| Title: | Identification of Dysregulated MiRNAs Based on MiRNA-MiRNA Interaction Network |
|---|---|
| Description: | A systematic biology tool was developed to identify dysregulated miRNAs via a miRNA-miRNA interaction network. 'IDMIR' first constructed a weighted miRNA interaction network through integrating miRNA-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific-disease context, and then, it used a network propagation algorithm on the network to identify significantly dysregulated miRNAs. |
| Authors: | Junwei Han [aut, cre, cph], Xilong Zhao [aut], Jiashuo Wu [aut] |
| Maintainer: | Junwei Han <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.0 |
| Built: | 2024-11-04 04:04:38 UTC |
| Source: | https://github.com/cran/IDMIR |
Get the example data
GetData_Mirna(Data)GetData_Mirna(Data)
Data |
A character should be one of"survival", "GEP", "MF_Target", "MiRNA_Target", "matrix_mirna_go_inter", "matrix_mirna_go_jaccard" |
data
Function "GetGDEscore" is used to calculate gene differential expression levels.
GetGDEscore(ExpData,Label)GetGDEscore(ExpData,Label)
ExpData |
A gene expression profile of interest (rows are genes, columns are samples). |
Label |
A character vector consists of "0" and "1" which represent sample class in the gene expression profile. "0" means normal sample and "1" means disease sample. |
A matrix with one column of GDEscore.
# Obtain the example data GEP<-GetData_Mirna("GEP") label<-GetData_Mirna("label") # Run the function GDEscore<-GetGDEscore(GEP,label)# Obtain the example data GEP<-GetData_Mirna("GEP") label<-GetData_Mirna("label") # Run the function GDEscore<-GetGDEscore(GEP,label)
The function "IdentifyMiRNA" is used to identify significantly dysregulated miRNAs by calculating the eigenvector centrality of miRNAs.
IdentifyMiRNA(GDEscore.table,nperm=1000,damping=0.90)IdentifyMiRNA(GDEscore.table,nperm=1000,damping=0.90)
GDEscore.table |
A matrix with one column of GDEscore. |
nperm |
The Number of random permutations (default: 100). |
damping |
Restart the probability of the random-walk algorithm (default: 0.9). |
A data frame with seven columns those are "MiRNA", "Target", "Number" (number of targets), "Score" (Centrality score), "P-value", and "FDR".
# Obtain the example data GEP<-GetData_Mirna("GEP") label<-GetData_Mirna("label") # Run the function GDEscore<-GetGDEscore(GEP,label) MiRNAScore<-IdentifyMiRNA(GDEscore,nperm=5, damping=0.90)# Obtain the example data GEP<-GetData_Mirna("GEP") label<-GetData_Mirna("label") # Run the function GDEscore<-GetGDEscore(GEP,label) MiRNAScore<-IdentifyMiRNA(GDEscore,nperm=5, damping=0.90)
An environment variable that includes some example data. matirx_mirna_go_inter:A matrix of Jaccard score between MiRNAs and GOMF. matirx_mirna_go_jaccard:A matrix consisting of genes shared by MiRNAs targets and GOMF. MiRNAScore:a ranked list of strong and weak associations with cancer. MF_Target:GOMF and corresponding targets. MiRNA_Target:MiRNAs and corresponding targets. zscore_BRCA:An example gene expression profile. label:A vector representing the label of the sample of BRCA, where "1" is the disease sample and "0" is the normal sample. survival:A dataframe including three columns which are sample, status, and time.
MirnaDataMirnaData
An environment variable
Function "MutiMiRNA_CRModel" can build a multivariate Cox model through integrating the models constructed separately based on different mirna targets.
MutiMiRNA_CRModel(ExpData, MiRNAs,SurvivalData,cutoff.point=NULL)MutiMiRNA_CRModel(ExpData, MiRNAs,SurvivalData,cutoff.point=NULL)
ExpData |
A gene expression profile of interest (rows are genes, columns are samples). |
MiRNAs |
An interest miRNA vector. |
SurvivalData |
Survival data (the column names are: "sample", "status", and "time") corresponding to the samples in gene expression profile of interest. |
cutoff.point |
A numeric value used to divide high-risk and low-risk groups. |
A list includes a data frame with seven parts those are "sample", "status", "time", "target gene expression", "risk score", "group", and a data frame with five columns those are "Gene", "HR", "HR.95L", "HR.95H", "beta", and "P-value".
