scholarly journals CuBlock: a cross-platform normalization method for gene-expression microarrays

2021 ◽  
Author(s):  
Valentin Junet ◽  
Judith Farrés ◽  
José M Mas ◽  
Xavier Daura
Keyword(s):  
Gene Expression ◽  
Microarray Data ◽  
Poor Performance ◽  
Supplementary Information ◽  
Data Sets ◽  
Real World Data ◽  
Normalization Methods ◽  
Cross Platform ◽  
Gene Expression Studies ◽  
Expression Microarrays

Abstract Motivation Cross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups. Results We present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct data sets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these data sets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), UPC (Piccolo et al., 2013), YuGene (Lê Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log2 transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study. Availability CuBlock can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/77882-cublock Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals CuBlock: A cross-platform normalization method for gene-expression microarrays

2020 ◽  
Author(s):  
Valentin Junet ◽  
Judith Farrés ◽  
José M. Mas ◽  
Xavier Daura
Keyword(s):  
Gene Expression ◽  
Microarray Data ◽  
Poor Performance ◽  
Supplementary Information ◽  
Data Sets ◽  
Real World Data ◽  
Normalization Methods ◽  
Cross Platform ◽  
Gene Expression Studies ◽  
Expression Microarrays

AbstractMotivationCross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups.ResultsWe present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct data sets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these data sets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), YuGene (Lê Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log2 transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study.AvailabilityCuBlock can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/[email protected], [email protected] informationSupplementary data are available at bioRxiv online.

SYSTEMATIC VARIATION NORMALIZATION IN MICROARRAY DATA TO GET GENE EXPRESSION COMPARISON UNBIASED

2005 ◽  
Vol 03 (02) ◽  
pp. 225-241 ◽  
Author(s):  
JEFF W. CHOU ◽  
RICHARD S. PAULES ◽  
PIERRE R. BUSHEL
Keyword(s):  
Gene Expression ◽  
Linear Regression ◽  
Microarray Data ◽  
Expression Patterns ◽  
Microarray Gene Expression Data ◽  
Systematic Variation ◽  
Data Sets ◽  
Microarray Gene Expression ◽  
Pixel Intensity ◽  
Non Linear

Normalization removes or minimizes the biases of systematic variation that exists in experimental data sets. This study presents a systematic variation normalization (SVN) procedure for removing systematic variation in two channel microarray gene expression data. Based on an analysis of how systematic variation contributes to variability in microarray data sets, our normalization procedure includes background subtraction determined from the distribution of pixel intensity values from each data acquisition channel and log conversion, linear or non-linear regression, restoration or transformation, and multiarray normalization. In the case when a non-linear regression is required, an empirical polynomial approximation approach is used. Either the high terminated points or their averaged values in the distributions of the pixel intensity values observed in control channels may be used for rescaling multiarray datasets. These pre-processing steps remove systematic variation in the data attributable to variability in microarray slides, assay-batches, the array process, or experimenters. Biologically meaningful comparisons of gene expression patterns between control and test channels or among multiple arrays are therefore unbiased using normalized but not unnormalized datasets.

SPECTRAL CLUSTERING ON GENE EXPRESSION PROFILE TO IDENTIFY CANCER TYPES OR SUBTYPES

2015 ◽  
Vol 76 (1) ◽  
Author(s):  
Ang Jun Chin ◽  
Andri Mirzal ◽  
Habibollah Haron
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Microarray Data ◽  
Spectral Clustering ◽  
Data Sets ◽  
Clustering Methods ◽  
Microarray Gene Expression ◽  
Cancer Types ◽  
Microarray Gene

Gene expression profile is eminent for its broad applications and achievements in disease discovery and analysis, especially in cancer research. Spectral clustering is robust to irrelevant features which are appropriated for gene expression analysis. However, previous works show that performance comparison with other clustering methods is limited and only a few microarray data sets were analyzed in each study. In this study, we demonstrate the use of spectral clustering in identifying cancer types or subtypes from microarray gene expression profiling. Spectral clustering was applied to eleven microarray data sets and its clustering performances were compared with the results in the literature. Based on the result, overall the spectral clustering slightly outperformed the corresponding results in the literature. The spectral clustering can also offer more stable clustering performances as it has smaller standard deviation value. Moreover, out of eleven data sets the spectral clustering outperformed the corresponding methods in the literature for six data sets. So, it can be stated that the spectral clustering is a promising method in identifying the cancer types or subtypes for microarray gene expression data sets.

scholarly journals DataRemix: a universal data transformation for optimal inference from gene expression datasets

2020 ◽  
Author(s):  
Weiguang Mao ◽  
Javad Rahimikollu ◽  
Ryan Hausler ◽  
Maria Chikina
Keyword(s):  
Gene Expression ◽  
R Package ◽  
Supplementary Information ◽  
Eqtl Analysis ◽  
Thompson Sampling ◽  
Normalization Methods ◽  
Special Cases ◽  
Biological Signals ◽  
Gene Correlation ◽  
Value Decomposition

Abstract Motivation RNA-seq technology provides unprecedented power in the assessment of the transcription abundance and can be used to perform a variety of downstream tasks such as inference of gene-correlation network and eQTL discovery. However, raw gene expression values have to be normalized for nuisance biological variation and technical covariates, and different normalization strategies can lead to dramatically different results in the downstream study. Results We describe a generalization of singular value decomposition-based reconstruction for which the common techniques of whitening, rank-k approximation and removing the top k principal components are special cases. Our simple three-parameter transformation, DataRemix, can be tuned to reweigh the contribution of hidden factors and reveal otherwise hidden biological signals. In particular, we demonstrate that the method can effectively prioritize biological signals over noise without leveraging external dataset-specific knowledge, and can outperform normalization methods that make explicit use of known technical factors. We also show that DataRemix can be efficiently optimized via Thompson sampling approach, which makes it feasible for computationally expensive objectives such as eQTL analysis. Finally, we apply our method to the Religious Orders Study and Memory and Aging Project dataset, and we report what to our knowledge is the first replicable trans-eQTL effect in human brain. Availabilityand implementation DataRemix is an R package which is freely available at GitHub (https://github.com/wgmao/DataRemix). Supplementary information Supplementary data are available at Bioinformatics online.

