| Title: | 'MicroRNA' Set Enrichment Analysis |
|---|---|
| Description: | The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results. |
| Authors: | Junwei Han, Siyao Liu |
| Maintainer: | Junwei Han <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.1 |
| Built: | 2024-11-10 03:13:51 UTC |
| Source: | https://github.com/hanjunwei-lab/mirsea |
This package can identify dysregulated pathways(or prior gene sets) regulated by microRNAs set in the context of miRNA expression data.
The package can identify dysregulated pathways(or prior gene sets) regulated by microRNAs set in the context of miRNA expression data. (1) The MiRSEA package constructs a correlation profile of miRNAs and pathways by hypergeometric.The gene sets of pathways derived from the three public databases(KEGG;Reactome;Biocarta;).The target gene sets of miRNAs are provided by four databases(TarBaseV6.0; mir2Disease; miRecords; miRTarBase;). (2) The MiRSEA package can quantify the change of correlation between miRNAs for each pathway (or prior gene set) based on miRNA expression data with cases and controls. (3) The MiRSEA package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score(ES)of a miRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) The MiRSEA package can provide the visualization of the results.
Junwei Han[email protected],Siyao Liu [email protected]
The function Corrp2miRfile create a p value matrix and a pathway-miRNA correlation profile
Corrp2miRfile(pathway="kegg", species = "hsa")Corrp2miRfile(pathway="kegg", species = "hsa")
pathway |
choose database of pathway,"kegg","biocarta" or"reactome" |
species |
Species of miRNAs(default: hsa) |
When users input interesting species and pathway, the function can calculate the p value between pathway and miRNA using hypergeometric.The p value can quantify the strength of the pathway regulated by each miRNA.The smaller p value is represent the bigger strength of regulate.Then p2m can get miRNA set(pmSET)for each pathway,which is a co-regulated miRNA set of this pathway(w>0).
p |
A p value weighted matrix (rows are pathway ,cols are miRNAs) |
p2miR |
pathway-miRNA correlation(pmSET) profile |
Junwei Han[email protected],Siyao Liu [email protected]
Rivals I, Personnaz L, Taing L, & Potier MC (2007) Enrichment or depletion of a GO category within a class of genes: which test? (Translated from eng) Bioinformatics 23(4):401-407 (in eng).
## Not run: p2m<-Corrp2miRfile(pathway="kegg", species = "example") p2m$p[1,1:10] p2m$p2miR[1,1:5] ## End(Not run)## Not run: p2m<-Corrp2miRfile(pathway="kegg", species = "example") p2m$p[1,1:10] p2m$p2miR[1,1:5] ## End(Not run)
Computes the enrichment score of a microRNA(miRNA) set in a ordered miRNA list.
EnrichmentScore(miR.list, miR.set, weighted.score.type = 1, correl.vector = NULL)EnrichmentScore(miR.list, miR.set, weighted.score.type = 1, correl.vector = NULL)
miR.list |
The ordered miRNA list ,integers indicating the original position in the input dataset. |
miR.set |
A miRNA set ,integers indicating the location of those miRNAs in the input dataset. |
weighted.score.type |
Type of score,weight=0,ES reduces to the standard Kolmogorov-Smirnov statistic,when weight=1, we are weighting the miRNAs by their tw-score normalized by the sum of the tw-scores over all of the miRNAs in the miRNA set. |
correl.vector |
A vector with the correlations(tw-scores) corresponding to the miRNAs in the miRNA list |
The function can computes the enrichment score of a miRNA set in a miRNA list. The weighted score type is the exponent of the correlation(e.g.tw-score) (1) Rank order the miRNAs in a miRNA set to form a list according to the correlation(e.g.tw-score) of their expression profiles and regulated pathway (2) Evaluate the fraction of miRNAs in the miRNA set(hits) weighted by their correlation and the fraction of miRNAs not in the miRNA set(misses)present up to a given position i in the miRNA list.The ES is the maximum deviation from zero of 'P(hit)-P(miss)'. For a randomly distributed miRNA set,The enrichment score will be relatively small, but if it is concentrated at the top or bottom of the list,or otherwise nonrandomly distributed, then the Enrichment score will be correspondingly high.
