repeated crossvalidations

32 files changed, 2191 insertions, 945 deletions

diff --git a/Makefile b/Makefile
index 766518c..4fd750e 100644
--- a/Makefile
+++ b/Makefile
@@ -1,9 +1,16 @@
+# Variables
+
+datasets = data/median-correlation.csv data/test.csv data/training.csv data/mazzatorta.csv data/swiss.csv data/test.json data/training.json data/mazzatorta.json data/swiss.json
+crossvalidations = data/training-cv-0.csv data/training-cv-1.csv data/training-cv-2.csv
+validations = data/training-test-predictions.csv $(crossvalidations)
+figures = figure/functional-groups.pdf  figure/test-prediction.pdf figure/test-correlation.pdf figure/crossvalidation.pdf figure/dataset-variability.pdf
+
 # Paper
 
 loael.pdf: loael.md references.bibtex
 	pandoc -r markdown+simple_tables+table_captions+yaml_metadata_block -s -S --bibliography=references.bibtex --latex-engine=pdflatex --filter pandoc-crossref --filter pandoc-citeproc -o loael.pdf loael.md
 
-loael.md: loael.Rmd figures # TODO: add further dependencies
+loael.md: loael.Rmd $(figures) $(datasets) $(validations) 
 	Rscript --vanilla -e "library(knitr); knit('loael.Rmd');"
 
 loael.docx: loael.md 
@@ -11,36 +18,36 @@ loael.docx: loael.md
 
 # Figures
 
-figures: datasets validations figure/functional-groups.pdf  figure/test-prediction.pdf figure/test-correlation.pdf figure/crossvalidation.pdf figure/dataset-variability.pdf
-
 figure/functional-groups.pdf: data/functional-groups-reduced4R.csv functional-groups.R
 	Rscript functional-groups.R
 
 figure/dataset-variability.pdf: data/mazzatorta.csv data/swiss.csv dataset-variability.R
 	Rscript dataset-variability.R
 
-figure/crossvalidation.pdf: data/combined-cv.csv 
+figure/crossvalidation.pdf: $(crossvalidations)
 	Rscript crossvalidation-plots.R
 
-figure/test-prediction.pdf: data/combined-test-predictions.csv data/median-correlation.csv  test-prediction-plot.R
+figure/test-prediction.pdf: data/training-test-predictions.csv data/median-correlation.csv  test-prediction-plot.R
 	Rscript test-prediction-plot.R
 
-figure/test-correlation.pdf: data/combined-test-predictions.csv data/median-correlation.csv  test-correlation-plot.R
+figure/test-correlation.pdf: data/training-test-predictions.csv data/median-correlation.csv  test-correlation-plot.R
 	Rscript test-correlation-plot.R
 
 # Validations
 
-validations: data/combined-test-predictions.csv data/combined-cv.csv
+data/training-test-predictions.csv: test-validation.rb data/test.csv data/training.csv
+	ruby test-validation.rb training.csv
 
-data/combined-test-predictions.csv: test-validation.rb data/test.csv data/combined.csv
-	ruby test-validation.rb combined.csv
+data/training-cv-0.csv: crossvalidation.rb data/training.csv
+	ruby crossvalidation.rb training.csv 0
 
-data/combined-cv.csv: crossvalidation.rb data/combined.csv
-	ruby crossvalidation.rb combined.csv
+data/training-cv-1.csv: crossvalidation.rb data/training.csv
+	ruby crossvalidation.rb training.csv 1
 
-# Datasets
+data/training-cv-2.csv: crossvalidation.rb data/training.csv
+	ruby crossvalidation.rb training.csv 2
 
-datasets: data/median-correlation.csv data/test.csv data/combined.csv data/mazzatorta.csv data/swiss.csv data/test.json data/combined.json data/mazzatorta.json data/swiss.json
+# Datasets
 
 data/functional-groups-reduced4R.csv: data/functional-groups-reduced.csv functional-groups4R.rb
 	ruby functional-groups4R.rb
@@ -57,11 +64,11 @@ data/test.json: data/mazzatorta.json
 	cp data/mazzatorta.json data/test.json
 
 # Combined training set
-data/combined.csv: create-combined.rb data/mazzatorta.csv data/swiss.csv
-	ruby create-combined.rb
+data/training.csv: create-training.rb data/mazzatorta.csv data/swiss.csv
+	ruby create-training.rb
 
-data/combined.json: data/mazzatorta.json 
-	cp data/mazzatorta.json data/combined.json
+data/training.json: data/mazzatorta.json 
+	cp data/mazzatorta.json data/training.json
 
 # Datasets with unique smiles
 data/mazzatorta.csv: unique-smiles.rb data/LOAEL_mg_corrected_smiles_mmol.csv 
@@ -77,4 +84,5 @@ data/swiss.json: data/swissRat_chron_LOAEL_mmol.json
 	cp data/swissRat_chron_LOAEL_mmol.json data/swiss.json
 
 clean:
+	rm figure/*pdf
 	cd data && rm `ls -I "*LOAEL*" -I "*functional*" -I "*SMARTS*"`
diff --git a/TODO b/TODO
index c6f49b5..0e671e5 100644
--- a/TODO
+++ b/TODO
@@ -17,23 +17,22 @@ warning if query compound contains new functional groups
 predictions fit experimental data
 
