Dataset Information

Systematic noise degrades gene co-expression signals but can be corrected.

ABSTRACT: In the past decade, the identification of gene co-expression has become a routine part of the analysis of high-dimensional microarray data. Gene co-expression, which is mostly detected via the Pearson correlation coefficient, has played an important role in the discovery of molecular pathways and networks. Unfortunately, the presence of systematic noise in high-dimensional microarray datasets corrupts estimates of gene co-expression. Removing systematic noise from microarray data is therefore crucial. Many cleaning approaches for microarray data exist, however these methods are aimed towards improving differential expression analysis and their performances have been primarily tested for this application. To our knowledge, the performances of these approaches have never been systematically compared in the context of gene co-expression estimation.Using simulations we demonstrate that standard cleaning procedures, such as background correction and quantile normalization, fail to adequately remove systematic noise that affects gene co-expression and at times further degrade true gene co-expression. Instead we show that a global version of removal of unwanted variation (RUV), a data-driven approach, removes systematic noise but also allows the estimation of the true underlying gene-gene correlations. We compare the performance of all noise removal methods when applied to five large published datasets on gene expression in the human brain. RUV retrieves the highest gene co-expression values for sets of genes known to interact, but also provides the greatest consistency across all five datasets. We apply the method to prioritize epileptic encephalopathy candidate genes.Our work raises serious concerns about the quality of many published gene co-expression analyses. RUV provides an efficient and flexible way to remove systematic noise from high-dimensional microarray datasets when the objective is gene co-expression analysis. The RUV method as applicable in the context of gene-gene correlation estimation is available as a BioconductoR-package: RUVcorr.

SUBMITTER: Freytag S

PROVIDER: S-EPMC4583191 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Systematic noise degrades gene co-expression signals but can be corrected.

Freytag Saskia S Gagnon-Bartsch Johann J Speed Terence P TP Bahlo Melanie M

BMC bioinformatics 20150924

<h4>Background</h4>In the past decade, the identification of gene co-expression has become a routine part of the analysis of high-dimensional microarray data. Gene co-expression, which is mostly detected via the Pearson correlation coefficient, has played an important role in the discovery of molecular pathways and networks. Unfortunately, the presence of systematic noise in high-dimensional microarray datasets corrupts estimates of gene co-expression. Removing systematic noise from microarray d ...[more]

PMID: 26403471

Similar Datasets

Project description:To replicate efficiently and evade the antiviral immune response of the host, some viruses degrade host mRNA to induce host gene shutoff via encoding shutoff factors. In this study, we found that feline calicivirus (FCV) infection promotes the degradation of endogenous and exogenous mRNAs and induces host gene shutoff, which results in global inhibition of host protein synthesis. Screening assays revealed that proteinase-polymerase (PP) is a most effective factor in reducing mRNA expression. Moreover, PP from differently virulent strains of FCV could induce mRNA degradation. Further, we found that the key sites of the PP protein required for its proteinase activity are also essential for its shutoff activity but also required for viral replication. The mechanism analysis showed that PP mainly targets Pol II-transcribed RNA in a ribosome-, 5' cap-, and 3' poly(A) tail-independent manner. Moreover, purified glutathione S-transferase (GST)-PP fusion protein exhibits RNase activity in vitro in assays using green fluorescent protein (GFP) RNA transcribed in vitro as a substrate in the absence of other viral or cellular proteins. Finally, PP-induced shutoff requires host Xrn1 to complete further RNA degradation. This study provides a newly discovered strategy in which FCV PP protein induces host gene shutoff by promoting the degradation of host mRNAs. IMPORTANCE Virus infection-induced shutoff is the result of targeted or global manipulation of cellular gene expression and leads to efficient viral replication and immune evasion. FCV is a highly contagious pathogen that persistently infects cats. It is unknown how FCV blocks the host immune response and persistently exists in cats. In this study, we found that FCV infection promotes the degradation of host mRNAs and induces host gene shutoff via a common strategy. Further, PP protein for different FCV strains is a key factor that enhances mRNA degradation. An in vitro assay showed that the GST-PP fusion protein possesses RNase activity in the absence of other viral or cellular proteins. This study demonstrates that FCV induces host gene shutoff by promoting the degradation of host mRNAs, thereby introducing a potential mechanism by which FCV infection inhibits the immune response.

Project description:BackgroundSystematic processing noise, which includes batch effects, is very common in microarray experiments but is often ignored despite its potential to confound or compromise experimental results. Compromised results are most likely when re-analysing or integrating datasets from public repositories due to the different conditions under which each dataset is generated. To better understand the relative noise-contributions of various factors in experimental-design, we assessed several Illumina and Affymetrix datasets for technical variation between replicate hybridisations of Universal Human Reference (UHRR) and individual or pooled breast-tumour RNA.ResultsA varying degree of systematic noise was observed in each of the datasets, however in all cases the relative amount of variation between standard control RNA replicates was found to be greatest at earlier points in the sample-preparation workflow. For example, 40.6% of the total variation in reported expressions were attributed to replicate extractions, compared to 13.9% due to amplification/labelling and 10.8% between replicate hybridisations. Deliberate probe-wise batch-correction methods were effective in reducing the magnitude of this variation, although the level of improvement was dependent on the sources of noise included in the model. Systematic noise introduced at the chip, run, and experiment levels of a combined Illumina dataset were found to be highly dependent upon the experimental design. Both UHRR and pools of RNA, which were derived from the samples of interest, modelled technical variation well although the pools were significantly better correlated (4% average improvement) and better emulated the effects of systematic noise, over all probes, than the UHRRs. The effect of this noise was not uniform over all probes, with low GC-content probes found to be more vulnerable to batch variation than probes with a higher GC-content.ConclusionsThe magnitude of systematic processing noise in a microarray experiment is variable across probes and experiments, however it is generally the case that procedures earlier in the sample-preparation workflow are liable to introduce the most noise. Careful experimental design is important to protect against noise, detailed meta-data should always be provided, and diagnostic procedures should be routinely performed prior to downstream analyses for the detection of bias in microarray studies.

Dataset Information

Systematic noise degrades gene co-expression signals but can be corrected.

Publications

Systematic noise degrades gene co-expression signals but can be corrected.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets