IDMIR User Guide
Xilong Zhao, Jiashuo Wu, Junwei Han
2023-11-09
Introduce
This vignette illustrates how to easily use the IDMIR
package. Here, we present a network-based computational
approach, IDMIR, identify dysregulated miRNAs by
integrating gene transcriptomic data and a miRNA interaction network.
Furthermore, the package can perform cox regression analysis to verify
miRNA’s prognostic efficacy. There are also some functions used to
visualize the results of survival analysis.
This network-based method consists of two major parts:
1.Calculating the GDEscore. We conducted a statistical comparison
of gene expression values between case and control groups (e.g. disease
and normal). In this case, we use Student’s t-test method to compute the
gene differentially expressed level for each gene between disease and
normal samples, and convert the t-test p-value of each gene to z-score.
The z-score is defined as GDEscore, and a larger GDEscore indicates the
gene regulated by disease to a greater extent.
2.Constructing network and performing randomization. In our
method, we fist constructed a miRNA-GO bipartite network. The molecular
function gene sets were downloaded from C5 Gene sets derived from the GO
Molecular Function ontology in the Molecular Signatures Database
(MSigDB). We then defined an edge between a miRNA and a GO term if they
have at least a common gene, and give the weight of this edge that
calculate by the Jaccard index and GDEscore. Next, we proposed to
construct a miRNA-miRNA interaction network. The information from the
miRNA-GO bipartite graph was used to evaluate the extent of interaction
between two miRNAs as the edge’s weight. We consider that the extent of
interaction between two miRNAs will be stronger when there are more GO
terms they target in common and the more related they are to these GO
terms. Then, we used the eigenvector centrality measure to calculate how
central each miRNA is in this miRNA-miRNA interaction network. Finally,
the statistical significance of these centrality scores is assessed
using a bootstrap-based randomization method. Statistically significant
miRNAs will serve as potential dysregulated miRNAs.
This package provides the GetData_Mirna function to
return example data set and environment variables, such as the gene
expression profile and so on.
Example 1 : Calculate the GDEscore.
The function GetGDEscore is used
to calculate the gene differential expression score (GDEscore). This
function requires users to input the gene expression matrix and the
sample label (“1” represents the disease sample. “0” represents the
normal sample.). These sample data are stored in this package.
The commands are as follows:
# Obtain the example data
GEP<-GetData_Mirna("GEP")
label<-GetData_Mirna("label")
# Calculate the zscore
GDEscore<-GetGDEscore(GEP,label)
head(GDEscore)
## GDEscore
## CFH 15.74888
## KLHL13 23.12946
## CYP26B1 13.81447
## CFLAR 17.97727
## HSPB6 26.25801
## PDK4 23.36144
Example 2 : Identify candidate miRNAs by
random-walk algorithm.
The function IdentifyMiRNA is
used to get a ranked list of strong and weak associations of miRNA with
disease. According to our method, in this function, the user needs to
enter the GDEscore which is the result of the GetGDEscore
function. The nperm is the number of perturbations, and the
damping is the restart probability of random walk. The commands
are as follows:
# load depend package
require(igraph)
# Calculate the centrality score of miRNAs
MiRNAScore<-IdentifyMiRNA(de_kich,nperm = 10,damping = 0.9)
###view the result
MiRNAScore[1:5,-2]
## MiRNA Number Score P-value FDR
## 255 hsa-miR-21-5p 49 0.00195 0.000 0.000
## 505 hsa-miR-369-5p 14 0.00118 0.000 0.000
## 294 hsa-miR-26a-5p 4 0.00180 0.000 0.000
## 65 hsa-miR-1238-3p 140 0.00099 0.001 0.001
## 30 hsa-miR-10b-5p 5 0.00016 0.002 0.002
Example 3 : Build a multivariate Cox
regression model using the targets of a single miRNA.
