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. 1999 Mar 16;96(6):2907-12.
doi: 10.1073/pnas.96.6.2907.

Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation

P Tamayo  1 , D Slonim, J Mesirov, Q Zhu, S Kitareewan, E Dmitrovsky, E S Lander, T R Golub
Affiliations

Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation

P Tamayo et al. Proc Natl Acad Sci U S A. .

Abstract

Array technologies have made it straightforward to monitor simultaneously the expression pattern of thousands of genes. The challenge now is to interpret such massive data sets. The first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidimensional data. The method has been implemented in a publicly available computer package, GENECLUSTER, that performs the analytical calculations and provides easy data visualization. To illustrate the value of such analysis, the approach is applied to hematopoietic differentiation in four well studied models (HL-60, U937, Jurkat, and NB4 cells). Expression patterns of some 6,000 human genes were assayed, and an online database was created. GENECLUSTER was used to organize the genes into biologically relevant clusters that suggest novel hypotheses about hematopoietic differentiation-for example, highlighting certain genes and pathways involved in "differentiation therapy" used in the treatment of acute promyelocytic leukemia.

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Figures

Figure 1
Figure 1
Principle of SOMs. Initial geometry of nodes in 3 × 2 rectangular grid is indicated by solid lines connecting the nodes. Hypothetical trajectories of nodes as they migrate to fit data during successive iterations of SOM algorithm are shown. Data points are represented by black dots, six nodes of SOM by large circles, and trajectories by arrows.
Figure 2
Figure 2
Yeast Cell Cycle SOM. (a) 6 × 5 SOM. The 828 genes that passed the variation filter were grouped into 30 clusters. Each cluster is represented by the centroid (average pattern) for genes in the cluster. Expression level of each gene was normalized to have mean = 0 and SD = 1 across time points. Expression levels are shown on y-axis and time points on x-axis. Error bars indicate the SD of average expression. n indicates the number of genes within each cluster. Note that multiple clusters exhibit periodic behavior and that adjacent clusters have similar behavior. (b) Cluster 29 detail. Cluster 29 contains 76 genes exhibiting periodic behavior with peak expression in late G1. Normalized expression pattern of 30 genes nearest the centroid are shown. (c) Centroids for SOM-derived clusters 29, 14, 1, and 5, corresponding to G1, S, G2 and M phases of the cell cycle, are shown. (d) Centroids for groups of genes identified by visual inspection by Cho et al. (4) as having peak expression in G1, S, G2, or M phase of the cell cycle are shown.
Figure 3
Figure 3
HL-60 SOM. HL-60 cells were treated with PMA for 0, 0.5, 4, or 24 hours, and expression levels of more than 6,000 genes were measured at each time point. The 567 genes passing the variation filter were grouped by a 4 × 3 SOM.
Figure 4
Figure 4
Hematopoietic-Differentiation SOM. The 1,036 genes varying in at least one of four cell lines were used to generate a 6 × 4 SOM. Time courses for four cell lines are shown (Left to Right): HL-60 + PMA, U937 + PMA, NB4 + ATRA, Jurkat + PMA.
Figure 5
Figure 5
G0S2 Regulation. Cells were treated with the neutrophil-differentiating agents ATRA or dimethyl sulfoxide for the times (in hours) indicated. RNA was subjected to Northern analysis with a G0S2 probe (Upper). The blots were then reprobed for glyceraldehyde-3-phosphate dehydrogenase as a loading control (Lower). NB4-S1 is an ATRA-sensitive subclone of NB4. NB4-R1 and NB4-R2 are subclones that fail to differentiate after ATRA treatment. NB4-R2 has a point mutation in PML/RARα; the mechanism of ATRA resistance in NB4-R1 is unknown.
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