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/ShapeSymmetry"
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)
# 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 = ""))
# Inputs about library are data specific
ModelTest = model(name = "AR-DBD R8 Nucleotides+Shape (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(8)),
rcSymmetric = TRUE,
verbose = FALSE,
includeShape = TRUE,
shapeTable = ST,
shapeParamsUsed = list(c("MGW", "HelT")))
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: 1 (8)
## Number of previous iterations: 0
##
## Slot "Shape":
## ShapeParamsUsed: HelT MGW
## Shape.upFootprintExtend: 4
## Shape.downFootprintExtend: 4
## Shape.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
# 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 R8 Nucleotides+Shape\nSeeding Model", ddG = TRUE)
Next we score the probes using topModelMatch
sample1 = sample(nrow(data.probeCounts), 1000000)
data = data.probeCounts[sample1,]
#data = data.probeCounts
data = topModelMatch(data, ModelTest)
# Uses aligned probes to build design matrix
data = addDesignMatrix(data, ModelTest)
designMatrixSummary = getDesignMatrix(ModelTest, data)
print("Round summary: ")
## [1] "Round summary: "
print (designMatrixSummary$Round)
## 8 Total
## Round 999427 999427
print("View/strand orientation summary: ")
## [1] "View/strand orientation summary: "
print (designMatrixSummary$Intercept)
## View.1 View.2 View.3 View.4 View.5 View.6 View.7 View.8 View.9
## Strand.F 5096 64640 96937 69835 107584 130300 141294 154280 110773
## Strand.R 0 0 0 0 0 0 0 0 0
## View.10 View.11 StrandTotal
## Strand.F 56619 62069 999427
## 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 582726 638699 349416 428013
## 2 719050 470765 372250 436789
## 3 909917 499020 171085 418832
## 4 927469 254495 408005 408885
## 5 1289591 18446 648508 42309
## 6 659 230 1982736 15229
## 7 844343 104861 93296 956354
## 8 1996417 415 1137 885
## 9 622 1997278 360 594
## 10 1880083 665 115150 2956
## 11 80245 1113446 111998 693165
## 12 343791 655636 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)+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+Shape.HelTA1+Shape.HelTB1+Shape.MGW1+Shape.HelTA2+Shape.HelTB2+Shape.MGW2+Shape.HelTA3+Shape.HelTB3+Shape.MGW3+Shape.HelTA4+Shape.HelTB4+Shape.MGW4+Shape.HelTA5+Shape.HelTB5+Shape.MGW5+Shape.HelTA6+Shape.HelTB6+Shape.MGW6+Shape.HelTA7+Shape.HelTB7+Shape.MGW7+Shape.HelTA8+Shape.HelTB8+Shape.MGW8+Shape.HelTA9+Shape.HelTB9+Shape.MGW9+Shape.HelTA10+Shape.HelTB10+Shape.MGW10+Shape.HelTA11+Shape.HelTB11+Shape.MGW11+Shape.HelTA12+Shape.MGW12"
fit = glm(regressionFormula,
data=data,
family = poisson(link="log"))
## Warning: glm.fit: fitted rates numerically 0 occurred
summary(fit)
##
## Call:
## glm(formula = regressionFormula, family = poisson(link = "log"),
## data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -36.094 -1.261 -0.622 0.299 54.660
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.165e+01 2.983e-01 173.131 < 2e-16 ***
## N.A1 2.261e-02 1.669e-04 135.454 < 2e-16 ***
## N.G1 1.580e-02 1.948e-04 81.117 < 2e-16 ***
## N.T1 -2.834e-02 1.829e-04 -154.924 < 2e-16 ***
## N.C2 -7.819e-02 1.802e-04 -433.960 < 2e-16 ***
## N.G2 -4.362e-03 1.845e-04 -23.646 < 2e-16 ***
## N.T2 -7.848e-02 2.112e-04 -371.530 < 2e-16 ***
## N.C3 -6.166e-02 1.897e-04 -325.090 < 2e-16 ***
## N.G3 -1.307e-01 2.775e-04 -471.064 < 2e-16 ***
## N.T3 -6.034e-02 1.977e-04 -305.295 < 2e-16 ***
## N.C4 -9.565e-02 3.483e-04 -274.621 < 2e-16 ***
## N.G4 1.772e-02 3.015e-04 58.767 < 2e-16 ***
## N.T4 2.609e-02 2.510e-04 103.936 < 2e-16 ***
## N.C5 -6.869e-01 3.051e-03 -225.172 < 2e-16 ***
## N.G5 -3.281e-01 3.194e-03 -102.705 < 2e-16 ***
## N.T5 -6.294e-01 3.871e-03 -162.585 < 2e-16 ***
## N.A6 -1.177e+00 2.835e-02 -41.528 < 2e-16 ***
## N.C6 -7.343e-01 2.132e-02 -34.439 < 2e-16 ***
## N.T6 -6.823e-01 6.708e-03 -101.715 < 2e-16 ***
## N.A7 -1.677e+00 2.946e-02 -56.934 < 2e-16 ***
## N.C7 -9.312e-01 1.648e-02 -56.500 < 2e-16 ***
## N.G7 -1.294e+00 2.284e-02 -56.658 < 2e-16 ***
## N.C8 -1.531e+00 2.640e-02 -58.010 < 2e-16 ***
## N.G8 -1.583e+00 2.639e-02 -59.985 < 2e-16 ***
## N.T8 -1.947e+00 3.723e-02 -52.296 < 2e-16 ***
## N.A9 -1.335e+00 4.669e-02 -28.584 < 2e-16 ***
## N.G9 -8.067e-01 6.945e-02 -11.615 < 2e-16 ***
## N.T9 -1.061e+00 5.662e-02 -18.732 < 2e-16 ***
## N.C10 -9.811e-01 3.180e-02 -30.850 < 2e-16 ***
## N.G10 -2.264e-01 5.674e-03 -39.893 < 2e-16 ***
## N.T10 -8.452e-01 6.520e-03 -129.627 < 2e-16 ***
## N.A11 -2.041e-01 1.243e-03 -164.176 < 2e-16 ***
## N.G11 -3.705e-01 1.010e-03 -367.010 < 2e-16 ***
## N.T11 -9.719e-02 7.340e-04 -132.422 < 2e-16 ***
## N.A12 -4.821e-02 4.589e-04 -105.065 < 2e-16 ***
## Shape.HelTA1 5.982e-03 8.999e-05 66.477 < 2e-16 ***
## Shape.HelTB1 -9.016e-04 1.725e-04 -5.227 1.73e-07 ***
## Shape.MGW1 3.205e-02 2.808e-04 114.144 < 2e-16 ***
## Shape.HelTA2 9.060e-03 1.788e-04 50.684 < 2e-16 ***
## Shape.HelTB2 -1.092e-02 2.014e-04 -54.202 < 2e-16 ***
## Shape.MGW2 6.105e-03 3.602e-04 16.949 < 2e-16 ***
## Shape.HelTA3 1.115e-02 2.058e-04 54.182 < 2e-16 ***
## Shape.HelTB3 -3.101e-02 2.189e-04 -141.661 < 2e-16 ***
## Shape.MGW3 5.586e-03 3.807e-04 14.672 < 2e-16 ***
## Shape.HelTA4 3.526e-02 2.264e-04 155.705 < 2e-16 ***
## Shape.HelTB4 -2.293e-02 2.827e-04 -81.102 < 2e-16 ***
## Shape.MGW4 -1.669e-01 5.556e-04 -300.443 < 2e-16 ***
## Shape.HelTA5 1.753e-02 2.899e-04 60.455 < 2e-16 ***
## Shape.HelTB5 -1.565e-03 4.139e-04 -3.781 0.000156 ***
## Shape.MGW5 -8.514e-02 5.963e-04 -142.784 < 2e-16 ***
## Shape.HelTA6 3.983e-02 5.837e-04 68.242 < 2e-16 ***
## Shape.HelTB6 -9.258e-02 7.439e-04 -124.448 < 2e-16 ***
## Shape.MGW6 1.881e-02 8.314e-04 22.626 < 2e-16 ***
## Shape.HelTA7 -1.678e-01 4.300e-03 -39.015 < 2e-16 ***
## Shape.HelTB7 4.478e-01 4.935e-03 90.745 < 2e-16 ***
## Shape.MGW7 -8.925e-01 9.612e-03 -92.858 < 2e-16 ***
## Shape.HelTA8 8.454e-03 6.073e-03 1.392 0.163917
## Shape.HelTB8 1.950e-01 2.211e-02 8.819 < 2e-16 ***
## Shape.MGW8 7.747e-01 2.797e-02 27.697 < 2e-16 ***
## Shape.HelTA9 9.082e-02 1.695e-03 53.587 < 2e-16 ***
## Shape.HelTB9 -1.747e-01 1.707e-03 -102.342 < 2e-16 ***
## Shape.MGW9 1.848e-02 9.525e-04 19.405 < 2e-16 ***
## Shape.HelTA10 5.684e-02 9.201e-04 61.778 < 2e-16 ***
## Shape.HelTB10 -2.442e-01 8.249e-04 -296.015 < 2e-16 ***
## Shape.MGW10 -1.449e-01 2.170e-03 -66.745 < 2e-16 ***
## Shape.HelTA11 1.336e-01 7.419e-04 180.132 < 2e-16 ***
## Shape.HelTB11 2.767e-02 7.403e-04 37.380 < 2e-16 ***
## Shape.MGW11 8.050e-02 1.069e-03 75.302 < 2e-16 ***
## Shape.HelTA12 1.203e-02 8.161e-04 14.736 < 2e-16 ***
## Shape.MGW12 -1.294e-01 1.008e-03 -128.394 < 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: 9231403 on 999426 degrees of freedom
## Residual deviance: 3425311 on 999357 degrees of freedom
## AIC: 4864583
##
## Number of Fisher Scoring iterations: 16
ModelTest = addNewBetas(ModelTest, data, fit)
# # Nucleotide Features after first round of fitting
summary(ModelTest)
## An object of class 'model'
##
## Slot "name": AR-DBD R8 Nucleotides+Shape (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 and shape features (shape = HelT, MGW) with 11 view(s) per strand of DNA and 1 round(s) of data (round = 8) with reverse complement symmetry.
##
## Slot "regressionFormula": ObservedCount ~ offset(logProb)+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+Shape.HelTA1+Shape.HelTB1+Shape.MGW1+Shape.HelTA2+Shape.HelTB2+Shape.MGW2+Shape.HelTA3+Shape.HelTB3+Shape.MGW3+Shape.HelTA4+Shape.HelTB4+Shape.MGW4+Shape.HelTA5+Shape.HelTB5+Shape.MGW5+Shape.HelTA6+Shape.HelTB6+Shape.MGW6+Shape.HelTA7+Shape.HelTB7+Shape.MGW7+Shape.HelTA8+Shape.HelTB8+Shape.MGW8+Shape.HelTA9+Shape.HelTB9+Shape.MGW9+Shape.HelTA10+Shape.HelTB10+Shape.MGW10+Shape.HelTA11+Shape.HelTB11+Shape.MGW11+Shape.HelTA12+Shape.MGW12
##
## Slot "shapeParamsUsed[[1]]": HelT MGW
##
## 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 0.02260685 0.000000000 0.00000000 0.00000000 0.0000000 -1.1774778
