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inst/doc/CSFA.R
In CSFA: Connectivity Scores with Factor Analysis
## ----load,echo=FALSE,warning=FALSE,message=FALSE,results='hide', eval=FALSE----
# require(viridis)
# library(CSFA)
# data("dataSIM",package="CSFA")
## ----data_heatmap,fig.keep="high", eval=FALSE, echo=FALSE, warning="FALSE",message=FALSE,fig.pos='H',dev='png',fig.cap="Heatmap of Reference and Query Matrix"----
#
# par(fig=c(0,0.25,0,1),xpd=TRUE)
# d <- dataSIM[,c(1:6)]
# image(c(1:dim(d)[2]),c(1:dim(d)[1]),t(d),xlab="condition",ylab="genes",col=viridis(75))
# par(fig=c(0.25,1,0,1), new=TRUE,xpd=TRUE)
# d <- dataSIM[,-c(1:6)]
# #d <- dataMFA
# image(c(1:dim(d)[2]),c(1:dim(d)[1]),t(d),xlab="condition",ylab="genes",col=viridis(75),axes=FALSE)
## ----packageload,echo=TRUE,eval=FALSE,warning=FALSE,message=FALSE--------
# library(CSFA)
# data("dataSIM",package="CSFA")
#
# querMat <- dataSIM[,c(1:6)]
# refMat <- dataSIM[,-c(1:6)]
## ----ZG,echo=TRUE,eval=FALSE,cache=FALSE,fig.keep="high",fig.pos='H',out.width="11cm",fig.align='center',fig.cap="CSanalysis Graphs for CSzhang"----
# out_ZG <- CSanalysis(querMat,refMat,"CSzhang",plot.type="sweave")
## ----MFA_hidden,echo=FALSE,eval=FALSE,cache=FALSE,fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSanalysis Graphs for CSmfa"----
# out_MFA <- CSanalysis(querMat,refMat,"CSmfa",plot.type="sweave",which=c(),
# profile.type="cmpd",gene.thresP=2.3,gene.thresN=-2.3,component.plot=1,
# column.interest=c(21,22,23))
## ----MFA,echo=TRUE,eval=FALSE,cache=FALSE,fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSanalysis Graphs for CSmfa"----
# out_MFA <- CSanalysis(querMat,refMat,"CSmfa",plot.type="sweave",which=c(2,3,4,7),
# profile.type="cmpd",gene.thresP=2.3,gene.thresN=-2.3,component.plot=1,
# column.interest=c(21,22,23))
## ----MFA2,echo=TRUE,eval=FALSE,cache=FALSE,fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSanalysis Graphs for CSmfa"----
# out_MFA <- CSanalysis(querMat,refMat,"CSmfa",plot.type="sweave",which=c(5),
# component.plot=1,column.interest=c(1,2,3),result.available=out_MFA)
## ----MFA3,echo=TRUE,eval=FALSE,cache=FALSE,fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSanalysis Graphs for CSfma"----
# out_MFA <- CSanalysis(querMat,refMat,"CSmfa",plot.type="sweave",which=c(7),
# profile.type="gene",component.plot=1,column.interest=c(1,2,3),
# row.interest=c(846,871,4,6),result.available=out_MFA)
## ----FABIA_hidden,echo=FALSE,eval=FALSE,cache=FALSE,message=FALSE,results='hide',fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSanalysis Graphs for CSfabia"----
# color.columns <- rep("black",dim(dataSIM)[2])
# color.columns[1:6] <- "blue"
# color.columns[c(29,30,31)] <- "red"
#
# set.seed(8956)
# out_FABIA <- CSanalysis(querMat,refMat,"CSfabia",plot.type="sweave",which=c(),
# color.columns=color.columns,
# legend.names=c("Queries","SP Connected"),
# legend.cols=c("blue","red"), component.plot=c(1,2),
# gene.thresP=2,gene.thresN=-2)
## ----FABIA,echo=TRUE,eval=FALSE,cache=FALSE,message=FALSE,results='hide',fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSanalysis Graphs for CSfabia"----
# color.columns <- rep("black",dim(dataSIM)[2])
# color.columns[1:6] <- "blue"
# color.columns[c(29,30,31)] <- "red"
#
# set.seed(8956)
# out_FABIA <- CSanalysis(refMat,querMat,"CSfabia",plot.type="sweave",which=c(2,5),
# color.columns=color.columns,
# legend.names=c("Queries","SP Connected"),
# legend.cols=c("blue","red"), component.plot=c(1,2),
# gene.thresP=2,gene.thresN=-2)
## ----CSpermute_compute,eval=FALSE,echo=TRUE,cache=FALSE------------------
# out_MFA <- CSpermute(querMat,refMat,CSresult=out_MFA,B=250,method.adjust="BH",
# which=c(),verbose=FALSE,MultiCores=TRUE)
# out_ZG <- CSpermute(querMat,refMat,CSresult=out_ZG,B=250,method.adjust="BH",
# which=c(),verbose=FALSE,MultiCores=TRUE)
#
## ----cheat1,echo=FALSE, eval=FALSE---------------------------------------
#
# # out_MFA@extra <- list()
# # save(list="out_MFA",file="vignettes/perm_data/out_MFA.RData")
# #save(list="out_ZG",file="vignettes/perm_data/out_ZG.RData")
#
#
# extra_temp <- out_MFA@extra
#
# load("perm_data/out_MFA.RData")
# load("perm_data/out_ZG.RData")
#
# out_MFA@extra <- extra_temp
#
## ----CSpermute_compute2,echo=FALSE,cache=FALSE, eval=FALSE---------------
# head(out_MFA@CS[[1]]$CS.query)
#
# ## CLoadings CLpvalues CLpvalues.adjusted CRankScores CRpvalues
# ## cWP-1 0.5858096 0.003984064 0.03813318 0.6800244 0.003984064
# ## cWP-2 0.5771857 0.003984064 0.03813318 0.6690638 0.003984064
# ## cWP-3 0.5739775 0.003984064 0.03813318 0.6649862 0.003984064
# ## cWP-4 0.5867910 0.003984064 0.03813318 0.6812717 0.003984064
# ## cWP-5 0.5770335 0.003984064 0.03813318 0.6688703 0.003984064
# ## cWP-6 0.5852554 0.003984064 0.03813318 0.6793200 0.003984064
# ## CRpvalues.adjusted CLrank CLabsrank CRrank CRabsrank
# ## cWP-1 0.03813318 14 24 14 24
# ## cWP-2 0.03813318 17 27 17 27
# ## cWP-3 0.03813318 19 29 19 29
# ## cWP-4 0.03813318 13 23 13 23
# ## cWP-5 0.03813318 18 28 18 28
# ## cWP-6 0.03813318 15 25 15 25
#
# head(out_ZG@CS$CS.query)
#
# ## ZGscore pvalues pvalues.adjusted ZGrank ZGabsrank
# ## cWP-1 0.5590061 0.003984064 0.03707393 14 24
# ## cWP-2 0.5473340 0.003984064 0.03707393 20 30
# ## cWP-3 0.5621351 0.003984064 0.03707393 13 23
# ## cWP-4 0.5649765 0.003984064 0.03707393 11 21
# ## cWP-5 0.5578107 0.003984064 0.03707393 16 26
# ## cWP-6 0.5468158 0.003984064 0.03707393 21 31
## ----CSpermuteplots,echo=TRUE,eval=FALSE,cache=FALSE,message=FALSE,results='hide',fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSpermute graphs for MFA result"----
# out_MFA <- CSpermute(querMat,refMat,out_MFA,B=250,method.adjust="BH",
# which=c(1,2),cmpd.hist=c(23,99),plot.type="sweave")
## ----CSpermuteplots2,echo=TRUE,eval=FALSE,cache=FALSE,message=FALSE,results='hide',fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSpermute graphs for MFA result"----
# out_MFA <- CSpermute(querMat,refMat,out_MFA,B=250,method.adjust="BH",
# which=c(3,4),cmpd.hist=c(23,99),plot.type="sweave")
## ----CScompare_hidden,echo=FALSE,eval=FALSE,cache=FALSE,message=FALSE,fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="Compare CSresults"----
# comp_ZG_MFA <- CScompare(out_ZG,out_MFA,component2.plot=1,plot.type="sweave",which=c())
#
# comp_MFA_FABIA <- CScompare(out_MFA,out_FABIA,component1.plot=1,component2.plot=1,
# gene.thresP=c(2,2),gene.thresN=c(-2,-2),plot.type="sweave",which=c())
#
# comp_ZG_MFA[[1]]
# comp_MFA_FABIA[[1]]
## ----CScompare,echo=FALSE,eval=FALSE,cache=FALSE,message=FALSE,fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="Compare CSresults"----
# comp_ZG_MFA <- CScompare(out_ZG,out_MFA,component2.plot=1,plot.type="sweave")
#
# comp_MFA_FABIA <- CScompare(out_MFA,out_FABIA,component1.plot=1,component2.plot=1,
# gene.thresP=c(2,2),gene.thresN=c(-2,-2),plot.type="sweave")
#
# comp_ZG_MFA[[1]]
#
# ## $scores
# ## CLoadings CRankScores GeneScores
# ## Correlation_Pearson 0.9965602 0.9982107 NA
# ## Correlation_Spearman 0.9627536 0.7273489 NA
# ##
# ## $pvalues
# ## CLoadings CRankScores
# ## Correlation_Pearson 0.9532248 0.9113716
# ## Correlation_Spearman 0.8737479 0.7128987
# ##
# ## $adj.pvalues
# ## CLoadings CRankScores
# ## Correlation_Pearson 0.9509475 0.946321
# ## Correlation_Spearman 0.5533918 0.542450
#
# comp_MFA_FABIA[[1]]
#
# ## $scores
# ## CLoadings CRankScores GeneScores
# ## Correlation_Pearson -0.9573119 0.9801271 -0.7752930
# ## Correlation_Spearman -0.5693159 0.5550448 -0.7996527
# ##
# ## $pvalues
# ## NULL
# ##
# ## $adj.p
## ----CScompare2,echo=FALSE, eval=FALSE-----------------------------------
# comp_ZG_MFA[[2]]
#
# ## $pvalues
# ## Result1.Sign Result1.NotSign
# ## Result2.Sign 35 0
# ## Result2.NotSign 9 291
# ##
# ## $adj.pvalues
# ## Result1.Sign Result1.NotSign
# ## Result2.Sign 35 0
# ## Result2.NotSign 1 299
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CSFA documentation built on May 2, 2019, 7:30 a.m.
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## ----load,echo=FALSE,warning=FALSE,message=FALSE,results='hide', eval=FALSE----
# require(viridis)
# library(CSFA)
# data("dataSIM",package="CSFA")
## ----data_heatmap,fig.keep="high", eval=FALSE, echo=FALSE, warning="FALSE",message=FALSE,fig.pos='H',dev='png',fig.cap="Heatmap of Reference and Query Matrix"----
#
# par(fig=c(0,0.25,0,1),xpd=TRUE)
# d <- dataSIM[,c(1:6)]
# image(c(1:dim(d)[2]),c(1:dim(d)[1]),t(d),xlab="condition",ylab="genes",col=viridis(75))
# par(fig=c(0.25,1,0,1), new=TRUE,xpd=TRUE)
# d <- dataSIM[,-c(1:6)]
# #d <- dataMFA
# image(c(1:dim(d)[2]),c(1:dim(d)[1]),t(d),xlab="condition",ylab="genes",col=viridis(75),axes=FALSE)
## ----packageload,echo=TRUE,eval=FALSE,warning=FALSE,message=FALSE--------
# library(CSFA)
# data("dataSIM",package="CSFA")
#
# querMat <- dataSIM[,c(1:6)]
# refMat <- dataSIM[,-c(1:6)]
## ----ZG,echo=TRUE,eval=FALSE,cache=FALSE,fig.keep="high",fig.pos='H',out.width="11cm",fig.align='center',fig.cap="CSanalysis Graphs for CSzhang"----
# out_ZG <- CSanalysis(querMat,refMat,"CSzhang",plot.type="sweave")
## ----MFA_hidden,echo=FALSE,eval=FALSE,cache=FALSE,fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSanalysis Graphs for CSmfa"----
# out_MFA <- CSanalysis(querMat,refMat,"CSmfa",plot.type="sweave",which=c(),
# profile.type="cmpd",gene.thresP=2.3,gene.thresN=-2.3,component.plot=1,
# column.interest=c(21,22,23))
## ----MFA,echo=TRUE,eval=FALSE,cache=FALSE,fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSanalysis Graphs for