The function SingleMiRNA_CRModel
is used to build a multivariate Cox model. According to our method, in
this function, the user needs to enter the miRNA of interest. The
ExpData is a gene expression profile of interest. The
MiRNA is the miRNA ID, and the SurvivalData is
survival data. The cutoff.point is a number used to divide
high-risk and low-risk groups by risk score (default is median). The
commands are as follows:
# load depend package
require(survival)
## Loading required package: survival
## Warning: package 'survival' was built under R version 4.2.3
###Get the survival and gene expression data
GEP<-GetData_Mirna("GEP")
survival<-GetData_Mirna("survival")
# Build a multivariate cox model
SingleMiRNA_CRData<-SingleMiRNA_CRModel(GEP,"hsa-miR-21-5p",cutoff.point=NULL,survival)
###view the result
data.frame(SingleMiRNA_CRData[[1]])[1:5,]
## status time TGFBR3 TIMP3 ACTA2 HIPK3 RECK NFIB TGFBR2 CFL2
## TCGA.3C.AAAU.01A 0 4047 -0.7 2.5 -0.6 -0.2 -0.9 -0.8 -0.7 -0.4
## TCGA.3C.AALI.01A 0 4005 -0.7 -0.3 -0.2 -1.2 -0.4 -0.2 -0.5 -0.5
## TCGA.3C.AALJ.01A 0 1474 -0.6 0.6 -0.3 -1.1 -0.7 -0.6 -0.4 -0.3
## TCGA.3C.AALK.01A 0 1448 -0.5 0.9 0.5 -0.7 0.3 -0.2 -0.2 -0.4
## TCGA.4H.AAAK.01A 0 348 -0.6 1.7 0.1 -0.7 1.7 -0.2 -0.1 -0.4
## score group
## TCGA.3C.AAAU.01A -0.70282866 low
## TCGA.3C.AALI.01A -0.12534517 low
## TCGA.3C.AALJ.01A -0.29593916 low
## TCGA.3C.AALK.01A -0.13408981 low
## TCGA.4H.AAAK.01A 0.04991242 high
###view the result
data.frame(SingleMiRNA_CRData[[2]])[1:5,]
## HR HR.95L HR.95H beta P.value
## TGFBR3 0.9898017 0.8218065 1.192139 -0.010250697 0.91398271
## TIMP3 0.8422946 0.6946581 1.021309 -0.171625436 0.08089412
## ACTA2 1.0060618 0.8649299 1.170223 0.006043537 0.93753659
## HIPK3 0.9954106 0.8358977 1.185363 -0.004599944 0.95882996
## RECK 1.2506863 1.0449974 1.496861 0.223692413 0.01468395
Example 4 : Build a multivariate Cox
regression model by integrating the models constructed separately based
on different miRNA targets.
The function MutiMiRNA_CRModel is
used to build a multivariate Cox model. According to our method, in this
function, the user needs to enter the miRNAs of interest. The
ExpData is a gene expression profile of interest, The
MiRNAs is the miRNA ID, and the SurvivalData is
survival data. The cutoff.point is a number used to divide
high-risk and low-risk groups by risk score (default is median). The
commands are as follows:
# load depend package
require(survival)
###Get the survival and gene expression 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")
# Build a multivariate cox model
MutiMiRNA_CRData<-MutiMiRNA_CRModel(GEP,MiRNAs,cutoff.point=NULL,survival)
###view the result
data.frame(MutiMiRNA_CRData[[1]])[1:5,]
## status time hsa.miR.21.5p hsa.miR.26a.5p hsa.miR.369.5p
## TCGA.3C.AAAU.01A 0 4047 -0.70282866 -0.12291216 -0.1552759
## TCGA.3C.AALI.01A 0 4005 -0.12534517 -0.08779440 -0.1552759
## TCGA.3C.AALJ.01A 0 1474 -0.29593916 -0.07023552 -0.1330936
## TCGA.3C.AALK.01A 0 1448 -0.13408981 -0.03511776 -0.1109113
## TCGA.4H.AAAK.01A 0 348 0.04991242 -0.01755888 -0.1330936
## hsa.miR.1238.3p hsa.miR.10b.5p score group
## TCGA.3C.AAAU.01A 0.141563149 -0.08886492 -0.2791553 low
## TCGA.3C.AALI.01A -0.141549442 -0.07109194 -0.1218944 low
## TCGA.3C.AALJ.01A 0.002988404 -0.08886492 -0.1340748 low
## TCGA.3C.AALK.01A -0.222947388 -0.05331895 -0.2123948 low
## TCGA.4H.AAAK.01A -0.251633884 -0.07109194 -0.1077162 low
###view the result
data.frame(MutiMiRNA_CRData[[2]])[1:5,]
## HR HR.95L HR.95H beta P.value
## hsa.miR.21.5p 1.9206555 1.1699532 3.153047 0.65266652 0.0098633164
## hsa.miR.26a.5p 0.9126143 0.3668481 2.270326 -0.09144195 0.8440989401
## hsa.miR.369.5p 0.6594771 0.2967931 1.465364 -0.41630808 0.3067975175
## hsa.miR.1238.3p 2.1555406 1.4509625 3.202257 0.76804155 0.0001428791
## hsa.miR.10b.5p 1.0584837 0.5469643 2.048375 0.05683745 0.8660063402
Visualize 1: Plot the Kaplan-Meier curve.