## N.C 0.00000000 -0.078185324 -0.06165896 -0.09564576 -0.6869431 -0.7342871
## N.G 0.01579906 -0.004362174 -0.13073732 0.01772096 -0.3280739 0.0000000
## N.T -0.02833779 -0.078481517 -0.06034341 0.02608989 -0.6293672 -0.6823032
## 7 8 9 10 11 12
## N.A -1.6772499 0.000000 -1.3345256 0.0000000 -0.20407440 -0.04821378
## N.C -0.9312197 -1.531309 0.0000000 -0.9811457 0.00000000 0.00000000
## N.G -1.2943400 -1.582956 -0.8066855 -0.2263536 -0.37053578 0.00000000
## N.T 0.0000000 -1.947109 -1.0606487 -0.8451675 -0.09719495 -0.04821378
## 13 14 15 16 17 18
## N.A -0.09719495 -0.8451675 -1.0606487 -1.947109 0.0000000 -0.6823032
## N.C -0.37053578 -0.2263536 -0.8066855 -1.582956 -1.2943400 0.0000000
## N.G 0.00000000 -0.9811457 0.0000000 -1.531309 -0.9312197 -0.7342871
## N.T -0.20407440 0.0000000 -1.3345256 0.000000 -1.6772499 -1.1774778
## 19 20 21 22 23
## N.A -0.6293672 0.02608989 -0.06034341 -0.078481517 -0.02833779
## N.C -0.3280739 0.01772096 -0.13073732 -0.004362174 0.01579906
## N.G -0.6869431 -0.09564576 -0.06165896 -0.078185324 0.00000000
## N.T 0.0000000 0.00000000 0.00000000 0.000000000 0.02260685
##
## Nucleotide beta errors:
## 1 2 3 4 5
## N.A 0.0001668965 0.0000000000 0.0000000000 0.0000000000 0.000000000
## N.C 0.0000000000 0.0001801672 0.0001896676 0.0003482823 0.003050745
## N.G 0.0001947680 0.0001844759 0.0002775361 0.0003015474 0.003194326
## N.T 0.0001829147 0.0002112387 0.0001976564 0.0002510180 0.003871002
## 6 7 8 9 10 11
## N.A 0.028353700 0.02945960 0.00000000 0.04668750 0.000000000 0.0012430224
## N.C 0.021321672 0.01648173 0.02639751 0.00000000 0.031803794 0.0000000000
## N.G 0.000000000 0.02284494 0.02638898 0.06945296 0.005674046 0.0010096062
## N.T 0.006707958 0.00000000 0.03723223 0.05662273 0.006519971 0.0007339785
## 12 13 14 15 16 17
## N.A 0.0004588926 0.0007339785 0.006519971 0.05662273 0.03723223 0.00000000
## N.C 0.0000000000 0.0010096062 0.005674046 0.06945296 0.02638898 0.02284494
## N.G 0.0000000000 0.0000000000 0.031803794 0.00000000 0.02639751 0.01648173
## N.T 0.0004588926 0.0012430224 0.000000000 0.04668750 0.00000000 0.02945960
## 18 19 20 21 22
## N.A 0.006707958 0.003871002 0.0002510180 0.0001976564 0.0002112387
## N.C 0.000000000 0.003194326 0.0003015474 0.0002775361 0.0001844759
## N.G 0.021321672 0.003050745 0.0003482823 0.0001896676 0.0001801672
## N.T 0.028353700 0.000000000 0.0000000000 0.0000000000 0.0000000000
## 23
## N.A 0.0001829147
## N.C 0.0001947680
## N.G 0.0000000000
## N.T 0.0001668965
##
##
## An object of class 'Intercept'
## Fits intercept(s) for 1 round(s) (round = 8).
## Intercept beta values:
## Round.8:
## [1] 51.64553
##
## Intercept beta errors:
## Round.8:
## [1] 0.2983034
##
##
##
## An object of class 'Shape'
## Fits 69 shape coefficients for 3 kinds of shape parameter(s) (shape = HelT, MGW) for a feature model of length 23.
## Shape features are reverse complement symmetric.
## Shape beta values:
## 1 2 3 4
## Shape.HelTA 0.0059821792 0.009060211 0.011148684 0.03525787
## Shape.HelTB -0.0009016091 -0.010918217 -0.031010352 -0.02293076
## Shape.MGW 0.0320530331 0.006105306 0.005586067 -0.16693758
## 5 6 7 8 9
## Shape.HelTA 0.017525985 0.03983166 -0.1677675 0.008453793 0.09081832
## Shape.HelTB -0.001565201 -0.09257650 0.4478214 0.195036745 -0.17471089
## Shape.MGW -0.085136659 0.01881214 -0.8925278 0.774687443 0.01848280
## 10 11 12 13 14
## Shape.HelTA 0.05684243 0.13364120 0.01202591 0.02767206 -0.24417232
## Shape.HelTB -0.24417232 0.02767206 0.01202591 0.13364120 0.05684243
## Shape.MGW -0.14485048 0.08050021 -0.12936393 0.08050021 -0.14485048
## 15 16 17 18 19
## Shape.HelTA -0.17471089 0.195036745 0.4478214 -0.09257650 -0.001565201
## Shape.HelTB 0.09081832 0.008453793 -0.1677675 0.03983166 0.017525985
## Shape.MGW 0.01848280 0.774687443 -0.8925278 0.01881214 -0.085136659
## 20 21 22 23
## Shape.HelTA -0.02293076 -0.031010352 -0.010918217 -0.0009016091
## Shape.HelTB 0.03525787 0.011148684 0.009060211 0.0059821792
## Shape.MGW -0.16693758 0.005586067 0.006105306 0.0320530331
##
## Shape beta errors:
## 1 2 3 4
## Shape.HelTA 0.0000899882 0.0001787605 0.0002057620 0.0002264408
## Shape.HelTB 0.0001725066 0.0002014362 0.0002189048 0.0002827388
## Shape.MGW 0.0002808112 0.0003602060 0.0003807293 0.0005556382
## 5 6 7 8 9
## Shape.HelTA 0.0002899026 0.0005836791 0.004300047 0.006073055 0.0016947832
## Shape.HelTB 0.0004139394 0.0007438972 0.004934955 0.022114468 0.0017071275
## Shape.MGW 0.0005962612 0.0008314487 0.009611724 0.027969870 0.0009524897
## 10 11 12 13
## Shape.HelTA 0.0009201045 0.0007419053 0.0008160645 0.0007402964
## Shape.HelTB 0.0008248641 0.0007402964 0.0008160645 0.0007419053
## Shape.MGW 0.0021702049 0.0010690371 0.0010075523 0.0010690371
## 14 15 16 17 18
## Shape.HelTA 0.0008248641 0.0017071275 0.022114468 0.004934955 0.0007438972
## Shape.HelTB 0.0009201045 0.0016947832 0.006073055 0.004300047 0.0005836791
## Shape.MGW 0.0021702049 0.0009524897 0.027969870 0.009611724 0.0008314487
## 19 20 21 22
## Shape.HelTA 0.0004139394 0.0002827388 0.0002189048 0.0002014362
## Shape.HelTB 0.0002899026 0.0002264408 0.0002057620 0.0001787605
## Shape.MGW 0.0005962612 0.0005556382 0.0003807293 0.0003602060
## 23
## Shape.HelTA 0.0001725066
## Shape.HelTB 0.0000899882
## Shape.MGW 0.0002808112
vPheight = verticalPlot_height(ModelTest)
pM <- plot(ModelTest, plotTitle = "AR-DBD R8 Nucleotide+Shape 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 = data.probeCounts[sample1,]
#data = data.probeCounts
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$Intercept))) {
print ("Stability Reached")
}
}
for (i in 2:20) {
if (nrow(data) == 0) {
break
} else if ((all(designMatrixSummary.v2$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 R8 Nucleotide+Shape 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: "
## 8 Total
## Round 999463 999463
## [1] "Mono-nucleotide summary: "
## N.A N.C N.G N.T
## 1 582766 638734 349409 428017
## 2 719082 470797 372247 436800
## 3 909912 499038 171123 418853
## 4 927450 254494 408071 408911
## 5 1289607 18477 648509 42333
## 6 676 295 1982710 15245
## 7 844287 104889 93278 956472
## 8 1996440 424 1146 916
## 9 637 1997235 413 641
## 10 1880044 694 115191 2997
## 11 80305 1113432 112017 693172
## 12 343838 655625 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 5099 64640 96937 69841 107578 130308 141300 154308 110778
## Strand.R 0 0 0 0 0 0 0 0 0
## View.10 View.11 StrandTotal
## Strand.F 56608 62066 999463
## Strand.R 0 0 0
## [1] "Regression Formula: "
## [1] "ObservedCount ~ offset(logProb)+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+Shape.HelTA1+Shape.HelTB1+Shape.MGW1+Shape.HelTA2+Shape.HelTB2+Shape.MGW2+Shape.HelTA3+Shape.HelTB3+Shape.MGW3+Shape.HelTA4+Shape.HelTB4+Shape.MGW4+Shape.HelTA5+Shape.HelTB5+Shape.MGW5+Shape.HelTA6+Shape.HelTB6+Shape.MGW6+Shape.HelTA7+Shape.HelTB7+Shape.MGW7+Shape.HelTA8+Shape.HelTB8+Shape.MGW8+Shape.HelTA9+Shape.HelTB9+Shape.MGW9+Shape.HelTA10+Shape.HelTB10+Shape.MGW10+Shape.HelTA11+Shape.HelTB11+Shape.MGW11+Shape.HelTA12+Shape.MGW12"
## Warning: glm.fit: fitted rates numerically 0 occurred
## An object of class 'model'
##
## Slot "name": AR-DBD R8 Nucleotides+Shape (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 and shape features (shape = HelT, MGW) with 11 view(s) per strand of DNA and 1 round(s) of data (round = 8) with reverse complement symmetry.
