The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge

Pham, VVH, Li, X, Truong, B, Nguyen, Thin, Liu, L, Li, J and Le, TD 2020, The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge, Briefings in Bioinformatics, pp. 1-4, doi: 10.1093/bib/bbaa181.

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Title The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge
Author(s) Pham, VVH
Li, X
Truong, B
Nguyen, ThinORCID iD for Nguyen, Thin orcid.org/0000-0003-3467-8963
Liu, L
Li, J
Le, TD
Journal name Briefings in Bioinformatics
Start page 1
End page 4
Total pages 4
Publisher Oxford University Press
Place of publication Oxford, Eng.
Publication date 2020-08-25
ISSN 1467-5463
1477-4054
Keyword(s) DREAM challenge
cellular position prediction
single-cell transcriptomics
Summary Motivation Predicting cell locations is important since with the understanding of cell locations, we may estimate the function of cells and their integration with the spatial environment. Thus, the DREAM challenge on single-cell transcriptomics required participants to predict the locations of single cells in the Drosophila embryo using single-cell transcriptomic data. Results We have developed over 50 pipelines by combining different ways of preprocessing the RNA-seq data, selecting the genes, predicting the cell locations and validating predicted cell locations, resulting in the winning methods which were ranked second in sub-challenge 1, first in sub-challenge 2 and third in sub-challenge 3. In this paper, we present an R package, SCTCwhatateam, which includes all the methods we developed and the Shiny web application to facilitate the research on single-cell spatial reconstruction. All the data and the example use cases are available in the Supplementary data.
Notes In Press
Language eng
DOI 10.1093/bib/bbaa181
Indigenous content off
Field of Research 0601 Biochemistry and Cell Biology
0802 Computation Theory and Mathematics
0899 Other Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30145409

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