ES |
Enrichment score. |
arg.ES |
Location in the miRNA list where the peak running enrichment occurs. |
RES |
Numerical vector containing the running enrichment score for all locations in the miRNA list. |
tag.indicator |
Binary vector indicating the location of the miRNA sets in the miRNA list. |
Junwei Han[email protected],Siyao Liu [email protected]
#Computes the enrichment score of a miRNA set in a ordered miRNA list. E1<-EnrichmentScore(miR.list=sample(1:1000),miR.set=c(39,281,37,381,39,11,3,34), correl.vector=rep(0.3,1000)) #show results #EnrichmentScore of this set E1$ES #peak running enrichment E1$arg.ES #running enrichment score of top ten miRNAs E1$RES[1:10] #Binary vector indicating the location of top ten miRNA in the miRNA list E1$tag.indicator[1:10]#Computes the enrichment score of a miRNA set in a ordered miRNA list. E1<-EnrichmentScore(miR.list=sample(1:1000),miR.set=c(39,281,37,381,39,11,3,34), correl.vector=rep(0.3,1000)) #show results #EnrichmentScore of this set E1$ES #peak running enrichment E1$arg.ES #running enrichment score of top ten miRNAs E1$RES[1:10] #Binary vector indicating the location of top ten miRNA in the miRNA list E1$tag.indicator[1:10]
Computes the enrichment score of a microRNA(miRNA) set in miRNA list.
EnrichmentScore2(miR.list, miR.set, weighted.score.type = 1, correl.vector = NULL)EnrichmentScore2(miR.list, miR.set, weighted.score.type = 1, correl.vector = NULL)
miR.list |
The ordered miRNA list,integers indicating the original position in the input dataset |
miR.set |
A miRNA set,integers indicating the location of those miRNAs in the input dataset |
weighted.score.type |
Type of score, weight=0 (unweighted = Kolmogorov-Smirnov), 1 (weighted), and 2 (over-weighted) |
correl.vector |
A vector with the correlations (e.g.tw-scores) corresponding to the miRNAs in the miRNA list |
Computes the weighted enrichment score of a miRNA set in miRNA list. It is the same calculation as in EnrichmentScore but faster without producing the RES, arg.RES and tag.indicator outputs. This call is intended to be used to asses the enrichment of random permutations rather than the observed one.The weighted score type is the exponent of the correlation.
ES |
Enrichment score (real number between -1 and +1) |
Junwei Han[email protected],Siyao Liu [email protected]
#Computes the enrichment score of a miRNA set in miRNA list R2<-EnrichmentScore2(miR.list=sample(1:100),miR.set=c(39,28,37,38,11,3,34), correl.vector=rep(0.04,100)) #show the result R2$ES#Computes the enrichment score of a miRNA set in miRNA list R2<-EnrichmentScore2(miR.list=sample(1:100),miR.set=c(39,28,37,38,11,3,34), correl.vector=rep(0.04,100)) #show the result R2$ES
The pathway information is download on the GSEA website,concluding three pathway database (KEGG,Biocarta,Reactome).We arranged the data for miRNAs and their target genes,which is according to four database including miRTarBase,TarBaseV6.0,miRecords and mir2Disease. example.GCT is an interesting miRNA expression data and example.cls is the vector of binary labels(class.labels) p is a p value weighted matrix (rows are pathway ,cols are miRNAs).p2miR is a correlation profile between kegg pathways and each human miRNA. miRList is a list of drawing parameters of KEGG ERBB signaling Pathway.
An environment variable
The environment variable includes the variable pathway, mfile,example.cls,example.gct,p,p2miR,miRList
Junwei Han[email protected],Siyao Liu [email protected]
Get the example data.
GetExampleData(exampleData)GetExampleData(exampleData)
exampleData |
A character string, must be one of "dataset", "class.labels" , "miRList","p_value"and "p2miR". |
The function GetExampleData(exampleData="dataset") obtains miRNA expression dataset from the environment variable envData.
The function GetExampleData(exampleData="class.labels") obtains class labels from the environment variable envData.
The function GetExampleData(exampleData="miRList") obtains the drawing parameters of a miRNA List from the environment variable envData.
The function GetExampleData(exampleData="p_value") obtains the weighting matrix from the environment variable envData.
The function GetExampleData(exampleData="p2miR") obtains the correlation profile between kegg pathways and each human miRNA from the environment variable envData.
