options(java.parameters = "-Xmx4000M")
library(SELEX)
library(SelexGLM)
library(grid)
workDir = "./cache/"
selex.config(workingDir=workDir, maxThreadNumber=4)
### LOCAL PATHS NEED TO BE RE-DEFINED TO RUN OFF OF MY COMPUTER
##################################################################
selexDir = "/Users/gabriella/Columbia/SELEX/"
#rawdataDir = "/Users/gabriella/Columbia/rawdata/Pufall/"
processedDataDir = "/Users/gabriella/Columbia/SplitFastqData/Pufall/ConcatFiles/"
# CLUSTER VERSIONS ARE COMMENTED OUT
#selexDir = "/vega/hblab/users/gdm2120/SELEX/SELEX/"
#rawdataDir = "/vega/hblab/projects/selex/rawdata/Pufall"
#processedDataDir = "/vega/hblab/users/gdm2120/SplitFastqData/Pufall/"
##################################################################
saveDir = "gabriella/SelexGLMtest/MultiroundViewSymmetry"
dir.create(file.path(selexDir, saveDir), showWarnings = FALSE, recursive = TRUE)
shapeTable = read.table(paste(selexDir, "gabriella/ShapeParamData/ShapeTableOrthogonal.txt", sep = ""), sep = "\t",
stringsAsFactors = FALSE)
ST = shapeTable[,c(1, 14:19)]
colnames(ST) = c("Sequence", "MGW", "ProT", "HelTA",
"HelTB", "RollA", "RollB")
selex.defineSample('r0.Pufall',
paste(processedDataDir, "/Demultiplexed.R0.fastq.gz", sep = ""),
'r0',
0, 23, '', 'TGGAA')
selex.defineSample('AR.R8',
paste(processedDataDir,"/AR.R8.fastq.gz",sep = ""),
'AR-DBD',
8, 23, '', 'TGGAA')
selex.defineSample('AR.R7',
paste(processedDataDir,"/AR.R7.fastq.gz",sep = ""),
'AR-DBD',
7, 23, '', 'TGGAA')
r0 = selex.sample(seqName = 'r0.Pufall', sampleName='r0', round = 0)
r0.split = selex.split(r0)
r0.train = r0.split$train
r0.test = r0.split$test
dataSample = selex.sample(seqName = 'AR.R8', sampleName = 'AR-DBD', round = 8)
dataSample.R7 = selex.sample(seqName = 'AR.R7', sampleName = 'AR-DBD', round = 7)
# MARKOV MODEL BUILT
kmax = selex.kmax(sample = r0.test)
mm = selex.mm(sample = r0.train, order = NA, crossValidationSample =r0.test, Kmax = kmax, mmMethod = "TRANSITION")
mmscores = selex.mmSummary(sample = r0.train)
ido = which(mmscores$R==max(mmscores$R))
mm.order = mmscores$Order[ido]
libLen = as.numeric(as.character(selex.getAttributes(dataSample)$VariableRegionLength))
kLen = 15
#data.probeCounts = getProbeCounts(dataSample, markovModel = mm)
#save(data.probeCounts, file = paste(selexDir, saveDir, "/data.probeCounts.RData", sep = ""))
load(file = paste(selexDir, saveDir, "/data.probeCounts.RData", sep = ""))
#data.kmerTable = getKmerCountAffinities(dataSample, k = kLen, minCount = 100, markovModel = mm)
#save(data.kmerTable, file = paste(selexDir, saveDir, "/data.kmerTable.RData", sep = ""))
load(file = paste(selexDir, saveDir, "/data.kmerTable.RData", sep = ""))
#data.probeCounts.R7 = getProbeCounts(dataSample.R7, markovModel = mm)
#save(data.probeCounts.R7, file = paste(selexDir, saveDir, "/data.probeCounts.R7.RData", sep = ""))
load(file = paste(selexDir, saveDir, "/data.probeCounts.R7.RData", sep = ""))
# Inputs about library are data specific
ModelTest = model(name = "AR-DBD R7+R8 Nucleotides+View (Rev. Comp. Sym.)",
varRegLen = libLen,
leftFixedSeq = "GTTCAGAGTTCTACAGTCCGACGATC",
rightFixedSeq ="TGGAATTCTCGGGTGCCAAGG",
consensusSeq = "RGWACANNNTGTWCY",
affinityType = "AffinitySym",
leftFixedSeqOverlap = 5,
minAffinity = 0.01,
missingValueSuppression = .5,
minSeedValue = .01,
upFootprintExtend = 4,
confidenceLevel = .99,
rounds = list(c(7,8)),
rcSymmetric = TRUE,
verbose = FALSE,
includeView = TRUE)
getFeatureDesign(ModelTest)
## Feature design for object of class 'model'
##
## seedLen: 15
## upFootprintExtend: 4
## downFootprintExtend: 4
## rcSymmetric: TRUE
##
## Slot "N":
## N.upFootprintExtend: 4
## N.downFootprintExtend: 4
## N.set: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
## Number of previous iterations: 0
##
## Slot "Intercept":
## Number of Views per Strand of DNA: 11
## Number of Rounds: 2 (7, 8)
## Number of previous iterations: 0
##
## Slot "Shape":
## "ShapeParamsUsed": NONE
# Add seed model
addSeedPsam(ModelTest) = seedTable2psam(ModelTest, data.kmerTable)
# Model nucleotide Betas after seed PSAM is added
print(getValues(getN(ModelTest)))
## 1 2 3 4 5 6 7 8 9
## N.A 0 0 0 0 0.00000000 -1.2968295 -0.03073087 0.000000 -1.296829
## N.C 0 0 0 0 -0.60728754 -1.2968295 -0.25628921 -1.296829 0.000000
## N.G 0 0 0 0 -0.09864725 0.0000000 -0.34036727 -1.296829 -1.296829
## N.T 0 0 0 0 -0.42644057 -0.5591611 0.00000000 -1.296829 -1.296829
## 10 11 12 13 14 15
## N.A 0.0000000 -0.40799975 -0.1359377 -0.09020211 -0.7968295 -1.296829
## N.C -1.2968295 0.00000000 0.0000000 -0.36546623 -0.3957275 -1.296829
## N.G -0.3957275 -0.36546623 0.0000000 0.00000000 -1.2968295 0.000000
## N.T -0.7968295 -0.09020211 -0.1359377 -0.40799975 0.0000000 -1.296829
## 16 17 18 19 20 21 22 23
## N.A -1.296829 0.00000000 -0.5591611 -0.42644057 0 0 0 0
## N.C -1.296829 -0.34036727 0.0000000 -0.09864725 0 0 0 0
## N.G -1.296829 -0.25628921 -1.2968295 -0.60728754 0 0 0 0
## N.T 0.000000 -0.03073087 -1.2968295 0.00000000 0 0 0 0
plot(ModelTest@features@N, Ntitle = "AR-DBD R7+R8 Nucleotides+View\nSeeding Model", ddG = TRUE)
Next we score the probes using topModelMatch
sample1 = sample(nrow(data.probeCounts), 500000)
sample2 = sample(nrow(data.probeCounts.R7), 500000)
data = rbind(data.probeCounts[sample1, ], data.probeCounts.R7[sample2, ])
#data = rbind(data.probeCounts, data.probeCounts.R7)
data = topModelMatch(data, ModelTest)
# Uses aligned probes to build design matrix
data = addDesignMatrix(data, ModelTest)
designMatrixSummary = getDesignMatrix(ModelTest, data)
