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pmid:34316700

On the impact of batch effect correction in TCGA isomiR expression data.

Susanne Ibing · Birgitta E Michels · Moritz Mosdzien · Helen R Meyer · Lars Feuerbach · Cindy Körner

NAR cancer · 2021 doi:10.1093/narcan/zcab007

Paper

complete

Experiment

complete

Data

partial

Analysis

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Execution

absent

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title
On the impact of batch effect correction in TCGA isomiR expression data.
doi
10.1093/narcan/zcab007
pmid
34316700
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Susanne Ibing
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Birgitta E Michels
[2]
Moritz Mosdzien
[3]
Helen R Meyer
[4]
Lars Feuerbach
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Cindy Körner
journal
NAR cancer
year
2021
abstract
MicroRNAs (miRNAs) are small non-coding RNAs with diverse functions in post-transcriptional regulation of gene expression. Sequence and length variants of miRNAs are called isomiRs and can exert different functions compared to their canonical counterparts. The Cancer Genome Atlas (TCGA) provides isomiR-level expression data for patients of various cancer entities collected in a multi-center approach over several years. However, the impact of batch effects within individual cohorts has not been systematically investigated and corrected for before. Therefore, the aim of this study was to identify relevant cohort-specific batch variables and generate batch-corrected isomiR expression data for 16 TCGA cohorts. The main batch variables included sequencing platform, plate, sample purity and sequencing depth. Platform bias was related to certain length and sequence features of individual recurrently affected isomiRs. Furthermore, significant downregulation of reported tumor suppressive isomiRs in lung tumor tissue compared to normal samples was only observed after batch correction, highlighting the importance of working with corrected data. Batch-corrected datasets for all cohorts including quality control are provided as supplement. In summary, this study reveals that batch effects present in the TCGA dataset might mask biologically relevant effects and provides a valuable resource for research on isomiRs in cancer (accessible through GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE164767).
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To assess the effect of the batch correction on differential expression between the platforms statistically, two-sided t-tests were performed comparing log2 transformed isomiR expression values for each isomiR on the two platforms. P-values were FDR corrected for multiple testing.
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To assess the effect of the batch correction on differential expression between the platforms statistically, two-sided t-tests were performed comparing log2 transformed isomiR expression values for each isomiR on the two platforms. P-values were FDR corrected for multiple testing.
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All data are publicly available and accessible as described in the ‘Data download and pre-processing’ section. The batch corrected data have been deposited in NCBI’s Gene Expression Omnibus (57) and are accessible through GEO Series accession number GSE164767 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE164767). R code for data download, pre-processing, annotation and batch correction as well as for the implementation of described analyses and main figures are publicly available on GitHub (https://github.com/susibing/BatchCor_TCGAisomiR).
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sections[19] "DATA AVAILABILITY" paragraph 2
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Annotation- and Batch Effect Correction in TCGA IsomiR Expression Data [Third-party re-analysis]
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The Cancer Genome Atlas (TCGA) provides isomiR-level expression data for patients of various cancer entities collected in a multi-center approach over several years. However, the impact of batch effects within individual cohorts has not been systematically investigated and corrected for before. Therefore, the aim of this study was to identify relevant cohort-specific batch variables and generate batch-corrected isomiR expression data for 16 TCGA cohorts. The main batch variables included sequencing platform, plate, sample purity and sequencing depth. Platform bias was related to certain length and sequence features of individual recurrently affected isomiRs. Furthermore, significant downregulation of reported tumor suppressive isomiRs in lung tumor tissue compared to normal samples was only observed after batch correction, highlighting the importance of working with corrected data. Batch-corrected datasets for all cohorts including quality control are provided as supplement. 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