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Computing the Rearrangement Distance of Natural Genomes Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Iterative Refinement of Cellular Identity from Single-Cell Data Using Online Learning

Authors : Chao Gao, Joshua D. Welch

Published in: Research in Computational Molecular Biology

Publisher: Springer International Publishing

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Abstract

Recent experimental advances have enabled high-throughput single-cell measurement of gene expression, chromatin accessibility and DNA methylation. We previously employed integrative non-negative matrix factorization (iNMF) to jointly align multiple single-cell datasets (\(X_i\)) and learn interpretable low-dimensional representations using dataset-specific (\(V_i)\) and shared metagene factors (W) and cell factor loadings (\(H_i\)). We developed an alternating nonnegative least squares (ANLS) algorithm to solve the iNMF optimization problem [2]:

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previous chapter Privacy-Preserving Biomedical Database Queries with Optimal Privacy-Utility Trade-Offs
next chapter A Guided Network Propagation Approach to Identify Disease Genes that Combines Prior and New Information
Literature
1.
Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res. 11(Jan), 19–60 (2010)MathSciNetMATH
2.
Welch, J.D., Kozareva, V., Ferreira, A., Vanderburg, C., Martin, C., Macosko, E.Z.: Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177(7), 1873–1887 (2019)CrossRef
Metadata
Title
Iterative Refinement of Cellular Identity from Single-Cell Data Using Online Learning
Authors
Chao Gao
Joshua D. Welch
Copyright Year
2020
Book
Research in Computational Molecular Biology

Print ISBN: 978-3-030-45256-8

Electronic ISBN: 978-3-030-45257-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-45257-5_24

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