Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments.

ABSTRACT: We introduce a general technique for making statistical inference from clustering tools applied to gene expression microarray data. The approach utilizes an analysis of variance model to achieve normalization and estimate differential expression of genes across multiple conditions. Statistical inference is based on the application of a randomization technique, bootstrapping. Bootstrapping has previously been used to obtain confidence intervals for estimates of differential expression for individual genes. Here we apply bootstrapping to assess the stability of results from a cluster analysis. We illustrate the technique with a publicly available data set and draw conclusions about the reliability of clustering results in light of variation in the data. The bootstrapping procedure relies on experimental replication. We discuss the implications of replication and good design in microarray experiments.

SUBMITTER: Kerr MK

PROVIDER: S-EPMC55356 | biostudies-literature | 2001 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments.

Kerr M K MK Churchill G A GA

Proceedings of the National Academy of Sciences of the United States of America 20010724 16

We introduce a general technique for making statistical inference from clustering tools applied to gene expression microarray data. The approach utilizes an analysis of variance model to achieve normalization and estimate differential expression of genes across multiple conditions. Statistical inference is based on the application of a randomization technique, bootstrapping. Bootstrapping has previously been used to obtain confidence intervals for estimates of differential expression for individ ...[more]

PMID: 11470909

Similar Datasets

Experimental approach for assessing the outcome accuracy of antibody microarray experiments.

Project description:An experimental strategy for quality control of antibody microarray analyses is proposed. The method utilizes proteins that are prepared for regular antibody microarray experiments. There is no need to use exogenous positive or negative reference markers and no need to determine the absolute concentration of each individual protein in the sample. Validation experiments support the basic principle of the proposed approach. This method can be a useful tool for assessing the outcome accuracy of microarray experiments.

| S-EPMC2291551 | biostudies-literature

Integrating multiple microarray data for cancer pathway analysis using bootstrapping K-S test.

Project description:Previous applications of microarray technology for cancer research have mostly focused on identifying genes that are differentially expressed between a particular cancer and normal cells. In a biological system, genes perform different molecular functions and regulate various biological processes via interactions with other genes thus forming a variety of complex networks. Therefore, it is critical to understand the relationship (e.g., interactions) between genes across different types of cancer in order to gain insights into the molecular mechanisms of cancer. Here we propose an integrative method based on the bootstrapping Kolmogorov-Smirnov test and a large set of microarray data produced with various types of cancer to discover common molecular changes in cells from normal state to cancerous state. We evaluate our method using three key pathways related to cancer and demonstrate that it is capable of finding meaningful alterations in gene relations.

| S-EPMC2688657 | biostudies-literature

Weighted analysis of general microarray experiments.

Project description:BackgroundIn DNA microarray experiments, measurements from different biological samples are often assumed to be independent and to have identical variance. For many datasets these assumptions have been shown to be invalid and typically lead to too optimistic p-values. A method called WAME has been proposed where a variance is estimated for each sample and a covariance is estimated for each pair of samples. The current version of WAME is, however, limited to experiments with paired design, e.g. two-channel microarrays.ResultsThe WAME procedure is extended to general microarray experiments, making it capable of handling both one- and two-channel datasets. Two public one-channel datasets are analysed and WAME detects both unequal variances and correlations. WAME is compared to other common methods: fold-change ranking, ordinary linear model with t-tests, LIMMA and weighted LIMMA. The p-value distributions are shown to differ greatly between the examined methods. In a resampling-based simulation study, the p-values generated by WAME are found to be substantially more correct than the alternatives when a relatively small proportion of the genes is regulated. WAME is also shown to have higher power than the other methods. WAME is available as an R-package.ConclusionThe WAME procedure is generalized and the limitation to paired-design microarray datasets is removed. The examined other methods produce invalid p-values in many cases, while WAME is shown to produce essentially valid p-values when a relatively small proportion of genes is regulated. WAME is also shown to have higher power than the examined alternative methods.

| S-EPMC2175522 | biostudies-literature

Significance analysis of time course microarray experiments.

Project description:Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes. This method is applied to two studies on humans. In one study, genes are identified that show differential expression over time in response to in vivo endotoxin administration. By using our method, 7,409 genes are called significant at a 1% false-discovery rate level, whereas several existing approaches fail to identify any genes. In another study, 417 genes are identified at a 10% false-discovery rate level that show expression changing with age in the kidney cortex. Here it is also shown that as many as 47% of the genes change with age in a manner more complex than simple exponential growth or decay. The methodology proposed here has been implemented in the freely distributed and open-source edge software package.

| S-EPMC1201697 | biostudies-literature

Assessing the importance of target type for oligonucleotide microarray experiments

2010-07-27 | GSE23168 | GEO

Quantitative noise analysis for gene expression microarray experiments.

