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

Hoang Vv PhamXiaomei LiBuu TruongThin NguyenLin LiuJiuyong LiThuc D Le
Published in: Briefings in bioinformatics (2021)
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.
Keyphrases
  • single cell
  • rna seq
  • high throughput
  • electronic health record
  • big data
  • data analysis
  • stem cells
  • artificial intelligence
  • cell therapy
  • bone marrow
  • transcription factor
  • genome wide identification
  • genome wide analysis