## No shape parameters included in fit.
print("Round summary: ")
## [1] "Round summary: "
print (designMatrixSummary$Round)
## 7 8 Total
## Round 499581 499718 999299
print("View/strand orientation summary: ")
## [1] "View/strand orientation summary: "
print (designMatrixSummary$Intercept)
## View.1 View.2 View.3 View.4 View.5 View.6 View.7 View.8 View.9
## Strand.F 5847 66854 97198 70552 105723 126490 138041 150703 111559
## Strand.R 0 0 0 0 0 0 0 0 0
## View.10 View.11 StrandTotal
## Strand.F 60870 65462 999299
## Strand.R 0 0 0
print("Mono-nucleotide summary: ")
## [1] "Mono-nucleotide summary: "
print (designMatrixSummary$N)
## N.A N.C N.G N.T
## 1 576865 634142 349626 437965
## 2 703666 481338 370837 442757
## 3 877164 517391 179687 424356
## 4 901809 270554 412957 413278
## 5 1272237 21324 656874 48163
## 6 809 183 1979327 18279
## 7 840111 110069 104813 943605
## 8 1996215 373 1113 897
## 9 544 1997271 275 508
## 10 1867156 555 127073 3814
## 11 91519 1088171 122255 696653
## 12 356730 642569 0 0
# # Constructs regression expression with independent features using design matrix
regressionFormula = updatedRegressionFormula(data, ModelTest)
print("Regression Formula: ")
## [1] "Regression Formula: "
print (regressionFormula)
## [1] "ObservedCount ~ offset(logProb)+Round.7+N.A1+N.G1+N.T1+N.C2+N.G2+N.T2+N.C3+N.G3+N.T3+N.C4+N.G4+N.T4+N.C5+N.G5+N.T5+N.A6+N.C6+N.T6+N.A7+N.C7+N.G7+N.C8+N.G8+N.T8+N.A9+N.G9+N.T9+N.C10+N.G10+N.T10+N.A11+N.G11+N.T11+N.A12+Strand.F1+Strand.F2+Strand.F3+Strand.F4+Strand.F5+Strand.F6+Strand.F7+Strand.F9+Strand.F10+Strand.F11"
fit = glm(regressionFormula,
data=data,
family = poisson(link="log"))
summary(fit)
##
## Call:
## glm(formula = regressionFormula, family = poisson(link = "log"),
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -19.886 -1.313 -0.677 0.276 54.807
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 36.6458963 0.0030051 12194.610 < 2e-16 ***
## Round.7 -0.7266037 0.0014436 -503.340 < 2e-16 ***
## N.A1 -0.0008111 0.0002253 -3.601 0.000317 ***
## N.G1 0.0005642 0.0002330 2.422 0.015451 *
## N.T1 -0.0394440 0.0002521 -156.463 < 2e-16 ***
## N.C2 -0.0783187 0.0002295 -341.211 < 2e-16 ***
## N.G2 -0.0187381 0.0001801 -104.015 < 2e-16 ***
## N.T2 -0.0883330 0.0002213 -399.105 < 2e-16 ***
## N.C3 -0.1108885 0.0002179 -508.901 < 2e-16 ***
## N.G3 -0.1610918 0.0002724 -591.361 < 2e-16 ***
## N.T3 -0.1256622 0.0001990 -631.615 < 2e-16 ***
## N.C4 -0.1816360 0.0003629 -500.451 < 2e-16 ***
## N.G4 -0.0818377 0.0001831 -447.023 < 2e-16 ***
## N.T4 -0.1061007 0.0001815 -584.589 < 2e-16 ***
## N.C5 -0.3177106 0.0013849 -229.411 < 2e-16 ***
## N.G5 -0.0810742 0.0001531 -529.493 < 2e-16 ***
## N.T5 -0.2910179 0.0007061 -412.129 < 2e-16 ***
## N.A6 -0.7021228 0.0236082 -29.741 < 2e-16 ***
## N.C6 -0.3973665 0.0172842 -22.990 < 2e-16 ***
## N.T6 -0.3342999 0.0012829 -260.588 < 2e-16 ***
## N.A7 0.0050363 0.0001312 38.395 < 2e-16 ***
## N.C7 -0.1629302 0.0003427 -475.454 < 2e-16 ***
## N.G7 -0.2011151 0.0004470 -449.942 < 2e-16 ***
## N.C8 -0.4159030 0.0133396 -31.178 < 2e-16 ***
## N.G8 -0.6945242 0.0189549 -36.641 < 2e-16 ***
## N.T8 -0.7927078 0.0255980 -30.968 < 2e-16 ***
## N.A9 -0.7511801 0.0386077 -19.457 < 2e-16 ***
## N.G9 -0.7576921 0.0496303 -15.267 < 2e-16 ***
## N.T9 -0.9045085 0.0547623 -16.517 < 2e-16 ***
## N.C10 -0.7473897 0.0381685 -19.581 < 2e-16 ***
## N.G10 -0.2146832 0.0003623 -592.618 < 2e-16 ***
## N.T10 -0.5432751 0.0048788 -111.354 < 2e-16 ***
## N.A11 -0.2735805 0.0004760 -574.775 < 2e-16 ***
## N.G11 -0.2095735 0.0003730 -561.859 < 2e-16 ***
## N.T11 -0.0675742 0.0001426 -473.771 < 2e-16 ***
## N.A12 -0.1045250 0.0002070 -504.858 < 2e-16 ***
## Strand.F1 -0.0705664 0.0026108 -27.029 < 2e-16 ***
## Strand.F2 0.0314530 0.0006508 48.328 < 2e-16 ***
## Strand.F3 0.0408631 0.0005189 78.754 < 2e-16 ***
## Strand.F4 0.0037351 0.0005347 6.986 2.83e-12 ***
## Strand.F5 -0.0070186 0.0003275 -21.431 < 2e-16 ***
## Strand.F6 0.0180938 0.0003407 53.109 < 2e-16 ***
## Strand.F7 0.0177024 0.0003743 47.293 < 2e-16 ***
## Strand.F9 -0.0542113 0.0004059 -133.567 < 2e-16 ***
## Strand.F10 -0.0373022 0.0006337 -58.864 < 2e-16 ***
## Strand.F11 0.0583660 0.0007480 78.025 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 6896588 on 999298 degrees of freedom
## Residual deviance: 3570321 on 999253 degrees of freedom
## AIC: 4891898
##
## Number of Fisher Scoring iterations: 12
ModelTest = addNewBetas(ModelTest, data, fit)
