Literature DB >> 16646784

Visualisation of gene expression data - the GE-biplot, the Chip-plot and the Gene-plot.

Yvonne E Pittelkow1, Susan R Wilson.   

Abstract

Visualisation methods for exploring microarray data are particularly important for gaining insight into data from gene expression experiments, such as those concerned with the development of an understanding of gene function and interactions. Further, good visualisation techniques are useful for outlier detection in microarray data and for aiding biological interpretation of results, as well as for presentation of overall summaries of the data. The biplot is particularly useful for the display of microarray data as both the genes and the chips can be simultaneously plotted. In this paper we describe several ordination techniques suitable for exploring microarray data, and we call these the GE-biplot, the Chip-plot and the Gene-plot. The general method is first evaluated on synthetic data simulated in accord with current biological interpretation of microarray data. Then it is applied to two well-known data sets, namely the colon data of Alon et al. (1999) and the leukaemia data of Golub et al. (1999). The usefulness of the approach for interpreting and comparing different analyses of the same data is demonstrated.

Entities:  

Year:  2003        PMID: 16646784     DOI: 10.2202/1544-6115.1019

Source DB:  PubMed          Journal:  Stat Appl Genet Mol Biol        ISSN: 1544-6115


  8 in total

1.  Mining gene expression profiles: an integrated implementation of kernel principal component analysis and singular value decomposition.

Authors:  Ferran Reverter; Esteban Vegas; Pedro Sánchez
Journal:  Genomics Proteomics Bioinformatics       Date:  2010-09       Impact factor: 7.691

2.  Primary metabolic pathways and signal transduction in sunflower (Helianthus annuus L.): comparison of transcriptional profiling in leaves and immature embryos using cDNA microarrays.

Authors:  Tarek Hewezi; Michel Petitprez; Laurent Gentzbittel
Journal:  Planta       Date:  2005-11-24       Impact factor: 4.116

3.  A comparison of methods for classifying clinical samples based on proteomics data: a case study for statistical and machine learning approaches.

Authors:  Dayle L Sampson; Tony J Parker; Zee Upton; Cameron P Hurst
Journal:  PLoS One       Date:  2011-09-28       Impact factor: 3.240

4.  Nonparametric estimation of LOH using Affymetrix SNP genotyping arrays for unpaired samples.

Authors:  Richard Huggins; Ling-Hui Li; You-Chin Lin; Alice L Yu; Hsin-Chou Yang
Journal:  J Hum Genet       Date:  2008-11-07       Impact factor: 3.172

5.  Kernel-PCA data integration with enhanced interpretability.

Authors:  Ferran Reverter; Esteban Vegas; Josep M Oller
Journal:  BMC Syst Biol       Date:  2014-03-13

6.  A new analysis tool for individual-level allele frequency for genomic studies.

Authors:  Hsin-Chou Yang; Hsin-Chi Lin; Mei-Chu Huang; Ling-Hui Li; Wen-Harn Pan; Jer-Yuarn Wu; Yuan-Tsong Chen
Journal:  BMC Genomics       Date:  2010-07-05       Impact factor: 3.969

7.  H-Profile plots for the discovery and exploration of patterns in gene expression data with an application to time course data.

Authors:  Yvonne E Pittelkow; Susan R Wilson
Journal:  BMC Bioinformatics       Date:  2007-12-20       Impact factor: 3.169

8.  Vascular microarray profiling in two models of hypertension identifies caveolin-1, Rgs2 and Rgs5 as antihypertensive targets.

Authors:  T Hilton Grayson; Stephen J Ohms; Therese D Brackenbury; Kate R Meaney; Kaiman Peng; Yvonne E Pittelkow; Susan R Wilson; Shaun L Sandow; Caryl E Hill
Journal:  BMC Genomics       Date:  2007-11-07       Impact factor: 3.969

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.