 #only combined dataset (training)
-combined -> training dataset
+#combined -> training dataset
 
-functional groups: remove, minfreq
+#functional groups: remove, minfreq
 
 smarts f functional groups
 
 error band (expermental variability) for fig 7
 
 # paper
-datasets:
-  rat chronic column
-  preprocessing remove first sentence
+#datasets:
+  #rat chronic column
+  #preprocessing remove first sentence
 
 results:
-  CheSMapper remove text, figures
-  light colors at bottom of histograms
-  fig 2 smiles -> smarts, leave out?
+  #CheSMapper remove text, figures
+  #fig 2 smiles -> smarts, leave out?
 
   variability from paolos paper
   https://en.wikipedia.org/wiki/Root_mean_square_deviation
diff --git a/create-combined.rb b/create-training.rb
index ef3c7a2..acba25b 100644
--- a/create-combined.rb
+++ b/create-training.rb
@@ -28,7 +28,7 @@ end
 
 data.sort!{|a,b| a[1] <=> b[1]}
 
-CSV.open(File.join(DATA,"combined.csv"),"w+") do |csv|
+CSV.open(File.join(DATA,"training.csv"),"w+") do |csv|
   csv << ["SMILES","LOAEL","Dataset"]
   data.each{|r| csv << r}
 end
diff --git a/crossvalidation-plots.R b/crossvalidation-plots.R
index 7b04984..2bc259f 100644
--- a/crossvalidation-plots.R
+++ b/crossvalidation-plots.R
@@ -1,7 +1,15 @@
 library(ggplot2)
+library(grid)
+library(gridExtra)
 
-combined = read.csv("data/combined-cv.csv",header=T)
+t0 = read.csv("data/training-cv-0.csv",header=T)
+t1 = read.csv("data/training-cv-1.csv",header=T)
+t2 = read.csv("data/training-cv-2.csv",header=T)
 
-p = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=combined,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0)  + xlim(-2,4.5) + ylim(-2,4.5)
+p0 = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=t0,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0)  + xlim(-2,4.5) + ylim(-2,4.5)
+p1 = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=t1,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0)  + xlim(-2,4.5) + ylim(-2,4.5)
+p2 = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=t2,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0)  + xlim(-2,4.5) + ylim(-2,4.5)
 
-ggsave(file='figure/crossvalidation.pdf', plot=p)
+pdf('figure/crossvalidation.pdf')
+grid.arrange(p0,p1,p2,ncol=2)
+dev.off()
diff --git a/crossvalidation.rb b/crossvalidation.rb
index 00fab96..79aeb83 100644
--- a/crossvalidation.rb
+++ b/crossvalidation.rb
@@ -2,12 +2,12 @@ require_relative 'include.rb'
 
 name = File.basename ARGV[0], ".csv"
 file = File.join DATA,ARGV[0]
-csv_file = File.join(DATA,ARGV[0].sub(/.csv/,'-cv.csv'))
-id_file = File.join(DATA,ARGV[0].sub(/.csv/,'-cv.id'))
 dataset = Dataset.from_csv_file file
 model = Model::LazarRegression.create(dataset, :prediction_algorithm => "OpenTox::Algorithm::Regression.local_fingerprint_regression")
 #model = Model::LazarRegression.create(dataset, :prediction_algorithm => "OpenTox::Algorithm::Regression.local_physchem_regression")
 #model = Model::LazarRegression.create(dataset, :prediction_algorithm => "OpenTox::Algorithm::Regression.local_weighted_average")
+csv_file = File.join(DATA,ARGV[0].sub(/.csv/,"-cv-#{ARGV[1]}.csv"))
+id_file = File.join(DATA,ARGV[0].sub(/.csv/,"-cv-#{ARGV[1]}.id"))
 cv = RegressionCrossValidation.create model
 File.open(id_file,"w+"){|f| f.puts cv.id}
 