Junwei Han[email protected],Siyao Liu [email protected]
## Not run: #obtain the gene expression dataset. dataset<-GetExampleData(exampleData="dataset") #obtain the class labels. class.labels<-GetExampleData(exampleData="class.labels") #obtain the drawing parameters of a miRNA List miRList<-GetExampleData(exampleData="miRList") #obtain the weighting matrix p_value<-GetExampleData(exampleData="p_value") #obtain the correlation profile p2miR<-GetExampleData(exampleData="p2miR") ## End(Not run)## Not run: #obtain the gene expression dataset. dataset<-GetExampleData(exampleData="dataset") #obtain the class labels. class.labels<-GetExampleData(exampleData="class.labels") #obtain the drawing parameters of a miRNA List miRList<-GetExampleData(exampleData="miRList") #obtain the weighting matrix p_value<-GetExampleData(exampleData="p_value") #obtain the correlation profile p2miR<-GetExampleData(exampleData="p2miR") ## End(Not run)
Get the data of miRNA and target genes
GetMiRTargetData()GetMiRTargetData()
The data for target genes of miRNAs are obtained from the environment variable envData,which is obtained from four database(TarBaseV6.0,mir2Disease,miRecords,miRTarBase).
Junwei Han[email protected],Siyao Liu [email protected]
#Get the data for target genes of miRNAs MiRTarget<-GetMiRTargetData()#Get the data for target genes of miRNAs MiRTarget<-GetMiRTargetData()
Get the gene sets of pathways for the three pathway databases (KEGG; Biocarta; Reactome)
GetPathwayData(pathway)GetPathwayData(pathway)
pathway |
choose database of pathway,"kegg","biocarta" or"reactome" |
The gene sets of pathways for the three pathway database (KEGG; Biocarta; Reactome)are obtained from the environment variable envData.
Junwei Han[email protected],Siyao Liu [email protected]
## Not run: #obtain the gene sets of kegg pathways. pathway<-GetPathwayData("kegg") ## End(Not run)## Not run: #obtain the gene sets of kegg pathways. pathway<-GetPathwayData("kegg") ## End(Not run)
Plot a heatmap of a microRNA(miRNA) expression
HeatMapPlot(V, row.names = FALSE, col.labels, col.classes, col.names = FALSE, main = " ", xlab = " ", ylab = " ")HeatMapPlot(V, row.names = FALSE, col.labels, col.classes, col.names = FALSE, main = " ", xlab = " ", ylab = " ")
V |
A miRNA expression matrix |
row.names |
A name list of row vector,default=FALSE |
col.labels |
Phenotype of class disticntion of interest. A vector of binary labels having first the 1's and then the 0's |
col.classes |
phenotype name |
col.names |
A name list of samples,default=FALSE |
main |
a main title for the heatmap |
xlab |
a label for the x axis, defaults to a description of x |
ylab |
a label for the y axis, defaults to a description of y |
Plots a heatmap of a miRNA expression matrix including phenotype vector and miRNA, sample and phenotype labels
return a heatmap
Junwei Han[email protected],Siyao Liu [email protected]
Andy Liaw, original,R. Gentleman, M. Maechler, W. Huber.
