Deciphering cell-type heterogeneity is crucial for systematically understanding tissue
homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient …

Motivation Principal component analysis (PCA) of genetic data is routinely used to infer
ancestry and control for population structure in various genetic analyses. However …

Obtaining a standardized benchmark of computational methods is a major issue in data-
science communities. Dedicated frameworks enabling fair benchmarking in a unified …

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk
expression of tumor samples, through a community-wide DREAM Challenge. We assess six …

Tumor deconvolution enables the identification of diverse cell types that comprise solid
tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and …

Background Codabench [1] is an open-source web-based portal developed to address the
growing difficulties of reproducibility and comparability in the data science communities …

Cellular heterogeneity in biological tissues reflects progression of disease state and is
therefore useful for improved diagnostic and prognosis. Cellular composition of tissues is …

The goal of this HDR thesis is to summarize my research activity over the past five years and
to present my future scientific prospects. My focus will be on studying tumor heterogeneity …