##
## Slot "regressionFormula": ObservedCount ~ offset(logProb)+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+Shape.HelTA1+Shape.HelTB1+Shape.MGW1+Shape.HelTA2+Shape.HelTB2+Shape.MGW2+Shape.HelTA3+Shape.HelTB3+Shape.MGW3+Shape.HelTA4+Shape.HelTB4+Shape.MGW4+Shape.HelTA5+Shape.HelTB5+Shape.MGW5+Shape.HelTA6+Shape.HelTB6+Shape.MGW6+Shape.HelTA7+Shape.HelTB7+Shape.MGW7+Shape.HelTA8+Shape.HelTB8+Shape.MGW8+Shape.HelTA9+Shape.HelTB9+Shape.MGW9+Shape.HelTA10+Shape.HelTB10+Shape.MGW10+Shape.HelTA11+Shape.HelTB11+Shape.MGW11+Shape.HelTA12+Shape.MGW12
##
## Slot "shapeParamsUsed[[1]]": HelT MGW
##
## 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 0.02264339 0.00000000 0.00000000 0.00000000 0.0000000 -1.3697684
## N.C 0.00000000 -0.07817806 -0.06166247 -0.09554759 -0.7379977 -0.7488641
## N.G 0.01581923 -0.00437085 -0.13084520 0.01726405 -0.3876488 0.0000000
## N.T -0.02834283 -0.07850260 -0.06037643 0.02612504 -0.6977610 -0.6706969
## 7 8 9 10 11 12
## N.A -2.190486 0.000000 -2.03909626 0.000000 -0.20349025 -0.04852396
## N.C -1.275356 -1.991353 0.00000000 -1.095198 0.00000000 0.00000000
## N.G -1.535048 -1.966988 0.07394985 -0.354096 -0.37316340 0.00000000
## N.T 0.000000 -1.987319 -0.55220803 -0.901120 -0.09860647 -0.04852396
## 13 14 15 16 17 18
## N.A -0.09860647 -0.901120 -0.55220803 -1.987319 0.000000 -0.6706969
## N.C -0.37316340 -0.354096 0.07394985 -1.966988 -1.535048 0.0000000
## N.G 0.00000000 -1.095198 0.00000000 -1.991353 -1.275356 -0.7488641
## N.T -0.20349025 0.000000 -2.03909626 0.000000 -2.190486 -1.3697684
## 19 20 21 22 23
## N.A -0.6977610 0.02612504 -0.06037643 -0.07850260 -0.02834283
## N.C -0.3876488 0.01726405 -0.13084520 -0.00437085 0.01581923
## N.G -0.7379977 -0.09554759 -0.06166247 -0.07817806 0.00000000
## N.T 0.0000000 0.00000000 0.00000000 0.00000000 0.02264339
##
## Nucleotide beta errors:
## 1 2 3 4 5
## N.A 0.0001668976 0.0000000000 0.0000000000 0.0000000000 0.000000000
## N.C 0.0000000000 0.0001801543 0.0001896889 0.0003483621 0.002831331
## N.G 0.0001947672 0.0001844876 0.0002775173 0.0003015979 0.002823411
## N.T 0.0001829249 0.0002112472 0.0001976495 0.0002510176 0.003825677
## 6 7 8 9 10 11
## N.A 0.024250533 0.03095806 0.00000000 0.03581687 0.000000000 0.0012418984
## N.C 0.019137625 0.01704326 0.04567665 0.00000000 0.027671278 0.0000000000
## N.G 0.000000000 0.02348721 0.02695834 0.01939258 0.003177973 0.0010114483
## N.T 0.005482773 0.00000000 0.03057258 0.01661620 0.006220218 0.0007343667
## 12 13 14 15 16 17
## N.A 0.000458647 0.0007343667 0.006220218 0.01661620 0.03057258 0.00000000
## N.C 0.000000000 0.0010114483 0.003177973 0.01939258 0.02695834 0.02348721
## N.G 0.000000000 0.0000000000 0.027671278 0.00000000 0.04567665 0.01704326
## N.T 0.000458647 0.0012418984 0.000000000 0.03581687 0.00000000 0.03095806
## 18 19 20 21 22
## N.A 0.005482773 0.003825677 0.0002510176 0.0001976495 0.0002112472
## N.C 0.000000000 0.002823411 0.0003015979 0.0002775173 0.0001844876
## N.G 0.019137625 0.002831331 0.0003483621 0.0001896889 0.0001801543
## N.T 0.024250533 0.000000000 0.0000000000 0.0000000000 0.0000000000
## 23
## N.A 0.0001829249
## N.C 0.0001947672
## N.G 0.0000000000
## N.T 0.0001668976
##
##
## An object of class 'Intercept'
## Fits intercept(s) for 1 round(s) (round = 8).
## Intercept beta values:
## Round.8:
## [1] 56.37078
##
## Intercept beta errors:
## Round.8:
## [1] 0.3122084
##
##
##
## An object of class 'Shape'
## Fits 69 shape coefficients for 3 kinds of shape parameter(s) (shape = HelT, MGW) for a feature model of length 23.
## Shape features are reverse complement symmetric.
## Shape beta values:
## 1 2 3 4
## Shape.HelTA 0.0059849107 0.009069712 0.011138455 0.03531386
## Shape.HelTB -0.0008980584 -0.010912938 -0.031054291 -0.02295622
## Shape.MGW 0.0320195312 0.006113506 0.005663408 -0.16716591
## 5 6 7 8 9
## Shape.HelTA 0.017410321 0.03973829 -0.2142963 0.08730818 0.09288284
## Shape.HelTB -0.001839003 -0.09521456 0.5454011 0.68237239 -0.17480088
## Shape.MGW -0.085484247 0.01648900 -1.0819123 1.24173157 0.01793459
## 10 11 12 13 14
## Shape.HelTA 0.05754169 0.13439061 0.01230215 0.02706371 -0.24612380
## Shape.HelTB -0.24612380 0.02706371 0.01230215 0.13439061 0.05754169
## Shape.MGW -0.14711762 0.08008281 -0.12873585 0.08008281 -0.14711762
## 15 16 17 18 19
## Shape.HelTA -0.17480088 0.68237239 0.5454011 -0.09521456 -0.001839003
## Shape.HelTB 0.09288284 0.08730818 -0.2142963 0.03973829 0.017410321
## Shape.MGW 0.01793459 1.24173157 -1.0819123 0.01648900 -0.085484247
## 20 21 22 23
## Shape.HelTA -0.02295622 -0.031054291 -0.010912938 -0.0008980584
## Shape.HelTB 0.03531386 0.011138455 0.009069712 0.0059849107
## Shape.MGW -0.16716591 0.005663408 0.006113506 0.0320195312
##
## Shape beta errors:
## 1 2 3 4
## Shape.HelTA 0.0000899896 0.0001787687 0.0002057911 0.0002264321
## Shape.HelTB 0.0001725146 0.0002014646 0.0002189075 0.0002828061
## Shape.MGW 0.0002808141 0.0003602363 0.0003807435 0.0005557480
## 5 6 7 8 9
## Shape.HelTA 0.0002899194 0.0005843959 0.004401282 0.006762059 0.0016942941
## Shape.HelTB 0.0004140882 0.0007398419 0.004575256 0.011592223 0.0017048909
## Shape.MGW 0.0005965069 0.0008298699 0.008851199 0.016473143 0.0009522115
## 10 11 12 13
## Shape.HelTA 0.0009201718 0.0007430716 0.0008159998 0.0007404835
## Shape.HelTB 0.0008269860 0.0007404835 0.0008159998 0.0007430716
## Shape.MGW 0.0021723879 0.0010691197 0.0010078851 0.0010691197
## 14 15 16 17 18
## Shape.HelTA 0.0008269860 0.0017048909 0.011592223 0.004575256 0.0007398419
## Shape.HelTB 0.0009201718 0.0016942941 0.006762059 0.004401282 0.0005843959
## Shape.MGW 0.0021723879 0.0009522115 0.016473143 0.008851199 0.0008298699
## 19 20 21 22
## Shape.HelTA 0.0004140882 0.0002828061 0.0002189075 0.0002014646
## Shape.HelTB 0.0002899194 0.0002264321 0.0002057911 0.0001787687
## Shape.MGW 0.0005965069 0.0005557480 0.0003807435 0.0003602363
## 23
## Shape.HelTA 0.0001725146
## Shape.HelTB 0.0000899896
## Shape.MGW 0.0002808141
##
## [1] "Number of Observations in Design Matrix: 998809"
## [1] "i = 3"
## [1] "Round summary: "
## 8 Total
## Round 998809 998809
## [1] "Mono-nucleotide summary: "
## N.A N.C N.G N.T
## 1 582357 638451 349111 427699
## 2 718634 470560 372105 436319
## 3 909437 498711 171038 418432
## 4 927035 254216 407772 408595
## 5 1288797 18440 648150 42231
## 6 665 301 1981436 15216
## 7 843723 104829 93184 955882
## 8 1995260 407 1091 860
## 9 597 1995934 431 656
## 10 1878864 683 115145 2926
## 11 80058 1112888 111978 692694
## 12 343435 655374 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 5095 64511 96873 69808 107544 130241 141243 154205 110738
## Strand.R 0 0 0 0 0 0 0 0 0
## View.10 View.11 StrandTotal
## Strand.F 56578 61973 998809
## Strand.R 0 0 0
## [1] "Regression Formula: "
## [1] "ObservedCount ~ offset(logProb)+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+Shape.HelTA1+Shape.HelTB1+Shape.MGW1+Shape.HelTA2+Shape.HelTB2+Shape.MGW2+Shape.HelTA3+Shape.HelTB3+Shape.MGW3+Shape.HelTA4+Shape.HelTB4+Shape.MGW4+Shape.HelTA5+Shape.HelTB5+Shape.MGW5+Shape.HelTA6+Shape.HelTB6+Shape.MGW6+Shape.HelTA7+Shape.HelTB7+Shape.MGW7+Shape.HelTA8+Shape.HelTB8+Shape.MGW8+Shape.HelTA9+Shape.HelTB9+Shape.MGW9+Shape.HelTA10+Shape.HelTB10+Shape.MGW10+Shape.HelTA11+Shape.HelTB11+Shape.MGW11+Shape.HelTA12+Shape.MGW12"
## An object of class 'model'
##
## Slot "name": AR-DBD R8 Nucleotides+Shape (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 and shape features (shape = HelT, MGW) with 11 view(s) per strand of DNA and 1 round(s) of data (round = 8) with reverse complement symmetry.