diff --git a/data/combined-cv.csv b/data/combined-cv.csv
deleted file mode 100644
index 853ea0c..0000000
--- a/data/combined-cv.csv
+++ /dev/null
@@ -1,637 +0,0 @@
-SMILES,LOAEL_measured_median,LOAEL_predicted,Confidence
-ClC12C3C4(C(C1(Cl)Cl)(C1(C2(C3(Cl)C(C41Cl)(Cl)Cl)Cl)Cl)Cl)Cl,1.9566e-05,0.0013256949622119727,1
-ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C=C2)(Cl)Cl)Cl,2.7404e-05,0.0016651169838136368,1
-N#Cc1nn(c(c1S(=O)C(F)(F)F)N)c1c(Cl)cc(cc1Cl)C(F)(F)F,0.00013611,0.011177655078409487,1
-Clc1ccc2c(c1)[n+]([O-])nc(n2)n1cncc1,0.00020191,0.048911153468458216,1
-OC(=O)C(Oc1ccc(cc1)Oc1ncc(cc1Cl)C(F)(F)F)C,0.00027647,0.015673431840531892,1
-CCOP(=S)(SCSC(C)(C)C)OCC,0.00027736000000000004,0.0024055968139354584,1
-ClC1C2OC2C2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,0.00028896749999999995,0.0003872005608077999,1
-ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C1C2O1)(Cl)Cl)Cl,0.000295345,0.0010281938554488118,1
-CCSCCSP(=S)(OCC)OCC,0.00036443,0.0031857123201829984,1
-CNC(=O)ON=CC(SC)(C)C,0.00052559,0.048676702040700716,1
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-OC1CCCCCc2cc(O)cc(c2C(=O)OC(CCC1)C)O,0.00062036,1.3394560489313465,1
-ClC1C=CC2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,0.000843785,0.00023878471503217986,1
-c1ccc(cc1)[Sn](c1ccccc1)c1ccccc1,0.00085711,0.5915467780579419,1
-CCOP(=O)(SC(CC)C)SC(CC)C,0.000872805,0.006810211870769872,1
-CCS(=O)CCSP(=O)(OC)OC,0.00089328,0.0016027297178080409,1
-ClC1CC2C(C1Cl)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,0.000939545,0.000474144996915765,1
-COP(=S)(Oc1ccc(cc1)N(=O)=O)OC,0.00094982,0.007284727000192782,1
-Clc1c(Cl)c(Cl)c(c(c1Cl)Cl)Cl,0.0010183,0.10209707294282794,1
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-COP(=O)(SCCS(=O)(=O)CC)OC,0.0011438,0.0016027297178080409,1
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-ClC12C(Cl)(Cl)C3(C4(C1(Cl)C1(C2(Cl)C3(C4(C1(Cl)Cl)Cl)Cl)Cl)Cl)Cl,0.0012831,6.240058509303287e-05,0.625
-CCOP(=O)(N1CCSC1=O)SC(CC)C,0.0013411,0.004862125791750852,1
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diff --git a/data/combined-cv.id b/data/combined-cv.id
deleted file mode 100644
index 19697a4..0000000
--- a/data/combined-cv.id
+++ /dev/null
@@ -1 +0,0 @@
-56d593572b72ed384a000001
diff --git a/data/combined-test-predictions.csv b/data/combined-test-predictions.csv
deleted file mode 100644
index 326c916..0000000
--- a/data/combined-test-predictions.csv
+++ /dev/null
@@ -1,151 +0,0 @@
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diff --git a/data/combined-test-predictions.id b/data/combined-test-predictions.id
deleted file mode 100644
index ab6c881..0000000
--- a/data/combined-test-predictions.id
+++ /dev/null
@@ -1 +0,0 @@
-56d593502b72ed3787000004
diff --git a/data/training-cv-0.csv b/data/training-cv-0.csv
new file mode 100644
index 0000000..da27aae
--- /dev/null
+++ b/data/training-cv-0.csv
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diff --git a/data/training-cv-0.id b/data/training-cv-0.id
new file mode 100644
index 0000000..8a2a7c7
--- /dev/null
+++ b/data/training-cv-0.id
@@ -0,0 +1 @@
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diff --git a/data/training-cv-1.csv b/data/training-cv-1.csv
new file mode 100644
index 0000000..2625aed
--- /dev/null
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diff --git a/data/training-cv-1.id b/data/training-cv-1.id
new file mode 100644
index 0000000..074349d
--- /dev/null
+++ b/data/training-cv-1.id
@@ -0,0 +1 @@
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diff --git a/data/training-cv-2.csv b/data/training-cv-2.csv
new file mode 100644
index 0000000..7cbbd67
--- /dev/null
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diff --git a/data/training-cv-2.id b/data/training-cv-2.id
new file mode 100644
index 0000000..973b4af
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diff --git a/data/training-test-predictions.csv b/data/training-test-predictions.csv
new file mode 100644
index 0000000..ca34b33
--- /dev/null
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+O=C(C1(C)CCCCC1)Nc1ccc(c(c1Cl)Cl)O,0.96626,0.38098624299804507,1,training-prediction
+ClCCP(=O)(O)O,0.9723550000000001,2.415446889200535,1,training-prediction
+COc1cccc(c1C)C(=O)NN(C(C)(C)C)C(=O)c1cc(C)cc(c1)C,1.1154,0.7700149356098216,1,training-prediction
+CCC(c1noc(c1)NC(=O)c1c(OC)cccc1OC)(CC)C,1.5855,0.4882234386036716,1,training-prediction
+OC(=O)COc1nc(F)c(c(c1Cl)N)Cl,1.9605,0.07824793915942921,1,training-prediction
+CC(OC(=O)Nc1cccc(c1)Cl)C,2.3402,0.2668742137819341,1,training-prediction
+Oc1ccccc1c1ccccc1,3.1197,0.8616321068040766,1,training-prediction
+OC(=O)CNCP(=O)(O)O,5.5597,0.7008374592406026,0.16666666666666666,training-prediction
diff --git a/data/training-test-predictions.id b/data/training-test-predictions.id
new file mode 100644
index 0000000..2dcc43a
--- /dev/null
+++ b/data/training-test-predictions.id
@@ -0,0 +1 @@
+56d5de732b72ed162f000005
diff --git a/data/combined.csv b/data/training.csv
index b788ab5..b788ab5 100644
--- a/data/combined.csv
+++ b/data/training.csv
diff --git a/data/combined.json b/data/training.json
index 10b52ac..10b52ac 100644
--- a/data/combined.json
+++ b/data/training.json
diff --git a/figure/crossvalidation.pdf b/figure/crossvalidation.pdf
index b8f512b..5c31c0c 100644
--- a/figure/crossvalidation.pdf
+++ b/figure/crossvalidation.pdf
diff --git a/figure/dataset-variability.pdf b/figure/dataset-variability.pdf
index 26a2483..fdcddf8 100644
--- a/figure/dataset-variability.pdf
+++ b/figure/dataset-variability.pdf
diff --git a/figure/functional-groups.pdf b/figure/functional-groups.pdf
