https://github.com/KChen-lab/SCMarker

Marker gene selection from scRNA-seq data
https://github.com/KChen-lab/SCMarker

feature-selection high-dimensional-data single-cell-rna-seq statistical-methods

Last synced: 23 days ago
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Marker gene selection from scRNA-seq data

• Host: GitHub
• URL: https://github.com/KChen-lab/SCMarker
• Owner: KChen-lab
• Created: 2018-06-25T15:28:30.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2020-09-15T03:40:48.000Z (almost 4 years ago)
• Last Synced: 2024-02-23T14:34:50.459Z (4 months ago)
• Topics: feature-selection, high-dimensional-data, single-cell-rna-seq, statistical-methods
• Language: HTML
• Size: 75.2 MB
• Stars: 15
• Watchers: 4
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

Lists

• awesome_single_cell - SCMarker - [R] - SCMarker is a method performing ab initial marker gene set selection from scRNA-seq data to achieve improved clustering/cell-typing results. [SCMarker: ab initio marker selection for single cell transcriptome profiling](https://www.biorxiv.org/content/early/2018/07/04/356634). (Software packages / RNA-seq)
• awesome-single-cell - SCMarker - [R] - SCMarker is a method performing ab initial marker gene set selection from scRNA-seq data to achieve improved clustering/cell-typing results. [SCMarker: ab initio marker selection for single cell transcriptome profiling](https://www.biorxiv.org/content/early/2018/07/04/356634). (Software packages / Marker and differential gene expression identification)
• awesome-single-cell - SCMarker - [R] - SCMarker is a method performing ab initial marker gene set selection from scRNA-seq data to achieve improved clustering/cell-typing results. [SCMarker: ab initio marker selection for single cell transcriptome profiling](https://www.biorxiv.org/content/early/2018/07/04/356634). (Software packages / RNA-seq)

README

        
# SCMarker

SCMarker performs cell-type-specific marker selection from single cell RNA sequencing data.

It provides users a tool for selecting features from tens of thousands of genes for further cell-type clustering analysis.

SCMarker is done based on two hypotheses:

1) The expression of a gene should follow bi/multi-modal distribution in a mixed cell population if it is a marker of a specific cell-type.

2) Marker genes of a cell type express synergistically in a subset of cells.

Developer

------------

Fang Wang ([email protected])

Marker selection

---------------------

The three main functions for this package are `ModalFilter()`, `GeneFilter()` and `getMarker()`.

`ModalFilter()` performs the initial filter based on the least expressed number of genes(cells) and whether the gene has unimodal distribution.

`GeneFilter()` takes the output of ModalFilter() and filters out genes that have unimodal distributed expressions and are expressed in more than maxexp cells.

`getMarker()` takes the output of GeneFilter() and selects the final markers based on synergistically (co- or mutual-exclusively) expressed gene pairs.

Installation

----------------------

Download SCMarker_2.0.tar.gz

```R

install.packages("SCMarker_2.0.tar.gz",repos=NULL,type="source")

```

or install through GitHub

```R

library(devtools)

install_github("KChen-lab/SCMarker")

```

Usage

----------------------

```R

library(SCMarker)

data(melanoma)

melanoma1=as.matrix(melanoma[,2:dim(melanoma)[2]])

row.names(melanoma1)=melanoma[,1]

res=ModalFilter(data=melanoma1,geneK=10,cellK=10,width=2)# default width = 1 for UMI data, width =2 for TPM data.

res=GeneFilter(obj=res)

res=getMarker(obj=res,k=300,n=30)

head(res$marker)

```

An example to show how SCMarker [improve identification of NK cell in GBM data.](https://github.com/KChen-lab/SCMarker/blob/master/test/NK%20cell%20identification%20from%20GBM%20data.pdf)

Publication

-----------------------

Wang, Fang, et al. "SCMarker: ab initio marker selection for single cell transcriptome profiling." PLoS computational biology 15.10 (2019): e1007445.