##
## Slot "regressionFormula": ObservedCount ~ offset(logProb)+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+Shape.HelTA1+Shape.HelTB1+Shape.MGW1+Shape.HelTA2+Shape.HelTB2+Shape.MGW2+Shape.HelTA3+Shape.HelTB3+Shape.MGW3+Shape.HelTA4+Shape.HelTB4+Shape.MGW4+Shape.HelTA5+Shape.HelTB5+Shape.MGW5+Shape.HelTA6+Shape.HelTB6+Shape.MGW6+Shape.HelTA7+Shape.HelTB7+Shape.MGW7+Shape.HelTA8+Shape.HelTB8+Shape.MGW8+Shape.HelTA9+Shape.HelTB9+Shape.MGW9+Shape.HelTA10+Shape.HelTB10+Shape.MGW10+Shape.HelTA11+Shape.HelTB11+Shape.MGW11+Shape.HelTA12+Shape.MGW12
##
## Slot "shapeParamsUsed[[1]]": HelT MGW
##
## 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 0.02263972 0.000000000 0.00000000 0.00000000 0.0000000 -1.3385856
## N.C 0.00000000 -0.078180675 -0.06166447 -0.09556934 -0.7329610 -0.7804856
## N.G 0.01581687 -0.004369761 -0.13083675 0.01734712 -0.3809242 0.0000000
## N.T -0.02834319 -0.078492475 -0.06036596 0.02613106 -0.6935194 -0.6818606
## 7 8 9 10 11 12
## N.A -2.170372 0.000000 -2.08829442 0.0000000 -0.2036001 -0.04853906
## N.C -1.251383 -2.181535 0.00000000 -1.1252606 0.0000000 0.00000000
## N.G -1.542776 -1.943166 -0.01097421 -0.3360303 -0.3731854 0.00000000
## N.T 0.000000 -2.031637 -0.62289564 -0.8921566 -0.0986699 -0.04853906
## 13 14 15 16 17 18
## N.A -0.0986699 -0.8921566 -0.62289564 -2.031637 0.000000 -0.6818606
## N.C -0.3731854 -0.3360303 -0.01097421 -1.943166 -1.542776 0.0000000
## N.G 0.0000000 -1.1252606 0.00000000 -2.181535 -1.251383 -0.7804856
## N.T -0.2036001 0.0000000 -2.08829442 0.000000 -2.170372 -1.3385856
## 19 20 21 22 23
## N.A -0.6935194 0.02613106 -0.06036596 -0.078492475 -0.02834319
## N.C -0.3809242 0.01734712 -0.13083675 -0.004369761 0.01581687
## N.G -0.7329610 -0.09556934 -0.06166447 -0.078180675 0.00000000
## N.T 0.0000000 0.00000000 0.00000000 0.000000000 0.02263972
##
## Nucleotide beta errors:
## 1 2 3 4 5
## N.A 0.0001669038 0.0000000000 0.0000000000 0.0000000000 0.000000000
## N.C 0.0000000000 0.0001801597 0.0001896908 0.0003483807 0.002804863
## N.G 0.0001947726 0.0001844918 0.0002775193 0.0003015995 0.002811038
## N.T 0.0001829291 0.0002112637 0.0001976595 0.0002510226 0.003733931
## 6 7 8 9 10 11
## N.A 0.025120855 0.03023172 0.00000000 0.05850522 0.000000000 0.0012421166
## N.C 0.019537458 0.01627414 0.06776635 0.00000000 0.033675058 0.0000000000
## N.G 0.000000000 0.02348513 0.02665048 0.02079203 0.002935863 0.0010117723
## N.T 0.005591702 0.00000000 0.03161940 0.01674234 0.006325569 0.0007346603
## 12 13 14 15 16 17
## N.A 0.0004586929 0.0007346603 0.006325569 0.01674234 0.03161940 0.00000000
## N.C 0.0000000000 0.0010117723 0.002935863 0.02079203 0.02665048 0.02348513
## N.G 0.0000000000 0.0000000000 0.033675058 0.00000000 0.06776635 0.01627414
## N.T 0.0004586929 0.0012421166 0.000000000 0.05850522 0.00000000 0.03023172
## 18 19 20 21 22
## N.A 0.005591702 0.003733931 0.0002510226 0.0001976595 0.0002112637
## N.C 0.000000000 0.002811038 0.0003015995 0.0002775193 0.0001844918
## N.G 0.019537458 0.002804863 0.0003483807 0.0001896908 0.0001801597
## N.T 0.025120855 0.000000000 0.0000000000 0.0000000000 0.0000000000
## 23
## N.A 0.0001829291
## N.C 0.0001947726
## N.G 0.0000000000
## N.T 0.0001669038
##
##
## An object of class 'Intercept'
## Fits intercept(s) for 1 round(s) (round = 8).
## Intercept beta values:
## Round.8:
## [1] 56.25446
##
## Intercept beta errors:
## Round.8:
## [1] 0.3070146
##
##
##
## An object of class 'Shape'
## Fits 69 shape coefficients for 3 kinds of shape parameter(s) (shape = HelT, MGW) for a feature model of length 23.
## Shape features are reverse complement symmetric.