#example of expression profile V<-matrix(runif(200),10,20) #example of class.labels ("0"or "1") a1<-rep(0,20) a1[sample(1:20,5)]=1 #plot heat map HeatMapPlot(V =V, row.names = FALSE, col.labels = a1, col.classes =c("a","b"), col.names =FALSE, main =" Heat Map for MiRs in MiR Set", xlab=" ", ylab=" ")#example of expression profile V<-matrix(runif(200),10,20) #example of class.labels ("0"or "1") a1<-rep(0,20) a1[sample(1:20,5)]=1 #plot heat map HeatMapPlot(V =V, row.names = FALSE, col.labels = a1, col.classes =c("a","b"), col.names =FALSE, main =" Heat Map for MiRs in MiR Set", xlab=" ", ylab=" ")
This function propose a novel method of miRNA set enrichment analysis(MiRSEA)to identify the dysregulated pathways by calculating the enrichment score of miRNA set which co-regulate a biological pathway(or prior gene set)
MirSEA(input.ds, input.cls, p_value,p2miR, reshuffling.type = "miR.labels", nperm = 1000, weighted.score.type = 1, ms.size.threshold.min = 10, ms.size.threshold.max = 500)MirSEA(input.ds, input.cls, p_value,p2miR, reshuffling.type = "miR.labels", nperm = 1000, weighted.score.type = 1, ms.size.threshold.min = 10, ms.size.threshold.max = 500)
input.ds |
Input miRNA expression Affymetrix dataset file in GCT format |
input.cls |
Input class vector (phenotype) file in CLS format |
p_value |
A weighting matrix of p value of the hypergeometric. (rows are pathway ,cols are microRNAs(miRNAs)) |
p2miR |
pathway-miRNA correlation(pmSET) profile |
reshuffling.type |
Type of permutation reshuffling: "sample.labels" or "miR.labels" (default: "miR.labels") |
nperm |
Number of random permutations (default: 1000) |
weighted.score.type |
Enrichment correlation based weighting:When weight= 0, ES reduces to the standard Kolmogorov-Smirnov statistic,when weight=1, we are weighting the miRNAs by their dw-score normalized by the sum of the dw-scores over all of the miRNAs in the miRNA set,when weight=2,it represent over weight (default: 1) |
ms.size.threshold.min |
Minimum size (in miRNAs) for database miRNA sets to be considered (default: 10) |
ms.size.threshold.max |
Maximum size (in miRNAs) for database miRNA sets to be considered (default: 500) |
MiRSEA integrates pathway (e.g.the strength of the pathway regulated by miRNAs.) and differential expression among miRNAs in identifying dysregulated pathways.MiRSEA can order pathway by the enrichment score of miRNA set, which is co-regulated by a miRNA set.
report.phen1 |
It is the summary of the result of the up regulated pathway |
report.phen2 |
It is the summary of the result of the down regulated pathway.Each rows of the dataframe represents a pathway. Its columns include "Pathway Name", "SIZE", "Pathway Source", "Pathway Enrichment Score", "NOM p-val", "FDR q-val", "Tag percentage"(Percent of miRNA set before running enrichment peak),"MiR percentage"(Percent of miRNA list before running enrichment peak),"Signal strength" (enrichment signal strength). |
Junwei Han[email protected],Siyao Liu [email protected]
Subramanian A, et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America 102(43):15545-15550.
Lu M, Shi B, Wang J, Cao Q, & Cui Q (2010) TAM: a method for enrichment and depletion analysis of a microRNA category in a list of microRNAs. BMC bioinformatics 11:419.
EnrichmentScore, EnrichmentScore2,S2N,Corrp2miRfile
## Not run: #get example of expression data #input.ds <- readLines("F:/lsy/xin data/GSE36915.gct") #input.cls <- readLines("F:/lsy/xin data/GSE36915.cls") input.ds <- GetExampleData("dataset") input.cls <- GetExampleData("class.labels") #get example of p value matrix p_value <- GetExampleData("p_value") #get example of correlation profile p2miR <- GetExampleData("p2miR") #identify dysregulated pathways by using the function MirSEA MirSEAresult <- MirSEA(input.ds,input.cls,p_value,p2miR, reshuffling.type = "miR.labels", nperm = 1000, weighted.score.type = 1, ms.size.threshold.min = 10, ms.size.threshold.max = 500) #print the summary results of pathways to screen summaryResult1 <- MirSEAresult$report.phen1 summaryResult1[1:5,] summaryResult2 <- MirSEAresult$report.phen2 summaryResult2[1:5,] #write the summary results of pathways to tab delimited file. write.table(summaryResult1,file="summaryResult1.txt",sep="\t",row.names=FALSE) write.table(summaryResult2,file="summaryResult2.txt",sep="\t",row.names=FALSE) ## End(Not