N. Alonso Moreda, A. Berral González, E. De La Rosa, Ó. González Velasco, J. M. Sánchez Santos, J. De Las Rivas
Recently, many detailed full transcriptomic studies on complex biological samples have been published and included in large gene expression repositories. However, the cellular heterogeneity does not allow the activity of specific genes and cell types to be identified. Thus, deconvolution methods have been developed to decompose cell mixtures into their primary components, identifying gene signatures and relative proportions. In this work, five deconvolution methods (CIBERSORT, FARDEEP, DECONICA, LINSEED and ABIS) were implemented to analyze blood, immune and cancer cells. Our study provides three analytical tools (corrplots, cell-signature plots and bar-mixture plots) that allow a thorough comparative analysis of the cell mixture data. As a result, CIBERSORT was a robust method for the identification of immune cell-types but not as efficient in the identification of cancer cells, whereas LINSEED was a very powerful unsupervised method to optimize specific gene markers selection.
Palabras clave: cell mixture; deconvolution; immune cells; blood cells; gene signature; bioinformatics
Programado
Bioestadística
8 de noviembre de 2023 17:20
HC4: Sala Sacristía