Biology – driven classification of Single-Cells RNAseq data in Galaxy
Creators: name: Bellenger, Léa
name: Saury, Mirca
name: Morvan-Dubois, Ghislaine
name: Chneiweiss, Hervé
name: El-Habr, Elias
name:  Naïra Naouar
name: Junier, Marie-Pierre
name: Antoniewski, Christophe
Publisher: name: F1000 Research Limited
Year: 2019

description: Single-cells RNA sequencing (scRNAseq) opens the possibility to quantify gene expression at a cell level, thereby allowing to identify specific patterns of gene expression levels in cell subpopulations of tissues or tumors. Expectations of biologists are high because, when relevant, these gene expression patterns may serve as proxy to capture the functional states of group of cells, thereby allowing to better understand the physiological processes in complex tissues, or to design better targeted therapies of cancers. However, capturing cellular functional states through scRNAseq and the classification of transcriptomes is limited by the curse of dimensionality, an expression first coined by Richard E. Bellman.<br><br>The problem is that obtaining a statistically sound classification of scRNAseq data of high dimension (the number of genes in transcriptomes) requires an exponential amount of data, which is not, and will likely never be, accessible. Thus, in high dimensional transcriptome data, single cells appear to be sparse and dissimilar in many ways. A common strategy to face the curse of dimensionality is to reduce the dimension of the data. This can be done using mathematical approaches that project the data in a lower dimensional space after filtering and statistical treatment (e.g. PCA, t-SNE). However, in a number of cases, these approaches fail to capture relevant functional states in the data. In these cases, incorporating a priori biology knowledge may further help to focus on a restricted set of functionally significant dimensions.<br><br>In collaboration with the Glial plasticity and Neuro-oncology team at the IBPS, we developed such a biology-driven data classification approach to analyse Glioblastomas, a class of extremely heterogeneous brain tumors, at single-cell resolution. We computed a score for each Glioblastoma single-cell transcriptome according to the expression of a set of genes which had been previously associated with the most aggressive state of the cells in independent studies. Cells were then split into two groups of “highly aggressive” and “lowly aggressive” cells and differential expression analysis between these two groups was performed using Mann-Whitney tests. The approach was applied to single-cell RNAseq from 4 glioblastomas [1] and the TCGA collection of bulk transcriptomes from 155 glioblastomas. Despite their very distinct origins (single-cell versus tissue RNAseq and 4 vs 155 tumors), we found a remarkable overlap between differentially expressed genes in these two series of datasets, thereby validating our biology-driven classification approach. With the aim to provide transparent and reproducible computational methods, we coded a set of functionally independent R scripts and wrapped these scripts into galaxy tools which will enrich the already available collection of tools for single-cells analyses in Galaxy. To further improve our biology-driven approach, we are currently working at classifying scRNAseq data using Pathway Deregulation Scores (PDS) computed with the Pathifier algorithm [2].<br><br>1. Darmanis S et al. 2017. Single-Cell RNA-Seq Analysis of Infiltrating Neoplastic Cells at the Migrating Front of Human Glioblastoma. Cell Rep Oct 31;21(5):1399-1410.<br><br>2. Drier et al. 2013. “Pathway-Based Personalized Analysis of Cancer.” Proceedings of the National Academy of Sciences of the United States of America 110 (16): 6388–93.