## No shape parameters included in fit.
# # Nucleotide Features after first round of fitting
summary(ModelTest)
## An object of class 'model'
##
## Slot "name": AR-DBD R7+R8 Nucleotides+View (Rev. Comp. Sym.)
## Slot "varRegLen": 23
## Slot "leftFixedSeq": GTTCAGAGTTCTACAGTCCGACGATC
## Slot "rightFixedSeq": TGGAATTCTCGGGTGCCAAGG
## Slot "leftFixedSeqOverlap": 5
## Slot "rightFixedSeqOverlap": 5
## Slot "confidenceLevel": 0.99
## Slot "minAffinity": 0.01
## Slot "missingValueSuppression": 0.5
## Slot "minSeedValue": 0.01
## Slot "seedLen": 15
## Slot "consensusSeq": [AG]G[AT]ACA[ACGT][ACGT][ACGT]TGT[AT]C[CT]
## Slot "upFootprintExtend": 4
## Slot "downFootprintExtend": 4
## Slot "fpLen": 23
##
## Fits a model of footprint length 23 for mono-nucleotide features with 11 view(s) per strand of DNA and 2 round(s) of data (round = 7, 8) with reverse complement symmetry.
##
## Slot "regressionFormula": ObservedCount ~ offset(logProb)+Round.7+Round.8+N.A1+N.C1+N.G1+N.T1+N.A2+N.C2+N.G2+N.T2+N.A3+N.C3+N.G3+N.T3+N.A4+N.C4+N.G4+N.T4+N.A5+N.C5+N.G5+N.T5+N.A6+N.C6+N.G6+N.T6+N.A7+N.C7+N.G7+N.T7+N.A8+N.C8+N.G8+N.T8+N.A9+N.C9+N.G9+N.T9+N.A10+N.C10+N.G10+N.T10+N.A11+N.C11+N.G11+N.T11+N.A12+N.C12+Strand.F1+Strand.F2+Strand.F3+Strand.F4+Strand.F5+Strand.F6+Strand.F7+Strand.F8+Strand.F9+Strand.F10+Strand.F11
