Single Cell RNA Sequencing Data Analysis Tools

Description

We integrated the common analysis methods utilized in single cell RNA sequencing data, which included cluster method, PCA, the filter of differentially expressed genes, pathway enrichment analysis and correlated analysis methods.

Author(s)

Qian Yang Maintainer: Qian Yang <bioqianyang@163.com>

Examples

####Here list three main function, cluster, PCA and t-SNE####
####cluster####
data(example1);##Example data in this package.
k<-6;##set K based on your own requirement.
scRNAtools_cluster(example1,k)
####PCA####
data(example1)
data(types)
pdf(file=file.path(tempdir(), "PCA_result-R.pdf"))##Save the figures of PCA results.
scRNAtools_pca(example1,types)
dev.off()
####t-SNE#####
data(exam)
scRNAtools_tsne(exam)

####Gene expression###
data(example)
types<-"1"
num<-0.8
scRNAtools_Geneexp(example,types,num)

Users interested genes or differentially expressed genes

Description

Gene list with two columns. The first column is Entrez ID of genes and the second column is gene symbol

correlation index

Description

The correlation index of the genes in the section of correlated analysis.

exam

Description

Example data in t-SNE method

exam1

Description

Example data in correlated analysis

example

Description

scRNA sequencing data in 50 cells and 1000 genes

example1

Description

scRNA sequencing data

Identification of differentially expressed genes

Description

Users can identify differentially expressed genes between two type of cells based on fold change value.

Usage

scRNAtools_DEGsA(example, types_all, type1, type2, num)

Arguments

Details

The output data is the fold change value of differentially expressed genes.

Author(s)

Qian Yang

Examples

data(example)
data(types)
type1<-"No malignant"
type2<-"Malignant"
num<-0.8;###type1 Vs type2
pdf(file=file.path(tempdir(), "DEGs.pdf"))
scRNAtools_DEGsA(example,types_all,type1,type2,num)
dev.off()

Present the expression of two genes

Description

This function can present the expression of two gene in appointed cell type.

Usage

scRNAtools_Gene2exp_1(example, types_all, gene1, gene2, n, col_1, col_2, pch, lwd)

Arguments

Author(s)

Qian Yang

Examples

data(example)
data(types_all)
gene1<-"CHD1"
gene2<-"CD82"
col_1="red"
col_2="blue"
pch=19
lwd=1
n<-2
scRNAtools_Gene2exp_1(example,types_all,gene1,gene2,n,col_1,col_2,pch,lwd)

Present gene expression

Description

This function can present the expression of two gene in appointed cell type.

Usage

scRNAtools_Gene3exp_1(example,types_all,gene1,gene2,gene3,n,col_1,col_2,col_3,pch,lwd)

Arguments

Author(s)

Qian Yang

Examples

data(example)
data(types_all)
gene1<-"CHD1"
gene2<-"CD82"
gene3<-"ASS1"
col_1="red"
col_2="blue"
col_3="green"
pch=19
lwd=2
n<-3
scRNAtools_Gene3exp_1(example,types_all,gene1,gene2,gene3,n,col_1,col_2,col_3,pch,lwd)

Expressed genes in scRNA sequencing data

Description

Extracted the genes expressed in cells. Users can set the threshold value.

Usage

scRNAtools_Geneexp(example, types, num)

Arguments

Value

Author(s)

Qian Yang

Examples

data(example)
types<-"1"
num<-0.8
scRNAtools_Geneexp(example,types,num)

Present gene expression

Description

This function can present the expression of one gene in appointed cell type.

Usage

scRNAtools_Geneexp_1(example, gene, types_all, n, col, pch, lwd)

Arguments

Author(s)

Qian Yang

Examples

data(example)
data(types_all)
gene<-"CHD1";###Set the gene you are interested in.
n<-3;###Set the type of cells you are interested in.
col<-"red";###Set the color of line in the figure.
pch<-19;###Set the shape of nodes in figure.
lwd<-2;###Set the width of lines in figure.
scRNAtools_Geneexp_1(example,gene,types_all,n,col,pch,lwd)

Pathway enrichment analysis

Description

Pathway enrichment analysis using the interested gene set or differentially expressed gene set provided by users. This data contains two column (Enterz ID and gene sybmols)

Usage

scRNAtools_PEA(DEGs,number)

Arguments

Details

This function integrated method to do the pathway enrichment analysis, TPEA.

