Genes sharing the protein family domain decrease the performance of classification with RNA-seq genomic signatures

Biology Direct
Anna LeśniewskaMichal J Okoniewski

Abstract

The experience with running various types of classification on the CAMDA neuroblastoma dataset have led us to the conclusion that the results are not always obvious and may differ depending on type of analysis and selection of genes used for classification. This paper aims in pointing out several factors that may influence the downstream machine learning analysis. In particular those factors are: type of the primary analysis, type of the classifier and increased correlation between the genes sharing a protein domain. They influence the analysis directly, but also interplay between them may be important. We have compiled the gene-domain database and used it for analysis to see the differences between the genes that share a domain versus the rest of the genes in the datasets. The major findings are: pairs of genes that share a domain have an increased Spearman's correlation coefficients of counts; genes sharing a domain are expected to have a lower predictive power due to increased correlation. For most of the cases it can be seen with the higher number of misclassified samples; classifiers performance may vary depending on a method, still in most cases using genes sharing a domain in the training set results in a higher misclass...Continue Reading

References

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Datasets Mentioned

BETA
GSE50760
GSE87340
GSE22260

Methods Mentioned

BETA
RNA-seq
RNA-deq

Software Mentioned

Ensembl
featureCount
MLInterfaces R
Hisat2
Subread
AceView
SRA Toolkit
Bioconductor
R
CAMDA

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