Model fitting.

<p>Train (a) and test (b) log likelihood of the negative control data for the two proposed models, and the baseline, varying the number of phenotypic classes. Green corresponds to the copula based model, red corresponds to the gaussian model, and black corresponds to the baseline model. For training log likelihood, we picked the best model among 10 random restarts of the algorithm. For the test log likelihood, the boxes account for the variability among ten different splits of the data in a cross validation setting. Given a data split, for each fold and each number of classes, we picked the best model among 5 random restarts of the algorithm.</p

Within population variability.

<p>Comparison of the dispersion of fields belonging to the same wells (boxplot A) and randomly selected fields (boxplot B). The measure of dispersion is the sum of squared pairwise distances. The population descriptors (cell count and proportions of cells in S, G2, M and apoptotic states) have been scaled beforehand.</p

Novelty detection and positive controls.

<p>Density plot of cell population descriptors averaged over wells (panel <b>(a)</b> to <b>(e)</b>) and log likelihood (panel <b>(f)</b>) given by the model trained on negative controls. Positive controls are very different from negative controls. It is easy to distinguish them from negative controls only looking at cell count. The log likelihood given by the model separates the two type of controls. We observe that the discriminative power of the univariate descriptors is not lost when considering the model likelihood.</p

Association between population descriptors.

<p>Association between cell count and proportion of cells in different states based on negative controls. The measure of association is Spearman's rho and the p-value is computed via the asymptotic t approximation <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042715#pone.0042715-Hollander1" target="_blank">[36]</a>.</p

Model and empirical distributions.

<p>Examples of classes found by the model (Copula model on the left, gaussian model on the right). The proportion of cells in apoptotic state is represented for the cell populations belonging to those classes. We compare for two classes the univariate marginal densities. For each class the empirical density is represented with a solid line and the density fitted by the model is represented with a broken line.</p

Example of a well.

<p><b>Panels</b> (<b>a</b>) <b>to</b> (<b>e</b>), the density plots represent the distribution of cell population descriptors averaged over wells for the negative control dataset. Red lines are the values of the 4 fields of the considered well and the blue lines are the population descriptors averaged over the 4 fields. <b>Panel</b> (<b>f</b>) represents the density of the log likelihood for all negative controls. The blue vertical line represents the log-likelihood of the considered well.</p

Experimental sensitivity, specificity and accuracy of functional assays and siRNA screening using the experimental best cut-off.

<p>Experimental sensitivity, specificity and accuracy of functional assays and siRNA screening using the experimental best cut-off.</p

High throughput siRNA screening and fluctuation of the best cut-off.

<p>An average of 150 human prostate tumoral cells were plated, treated with siRNAs targeting the indicated gene and grown for 72 hours before cell counting. The WT reference (No siRNA), the positive control of cell growth inhibition (siKIF11), the two negative controls of cell growth inhibition (siGOLGA2 and siGL2) and 8 among 406 siRNA targeted genes are shown. The complete analysis of the 406 targeted genes is available using the ProClass toolbox and the included siRNA full.txt file, as explained at the end of the README.doc file. (<b>A</b>) Waterfall distribution of cell growth after siRNA treatment, according to median values (standard method). As in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006096#pgen.1006096.g001" target="_blank">Fig 1A</a>, except that boxplot representation results from 12 (siRNA) or 1,140 (No siRNA) values. (<b>B</b>) Waterfall distribution according to p values (MWW method), as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006096#pgen.1006096.g001" target="_blank">Fig 1B</a>. (<b>C</b>) Classification of the siRNA targeted genes, as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006096#pgen.1006096.g002" target="_blank">Fig 2B</a>, except that probabilities are related to cell growth inhibition, with the corresponding five-class nomenclature: "no inhibition" (blue, class1), "likely no inhibition" (light blue, class2), "unclear inhibition" (grey, class3), "likely inhibition" (light red, class4) and "inhibition" (red, class5).</p

Relative position of the variants in the Colony Size assay and fluctuation of the best cut-off.

<p>(<b>A</b>) Waterfall distribution of colony sizes, according to median values (standard method). Boxplot representation results from 9 (mutants) or 36 (BRCA1 and Vector) colony size values. The red and blue colors of the boxes indicate the pathogenic and neutral mutations, respectively, according to their prior classification. Box central bar, median; box, interquartile range (50% of the distribution); whiskers, extreme values; dotted horizontal line, median of BRCA1; thick horizontal line, experimental best cut-off (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006096#pgen.1006096.s004" target="_blank">S2 Fig</a>). The distribution of the best cut-off fluctuation, obtained after random sampling (bootstrap), of the 9 mutants and 36 BRCA1 values, is visualized by the pink, grey and light blue areas, that delimit 4%, 90% and 4.9% of the distribution, respectively, which altogether represents a total coverage of 98.9%. (<b>B</b>) Waterfall distribution according to p values (MWW method). The p value assigned to each variant is symbolized by a segment. The upside-down representation facilitates the comparison of the mutation arrangement with the one obtained in <b>A</b>. Arrows pinpoint a modification of the mutation rank depending on the method used. Framed mutations indicate identical p values (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006096#pgen.1006096.s033" target="_blank">S4 Table</a>). Segment colors, thick horizontal line and colored areas, as in <b>A</b>.</p

Variant classification using the probability system.

<p>(<b>A</b>) Schematic of the probability system of classification. The left figure depicts a theoretical waterfall distribution of pathogenic and neutral missense mutations, as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006096#pgen.1006096.g001" target="_blank">Fig 1B</a>. Horizontal black line, experimental best cut-off. (1) Variant classification according to the experimental best cut-off (method used in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006096#pgen.1006096.t001" target="_blank">Table 1</a>). (2) Distribution of the best cut-off generated by bootstrap analysis from the experimental data. (3) Cumulative distribution function (CDF) derived from the distribution of the best cut-off. This CDF provides a probabilistic classification of the variants, depending on their positions in the CDF. (<b>B</b>) Classification of the <i>BRCA1</i> variants assessed in four functional assays. Colored background in the table indicates the five-class nomenclature, as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006096#pgen.1006096.s030" target="_blank">S1 Table</a>. Names in red and blue indicate the pathogenic and neutral mutations, respectively, according to their prior classification. The sensitivity, specificity and accuracy computation are detailed in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006096#pgen.1006096.s035" target="_blank">S6 Table</a>.</p