Mononucleotide+Shape Reverse Complement-Symmetric Example

Gabriella Martini

2016-07-10

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)

some text

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 = ""))

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