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Normalization of high dimensional genomics data where the distribution of the altered variables is skewed.

ABSTRACT: Genome-wide analysis of gene expression or protein binding patterns using different array or sequencing based technologies is now routinely performed to compare different populations, such as treatment and reference groups. It is often necessary to normalize the data obtained to remove technical variation introduced in the course of conducting experimental work, but standard normalization techniques are not capable of eliminating technical bias in cases where the distribution of the truly altered variables is skewed, i.e. when a large fraction of the variables are either positively or negatively affected by the treatment. However, several experiments are likely to generate such skewed distributions, including ChIP-chip experiments for the study of chromatin, gene expression experiments for the study of apoptosis, and SNP-studies of copy number variation in normal and tumour tissues. A preliminary study using spike-in array data established that the capacity of an experiment to identify altered variables and generate unbiased estimates of the fold change decreases as the fraction of altered variables and the skewness increases. We propose the following work-flow for analyzing high-dimensional experiments with regions of altered variables: (1) Pre-process raw data using one of the standard normalization techniques. (2) Investigate if the distribution of the altered variables is skewed. (3) If the distribution is not believed to be skewed, no additional normalization is needed. Otherwise, re-normalize the data using a novel HMM-assisted normalization procedure. (4) Perform downstream analysis. Here, ChIP-chip data and simulated data were used to evaluate the performance of the work-flow. It was found that skewed distributions can be detected by using the novel DSE-test (Detection of Skewed Experiments). Furthermore, applying the HMM-assisted normalization to experiments where the distribution of the truly altered variables is skewed results in considerably higher sensitivity and lower bias than can be attained using standard and invariant normalization methods.

SUBMITTER: Landfors M

PROVIDER: S-EPMC3222656 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Normalization of high dimensional genomics data where the distribution of the altered variables is skewed.

Landfors Mattias M Philip Philge P Rydén Patrik P Stenberg Per P

PloS one 20111122 11

Genome-wide analysis of gene expression or protein binding patterns using different array or sequencing based technologies is now routinely performed to compare different populations, such as treatment and reference groups. It is often necessary to normalize the data obtained to remove technical variation introduced in the course of conducting experimental work, but standard normalization techniques are not capable of eliminating technical bias in cases where the distribution of the truly altere ...[more]

PMID: 22132175

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Project description:MotivationThere are a number of well-established methods such as principal component analysis (PCA) for automatically capturing systematic variation due to latent variables in large-scale genomic data. PCA and related methods may directly provide a quantitative characterization of a complex biological variable that is otherwise difficult to precisely define or model. An unsolved problem in this context is how to systematically identify the genomic variables that are drivers of systematic variation captured by PCA. Principal components (PCs) (and other estimates of systematic variation) are directly constructed from the genomic variables themselves, making measures of statistical significance artificially inflated when using conventional methods due to over-fitting.ResultsWe introduce a new approach called the jackstraw that allows one to accurately identify genomic variables that are statistically significantly associated with any subset or linear combination of PCs. The proposed method can greatly simplify complex significance testing problems encountered in genomics and can be used to identify the genomic variables significantly associated with latent variables. Using simulation, we demonstrate that our method attains accurate measures of statistical significance over a range of relevant scenarios. We consider yeast cell-cycle gene expression data, and show that the proposed method can be used to straightforwardly identify genes that are cell-cycle regulated with an accurate measure of statistical significance. We also analyze gene expression data from post-trauma patients, allowing the gene expression data to provide a molecularly driven phenotype. Using our method, we find a greater enrichment for inflammatory-related gene sets compared to the original analysis that uses a clinically defined, although likely imprecise, phenotype. The proposed method provides a useful bridge between large-scale quantifications of systematic variation and gene-level significance analyses.Availability and implementationAn R software package, called jackstraw, is available in CRAN.Contactjstorey@princeton.edu.

Project description:Motivation: Gene-expression data obtained from high throughput technologies are subject to various sources of noise and accordingly the raw data are pre-processed before formally analyzed. Normalization of the data is a key pre-processing step, since it removes systematic variations across arrays. There are numerous normalization methods available in the literature. Based on our experience, in the context of oscillatory systems, such as cell-cycle, circadian clock, etc., the choice of the normalization method may substantially impact the determination of a gene to be rhythmic. Thus rhythmicity of a gene can purely be an artifact of how the data were normalized. Since the determination of rhythmic genes is an important component of modern toxicological and pharmacological studies, it is important to determine truly rhythmic genes that are robust to the choice of a normalization method. Results: In this paper we introduce a rhythmicity measure and a bootstrap methodology to detect rhythmic genes in an oscillatory system. Although the proposed methodology can be used for any high-throughput gene expression data, in this paper we illustrate the proposed methodology using several publicly available circadian clock microarray gene-expression datasets. We demonstrate that the choice of normalization method has very little effect on the proposed methodology. Specifically, for any pair of normalization methods considered in this paper, the resulting values of the rhythmicity measure are highly correlated. Thus it suggests that the proposed measure is robust to the choice of a normalization method. Consequently, the rhythmicity of a gene is potentially not a mere artifact of the normalization method used. Lastly, as demonstrated in the paper, the proposed bootstrap methodology can also be used for simulating data for genes participating in an oscillatory system using a reference dataset. Availability: A user friendly code implemented in R language can be downloaded from http://www.eio.uva.es/~miguel/robustdetectionprocedure.html.

Project description:BACKGROUND: High throughput methodologies such as microarrays, mass spectrometry and plate-based small molecule screens are increasingly used to facilitate discoveries from gene function to drug candidate identification. These large-scale experiments are typically carried out over the course of months and years, often without the controls needed to compare directly across the dataset. Few methods are available to facilitate comparisons of high throughput metabolic data generated in batches where explicit in-group controls for normalization are lacking. RESULTS: Here we describe MIPHENO (Mutant Identification by Probabilistic High throughput-Enabled Normalization), an approach for post-hoc normalization of quantitative first-pass screening data in the absence of explicit in-group controls. This approach includes a quality control step and facilitates cross-experiment comparisons that decrease the false non-discovery rates, while maintaining the high accuracy needed to limit false positives in first-pass screening. Results from simulation show an improvement in both accuracy and false non-discovery rate over a range of population parameters (p < 2.2 × 10(-16)) and a modest but significant (p < 2.2 × 10(-16)) improvement in area under the receiver operator characteristic curve of 0.955 for MIPHENO vs 0.923 for a group-based statistic (z-score). Analysis of the high throughput phenotypic data from the Arabidopsis Chloroplast 2010 Project (http://www.plastid.msu.edu/) showed ~ 4-fold increase in the ability to detect previously described or expected phenotypes over the group based statistic. CONCLUSIONS: Results demonstrate MIPHENO offers substantial benefit in improving the ability to detect putative mutant phenotypes from post-hoc analysis of large data sets. Additionally, it facilitates data interpretation and permits cross-dataset comparison where group-based controls are missing. MIPHENO is applicable to a wide range of high throughput screenings and the code is freely available as Additional file 1 as well as through an R package in CRAN.

Dataset Information

Normalization of high dimensional genomics data where the distribution of the altered variables is skewed.

Publications

Normalization of high dimensional genomics data where the distribution of the altered variables is skewed.

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