Project description:A major challenge in DNA microarray analysis is to effectively dissociate actual gene expression values from experimental noise. We report here a detailed noise analysis for oligonuleotide-based microarray experiments involving reverse transcription, generation of labeled cRNA (target) through in vitro transcription, and hybridization of the target to the probe immobilized on the substrate. By designing sets of replicate experiments that bifurcate at different steps of the assay, we are able to separate the noise caused by sample preparation and the hybridization processes. We quantitatively characterize the strength of these different sources of noise and their respective dependence on the gene expression level. We find that the sample preparation noise is small, implying that the amplification process during the sample preparation is relatively accurate. The hybridization noise is found to have very strong dependence on the expression level, with different characteristics for the low and high expression values. The hybridization noise characteristics at the high expression regime are mostly Poisson-like, whereas its characteristics for the small expression levels are more complex, probably due to cross-hybridization. A method to evaluate the significance of gene expression fold changes based on noise characteristics is proposed.

| S-EPMC137831 | biostudies-literature

HoughFeature, a novel method for assessing drug effects in three-color cDNA microarray experiments.

Project description:BACKGROUND: Three-color microarray experiments can be performed to assess drug effects on the genomic scale. The methodology may be useful in shortening the cycle, reducing the cost, and improving the efficiency in drug discovery and development compared with the commonly used dual-color technology. A visualization tool, the hexaMplot, is able to show the interrelations of gene expressions in normal-disease-drug samples in three-color microarray data. However, it is not enough to assess the complicated drug therapeutic effects based on the plot alone. It is important to explore more effective tools so that a deeper insight into gene expression patterns can be gained with three-color microarrays. RESULTS: Based on the celebrated Hough transform, a novel algorithm, HoughFeature, is proposed to extract line features in the hexaMplot corresponding to different drug effects. Drug therapy results can then be divided into a number of levels in relation to different groups of genes. We apply the framework to experimental microarray data to assess the complex effects of Rg1 (an extract of Chinese medicine) on Hcy-related HUVECs in details. Differentially expressed genes are classified into 15 functional groups corresponding to different levels of drug effects. CONCLUSION: Our study shows that the HoughFeature algorithm can reveal natural cluster patterns in gene expression data of normal-disease-drug samples. It provides both qualitative and quantitative information about up- or down-regulated genes. The methodology can be employed to predict disease susceptibility in gene therapy and assess drug effects on the disease based on three-color microarray data.

| S-EPMC1965489 | biostudies-literature

Assessing Statistical Significance in Microarray Experiments Using the Distance Between Microarrays

Project description:We propose a method to compare the location and variability of gene ex-pression between two groups of microarrays using a permutation test based on the pairwise distance between microarrays. The microarrays could be samples from distinct clinical or biological populations or microarrays prepared at two different levels of an experimental factor. For these tests the entire microarray or some pre-specifed subset of genes, not the individual gene, is the unit of analysis. We apply this method to compare results from two dfferent protocols for preparing labeled targets for microarray hybridization and their subsequent gene expression analysis. Keywords: Comparative genomic expression

2010-09-13 | GSE9424 | GEO

Assessing the importance of target type for oligonucleotide microarray experiments

Project description:In order to assess the effect on signal variability of the type of target used for oligonucleotide microarray experiments, we hybridized double and single stranded DNA of three genes on a Nimblegen High Density microarray. This slide carries specific probes (two lengths: 30 and 50 nucleotides, for a total of 7456 probes) targeting every region of selected genes. Probes of 30 nucleotides and 50 nucleotides, covering the full length of three genes (ahdA1C, mmoC and vcrA) by a one nucleotide step, were designed and in situ synthesized in six replicates on a 4*72K Nimblegen Custom Microarray. Double and single stranded DNA of the three genes were hybridized as target.

2010-07-27 | E-GEOD-23168 | biostudies-arrayexpress

Advanced spot quality analysis in two-colour microarray experiments.

Project description:BACKGROUND: Image analysis of microarrays and, in particular, spot quantification and spot quality control, is one of the most important steps in statistical analysis of microarray data. Recent methods of spot quality control are still in early age of development, often leading to underestimation of true positive microarray features and, consequently, to loss of important biological information. Therefore, improving and standardizing the statistical approaches of spot quality control are essential to facilitate the overall analysis of microarray data and subsequent extraction of biological information. FINDINGS: We evaluated the performance of two image analysis packages MAIA and GenePix (GP) using two complementary experimental approaches with a focus on the statistical analysis of spot quality factors. First, we developed control microarrays with a priori known fluorescence ratios to verify the accuracy and precision of the ratio estimation of signal intensities. Next, we developed advanced semi-automatic protocols of spot quality evaluation in MAIA and GP and compared their performance with available facilities of spot quantitative filtering in GP. We evaluated these algorithms for standardised spot quality analysis in a whole-genome microarray experiment assessing well-characterised transcriptional modifications induced by the transcription regulator SNAI1. Using a set of RT-PCR or qRT-PCR validated microarray data, we found that the semi-automatic protocol of spot quality control we developed with MAIA allowed recovering approximately 13% more spots and 38% more differentially expressed genes (at FDR = 5%) than GP with default spot filtering conditions. CONCLUSION: Careful control of spot quality characteristics with advanced spot quality evaluation can significantly increase the amount of confident and accurate data resulting in more meaningful biological conclusions.

| S-EPMC2556690 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data