run)## Not run: #get example of expression data #input.ds <- readLines("F:/lsy/xin data/GSE36915.gct") #input.cls <- readLines("F:/lsy/xin data/GSE36915.cls") input.ds <- GetExampleData("dataset") input.cls <- GetExampleData("class.labels") #get example of p value matrix p_value <- GetExampleData("p_value") #get example of correlation profile p2miR <- GetExampleData("p2miR") #identify dysregulated pathways by using the function MirSEA MirSEAresult <- MirSEA(input.ds,input.cls,p_value,p2miR, reshuffling.type = "miR.labels", nperm = 1000, weighted.score.type = 1, ms.size.threshold.min = 10, ms.size.threshold.max = 500) #print the summary results of pathways to screen summaryResult1 <- MirSEAresult$report.phen1 summaryResult1[1:5,] summaryResult2 <- MirSEAresult$report.phen2 summaryResult2[1:5,] #write the summary results of pathways to tab delimited file. write.table(summaryResult1,file="summaryResult1.txt",sep="\t",row.names=FALSE) write.table(summaryResult2,file="summaryResult2.txt",sep="\t",row.names=FALSE) ## End(Not run)
The miR.report includes miRNA names, locstion, S2N, RES and whether is core-enrichment miRNA,
MsReport(MsNAME = "", input.ds, input.cls, p_value, p2miR,weighted.score.type = 1)MsReport(MsNAME = "", input.ds, input.cls, p_value, p2miR,weighted.score.type = 1)
MsNAME |
An interesting pathway name |
input.ds |
Input miRNA expression Affymetrix dataset file in RES or GCT format |
input.cls |
Input class vector (phenotype) file in CLS format |
p_value |
A weighting matrix of p value of the hypergeometric. (rows are pathway ,cols are microRNAs(miRNAs)) |
p2miR |
pathway-miRNA correlation(pmSET) profile |
weighted.score.type |
Enrichment correlation-based weighting: 0=no weight (KS), 1=standard weigth, 2 = over-weigth (default: 1) |
When users input a interesting pathway,the function MsReport can create a report for a miRNA set that coordinated regulate this pathway. MiR : the name of miRNAs. For example the probe accession number, miRNA symbol or the miRNA identifier in the dataset. LIST LOC : location of the miRNA in the sorted miRNA list. S2N : correlation(tw-score) of the miRNA in the miRNA list. RES : value of the running enrichment score at the miRNA location. CORE_ENRICHMENT:whether this miRNA is the "core enrichment" section of the list, Yes or No variable specifying in the miRNA location is before (positive ES) or after (negative ES) the running enrichment peak.
A list. It includes two elements: Msreport and miRList.
Msreport is matrix of input pathway which present the detail results . Its columns include "miRNA name", "location of the miRNA in the sorted miRNA list", "tw-scoe of miRNA", "Running enrichment score", "Property of contribution".
miRList is a list of drawing parameters for function PlotHeatMap,PlotCorrelation and PlotRunEnrichment.
Junwei Han[email protected],Siyao Liu [email protected]
MirSEA,S2N,EnrichmentScore,PlotHeatMap,PlotCorrelation,PlotRunEnrichment
## Not run: #get example data #input.ds <- readLines("F:/lsy/xin data/GSE36915.gct") #input.cls <- readLines("F:/lsy/xin data/GSE36915.cls") input.ds <- GetExampleData("dataset") input.cls <- GetExampleData("class.labels") #get example of p value matrix p_value <- GetExampleData("p_value") #get example of correlation profile p2miR <- GetExampleData("p2miR") #get a miRNA.SET report for KEGG ERBB PATHWAY Results<-MsReport(MsNAME = "KEGG_ERBB_SIGNALING_PATHWAY", input.ds, input.cls,p_value,p2miR) # show the report of top five miRNA in the pathway Results[[1]][1:5,] ## End(Not run)## Not run: #get example data #input.ds <- readLines("F:/lsy/xin data/GSE36915.gct") #input.cls <- readLines("F:/lsy/xin data/GSE36915.cls") input.ds <- GetExampleData("dataset") input.cls <- GetExampleData("class.labels") #get example of p value matrix p_value <- GetExampleData("p_value") #get example of correlation profile p2miR <- GetExampleData("p2miR") #get a miRNA.SET report for KEGG ERBB PATHWAY Results<-MsReport(MsNAME = "KEGG_ERBB_SIGNALING_PATHWAY", input.ds, input.cls,p_value,p2miR) # show the report of top five miRNA in the pathway Results[[1]][1:5,] ## End(Not run)
plot global miRNA correlation profile for differential weighted scores(dw-score) of miRNAs