## Shape beta values:
## 1 2 3 4
## Shape.HelTA 0.0059821310 0.009067530 0.01113707 0.03531120
## Shape.HelTB -0.0008973217 -0.010906903 -0.03104910 -0.02296465
## Shape.MGW 0.0320226338 0.006112176 0.00564983 -0.16714494
## 5 6 7 8 9
## Shape.HelTA 0.017431755 0.03981302 -0.2151109 0.08266176 0.09264548
## Shape.HelTB -0.001820453 -0.09491022 0.5366550 0.61117847 -0.17494441
## Shape.MGW -0.085456521 0.01681844 -1.0643459 1.15881277 0.01786927
## 10 11 12 13 14
## Shape.HelTA 0.05735002 0.13446475 0.0124623 0.02697241 -0.24616851
## Shape.HelTB -0.24616851 0.02697241 0.0124623 0.13446475 0.05735002
## Shape.MGW -0.14746608 0.08025429 -0.1286961 0.08025429 -0.14746608
## 15 16 17 18 19
## Shape.HelTA -0.17494441 0.61117847 0.5366550 -0.09491022 -0.001820453
## Shape.HelTB 0.09264548 0.08266176 -0.2151109 0.03981302 0.017431755
## Shape.MGW 0.01786927 1.15881277 -1.0643459 0.01681844 -0.085456521
## 20 21 22 23
## Shape.HelTA -0.02296465 -0.03104910 -0.010906903 -0.0008973217
## Shape.HelTB 0.03531120 0.01113707 0.009067530 0.0059821310
## Shape.MGW -0.16714494 0.00564983 0.006112176 0.0320226338
##
## Shape beta errors:
## 1 2 3 4
## Shape.HelTA 8.999295e-05 0.0001787772 0.0002057951 0.0002264344
## Shape.HelTB 1.725220e-04 0.0002014681 0.0002189062 0.0002828196
## Shape.MGW 2.808208e-04 0.0003602455 0.0003807525 0.0005557653
## 5 6 7 8 9
## Shape.HelTA 0.0002899272 0.0005843531 0.004374649 0.006529105 0.0016941697
## Shape.HelTB 0.0004140743 0.0007397572 0.004479904 0.010967983 0.0017046201
## Shape.MGW 0.0005964881 0.0008298505 0.008681862 0.017551591 0.0009522383
## 10 11 12 13
## Shape.HelTA 0.0009201727 0.0007431925 0.0008162318 0.0007406496
## Shape.HelTB 0.0008271607 0.0007406496 0.0008162318 0.0007431925
## Shape.MGW 0.0021731615 0.0010692186 0.0010079685 0.0010692186
## 14 15 16 17 18
## Shape.HelTA 0.0008271607 0.0017046201 0.010967983 0.004479904 0.0007397572
## Shape.HelTB 0.0009201727 0.0016941697 0.006529105 0.004374649 0.0005843531
## Shape.MGW 0.0021731615 0.0009522383 0.017551591 0.008681862 0.0008298505
## 19 20 21 22
## Shape.HelTA 0.0004140743 0.0002828196 0.0002189062 0.0002014681
## Shape.HelTB 0.0002899272 0.0002264344 0.0002057951 0.0001787772
## Shape.MGW 0.0005964881 0.0005557653 0.0003807525 0.0003602455
## 23
## Shape.HelTA 1.725220e-04
## Shape.HelTB 8.999295e-05
## Shape.MGW 2.808208e-04
##
## [1] "Number of Observations in Design Matrix: 998793"
## [1] "i = 4"
## [1] "Round summary: "
## 8 Total
## Round 998793 998793
## [1] "Mono-nucleotide summary: "
## N.A N.C N.G N.T
## 1 582348 638448 349099 427691
## 2 718623 470555 372100 436308
## 3 909428 498700 171032 418426
## 4 927026 254201 407769 408590
## 5 1288778 18439 648145 42224
## 6 663 291 1981423 15209
## 7 843720 104828 93182 955856
## 8 1995251 399 1091 845
## 9 596 1995927 423 640
## 10 1878848 682 115135 2921
## 11 80051 1112881 111971 692683
## 12 343423 655370 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 5095 64508 96870 69807 107544 130239 141243 154204 110737
## Strand.R 0 0 0 0 0 0 0 0 0
## View.10 View.11 StrandTotal
## Strand.F 56576 61970 998793
## Strand.R 0 0 0
## [1] "Regression Formula: "
## [1] "ObservedCount ~ offset(logProb)+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+Shape.HelTA1+Shape.HelTB1+Shape.MGW1+Shape.HelTA2+Shape.HelTB2+Shape.MGW2+Shape.HelTA3+Shape.HelTB3+Shape.MGW3+Shape.HelTA4+Shape.HelTB4+Shape.MGW4+Shape.HelTA5+Shape.HelTB5+Shape.MGW5+Shape.HelTA6+Shape.HelTB6+Shape.MGW6+Shape.HelTA7+Shape.HelTB7+Shape.MGW7+Shape.HelTA8+Shape.HelTB8+Shape.MGW8+Shape.HelTA9+Shape.HelTB9+Shape.MGW9+Shape.HelTA10+Shape.HelTB10+Shape.MGW10+Shape.HelTA11+Shape.HelTB11+Shape.MGW11+Shape.HelTA12+Shape.MGW12"
## An object of class 'model'
##
## Slot "name": AR-DBD R8 Nucleotides+Shape (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 and shape features (shape = HelT, MGW) with 11 view(s) per strand of DNA and 1 round(s) of data (round = 8) with reverse complement symmetry.
##
## Slot "regressionFormula": ObservedCount ~ offset(logProb)+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+Shape.HelTA1+Shape.HelTB1+Shape.MGW1+Shape.HelTA2+Shape.HelTB2+Shape.MGW2+Shape.HelTA3+Shape.HelTB3+Shape.MGW3+Shape.HelTA4+Shape.HelTB4+Shape.MGW4+Shape.HelTA5+Shape.HelTB5+Shape.MGW5+Shape.HelTA6+Shape.HelTB6+Shape.MGW6+Shape.HelTA7+Shape.HelTB7+Shape.MGW7+Shape.HelTA8+Shape.HelTB8+Shape.MGW8+Shape.HelTA9+Shape.HelTB9+Shape.MGW9+Shape.HelTA10+Shape.HelTB10+Shape.MGW10+Shape.HelTA11+Shape.HelTB11+Shape.MGW11+Shape.HelTA12+Shape.MGW12
##
## Slot "shapeParamsUsed[[1]]": HelT MGW
##
## 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 0.02264111 0.000000000 0.00000000 0.00000000 0.0000000 -1.3551146
