library(SELEX)## Loading required package: rJava## Warning: package 'rJava' was built under R version 3.2.5## Loading required package: Biostrings## Warning: package 'Biostrings' was built under R version 3.2.2## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
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## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
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## as.data.frame, as.vector, cbind, colnames, do.call,
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## mapply, match, mget, order, paste, pmax, pmax.int, pmin,
## pmin.int, rank, rbind, rep.int, rownames, sapply, setdiff,
## sort, table, tapply, union, unique, unlist, unsplit## Loading required package: S4Vectors## Warning: package 'S4Vectors' was built under R version 3.2.2## Loading required package: stats4## Creating a generic function for 'nchar' from package 'base' in package 'S4Vectors'## Loading required package: IRanges## Warning: package 'IRanges' was built under R version 3.2.2## Warning in .recacheSubclasses(def@className, def, doSubclasses, env):
## undefined subclass "externalRefMethod" of class "expressionORfunction";
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## undefined subclass "externalRefMethod" of class "functionORNULL";
## definition not updated## Loading required package: XVectorlibrary(stringi)## Warning: package 'stringi' was built under R version 3.2.5library(Biostrings)
library(SelexGLM)## Loading required package: RColorBrewerlibrary(devtools)
library(reshape2)## Warning: package 'reshape2' was built under R version 3.2.5library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.2.5library(Rmisc)## Loading required package: lattice## Warning: package 'lattice' was built under R version 3.2.5## Loading required package: plyr## Warning: package 'plyr' was built under R version 3.2.5##
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## renameWe start with some initialization related to the SELEX package:
options(java.parameters = "-Xmx4000M")
workDir = tempdir()
selex.config(workingDir=workDir, maxThreadNumber=4)Next, we will define the SELEX samples that we want to analyze. We will use the example data from the SELEX package:
selex.loadAnnotation(system.file("extdata", "config.xml", package="SELEX"))
selex.sampleSummary()## seqName sampleName rounds leftBarcode rightBarcode
## 3 R0.libraries R0.barcodeCG 0 TGG CCACGTC
## 2 R0.libraries R0.barcodeGC 0 TGG CCAGCTG
## 1 R2.libraries ExdHox.R2 2 TGG CCAGCTG
## leftFlank rightFlank
## 3 GTTCAGAGTTCTACAGTCCGACGATCTGG CCACGTCTCGTATGCCGTCTTCTGCTTG
## 2 GTTCAGAGTTCTACAGTCCGACGATCTGG CCAGCTGTCGTATGCCGTCTTCTGCTTG
## 1 GTTCAGAGTTCTACAGTCCGACGATCTGG CCAGCTGTCGTATGCCGTCTTCTGCTTG
## seqFile
## 3 /Library/Frameworks/R.framework/Versions/3.2/Resources/library/SELEX/extdata/R0.fastq.gz
## 2 /Library/Frameworks/R.framework/Versions/3.2/Resources/library/SELEX/extdata/R0.fastq.gz
## 1 /Library/Frameworks/R.framework/Versions/3.2/Resources/library/SELEX/extdata/R2.fastq.gzr0.train = selex.sample(seqName = 'R0.libraries', sampleName='R0.barcodeGC', round = 0)
r0.test = selex.sample(seqName = 'R0.libraries', sampleName='R0.barcodeCG', round = 0)
dataSample = selex.sample(seqName = 'R2.libraries', sampleName = 'ExdHox.R2', round = 2)Markov model is built, information gain is used to identify k-mer length of binding site, kmer tables are built, and probes are counted in a way that corrects for the zero-deflated nature of data corrected.
