From 8718f27bc8f7532d5c6a20d9ba758b1369529334 Mon Sep 17 00:00:00 2001 From: Christoph Helma Date: Sat, 13 Feb 2016 13:17:00 +0100 Subject: improved handling of duplicates in validations --- create_nestec_models.rb | 3 +- paper/Makefile | 5 + paper/create-training-test-sets.rb | 42 ++++ paper/loael.Rmd | 249 +++++++++++++++++++++++ paper/loael.md | 2 +- paper/loael.pdf | Bin 323717 -> 323718 bytes paper/predict-testset.rb | 19 ++ regression/common-test.csv | 392 +++++++++++++++++++++++++++++++++++++ 8 files changed, 710 insertions(+), 2 deletions(-) create mode 100644 paper/create-training-test-sets.rb create mode 100644 paper/loael.Rmd create mode 100644 paper/predict-testset.rb create mode 100644 regression/common-test.csv diff --git a/create_nestec_models.rb b/create_nestec_models.rb index d9fa299..bfcf468 100644 --- a/create_nestec_models.rb +++ b/create_nestec_models.rb @@ -8,7 +8,8 @@ include OpenTox #"Mouse_TD50.csv", #"LOAEL_mg_corrected_smiles_mmol.csv", #"swissRat_chron_LOAEL_mmol.csv", - "LOAEL-rat-combined.csv" + #"LOAEL-rat-combined.csv", + "LOAEL-rat-combined_median.csv", #"LOAEL_mmol_corrected_smiles.csv", #"swissMouse_chron_LOAEL_mmol.csv", #"swissMultigen_LOAEL_mmol.csv", diff --git a/paper/Makefile b/paper/Makefile index 5fa2f47..22018a3 100644 --- a/paper/Makefile +++ b/paper/Makefile @@ -4,6 +4,11 @@ loael.pdf: loael.md functional-groups.pdf loael-dataset-correlation.pdf loael.docx: loael.md functional-groups.pdf loael-dataset-correlation.pdf pandoc --filter pandoc-citeproc loael.md -s -o loael.docx +%.md: %.Rmd + Rscript --vanilla -e "library(knitr); knit('$<');" + +%.pdf: %.md + pandoc --filter pandoc-citeproc $< -s -o $@ functional-groups.pdf: functional-groups-reduced4R.csv functional-groups.R R CMD BATCH functional-groups.R diff --git a/paper/create-training-test-sets.rb b/paper/create-training-test-sets.rb new file mode 100644 index 0000000..1079341 --- /dev/null +++ b/paper/create-training-test-sets.rb @@ -0,0 +1,42 @@ +require_relative '../../lazar/lib/lazar' +include OpenTox +dirpath = File.join(File.dirname(__FILE__),"..","regression") +old = CSV.read File.join(dirpath,"LOAEL_mg_corrected_smiles_mmol.csv") +old.shift +new = CSV.read File.join(dirpath,"swissRat_chron_LOAEL_mmol.csv") +new.shift +p old.size +p new.size +# canonical smiles +old.collect!{|r| [Compound.from_smiles(r.first).smiles, r.last]} +new.collect!{|r| [Compound.from_smiles(r.first).smiles, r.last]} +old_compounds = old.collect{|r| r.first}.uniq +new_compounds = new.collect{|r| r.first}.uniq +p old_compounds.size +p new_compounds.size +common_compounds = (old_compounds & new_compounds).uniq +p common_compounds.size +common = [] +# TODO: canonical smiles?? +common_compounds.each do |smi| + old_rows = old.select{|r| r.first == smi} + new_rows = new.select{|r| r.first == smi} + common += old_rows + new_rows + old -= old_rows + new -= new_rows +end +header = ["SMILES","LOAEL"] +p old.size +p new.size +p common.size +{ + "mazzatorta-loael-training.csv" => old.uniq, + "swiss-loael-training.csv" => new.uniq, + "combined-training.csv" => (old+new).uniq, + "common-test.csv" => common.uniq, +}.each do |file,data| + CSV.open(File.join(dirpath,file),"w+") do |csv| + csv << header + data.each{|row| csv << row} + end +end diff --git a/paper/loael.Rmd b/paper/loael.Rmd new file mode 100644 index 0000000..65f9b34 --- /dev/null +++ b/paper/loael.Rmd @@ -0,0 +1,249 @@ +--- +author: | + Christoph Helma^1^, David Vorgrimmler^1^, Denis Gebele^1^, Martin Gütlein^2^, Benoit Schilter^3^, Elena Lo Piparo^3^ +title: | + Modeling Chronic Toxicity: A comparison of experimental variability with read across predictions +include-before: ^1^ in silico toxicology gmbh, Basel, Switzerland\newline^2^ Inst. f. Computer Science, Johannes Gutenberg Universität Mainz, Germany\newline^3^ Chemical Food Safety Group, Nestlé Research Center, Lausanne, Switzerland +keywords: (Q)SAR, read-across, LOAEL +date: \today +abstract: " " +documentclass: achemso +bibliography: references.bib +bibliographystyle: achemso +biblio-style: achemso +... + +Introduction +============ + +Christoph + Elena + Benoit + +The main objectives of this study are + +- to investigate the experimental variability of LOAEL data + +- develop predictive model for lowest observed effect levels + +- compare the performance of model predictions with experimental + variability + +Materials and Methods +===================== + +Datasets +-------- + +### Mazzatorta dataset + +Just referred to the paper 2008. + +### Swiss Federal Office dataset + +Elena + Swiss Federal Office contribution (input) + +Only rat LOAEL values were used for the current investigation, because +they correspond directly to the Mazzatorta