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Jan 8, 2011 - are: (1) supervised learning, (2) reinforcement learning and (3) unsupervised learning. Unsupervised learning is distinguished from the others ...
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In order to determine if these clusters were recognizable using a distance-based method, we chose two hierarchical clustering methods that use Euclidean.
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Oncotarget Supplementary Materials
Model-based unsupervised learning informs metformininduced cell-migration inhibition through an AMPK-independent mechanism in breast cancer Supplementary Materials Supplementary Section 1 Cluster validation using distance-based hierarchical methods In order to determine if these clusters were recognizable using a distance-based method, we chose two hierarchical clustering methods that use Euclidean distances to find the clusters in metformin-treated cells. Agglomerative hierarchical clustering takes a bottom-up approach by iteratively joining the closest cells; divisive hierarchical clustering takes a top-down approach by first forming clusters with maximized intercluster distance and breaking them down until intercluster distance is minimized. We tested baseline and metformin-treated cells with both agglomerative hierarchical clustering and
divisive hierarchical clustering methods, each of which uses a distance matrix of pairwise Euclidean distances between cells as input. Both hierarchical methods found three clusters in metformin-treated cells, with one cluster comprising the same cells as M2, and with the difference in other clusters being that one cell from M1 was added to M3, with the rest remaining the same. Thus, we were able to establish consistency among the clustering results using different approaches that measured statistical distances between cells (where MiMoSA uses distribution-based distances and hierarchical clustering uses Euclidean distances). Because of the consistent clustering results and high statistical significance in the expression levels of the 230 genes in cluster M2 in comparison with all other clusters, we chose this set of 230 genes for further analyses.
Supplementary Figure 1: The probability density function (PDF) of gene expression within a cell, and the model-fit for the expressions under a Gaussian mixture model with exponential distributions. From a statistical perspective, we tested the samples drawn from the two distributions using the Mann-Whitney U-test and Kolmogorov-Smirnov test (KS-test), and the null hypothesis (samples drawn from the identical distribution) was accepted with p-value of 0.74 (0.5869 for KS-test) being greater than significance level of 0.05.
Supplementary Table 1: List of pathways significantly enriched in the 230-gene set. See Supplementary_Table_1
Supplementary Table 2: The 230 differentially expressed genes (bolded and highlighted are 24 genes with less literature evidence in metformin response) ATP5F1 ATP6V0B C1orf31 CCT3 CDC42 CNIH4 DDAH1 DNTTIP2 GAS5 NDUFS5 NME7 PARK7 PPT1 RHOC RPS8 SDHB SSR2 TAGLN2 UBE2T YBX1 ZBTB8OS ZMYM6NB CCT4 COX5B COX7A2L DPY30 FARSB HAT1 LINC00152 MRPL33 NCL OST4 PTRHD1 RNF181 RRM2 SF3B14 SSB TXNDC9 VAMP8 YWHAQ ARPC4 C3orf78 CCDC72 IMPDH2 LSM3 POLR2H