# MARKOV MODEL BUILT
kmax = selex.kmax(sample = r0.test)## Counting [R0.libraries.R0.barcodeCG.0][ K = 1 ]
## Counting [R0.libraries.R0.barcodeCG.0][ K = 2 ]
## Counting [R0.libraries.R0.barcodeCG.0][ K = 3 ]
## Counting [R0.libraries.R0.barcodeCG.0][ K = 4 ]
## Counting [R0.libraries.R0.barcodeCG.0][ K = 5 ]
## Counting [R0.libraries.R0.barcodeCG.0][ K = 6 ]
## [ sample id : R0.libraries.R0.barcodeCG.0, filter: variableRegionIncludeRegex:null,variableRegionExcludeRegex:null,variableRegionGroupRegex:null ]
## [ R0.libraries.R0.barcodeCG.0.kmax = 5 ]# Train Markov model on Hm 16bp library Round 0 data
mm = selex.mm(sample = r0.train, order = NA, crossValidationSample =r0.test, Kmax = kmax, mmMethod = "TRANSITION")## Overwriting Kmax = 5
## Counting [R0.libraries.R0.barcodeGC.0][ K = 1 ]
## Counting [R0.libraries.R0.barcodeGC.0][ K = 2 ]
## Counting [R0.libraries.R0.barcodeGC.0][ K = 3 ]
## Counting [R0.libraries.R0.barcodeGC.0][ K = 4 ]
## Counting [R0.libraries.R0.barcodeGC.0][ K = 5 ]
## [ markovLength = 3 ]
## [ maxR = 0.989094 ]
## [ Model = MarkovModelInfo [markovLength=3, markovLengthTotalCount=483784, markovR2=0.9890939798281818, markovCountsPath=/var/folders/6r/52dcl0sj1yg0z69w89t6fjyr0000gp/T/Rtmp5eCqOw//R0.libraries.R0.barcodeGC.0.3.dat_A7FE7F4E2E78A43F892C7F3227FFA520, markovObjPath=/var/folders/6r/52dcl0sj1yg0z69w89t6fjyr0000gp/T/Rtmp5eCqOw//R0.libraries.R0.barcodeGC.0.3.dat_A7FE7F4E2E78A43F892C7F3227FFA520.prob.obj, sample=config.ExperimentReference@133c3b45, markovModelMethod=TRANSITION, crossValidationSample=config.ExperimentReference@f5bfdbd, filter=variableRegionIncludeRegex:null,variableRegionExcludeRegex:null,variableRegionGroupRegex:null,kmerIncludeRegex:null,kmerExcludeRegex:null,kmerIncludeOnly:null] ]mmscores = selex.mmSummary(sample = r0.train)
ido = which(mmscores$R==max(mmscores$R))
mm.order = mmscores$Order[ido]More preliminaries:
# INFOGAIN USED TO CALCULATE KLEN
libLen = as.numeric(as.character(selex.getAttributes(dataSample)$VariableRegionLength))
selex.infogain(sample = dataSample, k = c((mm.order+1):libLen), markovModel = mm)## Counting [InfoGain][ K = 3 ]
## Counting [InfoGain][ K = 4 ]
## Counting [InfoGain][ K = 5 ]
## Counting [InfoGain][ K = 6 ]
## Counting [InfoGain][ K = 7 ]
## Counting [InfoGain][ K = 8 ]
## Counting [InfoGain][ K = 9 ]
## Counting [InfoGain][ K = 10 ]
## Counting [InfoGain][ K = 11 ]
## Counting [InfoGain][ K = 12 ]
## Counting [InfoGain][ K = 13 ]
## Counting [InfoGain][ K = 14 ]
## Counting [InfoGain][ K = 15 ]
## Counting [InfoGain][ K = 16 ]## [1] 2.420417infoscores = selex.infogainSummary(sample = dataSample)
#information gain barplot
idx = which(infoscores$InformationGain==max(infoscores$InformationGain))
colstring = rep('BLUE', nrow(infoscores))
colstring[idx] = 'RED'
barplot(height=infoscores$InformationGain, names.arg=infoscores$K, col=colstring,
xlab="Oligonucleotide Length (bp)", ylab="Information Gain (bits)")
kLen = infoscores$K[idx]# For the sake of previous analysis on the Hox data used in this example, I will use kLen.f = 12 as my k-mer length, even though kLen identified through the information gain analysis has kLen = 13
data.kmerTable = selex.affinities(sample=dataSample, k=kLen, markovModel=mm)## Counting [R2.libraries.ExdHox.R2.2][ K = 9 ]
## [ Lowest Count = 1 ]data.kmerTable = data.kmerTable[order(-data.kmerTable$Affinity), ]
rownames(data.kmerTable) = NULL
data.probeCounts = getProbeCounts(dataSample, markovModel = mm)## Counting [R2.libraries.split.1.ExdHox.R2.split.1.2][ K = 16 ]