##
##
## Includes the following feature sub-classes:
## An object of class 'N'
## Fits 23 nucleotides for a feature model of length 23.
## Nucleotide features are reverse complement symmetric.
## Nucleotide beta values:
## 1 2 3 4 5 6
## N.A -0.0008111264 0.00000000 0.0000000 0.0000000 0.00000000 -0.7021228
## N.C 0.0000000000 -0.07831867 -0.1108885 -0.1816360 -0.31771060 -0.3973665
## N.G 0.0005641848 -0.01873810 -0.1610918 -0.0818377 -0.08107421 0.0000000
## N.T -0.0394440451 -0.08833295 -0.1256622 -0.1061007 -0.29101786 -0.3342999
## 7 8 9 10 11 12
## N.A 0.005036262 0.0000000 -0.7511801 0.0000000 -0.2735805 -0.104525
## N.C -0.162930199 -0.4159030 0.0000000 -0.7473897 0.0000000 0.000000
## N.G -0.201115057 -0.6945242 -0.7576921 -0.2146832 -0.2095735 0.000000
## N.T 0.000000000 -0.7927078 -0.9045085 -0.5432751 -0.0675742 -0.104525
## 13 14 15 16 17 18
## N.A -0.0675742 -0.5432751 -0.9045085 -0.7927078 0.000000000 -0.3342999
## N.C -0.2095735 -0.2146832 -0.7576921 -0.6945242 -0.201115057 0.0000000
## N.G 0.0000000 -0.7473897 0.0000000 -0.4159030 -0.162930199 -0.3973665
## N.T -0.2735805 0.0000000 -0.7511801 0.0000000 0.005036262 -0.7021228
## 19 20 21 22 23
## N.A -0.29101786 -0.1061007 -0.1256622 -0.08833295 -0.0394440451
## N.C -0.08107421 -0.0818377 -0.1610918 -0.01873810 0.0005641848
## N.G -0.31771060 -0.1816360 -0.1108885 -0.07831867 0.0000000000
## N.T 0.00000000 0.0000000 0.0000000 0.00000000 -0.0008111264
##
## Nucleotide beta errors:
## 1 2 3 4 5
## N.A 0.0002252659 0.0000000000 0.0000000000 0.0000000000 0.0000000000
## N.C 0.0000000000 0.0002295318 0.0002178978 0.0003629445 0.0013848940
## N.G 0.0002329773 0.0001801475 0.0002724087 0.0001830725 0.0001531167
## N.T 0.0002520987 0.0002213274 0.0001989537 0.0001814962 0.0007061326
## 6 7 8 9 10
## N.A 0.023608223 0.0001311707 0.00000000 0.03860767 0.0000000000
## N.C 0.017284197 0.0003426834 0.01333963 0.00000000 0.0381684889
## N.G 0.000000000 0.0004469803 0.01895493 0.04963033 0.0003622626
## N.T 0.001282868 0.0000000000 0.02559804 0.05476229 0.0048788327
## 11 12 13 14 15
## N.A 0.0004759784 0.0002070382 0.0001426306 0.0048788327 0.05476229
## N.C 0.0000000000 0.0000000000 0.0003730000 0.0003622626 0.04963033
## N.G 0.0003730000 0.0000000000 0.0000000000 0.0381684889 0.00000000
## N.T 0.0001426306 0.0002070382 0.0004759784 0.0000000000 0.03860767
## 16 17 18 19 20
## N.A 0.02559804 0.0000000000 0.001282868 0.0007061326 0.0001814962
## N.C 0.01895493 0.0004469803 0.000000000 0.0001531167 0.0001830725
## N.G 0.01333963 0.0003426834 0.017284197 0.0013848940 0.0003629445
## N.T 0.00000000 0.0001311707 0.023608223 0.0000000000 0.0000000000
## 21 22 23
## N.A 0.0001989537 0.0002213274 0.0002520987
## N.C 0.0002724087 0.0001801475 0.0002329773
## N.G 0.0002178978 0.0002295318 0.0000000000
## N.T 0.0000000000 0.0000000000 0.0002252659
##
##
## An object of class 'Intercept'
## Fits 11 views and 2 round(s) (round = 7, 8).
## Intercept beta values:
## Round.7:
## View.1 View.2 View.3 View.4 View.5 View.6 View.7
## StrandView 35.84873 35.95075 35.96016 35.92303 35.91227 35.93739 35.937
## View.8 View.9 View.10 View.11
## StrandView 35.91929 35.86508 35.88199 35.97766
##
## Round.8:
## View.1 View.2 View.3 View.4 View.5 View.6 View.7 View.8
## StrandView 36.6459 36.6459 36.6459 36.6459 36.6459 36.6459 36.6459 36.6459
## View.9 View.10 View.11
## StrandView 36.6459 36.6459 36.6459
##
## Intercept beta errors:
## Round.7:
## View.1 View.2 View.3 View.4 View.5
## StrandView 0.004234477 0.003396764 0.003373969 0.003376436 0.003349881
## View.6 View.7 View.8 View.9 View.10
## StrandView 0.003351196 0.003354781 0.003333833 0.003358449 0.003393526
## View.11
## StrandView 0.003416724
##
## Round.8:
## View.1 View.2 View.3 View.4 View.5
## StrandView 0.00300509 0.00300509 0.00300509 0.00300509 0.00300509
## View.6 View.7 View.8 View.9 View.10
## StrandView 0.00300509 0.00300509 0.00300509 0.00300509 0.00300509
## View.11
## StrandView 0.00300509
##
##
##
## An object of class 'Shape'
## Fits 0 shape coefficients for 0 kinds of shape parameter(s) (shape = ) for a feature model of length 23.
vPheight = verticalPlot_height(ModelTest)
pM <- plot(ModelTest, plotTitle = "AR-DBD R7+R8 Nucleotide+View Fit", Nplot.ddG = TRUE, verticalPlots = TRUE)
ggplot2::ggsave(pM, file = paste(selexDir, saveDir, "/modelPlot.pdf", sep = ""), height = vPheight, width = 6)
ggplot2::ggsave(pM, file = paste(selexDir, saveDir, "/modelPlot.",1, ".pdf", sep = ""), height = vPheight, width = 6)