dataset. + +### Preprocessing + +Christoph + +Chemical structures in both datasets are represented as SMILES strings +(Weininger 1988). Syntactically incorrect and missing SMILES were +generated from other identifiers (e.g names, CAS numbers) when possible. +Studies with undefined (“0”) or empty LOAEL entries were removed for +this study. + +Algorithms +---------- + +Christoph + +For this study we are using the modular lazar (*la*zy *s*tructure +*a*ctivity *r*elationships) framework (Maunz et al. 2013) for model +development and validation. + +lazar follows the following basic workflow: For a given chemical +structure it searches in a database for similar structures (neighbors) +with experimental data, builds a local (Q)SAR model with these neighbors +and uses this model to predict the unknown activity of the query +compound. This procedure resembles an automated version of *read across* +predictions in toxicology, in machine learning terms it would be +classified as a *k-nearest-neighbor* algorithm. + +Apart from this basic workflow lazar is completely modular and allows +the researcher to use any algorithm for neighbor identification and +local (Q)SAR modelling. Within this study we are using the following +algorithms: + +### Neighbor identification + +Christoph + +Similarity calculations are based on MolPrint2D fingerprints (Bender et +al. 2004) from the OpenBabel chemoinformatics library (OBoyle et al. +2011). + +The MolPrint2D fingerprint uses atom environments as molecular +representation, which resemble basically the chemical concept of +functional groups. For each atom in a molecule it represents the +chemical environment with the atom types of connected atoms. + +The main advantage of MolPrint2D fingerprints over fingerprints with +predefined substructures (such as OpenBabel FP3, FP4 or MACCs +fingerprints) is that it may capture substructures of toxicological +relevance that are not included in predefined substructure lists. +Preliminary experiments have shown that predictions with MolPrint2D +fingerprints are indeed more accurate than fingerprints with predefined +substructures. + +From MolPrint2D fingerprints we can construct a feature vector with all +atom environments of a compound, which can be used to calculate chemical +similarities. + +[//]: # https://openbabel.org/docs/dev/FileFormats/MolPrint2D_format.html#molprint2d-format + +The chemical similarity between two compounds is expressed as the +proportion between atom environments common in both structures and the +total number of atom environments (Jaccard/Tanimoto index (1)). + +(1) $sim = \frac{|A \cap B|}{|A \cup B|}$, $A$ atom environments of + compound A, $B$ atom environments of compound B. + +### Local (Q)SAR models + +Christoph + +As soon as neighbors for a query compound have been identified, we can +use their experimental LOAEL values to predict the activity of the +untested compound. In this case we are using the weighted mean of the +neighbors LOAEL values, where the contribution of each neighbor is +weighted by its similarity to the query compound. + +### Validation + +Christoph + +Results +======= + +### Dataset comparison + +Christoph + Elena + +The main objective of this section is to compare the content of both +databases in terms of structural composition and LOAEL values, to +estimate the experimental variability of LOAEL values and to establish a +baseline for evaluating prediction performance. + +#### Applicability domain + +##### Ches-Mapper analysis + +Martin + +CheS-Mapper (Chemical Space Mapping and Visualization in 3D, +http://ches-mapper.org/, (Gutlein, Karwath, and Kramer 2012)) can be +used to analyze the relationship between the structure of chemical +compounds, their physico-chemical properties, and biological or toxic +effects. CheS-Mapper embeds a dataset into 3D space, such that compounds +with similar feature values are close to each other. The following two +screenshots visualise the comparison. The datasets are embeded into 3D +Space based on structural fragments from three Smart list (OpenBabel +FP3, OpenBabel FP4 and OpenBabel MACCS). + +##### Distribution of functional groups + +Christoph + +Figure 1 shows the frequency of selected functional groups in both +datasets. A complete table for 138 functional groups from OpenBabel FP4 +fingerprints can be found in the appendix. + +![Frequency of functional groups.](functional-groups.pdf) + +### Experimental variability versus prediction uncertainty + +Christoph + +Duplicated LOAEL values can be found in both datasets and there is a +substantial overlap of compounds, with LOAEL values in both datasets. + +##### Intra dataset variability + +The Mazzatorta dataset has 562 LOAEL values with 439 unique