## [ Lowest Count = 1 ]
## Counting [R2.libraries.split.2.ExdHox.R2.split.2.2][ K = 16 ]
## [ Lowest Count = 1 ]summary(data.probeCounts)## Probe ObservedCount Probability Round
## Length:24504 Min. :0.0000 Min. :3.036e-11 Min. :2
## Class :character 1st Qu.:0.0000 1st Qu.:2.537e-10 1st Qu.:2
## Mode :character Median :0.0000 Median :3.882e-10 Median :2
## Mean :0.0375 Mean :4.493e-10 Mean :2
## 3rd Qu.:0.0000 3rd Qu.:5.725e-10 3rd Qu.:2
## Max. :3.0000 Max. :3.307e-09 Max. :2print(data.probeCounts[1:10,])## Probe ObservedCount Probability Round
## 1 GTTGATTGATGGGTTT 3 1.198516e-09 2
## 2 GATGATTGATTGTTAT 3 1.081091e-09 2
## 3 GATGATTGATCGATGT 3 5.086886e-10 2
## 4 GAGAATGATTGATTAC 3 3.691869e-10 2
## 5 GAATGATTGATTACAT 3 5.432474e-10 2
## 6 ATGTTTGATTGATTAT 3 1.425483e-09 2
## 7 ATGATTGATGAGTCTA 3 4.712116e-10 2
## 8 AATGATTGATTATTGT 3 1.051582e-09 2
## 9 AAATGATTGATTAGCT 3 4.940620e-10 2
## 10 AAATGATTGATTACTT 3 6.086128e-10 2# Inputs about library are data specific
model = new("model",
varRegLen = libLen,
leftFixedSeq = "GTTCAGAGTTCTACAGTCCGACGATCTGG",
rightFixedSeq ="CCAGCTGTCGTATGCCGTCTTCTGCTTG",
seedLen = kLen,
leftFixedSeqOverlap = 4,
initialAffinityCutoff = 0.00,
missingValueSuppression = 1,
minSeedValue = .001,
upFootprintExtend = 2,
includeWindowFactor = FALSE,
confidenceLevel = .95,
verbose = FALSE,
useFixedValuesOffset.N = FALSE,
rounds = list(c(2)),
rcSymmetric = FALSE,
minAffinity = 0.01
)Inspect current state of model object:
model@features@N## An object of class 'N'
##
## Slot "seedLen": 9
##
## Slot "N.upFootprintExtend": 2
##
## Slot "N.downFootprintExtend": 2
##
## Slot "fS.upFootprintExtend": 2
##
## Slot "fS.downFootprintExtend": 2
##
## Slot "fpLen": 13
##
## Slot "N.set": 1 2 3 4 5 6 7 8 9 10 11 12 13
##
## Slot "N.equivMat":
## 13 x 13 null equivalence matrix
##
## Slot "N.values":
## 1 2 3 4 5 6 7 8 9 10 11 12 13
## N.A 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.C 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.G 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.T 0 0 0 0 0 0 0 0 0 0 0 0 0
##
##
## Slot "N.errors":
## 1 2 3 4 5 6 7 8 9 10 11 12 13
## N.A 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.C 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.G 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.T 0 0 0 0 0 0 0 0 0 0 0 0 0
##
##
## Slot "N.z":
## 1 2 3 4 5 6 7 8 9 10 11 12 13
## N.A 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.C 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.G 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.T 0 0 0 0 0 0 0 0 0 0 0 0 0
##
##
## Slot "N.sig":
## 1 2 3 4 5 6 7 8 9 10 11 12 13
## N.A 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.C 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.G 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.T 0 0 0 0 0 0 0 0 0 0 0 0 0
##
##
## Slot "N.oldValues":
## <4 x 13 x 0 array of double>
##
## Slot "N.oldErrors":
## <4 x 13 x 0 array of double>
##
## Slot "N.oldZ":
## <4 x 13 x 0 array of double>
##
## Slot "N.oldSig":
## <4 x 13 x 0 array of double># Model nucleotide Betas before seed PSAM is added
addSeedPsam(model) = seedTable2psam(model, data.kmerTable)
# Model nucleotide Betas after seed PSAM is added
model@features@N## An object of class 'N'