# Obtain the example data GEP<-GetData_Mirna("GEP") survival<-GetData_Mirna("survival") MiRNAs<-c("hsa-miR-21-5p","hsa-miR-26a-5p","hsa-miR-369-5p","hsa-miR-1238-3p","hsa-miR-10b-5p") # Run the function MutiMiRNA_CRData<-MutiMiRNA_CRModel(GEP, MiRNAs,survival,cutoff.point=NULL)# Obtain the example data GEP<-GetData_Mirna("GEP") survival<-GetData_Mirna("survival") MiRNAs<-c("hsa-miR-21-5p","hsa-miR-26a-5p","hsa-miR-369-5p","hsa-miR-1238-3p","hsa-miR-10b-5p") # Run the function MutiMiRNA_CRData<-MutiMiRNA_CRModel(GEP, MiRNAs,survival,cutoff.point=NULL)
Function "PlotForest" can visualize the result of Cox regression analysis through forest plot.
PlotForest(MiRNA_CRData,g.pos = 2,b.size = 3,col = c("#FE0101", "#1C61B6", "#A4A4A4"), lwd.zero = 2,lwd.ci = 3,x.lab = "Hazard Ratio Plot")PlotForest(MiRNA_CRData,g.pos = 2,b.size = 3,col = c("#FE0101", "#1C61B6", "#A4A4A4"), lwd.zero = 2,lwd.ci = 3,x.lab = "Hazard Ratio Plot")
MiRNA_CRData |
A list includes a data frame with seven parts those are "sample", "status", "time", "target genes expression", "risk score", "group", and a data frame with five columns those are "Gene", "HR", "HR.95L", "HR.95H", "beta", and "P-value". |
g.pos |
The position of the graph element within the table of text. The position can be 1-(ncol(labeltext) + 1). You can also choose set the position to "left" or "right". |
b.size |
Override the default box size based on precision. |
col |
Set the colors for all the elements in the plot. |
lwd.zero |
lwd for the vertical line that gives the no-effect line, see gpar. |
lwd.ci |
lwd for the confidence bands, see gpar. |
x.lab |
x-axis label. |
Forest maps associated with the Cox risk model.
# Obtain the example data GEP<-GetData_Mirna("GEP") survival<-GetData_Mirna("survival") MiRNAs<-c("hsa-miR-21-5p","hsa-miR-26a-5p","hsa-miR-369-5p","hsa-miR-1238-3p","hsa-miR-10b-5p") # Run the function SingleMiRNA_CRData<-SingleMiRNA_CRModel(GEP, "hsa-miR-21-5p",survival,cutoff.point=NULL) PlotForest(SingleMiRNA_CRData) MutiMiRNA_CRData<-MutiMiRNA_CRModel(GEP, MiRNAs,survival,cutoff.point=NULL) PlotForest(MutiMiRNA_CRData)# Obtain the example data GEP<-GetData_Mirna("GEP") survival<-GetData_Mirna("survival") MiRNAs<-c("hsa-miR-21-5p","hsa-miR-26a-5p","hsa-miR-369-5p","hsa-miR-1238-3p","hsa-miR-10b-5p") # Run the function SingleMiRNA_CRData<-SingleMiRNA_CRModel(GEP, "hsa-miR-21-5p",survival,cutoff.point=NULL) PlotForest(SingleMiRNA_CRData) MutiMiRNA_CRData<-MutiMiRNA_CRModel(GEP, MiRNAs,survival,cutoff.point=NULL) PlotForest(MutiMiRNA_CRData)
The function "PlotHeatmap" is used to plot a heat map of miRNA targets expression.
