Configuration Parameters¶
Config parameters are grouped into sections and needed by the framework to function properly. In the following, we provide brief explanations for the existing parameters and their values
General¶
- name - framework name, mainly used for logging
- numCores (int) - number of available cores that can be used for parallel running of gene selectors
- homePath - contains the path to the framework
- inputDir - the path to the directory where all input data is located
- intermediateDir - where to put intermediate results (recommended not to changed)
- externalKbDir - where to put intermediate query results from knowledge bases (recommended not to changed)
- resultsDir - where to put the final results (recommended not to changed)
- preprocessing - where to put preprocessed data sets (recommended not to changed)
- crossVal_preprocessing - where to put preprocessed data sets for cross-validation (recommended not to changed)
- outputDir_name - name of the overall output directory. If that directory already exists, Comprior adds numbering.
- log_filename - name of the log file to which processing information, warnings, etc. are written
R¶
- code - path to R source files (recommended not to changed)
- RscriptLocation - path to Rscript
Java¶
- code - path to Java jar files (recommended not to changed)
- JavaLocation - path to Java
Dataset¶
- input - path to expression dataset
- metadata - path to metadata file for expression dataset
- classLabelName - specify the metadata column name of the label to use for classification (=keywords that will also be used for search in knowledge bases)
- alternativeSearchTerms - specify alternative search terms that will be used by knowledge bases. Separate search terms by spaces and replace spaces within a search term with _, e.g. “Breast Cancer Kidney_Cancer” will be parsed to the following search terms:”Breast”, “Cancer”, “Kidney Cancer”
- genesInColumns (true/false) - specify if genes are in the columns so that the data can be transformed automatically
- metadataIDsInColumns (true/false) - specify if sample IDs in the metadata file are in the columns
- dataSeparator (sep) - specify file separator (must be the same for metadata and gene expression), e.g. ,, t
- currentGeneIDFormat - specify the current gene ID format with g:Convert IDs, e.g. ENTREZGENE, ENSG, AFFY_HG_U133_PLUS_2, HGNC (see https://biit.cs.ut.ee/gprofiler/convert)
- finalGeneIDFormat - specify the gene ID format with g:Convert IDs you want to have in your gene rankings, e.g. HGNC (see https://biit.cs.ut.ee/gprofiler/convert)
Preprocessing¶
- filterMissingsInGenes (true/false) - filter genes that have a higher percentage of missing fields than specified in treshold parameter
- filterMissingsInSamples (true/false) - filter samples that have a higher percentage of missing fields than specified in the treshold parameter
- threshold ([0..100]) - percentage used for filtering
Gene Selection - General¶
- outputDirectory - where the final gene rankings are stored (recommended not to changed)
- selectKgenes (int) - maximum number of genes to select (to reduce runtimes). Must be >= topKmax param in Evaluation.
Gene Selection - Methods¶
traditional_methods - select multiple traditional gene selection methods (separated by spaces):
- filter: Random, InfoGain, ReliefF, VB-FS (R-based variance selection), ANOVA, mRMR, Variance (Python-based variance selection)
- wrapper: SVMpRFE (SVM-RFE with polykernel), x-SFS, x-RFE (x = add the desired classifier: K-nearest Neighbor (KNN3, KNN5), Naive Bayes (NB), Linear Regression (LR), Support Vector Machines with linear kernel (SVMl))
- embedded: RandomForest, Lasso
modifying_methods - combine knowledge base with traditional approach from above. Currently implemented:
- Postfilter_trad_kb: Filter the features selected by traditional approach (trad) by the features/genes retrieved from knowledge base (kb)
- Prefilter_trad_kb: Filter the input features by the genes retrieved from the knowledge base (kb) first and forward this reduced input set to the traditional approach (trad) for feature selection
- Extension_trad_kb: Extends features selected by traditional approach (trad) by features/genes retrieved from knowledge base (kb). Traditionally selected features and external features/genes make up 50% of top k features, respectively (so if topKmax param is 2, 1 feature from traditional approach, 1 feature from external will be selected)
- KBonly_kb: Only use the scores from the knowledge base to rank features, setting a default score of 0.000001.