data = rbind(data.probeCounts[sample1, ], data.probeCounts.R7[sample2, ])
#data = rbind(data.probeCounts, data.probeCounts.R7)
data = topModelMatch(data, ModelTest)
data = addDesignMatrix(data, ModelTest)
if (nrow(data) > 0) {
designMatrixSummary.v2 = getDesignMatrix(ModelTest, data)
if ((all(designMatrixSummary.v2$N == designMatrixSummary$N)) & (all(designMatrixSummary.v2$Round == designMatrixSummary$Round)) & (all(designMatrixSummary.v2$Intercept == designMatrixSummary$Intercept))) {
print ("Stability Reached")
}
}
## No shape parameters included in fit.
for (i in 2:20) {
if (nrow(data) == 0) {
break
} else if ((all(designMatrixSummary.v2$N == designMatrixSummary$N)) & (all(designMatrixSummary.v2$Round == designMatrixSummary$Round)) & (all(designMatrixSummary.v2$Intercept == designMatrixSummary$Intercept))) {
break
}
data.nrow = nrow(data)
print (paste("i =",i))
designMatrixSummary = designMatrixSummary.v2
print("Round summary: ")
print (designMatrixSummary$Round)
print("Mono-nucleotide summary: ")
print (designMatrixSummary$N)
print("View/strand orientation summary: ")
print (designMatrixSummary$Intercept)
# # Constructs regression expression with independent features using design matrix
regressionFormula = updatedRegressionFormula(data, ModelTest)
print("Regression Formula: ")
print (regressionFormula)
fit = glm(regressionFormula,
data=data,
family = poisson(link="log"))
summary(fit)
ModelTest = addNewBetas(ModelTest, data, fit)
# # Nucleotide Features after first round of fitting
summary(ModelTest)
pM <- plot(ModelTest, plotTitle = "AR-DBD R7+R8 Nucleotide+View Fit", Nplot.ddG = TRUE, verticalPlots = TRUE)
ggplot2::ggsave(pM, file = paste(selexDir, saveDir, "/modelPlot.",i, ".pdf", sep = ""), height = vPheight, width = 6)
ggplot2::ggsave(pM, file = paste(selexDir, saveDir, "/modelPlot.pdf", sep = ""), height = vPheight, width = 6)
data = topModelMatch(data, ModelTest)
data = addDesignMatrix(data, ModelTest)
print(paste("Number of Observations in Design Matrix: ",nrow(data), sep = ""))
if (nrow(data) > 0) {
designMatrixSummary.v2 = getDesignMatrix(ModelTest, data)
if ((all(designMatrixSummary.v2$N == designMatrixSummary$N)) & (all(designMatrixSummary.v2$Round == designMatrixSummary$Round)) & (all(designMatrixSummary.v2$Intercept == designMatrixSummary$Intercept))) {
print (paste("Stability Reached after ", i, " iterations.", sep = ""))
break
}
} else {
print (paste("Algorithm failed to converge: No probes meet the confidence level requirement (Confidence Level:", ModelTest@confidenceLevel, ")", sep = ""))
}
}
## [1] "i = 2"
## [1] "Round summary: "
## 7 8 Total
## Round 499611 499764 999375
## [1] "Mono-nucleotide summary: "
## N.A N.C N.G N.T
## 1 576896 634212 349663 437979
## 2 703735 481366 370872 442777
## 3 877221 517408 179707 424414
## 4 901867 270584 412988 413311
## 5 1272314 21356 656899 48181
## 6 851 252 1979333 18314
## 7 840201 110086 104828 943635
## 8 1996253 447 1126 924
## 9 558 1997333 332 527
## 10 1867186 578 127143 3843
## 11 91570 1088192 122283 696705
## 12 356748 642627 0 0
## [1] "View/strand orientation summary: "
## View.1 View.2 View.3 View.4 View.5 View.6 View.7 View.8 View.9
## Strand.F 5854 66882 97201 70556 105732 126496 138047 150718 111570
## Strand.R 0 0 0 0 0 0 0 0 0
## View.10 View.11 StrandTotal
## Strand.F 60867 65452 999375
## Strand.R 0 0 0
## [1] "Regression Formula: "
## [1] "ObservedCount ~ offset(logProb)+Round.7+N.A1+N.G1+N.T1+N.C2+N.G2+N.T2+N.C3+N.G3+N.T3+N.C4+N.G4+N.T4+N.C5+N.G5+N.T5+N.A6+N.C6+N.T6+N.A7+N.C7+N.G7+N.C8+N.G8+N.T8+N.A9+N.G9+N.T9+N.C10+N.G10+N.T10+N.A11+N.G11+N.T11+N.A12+Strand.F1+Strand.F2+Strand.F3+Strand.F4+Strand.F5+Strand.F6+Strand.F7+Strand.F9+Strand.F10+Strand.F11"
## No shape parameters included in fit.
## An object of class 'model'
##
## Slot "name": AR-DBD R7+R8 Nucleotides+View (Rev. Comp. Sym.)
## Slot "varRegLen": 23
## Slot "leftFixedSeq": GTTCAGAGTTCTACAGTCCGACGATC
## Slot "rightFixedSeq": TGGAATTCTCGGGTGCCAAGG
## Slot "leftFixedSeqOverlap": 5
## Slot "rightFixedSeqOverlap": 5
## Slot "confidenceLevel": 0.99
## Slot "minAffinity": 0.01
## Slot "missingValueSuppression": 0.5
## Slot "minSeedValue": 0.01
## Slot "seedLen": 15
## Slot "consensusSeq": [AG]G[AT]ACA[ACGT][ACGT][ACGT]TGT[AT]C[CT]
## Slot "upFootprintExtend": 4
## Slot "downFootprintExtend": 4
## Slot "fpLen": 23
##
## Fits a model of footprint length 23 for mono-nucleotide features with 11 view(s) per strand of DNA and 2 round(s) of data (round = 7, 8) with reverse complement symmetry.
