| Title: | 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, principal components analysis (PCA), the filter of differentially expressed genes, pathway enrichment analysis and correlated analysis methods. |
| Authors: | Qian Yang |
| Maintainer: | Qian Yang <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0 |
| Built: | 2024-11-22 06:59:09 UTC |
| Source: | CRAN |
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.
Qian Yang Maintainer: Qian Yang <[email protected]>
####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)####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)
The correlation index of the genes in the section of correlated analysis.
Gene list with two columns. The first column is Entrez ID of genes and the second column is gene symbol
Do consistent clustering analysis use clusterProfiler method
scRNAtools_cluster(example1, k)scRNAtools_cluster(example1, k)
example1 |
scRNA sequencing data with header. |
k |
The number of class. If you set k is 6, you will obtain 6 results of cluster. |
The results are presented in your occurrent path.
Qian Yang
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.
##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)##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 of interested gene set or differentially expressed gene set.
scRNAtools_cor_map(exam1, types_all, type, methods)scRNAtools_cor_map(exam1, types_all, type, methods)
exam1 |
scRNA sequencing data of several genes and cells. |
types_all |
Cell names of each type. |
type |
Cell type. |
methods |
correlation methods including "pearson", "kendall" and "spearman". |
Return the correlation index of each two genes.
Qian Yang
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()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()
Correlation analysis with correlation index of interested gene set or differentially expressed gene set.
scRNAtools_cor_map_r(exam1, types_all, type, methods)scRNAtools_cor_map_r(exam1, types_all, type, methods)
exam1 |
scRNA sequencing data of several genes and cells. |
types_all |
Cell names of each type. |
type |
Cell type. |
methods |
correlation methods including "pearson", "kendall" and "spearman". |
Return the correlation index of each two genes.
Qian Yang
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()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()
Users can identify differentially expressed genes between two type of cells based on fold change value.
scRNAtools_DEGsA(example, types_all, type1, type2, num)scRNAtools_DEGsA(example, types_all, type1, type2, num)
example |
scRNA sequencing data with header. |
types_all |
Cell types in the example data. |
type1 |
Cell type one. |
type2 |
Cell type two. |
num |
Threshold value of expressed genes in appointed cell types. For example, we set 0.8 in example section. |
The output data is the fold change value of differentially expressed genes.
Qian Yang
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()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()
This function can present the expression of two gene in appointed cell type.
scRNAtools_Gene2exp_1(example, types_all, gene1, gene2, n, col_1, col_2, pch, lwd)scRNAtools_Gene2exp_1(example, types_all, gene1, gene2, n, col_1, col_2, pch, lwd)
example |
scRNA sequencing data without header. |
types_all |
Cell names of each type. |
gene1 |
Gene one you are interested in. |
gene2 |
Gene two you are interested in. |
n |
Number of cell names in scRNA sequening data. |
col_1 |
The color of line of gene one in the figure. |
col_2 |
The color of line of gene two in the figure. |
pch |
The shape of nodes in figure. |
lwd |
The width of lines in figure. |
Qian Yang
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)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)
This function can present the expression of two gene in appointed cell type.
scRNAtools_Gene3exp_1(example,types_all,gene1,gene2,gene3,n,col_1,col_2,col_3,pch,lwd)scRNAtools_Gene3exp_1(example,types_all,gene1,gene2,gene3,n,col_1,col_2,col_3,pch,lwd)
example |
scRNA sequencing data without header. |
types_all |
Cell names of each type. |
gene1 |
Gene one you are interested in. |
gene2 |
Gene two you are interested in. |
gene3 |
Gene three you are interested in. |
n |
Number of cell names in scRNA sequening data. |
col_1 |
The color of line of gene one in the figure. |
col_2 |
The color of line of gene two in the figure. |
col_3 |
The color of line of gene three in the figure. |
pch |
The shape of nodes in figure. |
lwd |
The width of lines in figure. |
Qian Yang
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)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)
Extracted the genes expressed in cells. Users can set the threshold value.
scRNAtools_Geneexp(example, types, num)scRNAtools_Geneexp(example, types, num)
example |
scRNA sequencing data without header. |
types |
Cell types in the example data. |
num |
Threshold value of expressed genes in appointed cell types. For example, we set 0.8 in example section. |
zset |
Gene expression data required the threshold value. |
Qian Yang
data(example) types<-"1" num<-0.8 scRNAtools_Geneexp(example,types,num)data(example) types<-"1" num<-0.8 scRNAtools_Geneexp(example,types,num)
This function can present the expression of one gene in appointed cell type.
scRNAtools_Geneexp_1(example, gene, types_all, n, col, pch, lwd)scRNAtools_Geneexp_1(example, gene, types_all, n, col, pch, lwd)
example |
scRNA sequencing data without header. |
gene |
One gene you are interested in. |
types_all |
Cell names of each type. |
n |
Number of cell names in scRNA sequening data. |
col |
The color of line in the figure. |
pch |
The shape of nodes in figure. |
lwd |
The width of lines in figure. |
Qian Yang
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)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)
Construction of interactive network based on scRNa sequencing data.
scRNAtools_inter_net(corr_re, p, r, size, color)scRNAtools_inter_net(corr_re, p, r, size, color)
corr_re |
The results of correlation analysis, which including four columns, the first two columns are genes and the last two columns are correlation index and p-value,respectively. |
p |
The p-value of correlation index. |
r |
Correlation index |
size |
The size of nodes in the network. |
color |
The color of nodes in the network. |
Qian Yang
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()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 for scRNA sequencing data
scRNAtools_pca(example1, types)scRNAtools_pca(example1, types)
example1 |
scRNA sequencing data with header. |
types |
Cell types in the example data. |
Qian Yang
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()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()
PCA analysis for scRNA sequencing data and present 3D figure.
scRNAtools_pca_3D(example1, types)scRNAtools_pca_3D(example1, types)
example1 |
scRNA sequencing data with header. |
types |
Cell types in the example data. |
Qian Yang
##3D PCA analysis data(example1) data(types) scRNAtools_pca_3D(example1,types)##3D figure of PCA results.##3D PCA analysis data(example1) data(types) scRNAtools_pca_3D(example1,types)##3D figure of PCA results.
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)
scRNAtools_PEA(DEGs,number)scRNAtools_PEA(DEGs,number)
DEGs |
Interested gene set of differentially expressed gene set. |
number |
The number of random, for example, users can set 1000, 5000 or more. |
This function integrated method to do the pathway enrichment analysis, TPEA.
The significant pathways are wrote in the occurrent path.
Qian Yang
Wei Jiang (2017). TPEA: A Novel Topology-Based Pathway Enrichment Analysis Approach.
data(DEGs) number<-10 pdf(file=file.path(tempdir(), "enrichment analysis.pdf")) scRNAtools_PEA(DEGs,number) dev.off()data(DEGs) number<-10 pdf(file=file.path(tempdir(), "enrichment analysis.pdf")) scRNAtools_PEA(DEGs,number) dev.off()
t-SNE analysis for scRNA sequencing data
scRNAtools_tsne(exam)scRNAtools_tsne(exam)
exam |
scRNA sequencing data with four genes. Users can reference the format and input their own data. |
Qian Yang
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.
data(exam) scRNAtools_tsne(exam)data(exam) scRNAtools_tsne(exam)