index 378d26d..ca189c8 100644
--- a/figure/functional-groups.pdf
+++ b/figure/functional-groups.pdf
diff --git a/figure/matching-ClC(C)Cl.png b/figure/matching-ClC(C)Cl.png
deleted file mode 100644
index 8641b24..0000000
--- a/figure/matching-ClC(C)Cl.png
+++ /dev/null
diff --git a/figure/test-correlation.pdf b/figure/test-correlation.pdf
index 8332ae9..6097332 100644
--- a/figure/test-correlation.pdf
+++ b/figure/test-correlation.pdf
diff --git a/figure/test-prediction.pdf b/figure/test-prediction.pdf
index 9fb9245..3403b8d 100644
--- a/figure/test-prediction.pdf
+++ b/figure/test-prediction.pdf
diff --git a/figure/unnamed-chunk-4-1.png b/figure/unnamed-chunk-4-1.png
deleted file mode 100644
index 27b9a79..0000000
--- a/figure/unnamed-chunk-4-1.png
+++ /dev/null
diff --git a/loael-variability.rb b/loael-variability.rb
deleted file mode 100644
index 5a8f1ca..0000000
--- a/loael-variability.rb
+++ /dev/null
@@ -1,61 +0,0 @@
-require_relative '../../lazar/lib/lazar'
-include OpenTox
-#$mongo.database.drop
-#$gridfs = $mongo.database.fs # recreate GridFS indexes
-# compare duplicates within datasets
-#old = Dataset.from_csv_file File.join(File.dirname(__FILE__),"regression","LOAEL_mmol_corrected_smiles.csv")
-old = Dataset.from_csv_file File.join(File.dirname(__FILE__),"regression","LOAEL_mg_corrected_smiles_mmol.csv")
-#new = Dataset.from_csv_file File.join(File.dirname(__FILE__),"regression","swissRat_chron_LOAEL.csv")
-new = Dataset.from_csv_file File.join(File.dirname(__FILE__),"regression","swissRat_chron_LOAEL_mmol.csv")
-#combined = Dataset.from_csv_file File.join(File.dirname(__FILE__),"regression","LOAEL-rat-combined.csv")
-
-compound_vector = []
-value_vector = []
-dataset_vector = []
-
-vars = []
-
-[old, new].each do |dataset|
-  vars << []
-  #vars[dataset.name] = []
-  p dataset.name
-  p dataset.compounds.size
-  p dataset.duplicates(dataset.features.first).size
-  dataset.duplicates.each do |cid,values|
-    R.assign "values", values
-    var = R.eval("var(-log(values))").to_f
-    vars.last << var
-    #smi = Compound.find(cid).smiles
-    smi = cid.to_s
-    values.each do |val|
-      compound_vector << smi
-      value_vector << - Math.log(val)
-      dataset_vector << dataset.name
-    end
-    #vars << { :var => var, :values => values, :smiles => smi }
-  end
-  #vars.sort!{|a,b| a[:var] <=> b[:var]}
-  #vars.each do |dup|
-    #dup[:values].each do |v|
-      #compound_vector << dup[:smiles]
-      #value_vector << v
-    #end
-  #end
-end
-p vars
-# TODO statistical test for variances
-R.assign "vars1", vars[0]
-R.assign "vars2", vars[1]
-print "p-value: #{R.eval("t.test(vars1,vars2)$p.value").to_f}"
-
-R.assign "smi", compound_vector
-R.assign "values", value_vector
-R.assign "dataset", dataset_vector
-R.eval "df <- data.frame(factor(smi),values,factor(dataset))"
-R.eval "df$smi <- reorder(df$factor.smi,df$values)"
-R.eval "img <- ggplot(df, aes(smi,values,ymin = min(values), ymax=max(values),color=dataset))"
-R.eval "img <- img + ylab('-log(LOAEL mg/kg_bw/day)') + xlab('Compound') + theme(axis.text.x = element_blank())"
-R.eval "img <- img + geom_point()"
-#R.eval "img <- img + scale_x_discrete(breaks=NULL) + geom_point() + coord_flip()"# + xlab('-log(LOAEL)'), ylab('Compound')"
-#R.eval "ggsave(file='/home/ch/opentox/lazar-nestec-data/loael_variance.svg', plot=img)"
-R.eval "ggsave(file='/home/ch/opentox/lazar-nestec-data/loael-variance.svg', plot=img,width=12, height=8)"
diff --git a/loael.Rmd b/loael.Rmd
index 5686698..8fb301f 100644
--- a/loael.Rmd
+++ b/loael.Rmd
@@ -41,7 +41,7 @@ Elena: do you have a reference and the name of the department?
 m = read.csv("data/mazzatorta.csv",header=T)
 s = read.csv("data/swiss.csv",header=T)
 t = read.csv("data/test.csv",header=T)
-c = read.csv("data/combined.csv",header=T)
+c = read.csv("data/training.csv",header=T)
 ```
 
 `r length(unique(t$SMILES))` compounds are common in both datasets and we use them as a test set in our investigation. For this test set we will
@@ -81,38 +81,42 @@ chemical structures.
 
 Elena + Swiss Federal Office contribution (input)
 
-The Swiss Federal Office dataset consists of `r length(s$SMILES)` LOAEL values
-for `r length(unique(s$SMILES))` unique chemical structures.
+The original Swiss Federal Office dataset has chronic toxicity data for rats,
+mice and multi generation effects. For the purpose of this study only rat LOAEL
+data was used. This leads to the *Swiss Federal Office*  dataset with `r length(s$SMILES)` rat LOAEL
+values for `r length(unique(s$SMILES))` unique chemical structures.
 
 ### Preprocessing
 
-Chemical structures in both datasets were initially represented as SMILES
-strings [@doi:10.1021/ci00057a005]. Syntactically incorrect and missing SMILES
-were generated from other identifiers (e.g names, CAS numbers). Unique smiles
-from the OpenBabel library [@OBoyle2011] were used for the identification of
-duplicated structures. 
+Chemical structures (represented as SMILES [@doi:10.1021/ci00057a005]) in both
+datasets were checked for correctness, syntactically incorrect and missing
+SMILES were generated from other identifiers (e.g names, CAS numbers). Unique
+smiles from the OpenBabel library [@OBoyle2011] were used for the
+identification of duplicated structures. 
 
 Studies with undefined or empty LOAEL entries were removed from the datasets.
-LOAEL values were converted to mmol/kg_bw/day units. For prediction, validation
-and visualisation purposes -log10 transformations are used.
-
-David: please check if we have missed something
+LOAEL values were converted to mmol/kg_bw/day units and rounded to five
+significant digits. For prediction, validation and visualisation purposes