structures, +the Swiss Federal Office dataset has 493 rat LOAEL values with 381 +unique structures. Figure 2 shows the intra-dataset variability, where +each vertical line represents a single compound and each dot represents +an individual LOAEL value. The experimental variance of LOAEL values is +similar in both datasets (p-value: 0.48). + +[//]: # p-value: 0.4750771581019402 + +![Intra dataset variability: Each vertical line represents a compound, dots are individual LOAEL values.](loael-dataset-comparison-all-compounds.pdf) + +##### Inter dataset variability + +Figure 3 shows the same situation for the combination of the Mazzatorta +and Swiss Federal Office datasets. Obviously the experimental +variability is larger than for individual datasets. + +![Inter dataset variability](loael-dataset-comparison-common-compounds.pdf) + + +##### LOAEL correlation between datasets + +Figure 4 depicts the correlation between LOAEL data from both datasets +(using means for multiple measurements). Correlation analysis shows a +significant correlation with r\^2: 0.61, RMSE: 1.22, MAE: 0.80 + +[//]: # MAE: 0.801626064534318 +[//]: # with identical values + +![LOAEL correlation](loael-dataset-correlation.pdf) + + +### Local (Q)SAR models + +Christoph + +Discussion +========== + +### Elena + Benoit + +### + +Summary +======= + +References +========== + +Bender, Andreas, Hamse Y. Mussa, and Robert C. Glen, and Stephan +Reiling. 2004. “Molecular Similarity Searching Using Atom Environments, +Information-Based Feature Selection, and a Naïve Bayesian Classifier.” +*Journal of Chemical Information and Computer Sciences* 44 (1): 170–78. +doi:[10.1021/ci034207y](https://doi.org/10.1021/ci034207y). + +Gütlein, Martin, Andreas Karwath, and Stefan Kramer. 2012. “CheS-Mapper +- Chemical Space Mapping and Visualization in 3D.” *Journal of +Cheminformatics* 4 (1): 7. +doi:[10.1186/1758-2946-4-7](https://doi.org/10.1186/1758-2946-4-7). + +Maunz, Andreas, Martin Gütlein, Micha Rautenberg, David Vorgrimmler, +Denis Gebele, and Christoph Helma. 2013. “Lazar: A Modular Predictive +Toxicology Framework.” *Frontiers in Pharmacology* 4. Frontiers Media +SA. +doi:[10.3389/fphar.2013.00038](https://doi.org/10.3389/fphar.2013.00038). + +OBoyle, Noel M, Michael Banck, Craig A James, Chris Morley, Tim +Vandermeersch, and Geoffrey R Hutchison. 2011. “Open Babel: An Open +Chemical Toolbox.” *Journal of Cheminformatics* 3 (1). Springer Science; +Business Media: 33. +doi:[10.1186/1758-2946-3-33](https://doi.org/10.1186/1758-2946-3-33). + +Weininger, David. 1988. “SMILES, a Chemical Language and Information +System. 1. Introduction to Methodology and Encoding Rules.” *Journal of +Chemical Information and Computer Sciences* 28 (1): 31–36. +doi:[10.1021/ci00057a005](https://doi.org/10.1021/ci00057a005). diff --git a/paper/loael.md b/paper/loael.md index ac7a50d..9926bec 100644 --- a/paper/loael.md +++ b/paper/loael.md @@ -142,7 +142,7 @@ baseline for evaluating prediction performance. ##### Ches-Mapper analysis -Christoph +Martin CheS-Mapper (Chemical Space Mapping and Visualization in 3D, http://ches-mapper.org/, (Gutlein, Karwath, and Kramer 2012)) can be diff --git a/paper/loael.pdf b/paper/loael.pdf index 93749fc..cbca010 100644 Binary files a/paper/loael.pdf and b/paper/loael.pdf differ diff --git a/paper/predict-testset.rb b/paper/predict-testset.rb new file mode 100644 index 0000000..7da6f31 --- /dev/null +++ b/paper/predict-testset.rb @@ -0,0 +1,19 @@ +require_relative '../../lazar/lib/lazar' +include OpenTox + +dir = File.join(File.dirname(__FILE__),"..","regression") +test = Dataset.from_csv_file(File.join(dir,"common-test.csv")) +[ + "LOAEL_mg_corrected_smiles_mmol.csv", + "swissRat_chron_LOAEL_mmol.csv", + "LOAEL-rat-combined.csv" +].each do |train| + file = File.join(dir,train) + params = { + :prediction_algorithm => "OpenTox::Algorithm::Regression.local_pls_regression", + } + dataset = Dataset.from_csv_file file + model = Model::LazarRegression.create dataset, params + validation = Validation.create model, dataset, test + puts "#{train}: #{validation.id.to_s}" +end diff --git a/regression/common-test.csv b/regression/common-test.csv new file mode 100644 index 0000000..864c4e6 --- /dev/null +++ b/regression/common-test.csv @@ -0,0 +1,392 @@ +SMILES,LOAEL +O=Cc1ccco1,0.624453213155231 +CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.18534506246313948 +CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.17607780933998252 +CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.24799169923196304 +CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.2557761861991325 +COCN(c1c(CC)cccc1CC)C(=O)CCl,0.05560351873894184 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