A Comparative Analysis of Gene Expression Profiling by Statistical and
  Machine Learning Approaches
Myriam Bontonou Anaïs Haget Maria Boulougouri Benjamin Audit Pierre Borgnat Jean-Michel Arbona
Many machine learning models have been proposed to classify phenotypes from
gene expression data. In addition to their good performance, these models can
potentially provide some understanding of phenotypes by extracting explanations
for their decisions. These explanations often take the form of a list of genes
ranked in order of importance for the predictions, the highest-ranked genes
being interpreted as linked to the phenotype. We discuss the biological and the
methodological limitations of such explanations. Experiments are performed on
several datasets gathering cancer and healthy tissue samples from the TCGA,
GTEx and TARGET databases. A collection of machine learning models including
logistic regression, multilayer perceptron, and graph neural network are
trained to classify samples according to their cancer type. Gene rankings are
obtained from explainability methods adapted to these models, and compared to
the ones from classical statistical feature selection methods such as mutual
information, DESeq2, and EdgeR. Interestingly, on simple tasks, we observe that
the information learned by black-box neural networks is related to the notion
of differential expression. In all cases, a small set containing the
best-ranked genes is sufficient to achieve a good classification. However,
these genes differ significantly between the methods and similar classification
performance can be achieved with numerous lower ranked genes. In conclusion,
although these methods enable the identification of biomarkers characteristic
of certain pathologies, our results question the completeness of the selected
gene sets and thus of explainability by the identification of the underlying
biological processes.