## N.C 0.00000000 -0.078179495 -0.06166659 -0.09556885 -0.7348230 -0.7880336
## N.G 0.01581948 -0.004369121 -0.13084210 0.01732844 -0.3831336 0.0000000
## N.T -0.02834222 -0.078491226 -0.06036771 0.02613115 -0.6954742 -0.6769246
## 7 8 9 10 11 12
## N.A -2.195763 0.000000 -2.14116805 0.0000000 -0.20362772 -0.0485379
## N.C -1.273227 -2.300367 0.00000000 -1.1373776 0.00000000 0.0000000
## N.G -1.551240 -1.966031 0.02741873 -0.3458347 -0.37323134 0.0000000
## N.T 0.000000 -2.022781 -0.60288301 -0.8950496 -0.09870798 -0.0485379
## 13 14 15 16 17 18
## N.A -0.09870798 -0.8950496 -0.60288301 -2.022781 0.000000 -0.6769246
## N.C -0.37323134 -0.3458347 0.02741873 -1.966031 -1.551240 0.0000000
## N.G 0.00000000 -1.1373776 0.00000000 -2.300367 -1.273227 -0.7880336
## N.T -0.20362772 0.0000000 -2.14116805 0.000000 -2.195763 -1.3551146
## 19 20 21 22 23
## N.A -0.6954742 0.02613115 -0.06036771 -0.078491226 -0.02834222
## N.C -0.3831336 0.01732844 -0.13084210 -0.004369121 0.01581948
## N.G -0.7348230 -0.09556885 -0.06166659 -0.078179495 0.00000000
## N.T 0.0000000 0.00000000 0.00000000 0.000000000 0.02264111
##
## Nucleotide beta errors:
## 1 2 3 4 5
## N.A 0.0001669043 0.0000000000 0.0000000000 0.0000000000 0.000000000
## N.C 0.0000000000 0.0001801593 0.0001896914 0.0003483842 0.002801159
## N.G 0.0001947732 0.0001844927 0.0002775219 0.0003016154 0.002799673
## N.T 0.0001829294 0.0002112638 0.0001976593 0.0002510248 0.003743901
## 6 7 8 9 10 11
## N.A 0.025275689 0.03093772 0.00000000 0.05942321 0.000000000 0.0012421679
## N.C 0.019619830 0.01708063 0.07864713 0.00000000 0.033762760 0.0000000000
## N.G 0.000000000 0.02367674 0.02718126 0.02153442 0.003475285 0.0010118873
## N.T 0.005601915 0.00000000 0.03134945 0.01652868 0.006320942 0.0007347398
## 12 13 14 15 16 17
## N.A 0.0004586787 0.0007347398 0.006320942 0.01652868 0.03134945 0.00000000
## N.C 0.0000000000 0.0010118873 0.003475285 0.02153442 0.02718126 0.02367674
## N.G 0.0000000000 0.0000000000 0.033762760 0.00000000 0.07864713 0.01708063
## N.T 0.0004586787 0.0012421679 0.000000000 0.05942321 0.00000000 0.03093772
## 18 19 20 21 22
## N.A 0.005601915 0.003743901 0.0002510248 0.0001976593 0.0002112638
## N.C 0.000000000 0.002799673 0.0003016154 0.0002775219 0.0001844927
## N.G 0.019619830 0.002801159 0.0003483842 0.0001896914 0.0001801593
## N.T 0.025275689 0.000000000 0.0000000000 0.0000000000 0.0000000000
## 23
## N.A 0.0001829294
## N.C 0.0001947732
## N.G 0.0000000000
## N.T 0.0001669043
##
##
## An object of class 'Intercept'
## Fits intercept(s) for 1 round(s) (round = 8).
## Intercept beta values:
## Round.8:
## [1] 56.46355
##
## Intercept beta errors:
## Round.8:
## [1] 0.31222
##
##
##
## An object of class 'Shape'
## Fits 69 shape coefficients for 3 kinds of shape parameter(s) (shape = HelT, MGW) for a feature model of length 23.
## Shape features are reverse complement symmetric.
## Shape beta values:
## 1 2 3 4
## Shape.HelTA 0.005981943 0.009069020 0.011136146 0.03531605
## Shape.HelTB -0.000897874 -0.010906646 -0.031052509 -0.02296732
## Shape.MGW 0.032019779 0.006116597 0.005650295 -0.16716164
## 5 6 7 8 9
## Shape.HelTA 0.017427450 0.03983680 -0.2156866 0.09001092 0.09273971
## Shape.HelTB -0.001829927 -0.09506268 0.5398271 0.64822193 -0.17485752
## Shape.MGW -0.085448275 0.01670212 -1.0706077 1.19118888 0.01788250
## 10 11 12 13 14
## Shape.HelTA 0.05737569 0.13448471 0.0124601 0.02696442 -0.24620280
## Shape.HelTB -0.24620280 0.02696442 0.0124601 0.13448471 0.05737569
## Shape.MGW -0.14749924 0.08024489 -0.1286951 0.08024489 -0.14749924
## 15 16 17 18 19
## Shape.HelTA -0.17485752 0.64822193 0.5398271 -0.09506268 -0.001829927
## Shape.HelTB 0.09273971 0.09001092 -0.2156866 0.03983680 0.017427450
## Shape.MGW 0.01788250 1.19118888 -1.0706077 0.01670212 -0.085448275
## 20 21 22 23
## Shape.HelTA -0.02296732 -0.031052509 -0.010906646 -0.000897874
## Shape.HelTB 0.03531605 0.011136146 0.009069020 0.005981943
## Shape.MGW -0.16716164 0.005650295 0.006116597 0.032019779
##
## Shape beta errors:
## 1 2 3 4
## Shape.HelTA 8.999298e-05 0.0001787779 0.0002057956 0.0002264385
## Shape.HelTB 1.725224e-04 0.0002014682 0.0002189092 0.0002828232
## Shape.MGW 2.808227e-04 0.0003602480 0.0003807528 0.0005557834
## 5 6 7 8 9
## Shape.HelTA 0.0002899303 0.0005844060 0.004395913 0.006784102 0.0016942058
## Shape.HelTB 0.0004140861 0.0007402325 0.004474219 0.012930764 0.0017048815
## Shape.MGW 0.0005964925 0.0008300915 0.008665973 0.018125062 0.0009522614
## 10 11 12 13
## Shape.HelTA 0.0009201543 0.0007432176 0.000816214 0.0007406419
## Shape.HelTB 0.0008272552 0.0007406419 0.000816214 0.0007432176
## Shape.MGW 0.0021731724 0.0010691851 0.001007959 0.0010691851
## 14 15 16 17 18
## Shape.HelTA 0.0008272552 0.0017048815 0.012930764 0.004474219 0.0007402325
## Shape.HelTB 0.0009201543 0.0016942058 0.006784102 0.004395913 0.0005844060
## Shape.MGW 0.0021731724 0.0009522614 0.018125062 0.008665973 0.0008300915
## 19 20 21 22
## Shape.HelTA 0.0004140861 0.0002828232 0.0002189092 0.0002014682
## Shape.HelTB 0.0002899303 0.0002264385 0.0002057956 0.0001787779
## Shape.MGW 0.0005964925 0.0005557834 0.0003807528 0.0003602480
## 23
## Shape.HelTA 1.725224e-04
## Shape.HelTB 8.999298e-05
## Shape.MGW 2.808227e-04
##
## [1] "Number of Observations in Design Matrix: 998790"
## [1] "i = 5"
## [1] "Round summary: "
## 8 Total
## Round 998790 998790
## [1] "Mono-nucleotide summary: "
## N.A N.C N.G N.T
## 1 582347 638446 349098 427689
## 2 718620 470555 372098 436307
## 3 909425 498698 171031 418426
## 4 927023 254200 407768 408589
## 5 1288775 18438 648143 42224
## 6 662 291 1981418 15209
## 7 843718 104828 93182 955852
## 8 1995248 396 1091 845
## 9 595 1995922 423 640
## 10 1878843 682 115134 2921
## 11 80050 1112878 111971 692681
## 12 343422 655368 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 5094 64508 96870 69807 107544 130239 141243 154203 110737
## Strand.R 0 0 0 0 0 0 0 0 0
## View.10 View.11 StrandTotal
## Strand.F 56576 61969 998790
## Strand.R 0 0 0
## [1] "Regression Formula: "
## [1] "ObservedCount ~ offset(logProb)+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+Shape.HelTA1+Shape.HelTB1+Shape.MGW1+Shape.HelTA2+Shape.HelTB2+Shape.MGW2+Shape.HelTA3+Shape.HelTB3+Shape.MGW3+Shape.HelTA4+Shape.HelTB4+Shape.MGW4+Shape.HelTA5+Shape.HelTB5+Shape.MGW5+Shape.HelTA6+Shape.HelTB6+Shape.MGW6+Shape.HelTA7+Shape.HelTB7+Shape.MGW7+Shape.HelTA8+Shape.HelTB8+Shape.MGW8+Shape.HelTA9+Shape.HelTB9+Shape.MGW9+Shape.HelTA10+Shape.HelTB10+Shape.MGW10+Shape.HelTA11+Shape.HelTB11+Shape.MGW11+Shape.HelTA12+Shape.MGW12"
## An object of class 'model'
##
## Slot "name": AR-DBD R8 Nucleotides+Shape (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 and shape features (shape = HelT, MGW) with 11 view(s) per strand of DNA and 1 round(s) of data (round = 8) with reverse complement symmetry.