##
## Slot "regressionFormula": ObservedCount ~ offset(logProb)+Round.7+Round.8+N.A1+N.C1+N.G1+N.T1+N.A2+N.C2+N.G2+N.T2+N.A3+N.C3+N.G3+N.T3+N.A4+N.C4+N.G4+N.T4+N.A5+N.C5+N.G5+N.T5+N.A6+N.C6+N.G6+N.T6+N.A7+N.C7+N.G7+N.T7+N.A8+N.C8+N.G8+N.T8+N.A9+N.C9+N.G9+N.T9+N.A10+N.C10+N.G10+N.T10+N.A11+N.C11+N.G11+N.T11+N.A12+N.C12+Strand.F1+Strand.F2+Strand.F3+Strand.F4+Strand.F5+Strand.F6+Strand.F7+Strand.F8+Strand.F9+Strand.F10+Strand.F11
##
##
## Includes the following feature sub-classes:
## An object of class 'N'
## Fits 23 nucleotides for a feature model of length 23.
## Nucleotide features are reverse complement symmetric.
## Nucleotide beta values:
## 1 2 3 4 5
## N.A -0.0008149542 0.00000000 0.0000000 0.00000000 0.00000000
## N.C 0.0000000000 -0.07830985 -0.1108709 -0.18159540 -0.31759490
## N.G 0.0005593578 -0.01873436 -0.1610667 -0.08182424 -0.08106457
## N.T -0.0394435268 -0.08832365 -0.1256426 -0.10608890 -0.29083336
## 6 7 8 9 10 11
## N.A -0.6695495 0.005040855 0.0000000 -0.7011121 0.0000000 -0.2735546
## N.C -0.3397277 -0.162905777 -0.3844257 0.0000000 -0.7473570 0.0000000
## N.G 0.0000000 -0.201064869 -0.6837596 -0.6454396 -0.2146403 -0.2095342
## N.T -0.3340028 0.000000000 -0.7754598 -0.7258555 -0.5413663 -0.0675646
## 12 13 14 15 16 17
## N.A -0.1045142 -0.0675646 -0.5413663 -0.7258555 -0.7754598 0.000000000
## N.C 0.0000000 -0.2095342 -0.2146403 -0.6454396 -0.6837596 -0.201064869
## N.G 0.0000000 0.0000000 -0.7473570 0.0000000 -0.3844257 -0.162905777
## N.T -0.1045142 -0.2735546 0.0000000 -0.7011121 0.0000000 0.005040855
## 18 19 20 21 22
## N.A -0.3340028 -0.29083336 -0.10608890 -0.1256426 -0.08832365
## N.C 0.0000000 -0.08106457 -0.08182424 -0.1610667 -0.01873436
## N.G -0.3397277 -0.31759490 -0.18159540 -0.1108709 -0.07830985
## N.T -0.6695495 0.00000000 0.00000000 0.0000000 0.00000000
## 23
## N.A -0.0394435268
## N.C 0.0005593578
## N.G 0.0000000000
## N.T -0.0008149542
##
## Nucleotide beta errors:
## 1 2 3 4 5
## N.A 0.0002252629 0.0000000000 0.0000000000 0.0000000000 0.0000000000
## N.C 0.0000000000 0.0002295322 0.0002178973 0.0003629179 0.0013844498
## N.G 0.0002329755 0.0001801465 0.0002724040 0.0001830727 0.0001531173
## N.T 0.0002520958 0.0002213266 0.0001989511 0.0001814960 0.0007057425
## 6 7 8 9 10
## N.A 0.020934337 0.0001311705 0.00000000 0.03180847 0.0000000000
## N.C 0.013749357 0.0003426779 0.01181693 0.00000000 0.0381684926
## N.G 0.000000000 0.0004469354 0.01820035 0.03197647 0.0003622392
## N.T 0.001281606 0.0000000000 0.02400388 0.02770075 0.0048445962
## 11 12 13 14 15
## N.A 0.0004759796 0.0002070368 0.0001426306 0.0048445962 0.02770075
## N.C 0.0000000000 0.0000000000 0.0003729790 0.0003622392 0.03197647
## N.G 0.0003729790 0.0000000000 0.0000000000 0.0381684926 0.00000000
## N.T 0.0001426306 0.0002070368 0.0004759796 0.0000000000 0.03180847
## 16 17 18 19 20
## N.A 0.02400388 0.0000000000 0.001281606 0.0007057425 0.0001814960
## N.C 0.01820035 0.0004469354 0.000000000 0.0001531173 0.0001830727
## N.G 0.01181693 0.0003426779 0.013749357 0.0013844498 0.0003629179
## N.T 0.00000000 0.0001311705 0.020934337 0.0000000000 0.0000000000
## 21 22 23
## N.A 0.0001989511 0.0002213266 0.0002520958
## N.C 0.0002724040 0.0001801465 0.0002329755
## N.G 0.0002178973 0.0002295322 0.0000000000
## N.T 0.0000000000 0.0000000000 0.0002252629
##
##
## An object of class 'Intercept'
## Fits 11 views and 2 round(s) (round = 7, 8).
## Intercept beta values:
## Round.7:
## View.1 View.2 View.3 View.4 View.5 View.6 View.7
## StrandView 35.84829 35.95017 35.9596 35.92248 35.91172 35.93683 35.93644
## View.8 View.9 View.10 View.11
## StrandView 35.91874 35.86454 35.88147 35.97708
##
## Round.8:
## View.1 View.2 View.3 View.4 View.5 View.6 View.7
## StrandView 36.64534 36.64534 36.64534 36.64534 36.64534 36.64534 36.64534
## View.8 View.9 View.10 View.11
## StrandView 36.64534 36.64534 36.64534 36.64534
##
## Intercept beta errors:
## Round.7:
## View.1 View.2 View.3 View.4 View.5
## StrandView 0.00423354 0.003396818 0.003374026 0.003376494 0.003349939
## View.6 View.7 View.8 View.9 View.10
## StrandView 0.003351254 0.003354839 0.003333892 0.003358507 0.003393578
## View.11
## StrandView 0.003416778
##
## Round.8:
## View.1 View.2 View.3 View.4 View.5
## StrandView 0.00300516 0.00300516 0.00300516 0.00300516 0.00300516
## View.6 View.7 View.8 View.9 View.10
## StrandView 0.00300516 0.00300516 0.00300516 0.00300516 0.00300516
## View.11
## StrandView 0.00300516
##
##
##
## An object of class 'Shape'
## Fits 0 shape coefficients for 0 kinds of shape parameter(s) (shape = ) for a feature model of length 23.
## [1] "Number of Observations in Design Matrix: 999340"
## No shape parameters included in fit.
## [1] "i = 3"
## [1] "Round summary: "
## 7 8 Total
## Round 499585 499755 999340
## [1] "Mono-nucleotide summary: "
## N.A N.C N.G N.T
## 1 576866 634190 349657 437967
## 2 703712 481347 370860 442761
## 3 877203 517384 179692 424401
## 4 901846 270563 412971 413300
## 5 1272289 21346 656889 48156
## 6 847 247 1979292 18294
## 7 840178 110081 104822 943599
## 8 1996213 440 1115 912
## 9 552 1997279 326 523
## 10 1867156 571 127127 3826
## 11 91552 1088178 122273 696677
## 12 356721 642619 0 0
## [1] "View/strand orientation summary: "
## View.1 View.2 View.3 View.4 View.5 View.6 View.7 View.8 View.9
## Strand.F 5852 66879 97199 70554 105730 126494 138044 150706 111569
## Strand.R 0 0 0 0 0 0 0 0 0
## View.10 View.11 StrandTotal
## Strand.F 60864 65449 999340
## Strand.R 0 0 0
## [1] "Regression Formula: "
## [1] "ObservedCount ~ offset(logProb)+Round.7+N.A1+N.G1+N.T1+N.C2+N.G2+N.T2+N.C3+N.G3+N.T3+N.C4+N.G4+N.T4+N.C5+N.G5+N.T5+N.A6+N.C6+N.T6+N.A7+N.C7+N.G7+N.C8+N.G8+N.T8+N.A9+N.G9+N.T9+N.C10+N.G10+N.T10+N.A11+N.G11+N.T11+N.A12+Strand.F1+Strand.F2+Strand.F3+Strand.F4+Strand.F5+Strand.F6+Strand.F7+Strand.F9+Strand.F10+Strand.F11"
## No shape parameters included in fit.
## An object of class 'model'
##
## Slot "name": AR-DBD R7+R8 Nucleotides+View (Rev. Comp. Sym.)
## Slot "varRegLen": 23
## Slot "leftFixedSeq": GTTCAGAGTTCTACAGTCCGACGATC
## Slot "rightFixedSeq": TGGAATTCTCGGGTGCCAAGG
## Slot "leftFixedSeqOverlap": 5
## Slot "rightFixedSeqOverlap": 5
## Slot "confidenceLevel": 0.99
## Slot "minAffinity": 0.01
## Slot "missingValueSuppression": 0.5
## Slot "minSeedValue": 0.01
## Slot "seedLen": 15
## Slot "consensusSeq": [AG]G[AT]ACA[ACGT][ACGT][ACGT]TGT[AT]C[CT]
## Slot "upFootprintExtend": 4
## Slot "downFootprintExtend": 4
## Slot "fpLen": 23
##
## Fits a model of footprint length 23 for mono-nucleotide features with 11 view(s) per strand of DNA and 2 round(s) of data (round = 7, 8) with reverse complement symmetry.
##
## Slot "regressionFormula": ObservedCount ~ offset(logProb)+Round.7+Round.8+N.A1+N.C1+N.G1+N.T1+N.A2+N.C2+N.G2+N.T2+N.A3+N.C3+N.G3+N.T3+N.A4+N.C4+N.G4+N.T4+N.A5+N.C5+N.G5+N.T5+N.A6+N.C6+N.G6+N.T6+N.A7+N.C7+N.G7+N.T7+N.A8+N.C8+N.G8+N.T8+N.A9+N.C9+N.G9+N.T9+N.A10+N.C10+N.G10+N.T10+N.A11+N.C11+N.G11+N.T11+N.A12+N.C12+Strand.F1+Strand.F2+Strand.F3+Strand.F4+Strand.F5+Strand.F6+Strand.F7+Strand.F8+Strand.F9+Strand.F10+Strand.F11
