https://github.com/bioconductor-source/roseq

https://github.com/bioconductor-source/roseq

Repository

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    fig.path = "man/figures/README-",
    out.width = "100%"
)
```


#  ROSeq

Modeling expression ranks for noise-tolerant differential expression analysis
of scRNA-Seq data

## Introduction

ROSeq - A rank based approach to modeling gene expression with filtered and
normalized read count matrix. ROSeq takes filtered and normalized read matrix 
and cell-annotation/condition as input and determines the differentially
expressed genes between the contrasting groups of single cells. One of the 
input parameters is the number of cores to be used.

## Installation

The developer's version of the R package can be installed 
with the following R commands:
```r
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("ROSeq")
```
The github's version of the R package can be installed 
with the following R commands:
```r
library(devtools)
install_github('krishan57gupta/ROSeq')
```
## Vignette tutorial

This vignette uses the Tung dataset, which is already inbuilt in the package, 
to demonstrate a standard pipeline.

## Example

Libraries need to be loaded before running.


```{r setup}
library(ROSeq)
library(edgeR)
library(limma)
```

### Loading tung dataset
```{r data, message=FALSE,warning = FALSE,include=TRUE, cache=FALSE}
samples<-list()
samples$count<-ROSeq::L_Tung_single$NA19098_NA19101_count
samples$group<-ROSeq::L_Tung_single$NA19098_NA19101_group
samples$count[1:5,1:5]
```

### Data Preprocessing:
#### Cells and genes filtering then voom transformation after TMM normalization
Below commands can be used for Cell/gene filtering, TMM normalization and voom 
transformation. The user is free to use an alternative preprocessing strategy 
while using different filtering/normalization methods.


```{r preprocesing, message=FALSE,warning = FALSE,include=TRUE, cache=FALSE}
gene_names<-rownames(samples$count)
samples$count<-apply(samples$count,2,function(x) as.numeric(x))
rownames(samples$count)<-gene_names
samples$count<-samples$count[,colSums(samples$count> 0) > 2000]
gkeep<-apply(samples$count,1,function(x) sum(x>2)>=3)
samples$count<-samples$count[gkeep,]
samples$count<-limma::voom(ROSeq::TMMnormalization(samples$count))
```

### ROSeq analysis. 
Input: gene expression matrix with genes in rows and cells in columns.
Condition/group annotation of cells also need to be supplied. 
User can set numCores based the hardware specifications in her computer. 

```{r main, message=FALSE,warning = FALSE, include=TRUE, cache=FALSE}
output<-ROSeq(countData=samples$count$E, condition = samples$group, numCores=1)
```

### Showing results are in the form of pVals and pAdj
##### p_Vals : p_value (unadjusted)
##### p_Adj : Adjusted p-value, based on FDR method

```{r output, message=FALSE,warning = FALSE,include=TRUE, cache=FALSE}
output[1:5,]
```

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