Dataset Information

Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes.

ABSTRACT: MOTIVATION: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. RESULTS: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. AVAILABILITY AND IMPLEMENTATION:The RUV-normalized expression data is available through the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) and can be accessed via the GSE series number GSE124814. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Weishaupt H

PROVIDER: S-EPMC6748729 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes.

Weishaupt Holger H Johansson Patrik P Sundström Anders A Lubovac-Pilav Zelmina Z Olsson Björn B Nelander Sven S Swartling Fredrik J FJ

Bioinformatics (Oxford, England) 20190901 18

<h4>Motivation</h4>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.<h4>Results</h4>We have integrated 23 transcription datasets, s ...[more]

PMID: 30715209

Similar Datasets

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.

Project description:Integration of single-cell RNA-sequencing (scRNA-seq) datasets has become a standard part of the analysis, with conditional variational autoencoders (cVAE) being among the most popular approaches. Increasingly, researchers are asking to map cells across challenging cases such as cross-organs, species, or organoids and primary tissue, as well as different scRNA-seq protocols, including single-cell and single-nuclei. Current computational methods struggle to harmonize datasets with such substantial differences, driven by technical or biological variation. Here, we propose to address these challenges for the popular cVAE-based approaches by introducing and comparing a series of regularization constraints. The two commonly used strategies for increasing batch correction in cVAEs, that is Kullback-Leibler divergence (KL) regularization strength tuning and adversarial learning, suffer from substantial loss of biological information. Therefore, we adapt, implement, and assess alternative regularization strategies for cVAEs and investigate how they improve batch effect removal or better preserve biological variation, enabling us to propose an optimal cVAE-based integration strategy for complex systems. We show that using a VampPrior instead of the commonly used Gaussian prior not only improves the preservation of biological variation but also unexpectedly batch correction. Moreover, we show that our implementation of cycle-consistency loss leads to significantly better biological preservation than adversarial learning implemented in the previously proposed GLUE model. Additionally, we do not recommend relying only on the KL regularization strength tuning for increasing batch correction, as it removes both biological and batch information without discriminating between the two. Based on our findings, we propose a new model that combines VampPrior and cycle-consistency loss. We show that using it for datasets with substantial batch effects improves downstream interpretation of cell states and biological conditions. To ease the use of the newly proposed model, we make it available in the scvi-tools package as an external model named sysVI. Moreover, in the future, these regularization techniques could be added to other established cVAE-based models to improve the integration of datasets with substantial batch effects.

Dataset Information

Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes.

Publications

Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets