Single Cell analysis pipeline

Single Cell analysis pipeline
Prof. Spang (Bioinformatik)
Robert Bosek
in Bearbeitung
Bioinformatik, Datenverarbeitung, Automatisierung


The gene expression profile of a tissue averages the expression profiles of all cells in this tissue. Digital tissue deconvolution (DTD) addresses the following inverse problem: Given the expression profile y of a tissue, what is the cellular composition c of that tissue? If X is a matrix whose columns are reference profiles of individual cell types, the com-position c can be computed by minimizing L(y−Xc) for a given loss function. In Görtler et al. 2018 loss-function learning digital tissue deconvolution (DTD) has been introduced. It adapts a deconvolution model to tissue by utilizing artificial mix-tures of single cell profiles.

Zielsetzung der Arbeit

The upcoming years will produce big resources of single cell data for all cell types oft he human body. There are public available resources e.g. the Human cell atlas These data sets consist of hundreds of thousands of single cell profiles. There are open question regarding the structure oft he data and the way to process it.

Konkrete Aufgaben

Understand the structure of HCA data sets. • Provide an R implementation to process the data to labelled count matrices • Show how subtle the profiles can be assigned to cell subgroups, until they can not be distinguish via DTD.

Erwartete Vorkenntnisse

Wissenschaftlicher Anspruch: praktischer Erkenntnisgewinn durch Automati-sieren der Datenaufbereitung • Praktischer Anteil: R-Implementation • Klare Aufgabenstellung • Thema aus der Medieninformatik: Softwaretechnik im Kontext Machine Learn-ing • Adäquater Umfang

Weiterführende Quellen

Görtler, F., et al. (2018). Loss-Function Learning for Digital Tissue Deconvolution Regev, A., et al. (2017). The Human Cell Atlas

arbeiten/single_cell_analysis_pipeline.txt · Zuletzt geändert: 01.04.2019 14:02 von Christian Wolff
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