PlotCorrelation(miRlist)PlotCorrelation(miRlist)
miRlist |
A list of miRNA LIST result obtained from the MsReport function |
Junwei Han[email protected],Siyao Liu [email protected]
## Not run: #get a list of miRNA list result miRlist<-GetExampleData("miRList") #Plot global miRNA correlation profile PlotCorrelation(miRlist) ## End(Not run)## Not run: #get a list of miRNA list result miRlist<-GetExampleData("miRList") #Plot global miRNA correlation profile PlotCorrelation(miRlist) ## End(Not run)
Plot a heat map for a microRNA(miRNA) set which co-regulate pathway
PlotHeatMap(miRlist,input.ds,input.cls)PlotHeatMap(miRlist,input.ds,input.cls)
miRlist |
A list of miRNA LIST result obtained from the MsReport function |
input.ds |
Input miRNA expression Affymetrix dataset file in GCT format |
input.cls |
Input class vector (phenotype) file in CLS format |
Plots a heatmap of a miRNA set in the expression matrix including phenotype vector and miRNA, sample and phenotype labels
Junwei Han[email protected],Siyao Liu [email protected]
## Not run: #get example data #input.ds <- readLines("F:/lsy/xin data/GSE36915.gct") #input.cls <- readLines("F:/lsy/xin data/GSE36915.cls") input.ds <- GetExampleData("dataset") input.cls <- GetExampleData("class.labels") #get a list of miRNA list result miRlist<-GetExampleData("miRList") #Plot a heat map PlotHeatMap(miRlist,input.ds,input.cls) ## End(Not run)## Not run: #get example data #input.ds <- readLines("F:/lsy/xin data/GSE36915.gct") #input.cls <- readLines("F:/lsy/xin data/GSE36915.cls") input.ds <- GetExampleData("dataset") input.cls <- GetExampleData("class.labels") #get a list of miRNA list result miRlist<-GetExampleData("miRList") #Plot a heat map PlotHeatMap(miRlist,input.ds,input.cls) ## End(Not run)
Plot running miRNAs enrichment score for the input pathway
PlotRunEnrichment(miRlist)PlotRunEnrichment(miRlist)
miRlist |
A list of miRNA LIST result obtained from the MsReport function |
Junwei Han[email protected],Siyao Liu [email protected]
## Not run: #get a list of miRNA list result miRlist<-GetExampleData("miRList") #Plot running miRNA enrichment score PlotRunEnrichment(miRlist) ## End(Not run)## Not run: #get a list of miRNA list result miRlist<-GetExampleData("miRList") #Plot running miRNA enrichment score PlotRunEnrichment(miRlist) ## End(Not run)
This function calculate the signal to noise ratio for miRNAs for the actual phenotype and also random permutations
S2N(A, class.labels, miR.labels, nperm )S2N(A, class.labels, miR.labels, nperm )
A |
Matrix of miRNAs expression values (rows are miRNAs, columns are samples) |
class.labels |
Phenotype of class disticntion of interest. A vector of binary labels having first the 1's and then the 0's |
miR.labels |
miRNA labels,Vector of probe ids or accession numbers for the rows of the expression matrix |
nperm |
Number of random permutations to perform |
The function uses matrix operations to implement the signal to noise calculation in stages and achieves fast execution speed.
s2n.matrix |
Matrix with random permuted or bootstraps signal to noise ratios (rows are miRNAs, columns are permutations or bootstrap subsamplings |
obs.s2n.matrix |
Matrix with observed signal to noise ratios (rows are miRNAs, columns are boostraps subsamplings. If fraction is set to 1.0 then all the columns have the same values |
Junwei Han[email protected],Siyao Liu [email protected]
Subramanian A, et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America 102(43):15545-15550.
##Matrix of miRNAs expression values A<-matrix(runif(200),10,20) ##class.labels("0" or "1") a1<-rep(0,20) a1[sample(1:20,5)]=1 a1<-sort(a1,decreasing=FALSE) #calculate signal to noise ratio for example data M1<-S2N(A, class.labels=a1, miR.labels=seq(1,10), nperm=100) #show actual results for top five in the matrix M1$obs.s2n.matrix[1:5,1] #show permutation results M1$s2n.matrix[1:5,1:5]##Matrix of miRNAs expression values A<-matrix(runif(200),10,20) ##class.labels("0" or "1") a1<-rep(0,20) a1[sample(1:20,5)]=1 a1<-sort(a1,decreasing=FALSE) #calculate signal to noise ratio for example data M1<-S2N(A, class.labels=a1, miR.labels=seq(1,10), nperm=100) #show actual results for top five in the matrix M1$obs.s2n.matrix[1:5,1] #show permutation results M1$s2n.matrix[1:5,1:5]