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Batch Effects Correction with Unknown Subtypes
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DOI：10.1080/01621459.2018.1497494.png)
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摘要
En 中文
High-throughput experimental data are accumulating exponentially in public databases. Unfortunately, however, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed batch effects, and the latter is often modeled by subtypes. Existing methods either tackle batch effects provided that subtypes are known or cluster subtypes assuming that batch effects are absent. Consequently, there is a lack of research on the correction of batch effects with the presence of unknown subtypes. Here, we combine a location-and-scale adjustment model and model-based clustering into a novel hybrid one, the batch-effects-correction-with-unknown-subtypes model (BUS). BUS is capable of (a) correcting batch effects explicitly, (b) grouping samples that share similar characteristics into subtypes, (c) identifying features that distinguish subtypes, (d) allowing the number of subtypes to vary from batch to batch, (e) integrating batches from different platforms, and (f) enjoying a linear-order computation complexity. We prove the identifiability of BUS and provide conditions for study designs under which batch effects can be corrected. BUS is evaluated by simulation studies and a real breast cancer dataset combined from three batches measured on two platforms. Results from the breast cancer dataset offer much better biological insights than existing methods. We implement BUS as a free Bioconductor package BUScorrect. Supplementary materials for this article are available online.
Keyword:
Disease subtype discovery
High-throughput experiments
Integrative analysis
Interlaboratory comparisons
Model-based clustering
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