##
##
## Includes the following feature sub-classes:
## An object of class 'N'
## Fits 23 nucleotides for a feature model of length 23.
## Nucleotide features are reverse complement symmetric.
## Nucleotide beta values:
## 1 2 3 4 5
## N.A -0.0008150682 0.00000000 0.0000000 0.00000000 0.00000000
## N.C 0.0000000000 -0.07831249 -0.1108769 -0.18160230 -0.31761892
## N.G 0.0005595451 -0.01873625 -0.1610734 -0.08182858 -0.08106755
## N.T -0.0394444898 -0.08832695 -0.1256460 -0.10609355 -0.29087120
## 6 7 8 9 10 11
## N.A -0.6805083 0.005040567 0.0000000 -0.7357226 0.0000000 -0.27356599
## N.C -0.3440666 -0.162914828 -0.3898515 0.0000000 -0.7473660 0.00000000
## N.G 0.0000000 -0.201075697 -0.6919688 -0.6917885 -0.2146580 -0.20954607
## N.T -0.3340863 0.000000000 -0.7837923 -0.7258670 -0.5420836 -0.06756632
## 12 13 14 15 16 17
## N.A -0.1045185 -0.06756632 -0.5420836 -0.7258670 -0.7837923 0.000000000
## N.C 0.0000000 -0.20954607 -0.2146580 -0.6917885 -0.6919688 -0.201075697
## N.G 0.0000000 0.00000000 -0.7473660 0.0000000 -0.3898515 -0.162914828
## N.T -0.1045185 -0.27356599 0.0000000 -0.7357226 0.0000000 0.005040567
## 18 19 20 21 22
## N.A -0.3340863 -0.29087120 -0.10609355 -0.1256460 -0.08832695
## N.C 0.0000000 -0.08106755 -0.08182858 -0.1610734 -0.01873625
## N.G -0.3440666 -0.31761892 -0.18160230 -0.1108769 -0.07831249
## N.T -0.6805083 0.00000000 0.00000000 0.0000000 0.00000000
## 23
## N.A -0.0394444898
## N.C 0.0005595451
## N.G 0.0000000000
## N.T -0.0008150682
##
## Nucleotide beta errors:
## 1 2 3 4 5
## N.A 0.0002252635 0.0000000000 0.0000000000 0.0000000000 0.0000000000
## N.C 0.0000000000 0.0002295322 0.0002178985 0.0003629198 0.0013845291
## N.G 0.0002329757 0.0001801471 0.0002724056 0.0001830730 0.0001531172
## N.T 0.0002520963 0.0002213271 0.0001989509 0.0001814967 0.0007058192
## 6 7 8 9 10
## N.A 0.021798700 0.0001311705 0.00000000 0.03636902 0.0000000000
## N.C 0.014111160 0.0003426825 0.01206635 0.00000000 0.0381684911
## N.G 0.000000000 0.0004469436 0.01877619 0.03834534 0.0003622531
## N.T 0.001281964 0.0000000000 0.02476263 0.02770077 0.0048575122
## 11 12 13 14 15
## N.A 0.0004759874 0.0002070377 0.0001426304 0.0048575122 0.02770077
## N.C 0.0000000000 0.0000000000 0.0003729868 0.0003622531 0.03834534
## N.G 0.0003729868 0.0000000000 0.0000000000 0.0381684911 0.00000000
## N.T 0.0001426304 0.0002070377 0.0004759874 0.0000000000 0.03636902
## 16 17 18 19 20
## N.A 0.02476263 0.0000000000 0.001281964 0.0007058192 0.0001814967
## N.C 0.01877619 0.0004469436 0.000000000 0.0001531172 0.0001830730
## N.G 0.01206635 0.0003426825 0.014111160 0.0013845291 0.0003629198
## N.T 0.00000000 0.0001311705 0.021798700 0.0000000000 0.0000000000
## 21 22 23
## N.A 0.0001989509 0.0002213271 0.0002520963
## N.C 0.0002724056 0.0001801471 0.0002329757
## N.G 0.0002178985 0.0002295322 0.0000000000
## N.T 0.0000000000 0.0000000000 0.0002252635
##
##
## An object of class 'Intercept'
## Fits 11 views and 2 round(s) (round = 7, 8).
## Intercept beta values:
## Round.7:
## View.1 View.2 View.3 View.4 View.5 View.6 View.7
## StrandView 35.84843 35.95033 35.95976 35.92264 35.91188 35.937 35.9366
## View.8 View.9 View.10 View.11
## StrandView 35.9189 35.8647 35.88163 35.97725
##
## Round.8:
## View.1 View.2 View.3 View.4 View.5 View.6 View.7 View.8
## StrandView 36.6455 36.6455 36.6455 36.6455 36.6455 36.6455 36.6455 36.6455
## View.9 View.10 View.11
## StrandView 36.6455 36.6455 36.6455
##
## Intercept beta errors:
## Round.7:
## View.1 View.2 View.3 View.4 View.5
## StrandView 0.00423372 0.003396802 0.00337401 0.003376478 0.003349922
## View.6 View.7 View.8 View.9 View.10
## StrandView 0.003351238 0.003354823 0.003333875 0.00335849 0.003393561
## View.11
## StrandView 0.003416762
##
## Round.8:
## View.1 View.2 View.3 View.4 View.5
## StrandView 0.00300514 0.00300514 0.00300514 0.00300514 0.00300514
## View.6 View.7 View.8 View.9 View.10
## StrandView 0.00300514 0.00300514 0.00300514 0.00300514 0.00300514
## View.11
## StrandView 0.00300514
##
##
##
## An object of class 'Shape'
## Fits 0 shape coefficients for 0 kinds of shape parameter(s) (shape = ) for a feature model of length 23.
## [1] "Number of Observations in Design Matrix: 999338"
## No shape parameters included in fit.
## [1] "i = 4"
## [1] "Round summary: "
## 7 8 Total
## Round 499583 499755 999338
## [1] "Mono-nucleotide summary: "
## N.A N.C N.G N.T
## 1 576865 634189 349655 437967
## 2 703711 481346 370859 442760
## 3 877202 517383 179692 424399
## 4 901845 270560 412971 413300
## 5 1272286 21346 656888 48156
## 6 847 246 1979289 18294
## 7 840177 110081 104822 943596
## 8 1996212 439 1114 911
## 9 551 1997278 324 523
## 10 1867154 571 127125 3826
## 11 91551 1088177 122272 696676
## 12 356720 642618 0 0
## [1] "View/strand orientation summary: "
## View.1 View.2 View.3 View.4 View.5 View.6 View.7 View.8 View.9
## Strand.F 5852 66878 97199 70554 105730 126494 138044 150706 111568
## Strand.R 0 0 0 0 0 0 0 0 0
## View.10 View.11 StrandTotal
## Strand.F 60864 65449 999338
## Strand.R 0 0 0
## [1] "Regression Formula: "
## [1] "ObservedCount ~ offset(logProb)+Round.7+N.A1+N.G1+N.T1+N.C2+N.G2+N.T2+N.C3+N.G3+N.T3+N.C4+N.G4+N.T4+N.C5+N.G5+N.T5+N.A6+N.C6+N.T6+N.A7+N.C7+N.G7+N.C8+N.G8+N.T8+N.A9+N.G9+N.T9+N.C10+N.G10+N.T10+N.A11+N.G11+N.T11+N.A12+Strand.F1+Strand.F2+Strand.F3+Strand.F4+Strand.F5+Strand.F6+Strand.F7+Strand.F9+Strand.F10+Strand.F11"
## No shape parameters included in fit.
## An object of class 'model'
##
## Slot "name": AR-DBD R7+R8 Nucleotides+View (Rev. Comp. Sym.)
## Slot "varRegLen": 23
## Slot "leftFixedSeq": GTTCAGAGTTCTACAGTCCGACGATC
## Slot "rightFixedSeq": TGGAATTCTCGGGTGCCAAGG
## Slot "leftFixedSeqOverlap": 5
## Slot "rightFixedSeqOverlap": 5
## Slot "confidenceLevel": 0.99
## Slot "minAffinity": 0.01
## Slot "missingValueSuppression": 0.5
## Slot "minSeedValue": 0.01
## Slot "seedLen": 15
## Slot "consensusSeq": [AG]G[AT]ACA[ACGT][ACGT][ACGT]TGT[AT]C[CT]
## Slot "upFootprintExtend": 4
## Slot "downFootprintExtend": 4
## Slot "fpLen": 23
##
## Fits a model of footprint length 23 for mono-nucleotide features with 11 view(s) per strand of DNA and 2 round(s) of data (round = 7, 8) with reverse complement symmetry.
