Abstract
Motivation: Single-cell RNA-sequencing technologies have greatly enhanced our understanding of heterogeneous cell populations and underlying regulatory processes. However, structural (spatial or temporal) relations between cells are lost during cell dissociation. These relations are crucial for identifying associated biological processes. Many existing tissue-reconstruction algorithms use prior information about subsets of genes that are informative with respect to the structure or process to be reconstructed. When such information is not available, and in the general case when the input genes code for multiple processes, including being susceptible to noise, biological reconstruction is often computationally challenging. Results: We propose an algorithm that iteratively identifies manifold-informative genes using existing reconstruction algorithms for single-cell RNA-seq data as subroutine. We show that our algorithm improves the quality of tissue reconstruction for diverse synthetic and real scRNA-seq data, including data from the mammalian intestinal epithelium and liver lobules.
| Original language | American English |
|---|---|
| Pages (from-to) | I423-I430 |
| Journal | Bioinformatics |
| Volume | 39 |
| DOIs | |
| State | Published - 1 Jun 2023 |
Bibliographical note
Funding Information:This work was supported by Simons Investigator Fellowship, NSF grant DMS-2134157, DARPA grant W911NF2010021, and DOE grant DE-SC0022199 (B.B.), an Azrieli Foundation Early Career Faculty Fellowship, the Israel Science Foundation (Grant no. 1079/21), and the European Union (ERC, DecodeSC, 101040660) (M.N.). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.
Funding Information:
This work was supported by Simons Investigator Fellowship, NSF grant DMS-2134157, DARPA grant W911NF2010021, and DOE grant DE-SC0022199 (B.B.), an Azrieli Foundation Early Career Faculty Fellowship, the Israel Science Foundation (Grant no. 1079/21), and the European Union (ERC, DecodeSC, 101040660) (M.N.). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.
Publisher Copyright:
© The Author(s) 2023. Published by Oxford University Press.