CSmfa"----
# out_MFA <- CSanalysis(querMat,refMat,"CSmfa",plot.type="sweave",which=c(2,3,4,7),
# profile.type="cmpd",gene.thresP=2.3,gene.thresN=-2.3,component.plot=1,
# column.interest=c(21,22,23))
## ----MFA2,echo=TRUE,eval=FALSE,cache=FALSE,fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSanalysis Graphs for CSmfa"----
# out_MFA <- CSanalysis(querMat,refMat,"CSmfa",plot.type="sweave",which=c(5),
# component.plot=1,column.interest=c(1,2,3),result.available=out_MFA)
## ----MFA3,echo=TRUE,eval=FALSE,cache=FALSE,fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSanalysis Graphs for CSfma"----
# out_MFA <- CSanalysis(querMat,refMat,"CSmfa",plot.type="sweave",which=c(7),
# profile.type="gene",component.plot=1,column.interest=c(1,2,3),
# row.interest=c(846,871,4,6),result.available=out_MFA)
## ----FABIA_hidden,echo=FALSE,eval=FALSE,cache=FALSE,message=FALSE,results='hide',fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSanalysis Graphs for CSfabia"----
# color.columns <- rep("black",dim(dataSIM)[2])
# color.columns[1:6] <- "blue"
# color.columns[c(29,30,31)] <- "red"
#
# set.seed(8956)
# out_FABIA <- CSanalysis(querMat,refMat,"CSfabia",plot.type="sweave",which=c(),
# color.columns=color.columns,
# legend.names=c("Queries","SP Connected"),
# legend.cols=c("blue","red"), component.plot=c(1,2),
# gene.thresP=2,gene.thresN=-2)
## ----FABIA,echo=TRUE,eval=FALSE,cache=FALSE,message=FALSE,results='hide',fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSanalysis Graphs for CSfabia"----
# color.columns <- rep("black",dim(dataSIM)[2])
# color.columns[1:6] <- "blue"
# color.columns[c(29,30,31)] <- "red"
#
# set.seed(8956)
# out_FABIA <- CSanalysis(refMat,querMat,"CSfabia",plot.type="sweave",which=c(2,5),
# color.columns=color.columns,
# legend.names=c("Queries","SP Connected"),
# legend.cols=c("blue","red"), component.plot=c(1,2),
# gene.thresP=2,gene.thresN=-2)
## ----CSpermute_compute,eval=FALSE,echo=TRUE,cache=FALSE------------------
# out_MFA <- CSpermute(querMat,refMat,CSresult=out_MFA,B=250,method.adjust="BH",
# which=c(),verbose=FALSE,MultiCores=TRUE)
# out_ZG <- CSpermute(querMat,refMat,CSresult=out_ZG,B=250,method.adjust="BH",
# which=c(),verbose=FALSE,MultiCores=TRUE)
#
## ----cheat1,echo=FALSE, eval=FALSE---------------------------------------
#
# # out_MFA@extra <- list()
# # save(list="out_MFA",file="vignettes/perm_data/out_MFA.RData")
# #save(list="out_ZG",file="vignettes/perm_data/out_ZG.RData")
#
#
# extra_temp <- out_MFA@extra
#
# load("perm_data/out_MFA.RData")
# load("perm_data/out_ZG.RData")
#
# out_MFA@extra <- extra_temp
#
## ----CSpermute_compute2,echo=FALSE,cache=FALSE, eval=FALSE---------------
# head(out_MFA@CS[[1]]$CS.query)
#
# ## CLoadings CLpvalues CLpvalues.adjusted CRankScores CRpvalues
# ## cWP-1 0.5858096 0.003984064 0.03813318 0.6800244 0.003984064
# ## cWP-2 0.5771857 0.003984064 0.03813318 0.6690638 0.003984064
# ## cWP-3 0.5739775 0.003984064 0.03813318 0.6649862 0.003984064
# ## cWP-4 0.5867910 0.003984064 0.03813318 0.6812717 0.003984064
# ## cWP-5 0.5770335 0.003984064 0.03813318 0.6688703 0.003984064
# ## cWP-6 0.5852554 0.003984064 0.03813318 0.6793200 