##
## Slot "regressionFormula": ObservedCount ~ offset(logProb)+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+Shape.HelTA1+Shape.HelTB1+Shape.MGW1+Shape.HelTA2+Shape.HelTB2+Shape.MGW2+Shape.HelTA3+Shape.HelTB3+Shape.MGW3+Shape.HelTA4+Shape.HelTB4+Shape.MGW4+Shape.HelTA5+Shape.HelTB5+Shape.MGW5+Shape.HelTA6+Shape.HelTB6+Shape.MGW6+Shape.HelTA7+Shape.HelTB7+Shape.MGW7+Shape.HelTA8+Shape.HelTB8+Shape.MGW8+Shape.HelTA9+Shape.HelTB9+Shape.MGW9+Shape.HelTA10+Shape.HelTB10+Shape.MGW10+Shape.HelTA11+Shape.HelTB11+Shape.MGW11+Shape.HelTA12+Shape.MGW12
##
## Slot "shapeParamsUsed[[1]]": HelT MGW
##
## 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 0.02264111 0.000000000 0.00000000 0.00000000 0.0000000 -1.3551147
## N.C 0.00000000 -0.078179495 -0.06166659 -0.09556885 -0.7348231 -0.7880335
## N.G 0.01581948 -0.004369121 -0.13084210 0.01732844 -0.3831337 0.0000000
## N.T -0.02834222 -0.078491226 -0.06036771 0.02613115 -0.6954742 -0.6769245
## 7 8 9 10 11 12
## N.A -2.195763 0.000000 -2.14116832 0.0000000 -0.20362772 -0.0485379
## N.C -1.273227 -2.300367 0.00000000 -1.1373777 0.00000000 0.0000000
## N.G -1.551240 -1.966031 0.02741899 -0.3458348 -0.37323134 0.0000000
## N.T 0.000000 -2.022781 -0.60288287 -0.8950496 -0.09870798 -0.0485379
## 13 14 15 16 17 18
## N.A -0.09870798 -0.8950496 -0.60288287 -2.022781 0.000000 -0.6769245
## N.C -0.37323134 -0.3458348 0.02741899 -1.966031 -1.551240 0.0000000
## N.G 0.00000000 -1.1373777 0.00000000 -2.300367 -1.273227 -0.7880335
## N.T -0.20362772 0.0000000 -2.14116832 0.000000 -2.195763 -1.3551147
## 19 20 21 22 23
## N.A -0.6954742 0.02613115 -0.06036771 -0.078491226 -0.02834222
## N.C -0.3831337 0.01732844 -0.13084210 -0.004369121 0.01581948
## N.G -0.7348231 -0.09556885 -0.06166659 -0.078179495 0.00000000
## N.T 0.0000000 0.00000000 0.00000000 0.000000000 0.02264111
##
## Nucleotide beta errors:
## 1 2 3 4 5
## N.A 0.0001669043 0.0000000000 0.0000000000 0.0000000000 0.000000000
## N.C 0.0000000000 0.0001801593 0.0001896914 0.0003483842 0.002801139
## N.G 0.0001947732 0.0001844927 0.0002775219 0.0003016154 0.002799640
## N.T 0.0001829294 0.0002112638 0.0001976593 0.0002510248 0.003743886
## 6 7 8 9 10 11
## N.A 0.025275530 0.03093768 0.00000000 0.05942295 0.000000000 0.0012421678
## N.C 0.019619708 0.01708057 0.07864534 0.00000000 0.033762733 0.0000000000
## N.G 0.000000000 0.02367669 0.02718122 0.02153340 0.003475114 0.0010118873
## N.T 0.005601832 0.00000000 0.03134919 0.01652822 0.006320910 0.0007347398
## 12 13 14 15 16 17
## N.A 0.0004586787 0.0007347398 0.006320910 0.01652822 0.03134919 0.00000000
## N.C 0.0000000000 0.0010118873 0.003475114 0.02153340 0.02718122 0.02367669
## N.G 0.0000000000 0.0000000000 0.033762733 0.00000000 0.07864534 0.01708057
## N.T 0.0004586787 0.0012421678 0.000000000 0.05942295 0.00000000 0.03093768
## 18 19 20 21 22
## N.A 0.005601832 0.003743886 0.0002510248 0.0001976593 0.0002112638
## N.C 0.000000000 0.002799640 0.0003016154 0.0002775219 0.0001844927
## N.G 0.019619708 0.002801139 0.0003483842 0.0001896914 0.0001801593
## N.T 0.025275530 0.000000000 0.0000000000 0.0000000000 0.0000000000
## 23
## N.A 0.0001829294
## N.C 0.0001947732
## N.G 0.0000000000
## N.T 0.0001669043
##
##
## An object of class 'Intercept'
## Fits intercept(s) for 1 round(s) (round = 8).
## Intercept beta values:
## Round.8:
## [1] 56.46355
##
## Intercept beta errors:
## Round.8:
## [1] 0.3122196
##
##
##
## An object of class 'Shape'
## Fits 69 shape coefficients for 3 kinds of shape parameter(s) (shape = HelT, MGW) for a feature model of length 23.
## Shape features are reverse complement symmetric.
## Shape beta values:
## 1 2 3 4
## Shape.HelTA 0.005981943 0.009069020 0.011136146 0.03531605
## Shape.HelTB -0.000897874 -0.010906646 -0.031052509 -0.02296732
## Shape.MGW 0.032019779 0.006116597 0.005650295 -0.16716164
## 5 6 7 8 9
## Shape.HelTA 0.017427450 0.03983680 -0.2156866 0.09001093 0.09273971
## Shape.HelTB -0.001829927 -0.09506269 0.5398271 0.64822213 -0.17485752
## Shape.MGW -0.085448275 0.01670212 -1.0706078 1.19118912 0.01788250
## 10 11 12 13 14
## Shape.HelTA 0.0573757 0.13448471 0.0124601 0.02696442 -0.2462028
## Shape.HelTB -0.2462028 0.02696442 0.0124601 0.13448471 0.0573757
## Shape.MGW -0.1474992 0.08024489 -0.1286951 0.08024489 -0.1474992
## 15 16 17 18 19
## Shape.HelTA -0.17485752 0.64822213 0.5398271 -0.09506269 -0.001829927
## Shape.HelTB 0.09273971 0.09001093 -0.2156866 0.03983680 0.017427450
## Shape.MGW 0.01788250 1.19118912 -1.0706078 0.01670212 -0.085448275
## 20 21 22 23
## Shape.HelTA -0.02296732 -0.031052509 -0.010906646 -0.000897874
## Shape.HelTB 0.03531605 0.011136146 0.009069020 0.005981943
## Shape.MGW -0.16716164 0.005650295 0.006116597 0.032019779
##
## Shape beta errors:
## 1 2 3 4
## Shape.HelTA 8.999298e-05 0.0001787779 0.0002057956 0.0002264385
## Shape.HelTB 1.725224e-04 0.0002014682 0.0002189092 0.0002828232
## Shape.MGW 2.808227e-04 0.0003602480 0.0003807528 0.0005557834
## 5 6 7 8 9
## Shape.HelTA 0.0002899303 0.0005844060 0.004395906 0.006784092 0.0016942057
## Shape.HelTB 0.0004140861 0.0007402322 0.004474182 0.012930047 0.0017048812
## Shape.MGW 0.0005964925 0.0008300913 0.008665898 0.018124367 0.0009522613
## 10 11 12 13
## Shape.HelTA 0.0009201542 0.0007432176 0.000816214 0.0007406419
## Shape.HelTB 0.0008272553 0.0007406419 0.000816214 0.0007432176
## Shape.MGW 0.0021731724 0.0010691851 0.001007959 0.0010691851
## 14 15 16 17 18
## Shape.HelTA 0.0008272553 0.0017048812 0.012930047 0.004474182 0.0007402322
## Shape.HelTB 0.0009201542 0.0016942057 0.006784092 0.004395906 0.0005844060
## Shape.MGW 0.0021731724 0.0009522613 0.018124367 0.008665898 0.0008300913
## 19 20 21 22
## Shape.HelTA 0.0004140861 0.0002828232 0.0002189092 0.0002014682
## Shape.HelTB 0.0002899303 0.0002264385 0.0002057956 0.0001787779
## Shape.MGW 0.0005964925 0.0005557834 0.0003807528 0.0003602480
## 23
## Shape.HelTA 1.725224e-04
## Shape.HelTB 8.999298e-05
## Shape.MGW 2.808227e-04
##
## [1] "Number of Observations in Design Matrix: 998790"
## [1] "Stability Reached after 5 iterations."
ModelTest <- finalizeFeatureBetas(ModelTest)
pM <- plot(ModelTest, plotTitle = "AR-DBD R8 Nucleotide+Shape 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 = ""))