Psychological diagnostics and its role in recruitment

Tato práce je rozdělena na dvě části. Cílem první části je přiblížit psychodiagnostiku jako aplikovanou vědní disciplínu, popsat její vývoj a nastínit prameny, ze kterých čerpá. Dále jsem se zaměřila na její jednotlivé diagnostické metody a bližší seznámení s jednotlivými diagnostickými nástroji. Návazně je zařazena druhá část. Jejím cílem je ukázat, jak se metody psychologické diagnostiky konkrétně uplatňují ve výběrových řízeních realizovaných formou Assessment center. K ilustraci slouží případové studie ve společnostech Image Lab, ČSOB, KPMG

Additional file 1: of EnrichedHeatmap: an R/Bioconductor package for comprehensive visualization of genomic signal associations

Data and source code for producing Figs. 1 and 2. (GZ 45195 kb

Data processing.

<p>(<b>A</b>) For each protein coding or lincRNA gene we consider the +/− 2kb region around the outmost TSS (left two vertical dashed lines), the entire transcript (the “gene body”), and the +/− 2kb region around the outmost TTS (right two vertical dashed lines) and count the number of tags for each mark that fall into each region. (<b>B</b>) For each constellation we obtain a matrix, where each entry contains the enrichment of a particular mark at a particular gene (for a given gene region). Here, as an illustrating example an excerpt of the matrix for the region around TSSs of protein coding genes in K562 is shown.</p

Pathways of EGF receptor internalization.

<p>EGF receptor can be internalized via clathrin dependent endocytosis (CDE) and clathrin independent endocytosis (CIE). <b>A</b>) Schematic representation of EGF receptor internalization according to Schmidt-Glenewinkel et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082593#pone.0082593-SchmidtGlenewinkel1" target="_blank">[21]</a>. <b>B</b>) Example trajectory representing class 0 (‘pathway off’) for clathrin dependent EGFR-internalization. <b>C</b>) Example trajectory representing class 1 (‘pathway on’) for clathrin independent EGFR- internalization.</p

Misclassification error and decision tree for EGF receptor internalization.

<p><b>A</b>) Misclassification error decreases depending on the number of terminal nodes of the decision tree in B). <b>B</b>) The full decision tree contains four terminal nodes and yields a misclassification of less than 1% for both training and test.</p

Predicting CAGE gene expression.

<p>(<b>A</b>) Scatter plot between predicted and measured values (when using 10-fold CV) for CAGE gene expression for protein coding genes in K562 cells when 40-bin resolution data was taken for the input marks of the MARS model (pseudocount <i>ε</i> optimized). (<b>B</b>) Barplot of Pearson’s r (when using 10-fold CV) for different models for protein coding genes. The bar labels are encoded by their model index, where the first letter represents the cell line (K = K562, G = GM12878, H = H1, I = IMR90), the middle symbols stands for the data input (1 = 1-bin resolution, 40 = 40-bin resolution, 40 m = middle two bins for each mark in 40-bin resolution), and the latter represents the model type (L = linear model, M = MARS model). For each of these the pseudocount <i>ε</i> was optimized. (<b>C</b>) Scatter plot between predicted and measured values for CAGE gene expression for the protein coding genes in GM12878, when a MARS model on 40-bin resolution data was fitted in K562 cells. The pseudocount <i>ε</i> is the same for calculating the logarithmized gene expression in both cell lines by using the optimized <i>ε</i> for K562 cells in the 10-fold CV setting. (<b>D</b>) Barplot of Pearson’s r values for protein coding genes, when considering each possible ordered pair of different cell lines (analogous to (<b>C</b>), with labels as in (<b>B</b>)), shown in blue, and the Pearson’s r (when using 10-fold CV) for individual cell lines, shown in red, when using MARS models with 40-bin resolution.</p

Prediction of a mark by all other marks.

<p>(<b>A</b>) Histogram of Pearson’s r values between measured and predicted values using 10-fold CV for all marks around the TSSs of protein coding genes in K562 cells. (<b>B,C,D</b>) Scatter plot comparing predicted and measured values (10-fold CV) for (<b>B</b>) DNA methylation, (<b>C</b>) H3K4me3, and (<b>D</b>) H3K27me3 around the TSSs of protein coding genes in K562 cells. The line “<i>y</i> = <i>x</i>” is indicated in red for reference. (<b>E</b>) Mark weight distribution in the linear model fitted for <i>CEBPB</i> on 100% of the data around TSSs of protein coding genes in K562. (<b>F</b>) Barplot of selected mark types for different mark types from the linear models fitted for all marks on 100% of the data for TSSs of protein coding genes in H1. We considered the four different mark types (chromatin remodelers, coregulators, epigenetic marks, and transcription factors) and calculated the relative frequency of each mark type (dark blue bars). Then, for each mark we considered all mark weights of these four types, i.e., without those with an unknown respectively not regulating function. We took a 95% quantile cutoff over all absolute weights, where we considered the weights for all mark models combined. For each mark type, we considered those weights in the linear models for each mark of that respective type, whose absolute weight was above the cutoff, grouped these weights according to the type of the input mark, and plotted the respective relative frequency of each input mark type in the bars of the same color. The bars, where the predicted mark type and the input mark type are identical, are marked with a red star.</p

Decision trees resulting from analysis of the caspase activation model.

<p>Decision tree resulting from analysis of the model illustrated in Fig. 5 for a misclassification error of 25.7% (A) and 17.43% (B), respectively.</p

Full rule set extracted from the decision tree of Fig. 4.

<p>Full rule set extracted from the decision tree of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082593#pone-0082593-g004" target="_blank">Fig. 4</a>.</p

Compression of information content.

<p>(<b>A</b>) Barplot of median Pearson’s r for each mark comparing the measured and predicted values for all other marks in the region around TSSs of protein coding genes in H9 cells. For each mark, we predicted for each other mark the enrichments using 10-fold CV and fitted linear models with solely the given mark as input (plus constant). Then we calculated the median Pearson’s r between predicted and measured values for all these other marks. (<b>B</b>) Median Pearson’s r performance on all other marks when using 10-fold CV and the IHEC marks or the top 6 selected marks for each respective cell line. (<b>C</b>) Number of marks that are needed to exceed a given median Pearson’s r threshold in K562 cells.</p