PlotHeatmap(MiRNA_CRData,yaxis=c(-2,2),scale="row", cluster.rows=FALSE,cluster.cols=FALSE,show.colnames=FALSE, ann_colors=c("#ef6d6d","#5470c6"),col=c("#ef6d6d","#5470c6"))PlotHeatmap(MiRNA_CRData,yaxis=c(-2,2),scale="row", cluster.rows=FALSE,cluster.cols=FALSE,show.colnames=FALSE, ann_colors=c("#ef6d6d","#5470c6"),col=c("#ef6d6d","#5470c6"))
MiRNA_CRData |
A list includes a data frame with seven parts those are "sample", "status", "time", "target genes expression", "risk score", "group", and a data frame with five columns those are "Gene", "HR", "HR.95L", "HR.95H", "beta", and "P-value". |
yaxis |
The upper and lower limits of this heat map. |
scale |
character indicating if the values should be centered and scaled in either the row direction or the column direction, or none. Corresponding values are "row", "column" and "none". |
cluster.rows |
A logical value that represents whether row clustering is used. |
cluster.cols |
A logical value that represents whether col clustering is used. |
show.colnames |
This parameter controls whether column names are displayed. |
ann_colors |
Vector of colors used to define groups. |
col |
Vector of colors used in the heatmap. |
A heat map of miRNA targets expression.
# Obtain the example data GEP<-GetData_Mirna("GEP") survival<-GetData_Mirna("survival") MiRNAs<-c("hsa-miR-21-5p","hsa-miR-26a-5p","hsa-miR-369-5p","hsa-miR-1238-3p","hsa-miR-10b-5p") # Run the function SingleMiRNA_CRData<-SingleMiRNA_CRModel(GEP, "hsa-miR-21-5p",survival,cutoff.point=NULL) PlotHeatmap(SingleMiRNA_CRData) MutiMiRNA_CRData<-MutiMiRNA_CRModel(GEP, MiRNAs,survival,cutoff.point=NULL) PlotHeatmap(MutiMiRNA_CRData)# Obtain the example data GEP<-GetData_Mirna("GEP") survival<-GetData_Mirna("survival") MiRNAs<-c("hsa-miR-21-5p","hsa-miR-26a-5p","hsa-miR-369-5p","hsa-miR-1238-3p","hsa-miR-10b-5p") # Run the function SingleMiRNA_CRData<-SingleMiRNA_CRModel(GEP, "hsa-miR-21-5p",survival,cutoff.point=NULL) PlotHeatmap(SingleMiRNA_CRData) MutiMiRNA_CRData<-MutiMiRNA_CRModel(GEP, MiRNAs,survival,cutoff.point=NULL) PlotHeatmap(MutiMiRNA_CRData)
Function "PlotScatter" is used to plot a scatter diagram.
PlotScatter(MiRNA_CRData,status.0='Alive',status.1='Dead', TitleYlab_A='Risk Score',TitleYlab_B='Survival Time',TitleXlab='Rank', TitleLegend_A='Risk Group',TitleLegend_B='Status', color.A=c(low='blue',high='red'),color.B=c(status.0='blue',status.1='red'))PlotScatter(MiRNA_CRData,status.0='Alive',status.1='Dead', TitleYlab_A='Risk Score',TitleYlab_B='Survival Time',TitleXlab='Rank', TitleLegend_A='Risk Group',TitleLegend_B='Status', color.A=c(low='blue',high='red'),color.B=c(status.0='blue',status.1='red'))
MiRNA_CRData |
A list includes a data frame with seven parts those are "sample", "status", "time", "target genes expression", "risk score", "group", and a data frame with five columns those are "Gene", "HR", "HR.95L", "HR.95H", "beta", and "P-value". |
status.0 |
string. Code for event 0. Default is 'Alive' |
status.1 |
string. Code for event 1. Default is 'Dead' |
TitleYlab_A |
string, y-lab title for figure A. Default is 'Riskscore' |
TitleYlab_B |
string, y-lab title for figure B. Default is 'Survival Time' |
TitleXlab |
string, x-lab title for figure B. Default is 'Rank' |
TitleLegend_A |
string, legend title for figure A. Default is 'Risk Group' |
TitleLegend_B |
string, legend title for figure B. Default is 'Status' |
color.A |
color for figure A. Default is low = 'blue', high = 'red' |
color.B |
color for figure B. Default is status.0 = 'blue', status.1 = 'red' |
A riskscore picture
# Obtain the example data GEP<-GetData_Mirna("GEP") survival<-GetData_Mirna("survival") MiRNAs<-c("hsa-miR-21-5p","hsa-miR-26a-5p","hsa-miR-369-5p","hsa-miR-1238-3p","hsa-miR-10b-5p") # Run the function SingleMiRNA_CRData<-SingleMiRNA_CRModel(GEP, "hsa-miR-21-5p",survival,cutoff.point=NULL) PlotScatter(SingleMiRNA_CRData) MutiMiRNA_CRData<-MutiMiRNA_CRModel(GEP, MiRNAs,survival,cutoff.point=NULL) PlotScatter(MutiMiRNA_CRData)# Obtain the example data GEP<-GetData_Mirna("GEP") survival<-GetData_Mirna("survival") MiRNAs<-c("hsa-miR-21-5p","hsa-miR-26a-5p","hsa-miR-369-5p","hsa-miR-1238-3p","hsa-miR-10b-5p") # Run the function SingleMiRNA_CRData<-SingleMiRNA_CRModel(GEP, "hsa-miR-21-5p",survival,cutoff.point=NULL) PlotScatter(SingleMiRNA_CRData) MutiMiRNA_CRData<-MutiMiRNA_CRModel(GEP, MiRNAs,survival,cutoff.point=NULL) PlotScatter(MutiMiRNA_CRData)
Function "PlotSurvival" is used to draw a Kaplan-Meier curve.