0.003984064
# ## CRpvalues.adjusted CLrank CLabsrank CRrank CRabsrank
# ## cWP-1 0.03813318 14 24 14 24
# ## cWP-2 0.03813318 17 27 17 27
# ## cWP-3 0.03813318 19 29 19 29
# ## cWP-4 0.03813318 13 23 13 23
# ## cWP-5 0.03813318 18 28 18 28
# ## cWP-6 0.03813318 15 25 15 25
#
# head(out_ZG@CS$CS.query)
#
# ## ZGscore pvalues pvalues.adjusted ZGrank ZGabsrank
# ## cWP-1 0.5590061 0.003984064 0.03707393 14 24
# ## cWP-2 0.5473340 0.003984064 0.03707393 20 30
# ## cWP-3 0.5621351 0.003984064 0.03707393 13 23
# ## cWP-4 0.5649765 0.003984064 0.03707393 11 21
# ## cWP-5 0.5578107 0.003984064 0.03707393 16 26
# ## cWP-6 0.5468158 0.003984064 0.03707393 21 31
## ----CSpermuteplots,echo=TRUE,eval=FALSE,cache=FALSE,message=FALSE,results='hide',fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSpermute graphs for MFA result"----
# out_MFA <- CSpermute(querMat,refMat,out_MFA,B=250,method.adjust="BH",
# which=c(1,2),cmpd.hist=c(23,99),plot.type="sweave")
## ----CSpermuteplots2,echo=TRUE,eval=FALSE,cache=FALSE,message=FALSE,results='hide',fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="CSpermute graphs for MFA result"----
# out_MFA <- CSpermute(querMat,refMat,out_MFA,B=250,method.adjust="BH",
# which=c(3,4),cmpd.hist=c(23,99),plot.type="sweave")
## ----CScompare_hidden,echo=FALSE,eval=FALSE,cache=FALSE,message=FALSE,fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="Compare CSresults"----
# comp_ZG_MFA <- CScompare(out_ZG,out_MFA,component2.plot=1,plot.type="sweave",which=c())
#
# comp_MFA_FABIA <- CScompare(out_MFA,out_FABIA,component1.plot=1,component2.plot=1,
# gene.thresP=c(2,2),gene.thresN=c(-2,-2),plot.type="sweave",which=c())
#
# comp_ZG_MFA[[1]]
# comp_MFA_FABIA[[1]]
## ----CScompare,echo=FALSE,eval=FALSE,cache=FALSE,message=FALSE,fig.keep="high",fig.show='hold',fig.pos='H',out.width='9cm',out.height='10cm',fig.align='left',fig.cap="Compare CSresults"----
# comp_ZG_MFA <- CScompare(out_ZG,out_MFA,component2.plot=1,plot.type="sweave")
#
# comp_MFA_FABIA <- CScompare(out_MFA,out_FABIA,component1.plot=1,component2.plot=1,
# gene.thresP=c(2,2),gene.thresN=c(-2,-2),plot.type="sweave")
#
# comp_ZG_MFA[[1]]
#
# ## $scores
# ## CLoadings CRankScores GeneScores
# ## Correlation_Pearson 0.9965602 0.9982107 NA
# ## Correlation_Spearman 0.9627536 0.7273489 NA
# ##
# ## $pvalues
# ## CLoadings CRankScores
# ## Correlation_Pearson 0.9532248 0.9113716
# ## Correlation_Spearman 0.8737479 0.7128987
# ##
# ## $adj.pvalues
# ## CLoadings CRankScores
# ## Correlation_Pearson 0.9509475 0.946321
# ## Correlation_Spearman 0.5533918 0.542450
#
# comp_MFA_FABIA[[1]]
#
# ## $scores
# ## CLoadings CRankScores GeneScores
# ## Correlation_Pearson -0.9573119 0.9801271 -0.7752930
# ## Correlation_Spearman -0.5693159 0.5550448 -0.7996527
# ##
# ## $pvalues
# ## NULL
# ##
# ## $adj.p
## ----CScompare2,echo=FALSE, eval=FALSE-----------------------------------
# comp_ZG_MFA[[2]]
#
# ## $pvalues
# ## Result1.Sign Result1.NotSign
# ## Result2.Sign 35 0
# ## Result2.NotSign 9 291
# ##
# ## $adj.pvalues
# ## Result1.Sign Result1.NotSign
# ## Result2.Sign 35 0
# ## Result2.NotSign 1 299
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