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https://github.com/ChenMengjie/Citrus

Last synced: 23 days ago
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Host: GitHub
URL: https://github.com/ChenMengjie/Citrus
Owner: ChenMengjie
Created: 2016-01-12T04:03:04.000Z (over 8 years ago)
Default Branch: master
Last Pushed: 2020-04-08T04:12:09.000Z (about 4 years ago)
Last Synced: 2024-02-24T14:39:02.373Z (4 months ago)
Language: C++
Size: 5.14 MB
Stars: 2
Watchers: 3
Forks: 1
Open Issues: 1
Metadata Files:
- Readme: README.md

Lists

awesome_single_cell - scPLS - [C++, R] - A normalization method to remove unwanted variation using both control and target genes. It takes advantage of the fact that genes in a scRNAseq study often can be naturally classified into two sets: a control set of genes that are free of effects of the predictor variables and a target set of genes that are of primary interest. By modeling the two sets of genes jointly using the partial least squares regression, scPLS is capable of making full use of the data to improve the inference of confounding effects. https://www.nature.com/articles/s41598-017-13665-w (Software packages / RNA-seq)
awesome-single-cell - scPLS - [C++, R] - A normalization method to remove unwanted variation using both control and target genes. It takes advantage of the fact that genes in a scRNAseq study often can be naturally classified into two sets: a control set of genes that are free of effects of the predictor variables and a target set of genes that are of primary interest. By modeling the two sets of genes jointly using the partial least squares regression, scPLS is capable of making full use of the data to improve the inference of confounding effects. https://www.nature.com/articles/s41598-017-13665-w (Software packages / Batch-effect removal)
awesome-single-cell - scPLS - [C++, R] - A normalization method to remove unwanted variation using both control and target genes. It takes advantage of the fact that genes in a scRNAseq study often can be naturally classified into two sets: a control set of genes that are free of effects of the predictor variables and a target set of genes that are of primary interest. By modeling the two sets of genes jointly using the partial least squares regression, scPLS is capable of making full use of the data to improve the inference of confounding effects. https://www.nature.com/articles/s41598-017-13665-w (Software packages / RNA-seq)

README

### Citrus

Citrus is a toolkit for single cell sequencing analysis under development.
Citrus is written in c++ with easy-to-use R wrappers.
To use Citrus, the installation of **Rcpp** and **RcppArmadillo** is required.

### Available methods

Now a normalized method and a clustering method are available in the distributed version.

* Method 1: **scPLS**: a normalization method to remove unwanted variation using both control and target genes.

The description of methodology and algorithm can be found in the manuscript:

Chen M, Zhou X: **Normalization of single cell RNA sequencing data using both control and target genes**. 2016

which can be downloaded at: http://biorxiv.org/content/early/2016/03/21/045070.

A **vignette** on how to use scPLS is distributed together with the package.

* Method 2: a clustering method which can account for confounding factors.

The description of method and a tutorial of software on clustering will be available shortly.

### Installation

**Citrus** relies on the following R packages: **Rcpp**, **RcppArmadillo**, **genlasso**, **gplots**. All packagess are hosted on CRAN.
```R
install.packages ("Rcpp")
install.packages ("RcppArmadillo")
install.packages ("genlasso")
install.packages ("gplots")
```

**Citrus** can be installed from github directly as follows:

```R
install.packages ("devtools")
library(devtools)
install_github("ChenMengjie/Citrus")
```

### Why do we call it citrus?
Citrus is not a short name for any of our methods. To honor Bowtie/Cufflinks and Salmon/Sailfish series, we are creating a fruit collection.

### Authors

**Mengjie Chen** (U Chicago)
**Xiang Zhou** (Umich)

Bug report, comments or questions please send to [email protected].