+-log10 transformations are used.
 
 ### Derived datasets
 
 Two derived datasets were obtained from the original datasets: 
 
 The *test* dataset contains data of compounds that occur in both datasets.
-Exact duplications of LOAEL values were removed, because it is very likely that
-they originate from the same study.  The test dataset has `r length(t$SMILES)`
-LOAEL values for `r length(unique(t$SMILES))` unique chemical structures.
-
-The *combined* dataset is the union of the Mazzatorta and the Swiss Federal
-Office dataset and it is used to build predictive models. Exact LOAEL
-duplications were removed, as for the test dataset.  The combined dataset has
-`r length(c$SMILES)` LOAEL values for `r length(unique(c$SMILES))` unique
+LOAEL values equal at five significant digits were considered as duplicates
+originating from the same study/publication and only one instance was kept in
+the test dataset.  Exact duplications of LOAEL values were removed, because it
+is very likely that they originate from the same study.  The test dataset has
+`r length(t$SMILES)` LOAEL values for `r length(unique(t$SMILES))` unique
 chemical structures.
 
+The *training* dataset is the union of the Mazzatorta and the Swiss Federal
+Office dataset and it is used to build predictive models. LOAEL duplicates were
+removed, as for the test dataset.  The training dataset has `r
+length(c$SMILES)` LOAEL values for `r length(unique(c$SMILES))` unique chemical
+structures.
+
 Algorithms
 ----------
 
@@ -203,7 +207,7 @@ Prediction intervals were obtained from the `predict` function.
 
 For the comparison of experimental variability with predictive accuracies we
 are using a test set of compounds that occur in both datasets. The
-*Mazzatorta*, *Swiss Federal Office* and *combined* datasets are used as
+*Mazzatorta*, *Swiss Federal Office* and *training* datasets are used as
 training data for read across predictions. In order to obtain unbiased
 predictions *all* information from the test compound is removed from the
 training set prior to predictions. This procedure is hardcoded into the
@@ -231,11 +235,17 @@ baseline for evaluating prediction performance.
 ##### Ches-Mapper analysis
 
 We applied the visualization tool CheS-Mapper (Chemical Space Mapping and Visualization in 3D,
-http://ches-mapper.org, @Gütlein2012) to compare both datasets. CheS-Mapper can be used to analyze the relationship between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects. It embeds a dataset into 3D space, such that compounds with similar feature values are close to each other. CheS-Mapper is generic and can be employed with different kinds of features. [@fig:ches-mapper-pc] shows an embedding that is based on physico-chemical (PC) descriptors: we determined that both datasets have very similar PC feature values.
+http://ches-mapper.org, @Gütlein2012) to compare both datasets. CheS-Mapper can be used to analyze the relationship between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects. It embeds a dataset into 3D space, such that compounds with similar feature values are close to each other. CheS-Mapper is generic and can be employed with different kinds of features. [@fig:ches-mapper-pc] shows an embedding that is based on physico-chemical (PC) descriptors.
+
+![Compounds from the Mazzatorta and the Swiss Federal Office dataset are highlighted in red and green. Compounds that occur in both datasets are highlighted in magenta. ](figure/pc-small-compounds-highlighted.png){#fig:ches-mapper-pc}
+
+Martin: explain  light colors at bottom of histograms
 
+In this example, CheS-Mapper applied a principal components analysis to map compounds according to their physico-chemical (PC) feature values into 3D space. Both datasets have in general very similar PC feature values. As an exception, the Mazzatorta dataset includes most of the tiny compound structures: we have selected the 78 smallest compounds (with 10 atoms and less, marked with a blue box in the screen-shot) and found that 61 of these compounds occur in the Mazzatorta dataset, whereas only 19 are contained in the Swiss dataset (p-value 3.7E-7).
 
-We extended CheS-Mapper with a functionality to mine the same MolPrint2D features that are utilized for model building in this work. Applying a minimum frequency of 3 yields 760 distinguished MolPrint2D fragments for the composed dataset of 671 unique compounds. Again, a visual inspection confirmed that both datasets are structurally very similar. However, CheS-Mapper allows the detection of features that help to distinguish groups of selected compounds from the entire dataset. Hence, we found discriminating features for compounds that occur in only one of both datasets, and for the most active or in-active compounds (see [@tbl:molprint]). As an example, [@fig:ches-mapper-alert] shows 9 compounds that match a specific fragment (all other compounds in the dataset do not match this fragment) and have very low mean LOAEL values.
+This result was confirmed for structural features (fingerprints) including MolPrint2D features that are utilized for model building in this work.
 
+In general we concluded that both datasets are very similar, in terms of chemical structures and physico-chemical properties. 
 
 ##### Distribution of functional groups
 
@@ -311,9 +321,9 @@ The Mazzatorta, the Swiss Federal Office dataset and a combined dataset were use
 ![Comparison of experimental with predicted LOAEL values, each vertical line represents a compound, dots are individual measurements (red) or predictions (green).](figure/test-prediction.pdf){#fig:comp}
 
