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Updated: May 8, 2026

Transcriptome Analysis of Single Cells
Published on: April 25, 2011
Missing data in single-cell transcriptomes reveals transcriptional shifts
Ruizhe Chen1, Yu-Che Chung2, Beth Kelly1
1Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
Abstract:
Profiling thousands of single cell transcriptomes is routine, yet cell prioritization based on response to biological perturbations is challenging and confounded by clustering, normalization and dimensionality reduction strategies. We developed a scoring approach independent of these obstacles that unbiasedly identifies distinct transcriptomes within a set based on missing data patterns, allowing cell prioritization and feature selection for downstream analysis. Our method applied to D. discoideum reveals a metabolic shift that marks the transition between the amoeboid and aggregated states of this model organism.
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