##
## Slot "regressionFormula": ObservedCount ~ offset(logProb)+Round.7+Round.8+N.A1+N.C1+N.G1+N.T1+N.A2+N.C2+N.G2+N.T2+N.A3+N.C3+N.G3+N.T3+N.A4+N.C4+N.G4+N.T4+N.A5+N.C5+N.G5+N.T5+N.A6+N.C6+N.G6+N.T6+N.A7+N.C7+N.G7+N.T7+N.A8+N.C8+N.G8+N.T8+N.A9+N.C9+N.G9+N.T9+N.A10+N.C10+N.G10+N.T10+N.A11+N.C11+N.G11+N.T11+N.A12+N.C12+Strand.F1+Strand.F2+Strand.F3+Strand.F4+Strand.F5+Strand.F6+Strand.F7+Strand.F8+Strand.F9+Strand.F10+Strand.F11
##
##
## Includes the following feature sub-classes:
## An object of class 'N'
## Fits 23 nucleotides for a feature model of length 23.
## Nucleotide features are reverse complement symmetric.
## Nucleotide beta values:
## 1 2 3 4 5 6
## N.A -0.0008152151 0.00000000 0.0000000 0.00000000 0.0000000 -0.6805089
## N.C 0.0000000000 -0.07831268 -0.1108771 -0.18160343 -0.3176194 -0.3440671
## N.G 0.0005595236 -0.01873646 -0.1610737 -0.08182874 -0.0810678 0.0000000
## N.T -0.0394445556 -0.08832711 -0.1256465 -0.10609372 -0.2908717 -0.3340869
## 7 8 9 10 11 12
## N.A 0.005040685 0.0000000 -0.7453305 0.0000000 -0.27356643 -0.104519
## N.C -0.162915092 -0.3898521 0.0000000 -0.7473666 0.00000000 0.000000
## N.G -0.201075962 -0.6919694 -0.7025205 -0.2146593 -0.20954730 0.000000
## N.T 0.000000000 -0.7881556 -0.7258679 -0.5420843 -0.06756637 -0.104519
## 13 14 15 16 17 18
## N.A -0.06756637 -0.5420843 -0.7258679 -0.7881556 0.000000000 -0.3340869
## N.C -0.20954730 -0.2146593 -0.7025205 -0.6919694 -0.201075962 0.0000000
## N.G 0.00000000 -0.7473666 0.0000000 -0.3898521 -0.162915092 -0.3440671
## N.T -0.27356643 0.0000000 -0.7453305 0.0000000 0.005040685 -0.6805089
## 19 20 21 22 23
## N.A -0.2908717 -0.10609372 -0.1256465 -0.08832711 -0.0394445556
## N.C -0.0810678 -0.08182874 -0.1610737 -0.01873646 0.0005595236
## N.G -0.3176194 -0.18160343 -0.1108771 -0.07831268 0.0000000000
## N.T 0.0000000 0.00000000 0.0000000 0.00000000 -0.0008152151
##
## Nucleotide beta errors:
## 1 2 3 4 5
## N.A 0.0002252636 0.0000000000 0.0000000000 0.0000000000 0.0000000000
## N.C 0.0000000000 0.0002295321 0.0002178985 0.0003629208 0.0013845291
## N.G 0.0002329757 0.0001801471 0.0002724056 0.0001830730 0.0001531172
## N.T 0.0002520962 0.0002213271 0.0001989510 0.0001814967 0.0007058191
## 6 7 8 9 10
## N.A 0.021798700 0.0001311705 0.00000000 0.03774671 0.0000000000
## N.C 0.014111007 0.0003426825 0.01206635 0.00000000 0.0381684902
## N.G 0.000000000 0.0004469436 0.01877619 0.03999185 0.0003622541
## N.T 0.001281964 0.0000000000 0.02516993 0.02770077 0.0048575120
## 11 12 13 14 15
## N.A 0.0004759873 0.0002070378 0.0001426304 0.0048575120 0.02770077
## N.C 0.0000000000 0.0000000000 0.0003729880 0.0003622541 0.03999185
## N.G 0.0003729880 0.0000000000 0.0000000000 0.0381684902 0.00000000
## N.T 0.0001426304 0.0002070378 0.0004759873 0.0000000000 0.03774671
## 16 17 18 19 20
## N.A 0.02516993 0.0000000000 0.001281964 0.0007058191 0.0001814967
## N.C 0.01877619 0.0004469436 0.000000000 0.0001531172 0.0001830730
## N.G 0.01206635 0.0003426825 0.014111007 0.0013845291 0.0003629208
## N.T 0.00000000 0.0001311705 0.021798700 0.0000000000 0.0000000000
## 21 22 23
## N.A 0.0001989510 0.0002213271 0.0002520962
## N.C 0.0002724056 0.0001801471 0.0002329757
## N.G 0.0002178985 0.0002295321 0.0000000000
## N.T 0.0000000000 0.0000000000 0.0002252636
##
##
## An object of class 'Intercept'
## Fits 11 views and 2 round(s) (round = 7, 8).
## Intercept beta values:
## Round.7:
## View.1 View.2 View.3 View.4 View.5 View.6 View.7
## StrandView 35.84844 35.95034 35.95977 35.92265 35.91189 35.93701 35.93661
## View.8 View.9 View.10 View.11
## StrandView 35.91891 35.86471 35.88164 35.97726
##
## Round.8:
## View.1 View.2 View.3 View.4 View.5 View.6 View.7
## StrandView 36.64551 36.64551 36.64551 36.64551 36.64551 36.64551 36.64551
## View.8 View.9 View.10 View.11
## StrandView 36.64551 36.64551 36.64551 36.64551
##
## Intercept beta errors:
## Round.7:
## View.1 View.2 View.3 View.4 View.5
## StrandView 0.004233719 0.003396801 0.003374009 0.003376476 0.003349921
## View.6 View.7 View.8 View.9 View.10
## StrandView 0.003351237 0.003354822 0.003333874 0.003358489 0.00339356
## View.11
## StrandView 0.003416761
##
## Round.8:
## View.1 View.2 View.3 View.4 View.5
## StrandView 0.003005138 0.003005138 0.003005138 0.003005138 0.003005138
## View.6 View.7 View.8 View.9 View.10
## StrandView 0.003005138 0.003005138 0.003005138 0.003005138 0.003005138
## View.11
## StrandView 0.003005138
##
##
##
## An object of class 'Shape'
## Fits 0 shape coefficients for 0 kinds of shape parameter(s) (shape = ) for a feature model of length 23.
## [1] "Number of Observations in Design Matrix: 999338"
## No shape parameters included in fit.
## [1] "Stability Reached after 4 iterations."
ModelTest <- finalizeFeatureBetas(ModelTest)
pM <- plot(ModelTest, plotTitle = "AR-DBD R7+R8 Nucleotide+View Fit", Nplot.ddG = TRUE, verticalPlots = TRUE)
ggplot2::ggsave(pM, file = paste(selexDir, saveDir, "/modelPlot.pdf", sep = ""), height = vPheight, width = 6)
save(ModelTest, file = paste(selexDir, saveDir, "/model.RData",sep = ""))
saveRDS(ModelTest, file = paste(selexDir, saveDir, "/model.rds",sep = ""))