Stepwise demonstration of the image analysis method. Scale bar represents 100 micrometer.

<p>(a) Image stack obtained from the Hoechst stained nuclei channel. (b) Image stack obtained from the Rhodamine stained F-actin channel. (c) In-focus 2D image projected from the stacks of Hoechst stained nuclei channel. (d) In-focus 2D image projected from the stacks of Rhodamine stained F-actin channel. (e) Binary nuclear mask after segmentation by Watershed Masked Clustering. (f) Binary cellular mask after segmentation. The subpopulation classification result is also shown here. The green contour represents branched and interconnected complex networks. The red contour represents spherical colonies. (g) Quantitative parameters measured for each well of the 384-well plates. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109688#s2" target="_blank">Methods</a> for further description.</p

Classification of human breast cancer cell lines.

<p>(a) According to the cross-validation result, the smallest error rate was achieved when 8 features were selected. (b) A 3 dimensional PCA plot was generated based on these 8 selected features. Percentages of data variation preserved in each principle component were shown with each axis. Different categories of breast cancer cells are colored differently to show the separation between the various human breast cancer cell classes.</p

2D PCA plot of phenotype profiles for various active compounds and their concentration dependent phenotypic trajectories.

<p>(a) 2D PCA plot of phenotype profiles for negative control (DMSO) and 12 active compounds at different concentrations. Percentages of data variation preserved in each principle component are shown with each axis. Compounds with the same biological target are colored identically. Red: BCR-ABL target inhibitor; Yellow: VEFGR inhibitor; Green: EGFR inhibitor; Purple: HDAC inhibitor; Blue: c-MET inhibitor. Concentration is represented by the size of data points. The trend lines were added for each effective compound using 2nd polynomial regression models. (b) Comparison of microscope images of four example compounds with two DMSO control images. Each compound has a different biological target. 2D projected images from the Rhoadamine stained F-actin channel are shown here. Scale bar represents 500 micrometer</p

Characterization of cellular phenotype by clustering and classification.

<p>(a) Hierarchical clustering result using an average matrix as distance matrix. The scale of dendrogram is the natural logarithm of . (b) Five defined classes of test compounds and corresponding compounds and number of data points. (c) Classification result using multiple classification methods. Feature selection with search algorithm “forward” and criterion “Mahalanobis distance” was applied to detect optimal number of features. For each classification method and each number of selected features, 10 fold cross-validation was repeated 10 times, resulting in 10 error rates. The average error rates are shown in the chart with standard deviation as error bar. SVC means support vector machine classification.</p

An overview of the project workflow.

<p>The details are explained in the Methods and Results of the manuscript and more information on the individual data analysis steps can be found in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109688#pone.0109688.s014" target="_blank">File S1</a>.</p