The function plotSurvival is used
to plot the Kaplan-Meier. The user needs to enter the result of the
SingleMiRNA_CRModel function or the
MutiMiRNA_CRModel function. The commands are as follows:
# load depend package
require(survminer)
## Warning: package 'survminer' was built under R version 4.2.2
## Warning: package 'ggplot2' was built under R version 4.2.3
## Warning: package 'ggpubr' was built under R version 4.2.3
require(survival)
###Get the survival and gene expression data
GEP<-GetData_Mirna("GEP")
survival<-GetData_Mirna("survival")
# plot the Kaplan-Meier curve
SingleMiRNA_CRData<-SingleMiRNA_CRModel(GEP,"hsa-miR-21-5p",cutoff.point=NULL,survival)
PlotSurvival(SingleMiRNA_CRData)
Visualize 2: Plot the heatmap of miRNA
targets expression.
The function PlotHeatmap can plot
the heatmap of miRNA target expression. The user needs to enter the
result of the SingleMiRNA_CRModel function or the
MutiMiRNA_CRModel. The commands are as follows:
# load depend package
require(pheatmap)
require(survival)
###Get the survival and gene expression data
GEP<-GetData_Mirna("GEP")
survival<-GetData_Mirna("survival")
# plot the heatmap of miRNA targets expression
SingleMiRNA_CRData<-SingleMiRNA_CRModel(GEP,"hsa-miR-21-5p",cutoff.point=NULL,survival)
PlotHeatmap(SingleMiRNA_CRData)
Visualize 3: Plot the forest
diagram.
The function PlotForest can
visualize the result of Cox regression analysis through forest plot. The
user needs to enter the result of the SingleMiRNA_CRModel
function or the MutiMiRNA_CRModel. The commands are as
follows:
# load depend package
require(forestplot)
## Warning: package 'forestplot' was built under R version 4.2.3
## Warning: package 'checkmate' was built under R version 4.2.3
require(survival)
###Get the survival and gene expression data
GEP<-GetData_Mirna("GEP")
survival<-GetData_Mirna("survival")
# plot the forest diagram
SingleMiRNA_CRData<-SingleMiRNA_CRModel(GEP,"hsa-miR-21-5p",cutoff.point=NULL,survival)
PlotForest(SingleMiRNA_CRData)
Visualize 4: Plot the Scatter
diagram.
The function PlotScatter can
visualize the clinical information of samples through scatter diagram.
The user needs to enter the result of the
SingleMiRNA_CRModel function or the
MutiMiRNA_CRModel. The commands are as follows:
# load depend package
require(egg)
## Warning: package 'egg' was built under R version 4.2.3
require(survival)
require(ggplot2)
###Get the survival and gene expression data
GEP<-GetData_Mirna("GEP")
survival<-GetData_Mirna("survival")
# plot the scatter diagram
SingleMiRNA_CRData<-SingleMiRNA_CRModel(GEP,"hsa-miR-21-5p",cutoff.point=NULL,survival)
PlotScatter(SingleMiRNA_CRData)