 ```{r echo=F}
-combined = read.csv("data/combined-test-predictions.csv",header=T)
-combined.r_square = round(rsquare(-log(combined$LOAEL_measured_median),-log(combined$LOAEL_predicted)),2)
-combined.rmse = round(rmse(-log(combined$LOAEL_measured_median),-log(combined$LOAEL_predicted)),2)
+training = read.csv("data/training-test-predictions.csv",header=T)
+training.r_square = round(rsquare(-log(training$LOAEL_measured_median),-log(training$LOAEL_predicted)),2)
+training.rmse = round(rmse(-log(training$LOAEL_measured_median),-log(training$LOAEL_predicted)),2)
 ```
 
 TODO: nr unpredicted, nr predictions outside of experimental values
@@ -325,16 +335,22 @@ These results are presented in [@fig:corr] and [@tbl:cv]. Please bear in mind th
 Training data | $r^2$                     | RMSE                    
 --------------|---------------------------|-------------------------
 Experimental | `r median.r.square`      | `r median.rmse`           
-Combined             | `r combined.r_square` | `r combined.rmse` 
+Combined             | `r training.r_square` | `r training.rmse` 
 
 : Comparison of model predictions with experimental variability. {#tbl:common-pred}
 
 ![Correlation of experimental with predicted LOAEL values (test set)](figure/test-correlation.pdf){#fig:corr}
 
