Nov 4, 2018

Fast, sensitive, and accurate integration of single cell data with Harmony

BioRxiv : the Preprint Server for Biology
Ilya KorsunskySoumya Raychaudhuri

Abstract

The rapidly emerging diversity of single cell RNAseq datasets allows us to characterize the transcriptional behavior of cell types across a wide variety of biological and clinical conditions. With this comprehensive breadth comes a major analytical challenge. The same cell type across tissues, from different donors, or in different disease states, may appear to express different genes. A joint analysis of multiple datasets requires the integration of cells across diverse conditions. This is particularly challenging when datasets are assayed with different technologies in which real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Unlike available single-cell integration methods, Harmony can simultaneously account for multiple experimental and biological factors. We develop objective metrics to evaluate the quality of data integration. In four separate analyses, we demonstrate the superior performance of Harmony to four single-cell-specific integration algorithms. Moreover, we show that Harmony requires dramatically fewer computational resources. It is the only...Continue Reading

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Mentioned in this Paper

Study
Biological Adaptation to Stress
Cell Projection
Meta-Analysis (Publications)
Meta Analysis (Statistical Procedure)
Structure of Beta Cell of Islet
Genes
Response to Endoplasmic Reticulum Stress
Transcription, Genetic
Donor Person

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