Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes
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ABSTRACT:
PROVIDER: PRJNA513861 | ENA |
REPOSITORIES: ENA
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Project description:Medulloblastoma (MB) is a brain cancer predominantly arising in children. Roughly 70% of patients are cured today, but survivors often suffer from severe sequelae. MB has been extensively studied by molecular profiling, but often in small and scattered cohorts. To improve cure rates and reduce treatment side effects, accurate integration of such data to increase analytical power will be important, if not essential. We have integrated 23 transcription datasets, spanning 1350 MB and 291 normal brain samples. To remove batch effects, we combined the Removal of Unwanted Variation (RUV) method with a novel pipeline for determining empirical negative control genes and a panel of metrics to evaluate normalization performance. The documented approach enabled the removal of a majority of batch effects, producing a large-scale, integrative dataset of MB and cerebellar expression data. The proposed strategy will be broadly applicable for accurate integration of data and incorporation of normal reference samples for studies of various diseases. We hope that the integrated dataset will improve current research in the field of MB by allowing more large-scale gene expression analyses. For all selected samples, raw CEL files were downloaded from GEO or AE. Subsequently, all raw CEL files from the same platform were processed together using the R/Bioconductor package oligo in conjunction with the RMA algorithm. The Human Gene 1.0 ST and Human Gene 1.1 ST arrays were analysed at the core level, while the Human Exon 1.0 ST arrays were processed at the extended level. Subsequently, we mapped the identifiers of the HG-U133 Plus 2 and Human Exon 1.0 ST to Human Gene 1.0/1.1 ST identifiers using `Best Match' information from Affymetrix (https://www.affymetrix.com/support/technical/byproduct.affx?product=hugene-1_0-st-v1). In addition, to increase the overlap between the Human Exon 1.0 ST and Human Gene 1.0/1.1 ST data we also inspected and added probe mappings from the `Good Match' and `Complex Match' files, including probes for the genes MYCN, PTCH1, NPR3, UNC5D, DKK2, and GABRA5. After mapping of probe identifiers within each platform, multiple rows mapping to the same identifier were collapsed using the mean value. Subsequently, all platform datasets were merged on probe identifiers, and gene symbols were assigned using the hugene11sttranscriptcluster.db package. Multiple rows mapping to the same gene or multiple columns mapping to the same patient were collapsed using the mean value. Finally, the resulting gene expression matrix was quantile normalized using the respective function in the preprocessCore package.
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Project description:Profiling miRNA levels in cells with miRNA-microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. Here, we developed a method to select non-changing miRNAs (“invariants”) and used them to compute linear regression normalization coefficients or Variance Stabilizing Normalization (VSN) parameters. We compared the invariant normalizations to normalization by, scaling, quantile and VSN with default parameters as well as to no normalization using samples with strong differential expression of miRNAs (heart-brain comparison) and samples where only few miRNAs are affected (p53 overexpression in SCC13 cells versus GFP vector control transfected cells). All normalization methods performed better than no normalization. Normalizations procedures based on the set of invariants and quantile were the most robust over all experimental conditions tested. Our method of invariant selection and normalization is not limited to Agilent miRNA arrays and can be applied to other datasets from one color miRNA microarray platforms, focused gene expression arrays and gene expression analysis using quantitative PCR. Keywords: miRNA profiling To assay technical reproducibility, 3 technical replicates from brain and heart RNA (Stratagene) were hybridized on Agilent human miRNA microarrays. To determine sensitivity and specificity, heart and brain RNA were mixed in the following ratios: 50% heart 50% brain, 25% heart 75% brain and 5% heart 95% brain. Each of the dilutions was hybridized in a technical duplicate on Agilent human miRNA arrays. To assay the effect of p53 expression on miRNA levels in the human SCC13 cell line, RNA from 3 biological replicates of p53-expressing or control cells were hybridized in technical duplicates on the microarrays, resulting in a total of 12 hybridizations.
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