PlotSurvival(MiRNA_CRData,colors=c("#ef6d6d","#5470c6"))PlotSurvival(MiRNA_CRData,colors=c("#ef6d6d","#5470c6"))
MiRNA_CRData |
A list includes a data frame with seven parts those are "sample", "status", "time", "target genes expression", "risk score", "group", and a data frame with five columns those are "Gene", "HR", "HR.95L", "HR.95H", "beta", and "P-value". |
colors |
Vector of colors used to define groups. |
A survival curve of a data set.
# Obtain the example data GEP<-GetData_Mirna("GEP") survival<-GetData_Mirna("survival") MiRNAs<-c("hsa-miR-21-5p","hsa-miR-26a-5p","hsa-miR-369-5p","hsa-miR-1238-3p","hsa-miR-10b-5p") # Run the function SingleMiRNA_CRData<-SingleMiRNA_CRModel(GEP, "hsa-miR-21-5p",survival,cutoff.point=NULL) PlotSurvival(SingleMiRNA_CRData) MutiMiRNA_CRData<-MutiMiRNA_CRModel(GEP, MiRNAs,survival,cutoff.point=NULL) PlotSurvival(MutiMiRNA_CRData)# Obtain the example data GEP<-GetData_Mirna("GEP") survival<-GetData_Mirna("survival") MiRNAs<-c("hsa-miR-21-5p","hsa-miR-26a-5p","hsa-miR-369-5p","hsa-miR-1238-3p","hsa-miR-10b-5p") # Run the function SingleMiRNA_CRData<-SingleMiRNA_CRModel(GEP, "hsa-miR-21-5p",survival,cutoff.point=NULL) PlotSurvival(SingleMiRNA_CRData) MutiMiRNA_CRData<-MutiMiRNA_CRModel(GEP, MiRNAs,survival,cutoff.point=NULL) PlotSurvival(MutiMiRNA_CRData)
Function "SingleMiRNA_CRModel" uses survival data to build a multivariate Cox model using the targets of a single miRNA.
SingleMiRNA_CRModel(ExpData,MiRNA,cutoff.point=NULL,SurvivalData)SingleMiRNA_CRModel(ExpData,MiRNA,cutoff.point=NULL,SurvivalData)
ExpData |
A gene expression profile of interest (rows are genes, columns are samples). |
MiRNA |
A miRNA ID. |
cutoff.point |
A numeric value is used to divide high-risk and low-risk groups. |
SurvivalData |
Survival data (the column names are: "sample", "status", "time") corresponding to samples in the gene expression profile of interest. |
A list includes a data frame with seven parts those are "sample", "status", "time", "target genes expression", "risk score", "group", and a dataframe with five columns those are "Gene", "HR", "HR.95L", "HR.95H", "beta", and "P-value".
# Obtain the example data GEP<-GetData_Mirna("GEP") survival<-GetData_Mirna("survival") # Run the function SingleMiRNA_CRData<-SingleMiRNA_CRModel(GEP, "hsa-miR-21-5p",cutoff.point=NULL,survival)# Obtain the example data GEP<-GetData_Mirna("GEP") survival<-GetData_Mirna("survival") # Run the function SingleMiRNA_CRData<-SingleMiRNA_CRModel(GEP, "hsa-miR-21-5p",cutoff.point=NULL,survival)