<> "The repository administrator has not yet configured an RDF license."^^ . <> . . "Cell type classification for multi-sample multi-condition\r\ncomparisons in single-cell RNA sequencing data"^^ . "Multicellular organisms require specialized cell types in order to function.\r\nWhile a widely accepted definition does not exist, cell types are regarded\r\nas groups of cells with similar properties, such as RNA expression, protein\r\nabundance and epigenetic modification.\r\nSingle-cell RNA sequencing (scRNAseq) is a recent breakthrough for explor-\r\ning cell types, providing expression estimates for all genes in thousands of\r\nindividual cells. Using data-driven algorithms, such as unsupervised clus-\r\ntering, scRNAseq has discovered new cell types and created large reference\r\ndata sets, next to other exploratory achievements. More recently, scRNA-\r\nseq was applied to patient cohorts that include different groups, for example\r\ndisease and healthy or disease subtypes. These multi-sample multi-condition\r\ndata sets enable statistical inferences between groups, such as differential ex-\r\npression testing. In contrast to projects exploring unknown tissues or species,\r\npatient cohorts often study known cell types defined by specific marker genes.\r\nHere, I present Pooled Count Poisson Classification (PCPC), a novel cell\r\ntype classification approach designed for inference with multi-sample multi-\r\ncondition scRNAseq data sets. PCPC implements a statistical model that\r\nallows researchers to distinguish cells according to marker-based cell type\r\ndefinitions, enabling reproducible and comparable analysis between data sets\r\nand technologies (e.g. scRNAseq and flow cytometry). Specifically, PCPC\r\npools marker gene counts across related cells to overcome technical noise,\r\nand compares them to a user-defined threshold using the Poisson model.\r\nIn this work, I apply PCPC to three different data sets to demonstrate its\r\nutility. The first application shows it is able to annotate all lineages in data\r\nfrom human cord blood mononuclear cells (CBMCs), with a single marker\r\ngene per cell type.\r\nThe second application shows PCPC is able to discriminate fine cell type sub-\r\nsets, using data from a human tumor of mucosa-associated lymphoid tissue\r\n(MALT). Many cell types in the MALT tumor microenvironment, and T cell\r\nsubsets in particular, are transcriptionally related, making their classification\r\ndifficult. In spite of this challenging complexity, PCPC can even use lowly\r\nexpressed marker genes, such as FOXP3 marking CD3E + CD4 + FOXP3 + reg-\r\nulatory T (T reg ) cells. Furthermore, I find T reg cells isolated from the MALT\r\ntumor can further be subdivided into CCR7 + and ICOS + subsets, indicating\r\na mixture of naive-like and activated T reg cells. In comparison to unsuper-\r\nvised clustering and the marker-based tool Garnett, classification with PCPC\r\nhas more flexibility and fewer misclassifications, respectively. Thus, PCPC\r\nremoves obstacles in studying complex tissues with scRNAseq, such as the\r\nmicroenvironment in human tumors.\r\nFurthermore, I demonstrate a multi-sample multi-condition comparison using\r\ndata from a patient cohort of aggressive and indolent lymphoma subtypes.\r\nPCPC is applied to classify CD3E + CD8B + cytotoxic T cells, followed by\r\ndifferential expression testing between the aggressive and indolent subtypes.\r\nThis uncovers significantly lower LGALS1 expression in indolent tumors,\r\nfurther implicating this gene in tumor aggressiveness and T cell inhibition.\r\nCurrently, PCPC requires data generated with unique molecular identifiers\r\n(UMI), as well as substantial manual work. Due to its ability to resolve com-\r\nplex tissues with few marker genes, PCPC may bring clarity to transcrip-\r\ntomic cell type definitions and prove useful for multi-sample multi-condition\r\ncomparisons in scRNAseq data."^^ . "2021" . . . . . . . . "Felix"^^ . "Frauhammer"^^ . "Felix Frauhammer"^^ . . . . . . "Cell type classification for multi-sample multi-condition\r\ncomparisons in single-cell RNA sequencing data (Other)"^^ . . . . . . "preview.jpg"^^ . . . "Cell type classification for multi-sample multi-condition\r\ncomparisons in single-cell RNA sequencing data (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "Cell type classification for multi-sample multi-condition\r\ncomparisons in single-cell RNA sequencing data (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "Cell type classification for multi-sample multi-condition\r\ncomparisons in single-cell RNA sequencing data (Other)"^^ . . . . . . "preview.jpg"^^ . . . "Cell type classification for multi-sample multi-condition\r\ncomparisons in single-cell RNA sequencing data (Other)"^^ . . . . . . "medium.jpg"^^ . . . "Cell type classification for multi-sample multi-condition\r\ncomparisons in single-cell RNA sequencing data (Other)"^^ . . . . . . "small.jpg"^^ . . . "Cell type classification for multi-sample multi-condition\r\ncomparisons in single-cell RNA sequencing data (PDF)"^^ . . . "Thesis_Frauhammer_blacklinks.pdf"^^ . . . "Cell type classification for multi-sample multi-condition\r\ncomparisons in single-cell RNA sequencing data (PDF)"^^ . . . "Thesis_Frauhammer.pdf"^^ . . . "Cell type classification for multi-sample multi-condition\r\ncomparisons in single-cell RNA sequencing data (Other)"^^ . . . . . . "indexcodes.txt"^^ . . . "Cell type classification for multi-sample multi-condition\r\ncomparisons in single-cell RNA sequencing data (Other)"^^ . . . . . . "indexcodes.txt"^^ . . . "Cell type classification for multi-sample multi-condition\r\ncomparisons in single-cell RNA sequencing data (Other)"^^ . . . . . . "small.jpg"^^ . . . "Cell type classification for multi-sample multi-condition\r\ncomparisons in single-cell RNA sequencing data (Other)"^^ . . . . . . "medium.jpg"^^ . . "HTML Summary of #30977 \n\nCell type classification for multi-sample multi-condition \ncomparisons in single-cell RNA sequencing data\n\n" . "text/html" . . . "500 Naturwissenschaften und Mathematik"@de . "500 Natural sciences and mathematics"@en . .