Burman, B. | bioRxiv Science

bioRxiv2022

TCRi: Information theoretic metrics for single cell RNA and TCR sequencing in cancer

Single-cell T cell repertoire sequencing can pair both T cell receptor (TCR) and gene expression sequence data, providing an enriched view of T cell behavior. This powerful tool can identify and characterize specific clonotypes and phenotypes as well as track their changes in response to therapy, such as immune checkpoint blockade (ICB). We present a novel information theoretic framework called TCRi for characterizing single cell T cell repertoires by formalizing the relationship between clonotype and phenotype in a joint probability distribution. Our strategy allows for the identification of subpopulations of T cells and jointly quantifies their TCR and expression profiles in response to stimuli, in addition the framework tracks the phenotypic changes in individual T cell clones over time. We applied this framework to four datasets of T cells sequenced from cancer patients treated with anti-PD-(L)1 ICB immunotherapies and examined evolution of T cell responses pre- and post-treatment. Quantitative of phenotypic and clonotypic entropy analysis with TCRi demonstrated improvements in characterization of the transcriptional signature of clonotypes. Furthermore, TCRi highlighted the importance of phenotypic flux and specific T-cell phenotypes as determinants of therapeutic response.

bioinformatics↗

bioRxiv2022

GeneVector: Identification of transcriptional programs using dense vector representations defined by mutual information.

Deciphering individual cell phenotypes from cell-specific transcriptional processes requires high dimensional single cell RNA sequencing. However, current dimensionality reduction methods aggregate sparse gene information across cells, without directly measuring the relationships that exist between genes. By performing dimensionality reduction with respect to gene co-expression, low-dimensional features can model these gene-specific relationships and leverage shared signal to overcome sparsity. We describe GeneVector, a scalable framework for dimensionality reduction implemented as a vector space model using mutual information between gene expression. Unlike other methods, including principal component analysis and variational autoencoders, GeneVector uses latent space arithmetic in a lower dimensional gene embedding to identify transcriptional programs and classify cell types. In this work, we show in four single cell RNA-seq datasets that GeneVector was able to capture phenotypespecific pathways, perform batch effect correction, interactively annotate cell types, and identify pathway variation with treatment over time.

bioinformatics↗