KB
pagoda.view.aspects.Rd 1KB
pagoda.top.aspects.Rd 3KB
knn.error.models.Rd 3KB
scde.Rd 1KB
tests
tests.R 2KB
.gitignore 114B
license.txt 1KB
共 96 条
- 1
盗心魔幻
- 粉丝: 15
- 资源: 4478
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功
评论2