##
## Slot "seedLen": 9
##
## Slot "N.upFootprintExtend": 2
##
## Slot "N.downFootprintExtend": 2
##
## Slot "fS.upFootprintExtend": 2
##
## Slot "fS.downFootprintExtend": 2
##
## Slot "fpLen": 13
##
## Slot "N.set": 1 2 3 4 5 6 7 8 9 10 11 12 13
##
## Slot "N.equivMat":
## 13 x 13 null equivalence matrix
##
## Slot "N.values":
## 1 2 3 4 5 6 7 8
## N.A 0 0 0.0000000 -1.100719 -1.748610 0.000000 -3.042044 -1.808151
## N.C 0 0 -1.5065161 -3.042044 -3.042044 -3.042044 -1.841388 -1.016157
## N.G 0 0 -0.5525097 -3.042044 0.000000 -3.042044 -3.042044 -1.012792
## N.T 0 0 -0.4682265 0.000000 -3.042044 -3.042044 0.000000 0.000000
## 9 10 11 12 13
## N.A -1.293313 0.000000 -3.042044 0 0
## N.C -3.042044 -3.042044 -3.042044 0 0
## N.G 0.000000 -3.042044 -3.042044 0 0
## N.T -2.042044 -3.042044 0.000000 0 0
##
##
## Slot "N.errors":
## 1 2 3 4 5 6 7 8 9 10 11 12 13
## N.A 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.C 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.G 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.T 0 0 0 0 0 0 0 0 0 0 0 0 0
##
##
## Slot "N.z":
## 1 2 3 4 5 6 7 8 9 10 11 12 13
## N.A 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.C 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.G 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.T 0 0 0 0 0 0 0 0 0 0 0 0 0
##
##
## Slot "N.sig":
## 1 2 3 4 5 6 7 8 9 10 11 12 13
## N.A 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.C 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.G 0 0 0 0 0 0 0 0 0 0 0 0 0
## N.T 0 0 0 0 0 0 0 0 0 0 0 0 0
##
##
## Slot "N.oldValues":
## <4 x 13 x 0 array of double>
##
## Slot "N.oldErrors":
## <4 x 13 x 0 array of double>
##
## Slot "N.oldZ":
## <4 x 13 x 0 array of double>
##
## Slot "N.oldSig":
## <4 x 13 x 0 array of double>#Use this definition of data for complete analysis
data = data.probeCounts
data = topModelMatch(data, model)
# Uses aligned probes to build design matrix
data = addDesignMatrix(data, model)
# Constructs regression expression with independent features using design matrix
regressionFormula = updatedRegressionFormula(data, model)
fit = glm(regressionFormula,
data=data,
family = poisson(link="log"))
model = addNewBetas(model, data, fit)
# Nucleotide Features after first round of fitting
# GABRIELLA: this plotting commmand is not working, can you fix it?
#plot(model, Nplot.ddG = TRUE, verticalPlots = TRUE)
data = data.probeCounts
data.nrow = nrow(data)
for (i in 2:3) {
data = topModelMatch(data, model)
data = addDesignMatrix(data, model)
if (data.nrow == nrow(data)) {
print ("Stability Reached")
break
} else {
data.nrow = nrow(data)
}
regressionFormula = updatedRegressionFormula(data, model)
fit = glm(regressionFormula,
data=data,
family = poisson(link="log"))
model = addNewBetas(model,data,fit)
# Nucleotide Features after i'th round of fitting
}## Warning: glm.fit: fitted rates numerically 0 occurredmodel@features@N@N.values## 1 2 3 4 5 6
## N.A 0.04866333 0.05268261 0.0000000 -8.007723 -1.141010 0.0000000
## N.C -0.29778959 -0.22239446 -0.9323824 -7.813690 -7.686484 -7.3486044
## N.G 0.00000000 0.00000000 -0.5159343 -8.431065 0.000000 0.2000812
## N.T -0.25621848 -0.08685875 -0.3569731 0.000000 -8.066953 -8.0201639
## 7 8 9 10 11 12
## N.A -8.382165 -8.0709673 -0.8518232 0.000000 -1.0000000 -0.73702293
## N.C -7.601662 -1.3972185 -8.3405496 -1.000000 -1.0000000 -0.14461245
## N.G -7.793026 -0.6016221 0.0000000 -7.628075 -0.8703377 -0.07539009
## N.T 0.000000 0.0000000 -1.4875563 -7.653693 0.0000000 0.00000000
## 13
## N.A 0.0000000
## N.C -0.9846406
## N.G -0.1673405
## N.T -0.9563936Save model object for future reference:
save(model, file = "HowToFitMononucleotideModel.Result.RData")