 ```{r echo=F}
-combined = read.csv("data/combined-cv.csv",header=T)
-cv.combined.r_square = round(rsquare(-log(combined$LOAEL_measured_median),-log(combined$LOAEL_predicted)),2)
-cv.combined.rmse = round(rmse(-log(combined$LOAEL_measured_median),-log(combined$LOAEL_predicted)),2)
+t0 = read.csv("data/training-cv-0.csv",header=T)
+cv.t0.r_square = round(rsquare(-log(t0$LOAEL_measured_median),-log(t0$LOAEL_predicted)),2)
+cv.t0.rmse = round(rmse(-log(t0$LOAEL_measured_median),-log(t0$LOAEL_predicted)),2)
+t1 = read.csv("data/training-cv-1.csv",header=T)
+cv.t1.r_square = round(rsquare(-log(t1$LOAEL_measured_median),-log(t1$LOAEL_predicted)),2)
+cv.t1.rmse = round(rmse(-log(t1$LOAEL_measured_median),-log(t1$LOAEL_predicted)),2)
+t2 = read.csv("data/training-cv-2.csv",header=T)
+cv.t2.r_square = round(rsquare(-log(t2$LOAEL_measured_median),-log(t2$LOAEL_predicted)),2)
+cv.t2.rmse = round(rmse(-log(t2$LOAEL_measured_median),-log(t2$LOAEL_predicted)),2)
 ```
 TODO: repeated CV
 
@@ -343,7 +359,9 @@ All correlations are statistically highly significant with a p-value < 2.2e-16.
 
 Training dataset | $r^2$ | RMSE 
 -----------------|-------|------
-Combined | `r round(cv.combined.r_square,2)`  | `r round(cv.combined.rmse,2)` 
+Combined | `r round(cv.t0.r_square,2)`  | `r round(cv.t0.rmse,2)` 
+Combined | `r round(cv.t1.r_square,2)`  | `r round(cv.t1.rmse,2)` 
+Combined | `r round(cv.t2.r_square,2)`  | `r round(cv.t2.rmse,2)` 
 
 : 10-fold crossvalidation results {#tbl:cv}
 
diff --git a/loael.md b/loael.md
index 07efadd..9f6224e 100644
--- a/loael.md
+++ b/loael.md
@@ -73,38 +73,42 @@ chemical structures.
 
 Elena + Swiss Federal Office contribution (input)
 
-The Swiss Federal Office dataset consists of 493 LOAEL values
-for 381 unique chemical structures.
+The original Swiss Federal Office dataset has chronic toxicity data for rats,
+mice and multi generation effects. For the purpose of this study only rat LOAEL
+data was used. This leads to the *Swiss Federal Office*  dataset with 493 rat LOAEL
+values for 381 unique chemical structures.
 
 ### Preprocessing
 
-Chemical structures in both datasets were initially represented as SMILES
-strings [@doi:10.1021/ci00057a005]. Syntactically incorrect and missing SMILES
-were generated from other identifiers (e.g names, CAS numbers). Unique smiles
-from the OpenBabel library [@OBoyle2011] were used for the identification of
-duplicated structures. 
+Chemical structures (represented as SMILES [@doi:10.1021/ci00057a005]) in both
+datasets were checked for correctness, syntactically incorrect and missing
+SMILES were generated from other identifiers (e.g names, CAS numbers). Unique
+smiles from the OpenBabel library [@OBoyle2011] were used for the
+identification of duplicated structures. 
 
 Studies with undefined or empty LOAEL entries were removed from the datasets.
-LOAEL values were converted to mmol/kg_bw/day units. For prediction, validation
-and visualisation purposes -log10 transformations are used.
-
-David: please check if we have missed something
+LOAEL values were converted to mmol/kg_bw/day units and rounded to five
+significant digits. For prediction, validation and visualisation purposes
+-log10 transformations are used.
 
 ### Derived datasets
 
 Two derived datasets were obtained from the original datasets: 
 
 The *test* dataset contains data of compounds that occur in both datasets.
-Exact duplications of LOAEL values were removed, because it is very likely that
-they originate from the same study.  The test dataset has 375
-LOAEL values for 155 unique chemical structures.
-
-The *combined* dataset is the union of the Mazzatorta and the Swiss Federal
-Office dataset and it is used to build predictive models. Exact LOAEL
-duplications were removed, as for the test dataset.  The combined dataset has
-998 LOAEL values for 671 unique
+LOAEL values equal at five significant digits were considered as duplicates
+originating from the same study/publication and only one instance was kept in
+the test dataset.  Exact duplications of LOAEL values were removed, because it
+is very likely that they originate from the same study.  The test dataset has
+375 LOAEL values for 155 unique
 chemical structures.
 
+The *training* dataset is the union of the Mazzatorta and the Swiss Federal
+Office dataset and it is used to build predictive models. LOAEL duplicates were
+removed, as for the test dataset.  The training dataset has `r
+length(c$SMILES)` LOAEL values for 671 unique chemical
+structures.
+
 Algorithms
 ----------
 
@@ -195,7 +199,7 @@ Prediction intervals were obtained from the `predict` function.
 
 For the comparison of experimental variability with predictive accuracies we
 are using a test set of compounds that occur in both datasets. The
-*Mazzatorta*, *Swiss Federal Office* and *combined* datasets are used as
+*Mazzatorta*, *Swiss Federal Office* and *training* datasets are used as
 training data for read across predictions. In order to obtain unbiased
 predictions *all* information from the test compound is removed from the
 training set prior to predictions. This procedure is hardcoded into the
@@ -223,13 +227,17 @@ baseline for evaluating prediction performance.
 ##### Ches-Mapper analysis
 
 We applied the visualization tool CheS-Mapper (Chemical Space Mapping and Visualization in 3D,
-http://ches-mapper.org, @Gütlein2012) to compare both datasets. CheS-Mapper can be used to analyze the relationship between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects. It embeds a dataset into 3D space, such that compounds with similar feature values are close to each other. CheS-Mapper is generic and can be employed with different kinds of features. [@fig:ches-mapper-pc] shows an embedding that is based on physico-chemical (PC) descriptors: we determined that both datasets have very similar PC feature values.
+http://ches-mapper.org, @Gütlein2012) to compare both datasets. CheS-Mapper can be used to analyze the relationship between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects. It embeds a dataset into 3D space, such that compounds with similar feature values are close to each other. CheS-Mapper is generic and can be employed with different kinds of features. [@fig:ches-mapper-pc] shows an embedding that is based on physico-chemical (PC) descriptors.
+
+![Compounds from the Mazzatorta and the Swiss Federal Office dataset are highlighted in red and green. Compounds that occur in both datasets are highlighted in magenta. ](figure/pc-small-compounds-highlighted.png){#fig:ches-mapper-pc}
