PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 12707371

Analysis of microarray data using Z score transformation.

Chris Cheadle·Marquis P Vawter·William J Freed·Kevin G Becker
View original source ↗Export metadata ↓

Abstract

High-throughput cDNA microarray technology allows for the simultaneous analysis of gene expression levels for thousands of genes and as such, rapid, relatively simple methods are needed to store, analyze, and cross-compare basic microarray data. The application of a classical method of data normalization, Z score transformation, provides a way of standardizing data across a wide range of experiments and allows the comparison of microarray data independent of the original hybridization intensities. Data normalized by Z score transformation can be used directly in the calculation of significant changes in gene expression between different samples and conditions. We used Z scores to compare several different methods for predicting significant changes in gene expression including fold changes, Z ratios, Z and t statistical tests. We conclude that the Z score transformation normalization method accompanied by either Z ratios or Z tests for significance estimates offers a useful method for the basic analysis of microarray data. The results provided by these methods can be as rigorous and are no more arbitrary than other test methods, and, in addition, they have the advantage that they can be easily adapted to standard spreadsheet programs.

Explore related subjects

Cluster AnalysisDNA, ComplementaryData Interpretation, StatisticalDown-RegulationHumansJurkat CellsModels, StatisticalNucleic Acid HybridizationOligonucleotide Array Sequence AnalysisRNAStatistics as TopicUp-RegulationJournal ArticleResearch Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chris Cheadle, Marquis P Vawter, William J Freed, Kevin G Becker. 2003. Analysis of microarray data using Z score transformation.. https://doi.org/10.1016/s1525-1578(10)60455-2

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data.

MOTIVATION: Combining omics and images can lead to a more comprehensive clustering of individuals than classic single-view approaches. Among the various approaches for multi-view clustering, nonnegative matrix tri-factorization (NMTF) and nonnegative Tucker decomposition (NTD) are advantageous in learning low-rank embeddings with promising interpretability. Besides, there is a need to handle unwanted drivers of clusterings (i.e. confounders). RESULTS: In this work, we introduce a novel multi-view clustering method based on NMTF and NTD, named INMTD, which integrates omics and 3D imaging data to derive unconfounded subgroups of individuals. According to the adjusted Rand index, INMTD outperformed other clustering methods on a synthetic dataset with known clusters. In the application to real-life facial-genomic data, INMTD generated biologically relevant embeddings for individuals, genetics, and facial morphology. By removing confounded embedding vectors, we derived an unconfounded clustering with better internal and external quality; the genetic and facial annotations of each derived subgroup highlighted distinctive characteristics. In conclusion, INMTD can effectively integrate omics data and 3D images for unconfounded clustering with biologically meaningful interpretation. AVAILABILITY AND IMPLEMENTATION: INMTD is freely available at https://github.com/ZuqiLi/INMTD.

Cluster Analysis↗
PubMed2005

Fuzzy species among recombinogenic bacteria.

BACKGROUND: It is a matter of ongoing debate whether a universal species concept is possible for bacteria. Indeed, it is not clear whether closely related isolates of bacteria typically form discrete genotypic clusters that can be assigned as species. The most challenging test of whether species can be clearly delineated is provided by analysis of large populations of closely-related, highly recombinogenic, bacteria that colonise the same body site. We have used concatenated sequences of seven house-keeping loci from 770 strains of 11 named Neisseria species, and phylogenetic trees, to investigate whether genotypic clusters can be resolved among these recombinogenic bacteria and, if so, the extent to which they correspond to named species. RESULTS: Alleles at individual loci were widely distributed among the named species but this distorting effect of recombination was largely buffered by using concatenated sequences, which resolved clusters corresponding to the three species most numerous in the sample, N. meningitidis, N. lactamica and N. gonorrhoeae. A few isolates arose from the branch that separated N. meningitidis from N. lactamica leading us to describe these species as 'fuzzy'. CONCLUSION: A multilocus approach using large samples of closely related isolates delineates species even in the highly recombinogenic human Neisseria where individual loci are inadequate for the task. This approach should be applied by taxonomists to large samples of other groups of closely-related bacteria, and especially to those where species delineation has historically been difficult, to determine whether genotypic clusters can be delineated, and to guide the definition of species.

Cluster Analysis↗
PubMed2003

Scaling and clustering in the study of semantic disruptions in patients with schizophrenia: a re-evaluation.

Some recent studies of semantics in schizophrenia have employed multidimensional scaling and clustering techniques to analyse verbal fluency and triadic comparison data. The conclusions have been: (i) patients generate fewer words in fluency tasks and display more variable similarity groupings of words in triadic tasks, and (ii) this is due to deficits in semantics. We analysed data from both tasks. On the verbal fluency task, patients produced significantly fewer responses than controls. The results also showed little patient-specific inter-individual consistency. Similarly, for triadic comparison data, we did not find much patient-specific inter-individual consistency. When correlating patients' results at different measurement times with means of controls, the data of individual patients (at either of the two measurement times) were not predicted better from their data at the other measurement time than from controls. This latter finding suggests little patient-specific intra-individual consistency and, thus, pleads against idiosyncratic semantic deficits. Our findings do not refute the hypothesis that schizophrenia is associated with semantic disruptions. However, our results demonstrate that because of severe statistical restrictions and requirements associated with some scaling and clustering techniques, these methods may not be as useful in this enterprise as previously thought.

Cluster Analysis↗