Defining Cell-Type Specificity at the Transcriptional Level in Human Disease
Downloaded from genome.cshlp.org on September 30, 2021 - Published by Cold Spring Harbor Laboratory Press Method Defining cell-type specificity at the transcriptional level in human disease Wenjun Ju,1,2,9 Casey S. Greene,3,4,5,9 Felix Eichinger,1 Viji Nair,1 Jeffrey B. Hodgin,6 Markus Bitzer,1 Young-suk Lee,3,4 Qian Zhu,3,4 Masami Kehata,7 Min Li,7 Song Jiang,1 Maria Pia Rastaldi,7 Clemens D. Cohen,8 Olga G. Troyanskaya,3,4,10 and Matthias Kretzler1,2,10 1Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan 48109, USA; 2Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA; 3Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA; 4Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA; 5Department of Genetics, The Geisel School of Medicine at Dartmouth, Hanover, New Hampshire 03755, USA; 6Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA; 7Laboratorio di Ricerca Nefrologica, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milano, Italy; 8Division of Nephrology, University of Zurich, 8057 Zurich, Switzerland Cell-lineage–specific transcripts are essential for differentiated tissue function, implicated in hereditary organ failure, and mediate acquired chronic diseases. However, experimental identification of cell-lineage–specific genes in a genome-scale manner is infeasible for most solid human tissues. We developed the first genome-scale method to identify genes with cell- lineage–specific expression, even in lineages not separable by experimental microdissection. Our machine-learning–based approach leverages high-throughput data from tissue homogenates in a novel iterative statistical framework.
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