+
+Martin: explain  light colors at bottom of histograms
 
-![Compounds from the Mazzatorta and the Swiss dataset are highlighted in red and green. Compounds that occur in both datasets are highlighted in magenta. In this example, CheS-Mapper applied a principal components analysis to map compounds according to their physico-chemical (PC) feature values into 3D space. Both datasets have in general similar PC feature values. As an exception, the Mazzatorta dataset includes most of the tiny compound structures: we have selected the 78 smallest compounds (with 10 atoms and less, marked with a blue box in the screen-shot) and found that 61 of these compounds occur in the Mazzatorta dataset, whereas only 19 are contained in the Swiss dataset (p-value 3.7E-7).](figure/pc-small-compounds-highlighted.png){#fig:ches-mapper-pc}
+In this example, CheS-Mapper applied a principal components analysis to map compounds according to their physico-chemical (PC) feature values into 3D space. Both datasets have in general very similar PC feature values. As an exception, the Mazzatorta dataset includes most of the tiny compound structures: we have selected the 78 smallest compounds (with 10 atoms and less, marked with a blue box in the screen-shot) and found that 61 of these compounds occur in the Mazzatorta dataset, whereas only 19 are contained in the Swiss dataset (p-value 3.7E-7).
 
-We extended CheS-Mapper with a functionality to mine the same MolPrint2D features that are utilized for model building in this work. Applying a minimum frequency of 3 yields 760 distinguished MolPrint2D fragments for the composed dataset of 671 unique compounds. Again, a visual inspection confirmed that both datasets are structurally very similar. However, CheS-Mapper allows the detection of features that help to distinguish groups of selected compounds from the entire dataset. Hence, we found discriminating features for compounds that occur in only one of both datasets, and for the most active or in-active compounds (see [@tbl:molprint]). As an example, [@fig:ches-mapper-alert] shows 9 compounds that match a specific fragment (all other compounds in the dataset do not match this fragment) and have very low mean LOAEL values.
+This result was confirmed for structural features (fingerprints) including MolPrint2D features that are utilized for model building in this work.
 
-![A CheS-Mapper screen-shot showing 9 compounds that match the MolPrint2D fragment 15;1-1-1;2-2-1;2-1-15; (as SMILES syntax: ClC(C)Cl). Apart from the selected compound (blue box), the other 8 compounds belong to the top 10 percent of compounds with the lowest LOAEL values. I.e., this feature can be regarded as a structural alert in our dataset, as it is matched by only 9 compounds in the entire dataset and 8 of these compounds are highly active.](figure/matching-ClC(C)Cl.png){#fig:ches-mapper-alert}
+In general we concluded that both datasets are very similar, in terms of chemical structures and physico-chemical properties. 
 
 ##### Distribution of functional groups
 
@@ -239,7 +247,7 @@ In order to confirm the results of CheS-Mapper analysis we have evaluated the
 frequency of functional groups from the OpenBabel FP4
 fingerprint. [@fig:fg] shows the frequency of functional groups 
 in
-both datasets. Only 139 functional groups with a frequency > 25 are depicted, the complete table for all functional groups can be found in the
+both datasets. 139 functional groups with a frequency > 25 are depicted, the complete table for all functional groups can be found in the
 data directory of the supplemental material (`data/functional-groups.csv`).
  
 ![Frequency of functional groups.](figure/functional-groups.pdf){#fig:fg}
@@ -293,7 +301,7 @@ These results are presented in [@fig:corr] and [@tbl:cv]. Please bear in mind th
 Training data | $r^2$                     | RMSE                    
 --------------|---------------------------|-------------------------
 Experimental | 0.49      | 1.41           
-Combined             | 0.41 | 1.47 
+Combined             | 0.4 | 1.47 
 
 : Comparison of model predictions with experimental variability. {#tbl:common-pred}
 
@@ -307,7 +315,9 @@ All correlations are statistically highly significant with a p-value < 2.2e-16.
 
 Training dataset | $r^2$ | RMSE 
 -----------------|-------|------
-Combined | 0.39  | 1.84 
+Combined | 0.4  | 1.8 
+Combined | 0.38  | 1.84 
+Combined | 0.4  | 1.81 
 
 : 10-fold crossvalidation results {#tbl:cv}
 
diff --git a/loael.pdf b/loael.pdf
index 1cc1417..db69185 100644
--- a/loael.pdf
+++ b/loael.pdf
diff --git a/test-correlation-plot.R b/test-correlation-plot.R
index b8cbb76..74a2739 100644
--- a/test-correlation-plot.R
+++ b/test-correlation-plot.R
@@ -5,11 +5,11 @@ library(gridExtra)
 experimental <- read.csv("data/median-correlation.csv",header=T)
 p1 = qplot(-log10(mazzatorta),-log10(swiss),data=experimental,xlab="-log10(LOAEL Mazzatorta median)",ylab="-log10(LOAEL Swiss Federal Office median)",main="Experimental data") + geom_point() + geom_abline(intercept=0.0) + xlim(-1,4) + ylim(-1,4)
 
-combined = read.csv("data/combined-test-predictions.csv",header=T)
+training = read.csv("data/training-test-predictions.csv",header=T)
 
-p2 = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=combined,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0)  + xlim(-1,4) + ylim(-1,4)
+p2 = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=training,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0)  + xlim(-1,4) + ylim(-1,4)
 
 pdf('figure/test-correlation.pdf')
-grid.arrange(p1,p2,ncol=1)
+grid.arrange(p1,p2,ncol=1,respect=T)
 dev.off()
 
diff --git a/test-prediction-plot.R b/test-prediction-plot.R
index d1d6acb..4cee05e 100644
--- a/test-prediction-plot.R
+++ b/test-prediction-plot.R
@@ -1,13 +1,13 @@
 library(ggplot2)
 
-combined = read.csv("data/combined-test-predictions.csv",header=T)
+training = read.csv("data/training-test-predictions.csv",header=T)
 test <- read.csv("data/test.csv",header=T)
 n = c("SMILES","LOAEL","Source")
 
 data = data.frame(factor(test$SMILES),test$LOAEL,factor(test$Dataset))
 names(data) = n
 data$Type = "experimental"
-comb = data.frame(factor(combined$SMILES),combined$LOAEL_predicted,factor(combined$Dataset))
+comb = data.frame(factor(training$SMILES),training$LOAEL_predicted,factor(training$Dataset))
 names(comb) = n
 comb$Type = "predicted"
 data = rbind(data,comb)