Value

The significant pathways are wrote in the occurrent path.

Author(s)

Qian Yang

References

Wei Jiang (2017). TPEA: A Novel Topology-Based Pathway Enrichment Analysis Approach.

Examples

data(DEGs)
number<-10
pdf(file=file.path(tempdir(), "enrichment analysis.pdf"))
scRNAtools_PEA(DEGs,number)
dev.off()

Cluster section

Description

Do consistent clustering analysis use clusterProfiler method

Usage

scRNAtools_cluster(example1, k)

Arguments

Details

The results are presented in your occurrent path.

Author(s)

Qian Yang

References

Guangchuang Yu, Li-Gen Wang, Yanyan Han and Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology 2012, 16(5):284-287.

Examples

##setwd("")###Set the path your data in.
data(example1)##Example data in this package.
k<-6##set K based on your own requirement.
scRNAtools_cluster(example1,k)

Correlation analysis

Description

Correlation analysis of interested gene set or differentially expressed gene set.

Usage

scRNAtools_cor_map(exam1, types_all, type, methods)

Arguments

Details

Return the correlation index of each two genes.

Author(s)

Qian Yang

Examples

data(exam1)
data(types_all)
type<-"Malignant";
methods<-"pearson";##methods = c("pearson", "kendall", "spearman").
pdf(file=file.path(tempdir(), "correlation_color.pdf"))
scRNAtools_cor_map(exam1,types_all,type,methods)
dev.off()

Present correlation index in figure

Description

Correlation analysis with correlation index of interested gene set or differentially expressed gene set.

Usage

scRNAtools_cor_map_r(exam1, types_all, type, methods)

Arguments

Details

Return the correlation index of each two genes.

Author(s)

Qian Yang

Examples

data(exam1)
data(types_all)
type<-"Malignant";
methods<-"pearson";##methods = c("pearson", "kendall", "spearman").
pdf(file=file.path(tempdir(), "correlation_num.pdf"))
scRNAtools_cor_map_r(exam1,types_all,type,methods)
dev.off()

Construction of interactive network in scRNA sequencing data

Description

Construction of interactive network based on scRNa sequencing data.

Usage

scRNAtools_inter_net(corr_re, p, r, size, color)

Arguments

Author(s)

Qian Yang

Examples

data(corr_re)
p<-0.05
r<-0.9
size<-5 #nodes size
color<-"#00B2EE" ##Color of nodes.
pdf(file=file.path(tempdir(), "interact_net.pdf"))
scRNAtools_inter_net(corr_re,p,r,size,color)
dev.off()

PCA analysis

Description

PCA analysis for scRNA sequencing data

Usage

scRNAtools_pca(example1, types)

Arguments

Author(s)

Qian Yang

Examples

data(example1)
data(types)
pdf(file=file.path(tempdir(), "PCA_result-R.pdf"))##Save the figures of PCA results.
scRNAtools_pca(example1,types)
dev.off()

3D PCA analysis

Description

PCA analysis for scRNA sequencing data and present 3D figure.

Usage

scRNAtools_pca_3D(example1, types)

Arguments

Author(s)

Qian Yang

Examples

##3D PCA analysis
data(example1)
data(types)
scRNAtools_pca_3D(example1,types)##3D figure of PCA results.

t-SNE analysis

Description

t-SNE analysis for scRNA sequencing data

Usage

scRNAtools_tsne(exam)

Arguments

Author(s)

Qian Yang

References

L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579-2605, 2008.

Examples

data(exam)
scRNAtools_tsne(exam)

types

Description

Cell types in the example data

types_all

Description

Cell names of each type