combining_methods - select combining methods to apply. Combining methods should combine a traditional approach (trad) with a knowledge base (kb), the prefix indicates the actual combining strategy. Currently implemented:
- Weighted_trad_kb: weights the score from traditional approach trad by the score retrieved from knowledge base kb)
- LassoPenalty_kb: includes external knowledge via Lasso penalty as described by Zeng et al.: “Incorporating prior knowledge into regularized regression”
network_methods - select network approaches to apply. Currently implemented:
- NetworkActivity_kb: retrieves pathway from knowledge base kb, ranks them via average of (ANOVA of gene expression value/sample class) for every gene in pathway, and creates an activity score as new feature value for the pathway for every sample (= average of (gene expression value x variance x average of (Pearson correlation with network neighbors))) for all genes in the pathway)
- CorgsNetworkActivity_kb: retrieves pathway from knowledge base kb, ranks them via average of (ANOVA of gene expression value/sample class) for every gene in pathway, and creates an activity score as new feature value for the pathway for every sample as described by Lee et al.: “Inferring Pathway Activity toward Precise Disease Classification”
Evaluation¶
topKmin (int) - minimum number of features to select
topKmax (int) - maximum number of features to select
kfold (int) - k parameter for k-fold cross-validation during classification
results - where to put the results (recommended not to changed)
reducedDataset - where to put the reduced data sets (with k features) (recommended not to changed)
preanalysis - where to put plots created during preanalysis
preanalysis_plots - create plots on input data before any analysis. Currently implemented:
- density: density plot showing the average density distribution of expression values (per class)
- box: box plot showing the average gene expression (per class)
- mds: multidimensional scaling plot showing dis-/similarities between samples
evaluateKBcoverage (true/false) - create diagrams showing coverage of search terms in the used knowledge bases
robustnessResults - where to put the cross-validation results (recommended not to changed)
enableCrossEvaluation (true/false) - whether to use a second data set for cross-validation
crossEvaluationData - path to second data set for cross-validation (must have genes in columns and be already labeled)
crossEvaluationClassLabel - column name of second data set for cross-validation containing the class label
crossEvaluationGeneIDFormat - current g:Convert gene ID format of the second data set for cross-validation (see https://biit.cs.ut.ee/gprofiler/convert)
enableClassification (true/false) - use classification algorithms for evaluation
enablePrediction (true/false) - use predictive algorithms for evaluation (functionality not implemented yet)
Rankings¶
results - where to put the actual rankings (recommended not to change)
metricsDir - where to put the metric results (recommended not to change)
annotationsDir - where to put the annotation information(recommended not to change)
metrics - specify which evaluation metrics to apply/compare on feature rankings. Currently provided:
- top_k_overlap: overlap of top k features of rankings
- kendall_w: Kendall’s W ranking comparison
- fleiss_kappa: Fleiss’ Kappa ranking comparison
- annotation_overlap: shows gene annotation overlap in rankings (e.g. which annotated genes where found jointly by all rankings)
- enrichment_overlap: shows enrichment term overlap of rankings (e.g. which enrichment terms where found jointly by all rankings)
- annotation_percentage: compares average p
- average_foldchange: compares average fold change of genes in rankings
Classification¶
classifiers - specify classifiers to use for classsification task. Currently provided:
- KNN(int): K-nearest Neighbor, e.g. KNN3
- NB: Naive Bayes
- LR: Linear Regression
- SMO: Support Vector Machines
- RF: Random Forest
metrics - specify which evaluation metrics to apply on classification results. Currently provided:
- accuracy
- sensitivity
- specificity
- F1
- kappa
- AUROC
- precision
- matthewcoef: Matthews Correlation Coefficient
results - where to put the classification results (recommended not to change)
crossEvaluationDir - where to put classification results from cross-validation (recommended not to change)
metricsDir - where to put the evaluation results (recommended not to change)
Prediction (not implemented yet)¶
- predictors - specify predictors to use for prediction task (still under construction)
- metrics - specify which evaluation metrics to apply on prediction results (still under construction)
- results - where to put the classification results (recommended not to change)
- crossEvaluationDir - where to put classification results from cross-validation (recommended not to change)
- metricsDir - where to put the evaluation results (recommended not to change)
Enrichr¶
- webservice_uri - URL of Enrichr web service (recommended not to change)
- outputDir - output directory for intermediate results from web service (recommended not to change)
- geneSetLibrary - gene set library to use for annotation/enrichment. Choose any available at https://maayanlab.cloud/Enrichr/#stats
OpenTargets¶
- outputDir - output directory for intermediate results from web service (recommended not to change)
KEGG¶
- outputDir - output directory for intermediate results from web service (recommended not to change)
- maxNumPathways (int) - specify the maximum number of pathways to retrieve per search term (for performance reasons)
UMLS (needed to transform search terms into CUIs for using DisGeNET)¶
- login_uri - URL of login web service (recommended not to change)
- loginservice_uri - URL of login web service (recommended not to change)
- auth_endpoint - authentication endpoint for API (recommended not to change)
- apikey - API key for accessing UMLS (recommended not to change unless own API key available, register for free at UMLS to create an own key)
- webservice_uri - URL of UMLS web service (recommended not to change)
DisGeNET¶
- associationScore (score/gene_dsi/gene_dpi) - which association score to use for knowledge retrieval: score (overall score), gene_dsi (disease specificity), gene_dpi (disease pleiotropy)
- webservice_url - URL of DisGeNET web service (recommended not to change)
- outputDir - output directory for intermediate results from web service (recommended not to change)
- apikey - API key for accessing DisGeNET (recommended not to change unless own API key is available, register at DisGeNET to create your own key)
PathwayCommons¶
- webservice_url - URL of PathwayCommons web service (recommended not to change)
- outputDir - output directory for intermediate results from web service (recommended not to change)
- maxNumPathways (int) - specify the maximum number of pathways to retrieve per search term (for performance reasons)
BiomaRt¶
- outputDir - output directory for intermediate results from web service (recommended not to change)
gConvert¶
- webservice_url - URL of g:Convert web service (recommended not to change)
- outputDir - output directory for intermediate results from web service (recommended not to change)