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. 2012;7(7):e40321.
doi: 10.1371/journal.pone.0040321. Epub 2012 Jul 18.

Gene Expression Commons: An Open Platform for Absolute Gene Expression Profiling

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Free PMC article

Gene Expression Commons: An Open Platform for Absolute Gene Expression Profiling

Jun Seita et al. PLoS One. .
Free PMC article

Abstract

Gene expression profiling using microarrays has been limited to comparisons of gene expression between small numbers of samples within individual experiments. However, the unknown and variable sensitivities of each probeset have rendered the absolute expression of any given gene nearly impossible to estimate. We have overcome this limitation by using a very large number (>10,000) of varied microarray data as a common reference, so that statistical attributes of each probeset, such as the dynamic range and threshold between low and high expression, can be reliably discovered through meta-analysis. This strategy is implemented in a web-based platform named "Gene Expression Commons" (https://gexc.stanford.edu/) which contains data of 39 distinct highly purified mouse hematopoietic stem/progenitor/differentiated cell populations covering almost the entire hematopoietic system. Since the Gene Expression Commons is designed as an open platform, investigators can explore the expression level of any gene, search by expression patterns of interest, submit their own microarray data, and design their own working models representing biological relationship among samples.

Conflict of interest statement

Competing Interests: ILW, DLD, DS, and JS are named as inventors on a patent application for the Digital Pattern Profiling of Gene Expression filed April 16, 2012. This does not alter the authors’ adherence to all PLoS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Absolute gene expression profiling with a large-scale common reference and a probeset meta profile database.
(A) Relative vs. absolute gene expression profiling. Conventional methods compare differences in gene expression between samples within an individual experiment, and result in relative values only (left). In Gene Expression Commons, raw microarray data is individually normalized against a large-scale common reference, then mapped onto the probeset meta profile. This strategy enables profiling of absolute expression levels of all genes on the microarray (right). (B) Relationship between the size of the common reference and the accuracy of the probeset dynamic range estimation. The result of one out of five experiments is shown. The Y-axis represents % probesets with false estimation of dynamic range in mean ± S.E.M (n = 10). (C) The dynamic range versus the mean of each probeset in Affymetrix Mouse 430 2.0 (n = 11,939) (left) and Affymetrix Human U133 Plus 2 (n = 25,229) (right) presented by a density plot and histograms. (D) Graphical representation of probeset meta profile. The Y-axis represents expression intensity without units in log2 scale. The distribution of expression levels is displayed by a histogram (right side of the axis). The high/low threshold computed is shown by a solid bar, and the distribution of percentiles in either the high or low expression range is indicated by a gradation of color, displayed as highest (+100%) in dark red, threshold (0%) in white, lowest (−100%) in dark blue (left side of the axis). Four diverse distributions of probesets for four different genes (Aak1, Rbx1, Hif1a and Ikzf1) (left), and diverse distribution of four probesets of one gene (Il16) (right) are shown.
Figure 2
Figure 2. Structure and Workflow of Gene Expression Commons.
(A) Functional layers of Gene Expression Commons system. Users can select a Model of interest, and search for absolute gene expression through an intuitive web interface. A Model is a searchable category representing a biological context and displaying relationships among Populations. A Population contains several microarray data, which are biological replicates. Users can submit their own Populations, and design Models with a privacy control feature. (B) Seamless search flow at Gene Expression Commons. Gene Name Search provides absolute gene expression of a particular gene (path a). Expression Pattern Search provides a list of genes with expression patterns matching the expression pattern of interest designed by user (path b). From the list of genes, absolute expression of a particular gene is displayed with one click. From the absolute gene expression of a particular gene, the user can obtain a list of genes with the same expression pattern (path d).
Figure 3
Figure 3. Gene expression pattern profiling of mouse and human hematopoiesis.
Number of genes expressed at very low levels (dark blue), low levels (light blue), high levels (light red) and very high levels (dark red) in either the Mouse Hematopoiesis Model (A, left) or Human Hematooiesis Model (B, left). Number of genes expressed at high (red) and low (blue) levels in a specific population in Mouse Hematopoiesis Model (A, right) and Human Hematopoiesis Model (B, right). A list of genes matching each criterion can be obtained with a few clicks on Gene Expression Commons (https://gexc.stanford.edu/).

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