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propeller: testing for differences in cell type proportions in single cell data.
Phipson, Belinda; Sim, Choon Boon; Porrello, Enzo R; Hewitt, Alex W; Powell, Joseph; Oshlack, Alicia.
  • Phipson B; Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC 3052, Australia.
  • Sim CB; Department of Pediatrics, University of Melbourne, Melbourne, VIC 3010, Australia.
  • Porrello ER; Department of Medical Biology, University of Melbourne, Melbourne, VIC 3010, Australia.
  • Hewitt AW; Heart Regeneration Group, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, VIC 3052, Australia.
  • Powell J; Melbourne Centre for Cardiovascular Genomics and Regenerative Medicine, The Royal Children's Hospital, Melbourne, VIC 3052, Australia.
  • Oshlack A; Heart Regeneration Group, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, VIC 3052, Australia.
Bioinformatics ; 38(20): 4720-4726, 2022 10 14.
Статья в английский | MEDLINE | ID: covidwho-2008507
ABSTRACT
MOTIVATION Single cell RNA-Sequencing (scRNA-seq) has rapidly gained popularity over the last few years for profiling the transcriptomes of thousands to millions of single cells. This technology is now being used to analyse experiments with complex designs including biological replication. One question that can be asked from single cell experiments, which has been difficult to directly address with bulk RNA-seq data, is whether the cell type proportions are different between two or more experimental conditions. As well as gene expression changes, the relative depletion or enrichment of a particular cell type can be the functional consequence of disease or treatment. However, cell type proportion estimates from scRNA-seq data are variable and statistical methods that can correctly account for different sources of variability are needed to confidently identify statistically significant shifts in cell type composition between experimental conditions.

RESULTS:

We have developed propeller, a robust and flexible method that leverages biological replication to find statistically significant differences in cell type proportions between groups. Using simulated cell type proportions data, we show that propeller performs well under a variety of scenarios. We applied propeller to test for significant changes in cell type proportions related to human heart development, ageing and COVID-19 disease severity. AVAILABILITY AND IMPLEMENTATION The propeller method is publicly available in the open source speckle R package (https//github.com/phipsonlab/speckle). All the analysis code for the article is available at the associated analysis website https//phipsonlab.github.io/propeller-paper-analysis/. The speckle package, analysis scripts and datasets have been deposited at https//doi.org/10.5281/zenodo.7009042. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Тема - темы

Полный текст: Имеется в наличии Коллекция: Международные базы данных база данных: MEDLINE Основная тема: Single-Cell Analysis / COVID-19 Тип исследования: Экспериментальные исследования / Прогностическое исследование / Рандомизированные контролируемые испытания Пределы темы: Люди Язык: английский Журнал: Bioinformatics Тематика журнала: Медицинская информатика Год: 2022 Тип: Статья Аффилированная страна: Bioinformatics

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Полный текст: Имеется в наличии Коллекция: Международные базы данных база данных: MEDLINE Основная тема: Single-Cell Analysis / COVID-19 Тип исследования: Экспериментальные исследования / Прогностическое исследование / Рандомизированные контролируемые испытания Пределы темы: Люди Язык: английский Журнал: Bioinformatics Тематика журнала: Медицинская информатика Год: 2022 Тип: Статья Аффилированная страна: Bioinformatics