Cluster analysis and display of genome-wide expression patterns.
Eisen MB; Spellman PT; Brown PO; Botstein D.
Proceedings of the National Academy of Sciences of the United States of America, 1998 Dec 8, 95(25):14863-8.
A system of cluster analysis for genome-wide expression data from DNA
microarray hybridization is described that uses standard statistical
algorithms to arrange genes according to similarity in pattern of gene
expression. The output is displayed graphically, conveying the clustering
and the underlying expression data simultaneously in a form intuitive for
biologists. We have found in the budding yeast Saccharomyces cerevisiae
that clustering gene expression data groups together efficiently genes of
known similar function, and we find a similar tendency in human data. Thus
patterns seen in genome-wide expression experiments can be interpreted as
indications of the status of cellular processes. Also, coexpression of
genes of known function with poorly characterized or novel genes may
provide a simple means of gaining leads to the functions of many genes for
which information is not available currently.
med_UI: 99061959 ID: 57
PDF
van Ruissen F, Ruijter JM, Schaaf GJ, Asgharnegad L, Zwijnenburg DA, Kool M, Baas F.
BMC Genomics. 2005 Jun 14;6:91.
BACKGROUND: Serial Analysis of Gene Expression (SAGE) and microarrays have found
a widespread application, but much ambiguity exists regarding the evaluation of
these technologies. Cross-platform utilization of gene expression data from the
SAGE and microarray technology could reduce the need for duplicate experiments
and facilitate a more extensive exchange of data within the research community.
This requires a measure for the correspondence of the different gene expression
platforms. To date, a number of cross-platform evaluations (including a few
studies using SAGE and Affymetrix GeneChips) have been conducted showing a
variable, but overall low, concordance. This study evaluates these overall
measures and introduces the between-ratio difference as a concordance measure
pergene. RESULTS: In this study, gene expression measurements of Unigene clusters
represented by both Affymetrix GeneChips HG-U133A and SAGE were compared using
two independent RNA samples. After matching of the data sets the final comparison
contains a small data set of 1094 unique Unigene clusters, which is unbiased with
respect to expression level. Different overall correlation approaches, like
Up/Down classification, contingency tables and correlation coefficients were used
to compare both platforms. In addition, we introduce a novel approach to compare
two platforms based on the calculation of differences between expression ratios
observed in each platform for each individual transcript. This approach results
in a concordance measure per gene (with statistical probability value), as
opposed to the commonly used overall concordance measures between platforms.
CONCLUSION: We can conclude that intra-platform correlations are generally good,
but that overall agreement between the two platforms is modest. This might be due
to the binomially distributed sampling variation in SAGE tag counts, SAGE
annotation errors and the intensity variation between probe sets of a single gene
in Affymetrix GeneChips. We cannot identify or advice which platform performs
better since both have their (dis)-advantages. Therefore it is strongly
recommended to perform follow-up studies of interesting genes using additional
techniques. The newly introduced between-ratio difference is a
filtering-independent measure for between-platform concordance. Moreover, the
between-ratio difference per gene can be used to detect transcripts with similar
regulation on both platforms.
med_UI: 15955238 ID: 157
PDF