CLASSIFYING TEMPORAL MICROARRAY DATA BY SELECTING INFORMATIVE GENES

2013 ◽  
Vol 11 (03) ◽  
pp. 1341006
Author(s):  
QIANG LOU ◽  
ZORAN OBRADOVIC
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Microarray Data ◽  
Data Sets ◽  
Temporal Data ◽  
Expression Data ◽  
Selection Methods ◽  
Temporal Gene Expression ◽  
Single Matrix

In order to more accurately predict an individual's health status, in clinical applications it is often important to perform analysis of high-dimensional gene expression data that varies with time. A major challenge in predicting from such temporal microarray data is that the number of biomarkers used as features is typically much larger than the number of labeled subjects. One way to address this challenge is to perform feature selection as a preprocessing step and then apply a classification method on selected features. However, traditional feature selection methods cannot handle multivariate temporal data without applying techniques that flatten temporal data into a single matrix in advance. In this study, a feature selection filter that can directly select informative features from temporal gene expression data is proposed. In our approach, we measure the distance between multivariate temporal data from two subjects. Based on this distance, we define the objective function of temporal margin based feature selection to maximize each subject's temporal margin in its own relevant subspace. The experimental results on synthetic and two real flu data sets provide evidence that our method outperforms the alternatives, which flatten the temporal data in advance.

P3M— POSSIBILISTIC MULTI-STEP MAXMIN AND MERGING ALGORITHM WITH APPLICATION TO GENE EXPRESSION DATA MINING

2009 ◽  
Vol 18 (04) ◽  
pp. 545-567
Author(s):  
LOTFI BEN ROMDHANE ◽  
HECHMI SHILI ◽  
BECHIR AYEB
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Optimal Number ◽  
Data Sets ◽  
Expression Data ◽  
Real World Data ◽  
Possibilistic Clustering ◽  
Medical Diagnostic ◽  
High Prediction ◽  
Proximity Graph

Gene expression data generated by DNA microarray experiments provide a vast resource of medical diagnostic and disease understanding. Unfortunately, the large amount of data makes it hard, sometimes impossible, to understand the correct behavior of genes. In this work, we develop a possibilistic approach for mining gene microarray data. Our model consists of two steps. In the first step, we use possibilistic clustering to partition the data into groups (or clusters). The optimal number of clusters is evaluated automatically from data using the Partition Information Entropy as a validity measure. In the second step, we select from each computed cluster the most representative genes and model them as a graph called a proximity graph. This set of graphs (or hyper-graph) will be used to predict the function of new and previously unknown genes. Benchmark results on real-world data sets reveal a good performance of our model in computing optimal partitions even in the presence of noise; and a high prediction accuracy on unknown genes.

scholarly journals Statistical design and the analysis of gene expression microarray data

2001 ◽  
Vol 77 (2) ◽  
pp. 123-128 ◽  
Author(s):  
M. KATHLEEN KERR ◽  
GARY A. CHURCHILL
Keyword(s):  
Gene Expression ◽  
Microarray Data ◽  
Statistical Design ◽  
Innovative Technology ◽  
Gene Expression Microarray ◽  
Expression Microarray ◽  
Gene Expression Microarrays ◽  
Gene Expression Microarray Data ◽  
Expression Microarrays ◽  
Microarray Studies

Gene expression microarrays are an innovative technology with enormous promise to help geneticists explore and understand the genome. Although the potential of this technology has been clearly demonstrated, many important and interesting statistical questions persist. We relate certain features of microarrays to other kinds of experimental data and argue that classical statistical techniques are appropriate and useful. We advocate greater attention to experimental design issues and a more prominent role for the ideas of statistical inference in microarray studies.

Normalization for Two-channel Microarray Data

2005 ◽  
Vol 44 (03) ◽  
pp. 418-422 ◽  
Author(s):  
C. Ittrich
Keyword(s):  
Gene Expression ◽  
Microarray Data ◽  
Single Channel ◽  
Single Channels ◽  
Data Set ◽  
Microarray Experiments ◽  
Normalization Methods ◽  
Sources Of Variation ◽  
Many Sources ◽  
Gene Expression Levels

Summary Objectives: In two-channel microarray experiments the measured gene expression levels are affected by many sources of systematic variation. Normalization refers to the process of removing such systematic sources of variation, to make measured intensities within and between slides comparable. Some commonly used normalization methods removing intensity-dependent dye bias and adjusting differences in variability between slides will be reviewed with the main focus on intensity-dependent normalization methods. Methods: This article describes different intensity-dependent within-slide normalization methods for the log ratios of red and green channel intensities but also refers to single channel normalization methods incorporating all single channels of the slides at once. Results: The described procedures provide a useful approach to remove systematic sources of variation like intensity-dependent dye bias and variability between slides in cDNA microarray experiments. This is illustrated by an experimental data set. Conclusions: Several reasonable normalization procedures for two-channel microarray data have recently been proposed. Deciding on which method would perform well for a concrete experiment is difficult. Designed spike-in experiments or dilution series with known differences for some selected genes would be helpful to assess the different methods, but may be impractical for most laboratories due to the high costs.

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