期刊
BIOINFORMATICS
卷 26, 期 20, 页码 2571-2577出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btq406
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资金
- Academy of Finland [134290, 213462]
- Finnish Programme for Centres of Excellence in Research
- Finnish Graduate School in Computational Sciences
- National Institutes of Health [NIHP50GMO76547]
- Academy of Finland (AKA) [134290, 134290] Funding Source: Academy of Finland (AKA)
Motivation: Tissue heterogeneity, arising from multiple cell types, is a major confounding factor in experiments that focus on studying cell types, e. g. their expression profiles, in isolation. Although sample heterogeneity can be addressed by manual microdissection, prior to conducting experiments, computational treatment on heterogeneous measurements have become a reliable alternative to perform this microdissection in silico. Favoring computation over manual purification has its advantages, such as time consumption, measuring responses of multiple cell types simultaneously, keeping samples intact of external perturbations and unaltered yield of molecular content. Results: We formalize a probabilistic model, DSection, and show with simulations as well as with real microarray data that DSection attains increased modeling accuracy in terms of (i) estimating cell-type proportions of heterogeneous tissue samples, (ii) estimating replication variance and (iii) identifying differential expression across cell types under various experimental conditions. As our reference we use the corresponding linear regression model, which mirrors the performance of the majority of current non-probabilistic modeling approaches.
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