An article published in GenomeBiology presents CimpleG: a computational framework for the detection of small CpG methylation signatures used for cell-type classification and deconvolution.
] and it needs to be installed prior to running minfi to deconvolve the data. To perform deconvolution it first computes which probes would be best to perform this task. By default, for the type of data in this study, it selects the 100 probes with the highest magnitude in effect between contrasts as selected by its F-stat p-value threshold
To process these data, we have acquired the raw data from GEO. We have pre-processed the data with the data processing pipeline from SeSAMe [ In the experiments where we needed to combine the leukocyte subsets into larger groups, we simply summed up the predictions of the more refined subsets into the combined groups. We have combined neutrophils, eosinophils and basophils into granulocytes ; B naive cells and B memory cells into B cells ; CD4 T naive cells, CD4 T memory cells and T regulatory cells into CD4 T cells ; and finally, CD8 T naive cells and CD8 T memory cells into CD8 T cells.
First we focus on the methods that need to be trained. These are IDOL and CimpleG . We train CimpleG, CimpleG.10, elasticnet , boostedtree , neuralnet , randforest and decisiontree with the “Purified 12 leukocytes data” with default parameters. The exception being the trainonly parameter which was set to “TRUE” as this dataset is too small for a train-test split to be performed. Here, the cross-validation step described in the “CimpleG for signature selection” section is still performed.
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