Figure 1

Overview of InfectX high-content datasets, image analysis, and Parallel Mixed Model (PMM). (A) The figure shows example images of the different pathogens after siRNA transfection and the infection phase. The arrows indicate typical infectious phenotypes for each pathogen. The list shows an example of three single cell features that we extracted to identify infected cells for L. monocytogenes. The scale bar has a length of 50 μm. (B) For each selected feature, we defined the optimal threshold that separated best between uninfected and infected cells via histograms. We used the thresholds in the Decision Tree Infection Scoring (DTIS) algorithm to classify between infected (green) and non-infected cells (red). We optimized this procedure for each pathogen separately. (C) For each well in a 384-well assay plate, we calculated the infection index by dividing the number of infected cells (green) by the total number of cells (green and red). (D) The figure shows a schematic representation of the input data for the statistical analysis. Each point represents the average infection index over all its replicate wells (wells with the same siRNA set targeting the same gene and pathogen). (E) The Parallel Mixed Model (PMM) algorithm fits via a normal distribution for an overall effect a _g to all data of gene g. The second plot shows the correction of the overall effect a _g within every pathogen by an estimate b _pg in order to obtain to an pathogen and gene specific effect c _pg . The different sizes of the data points refer to weights w _s which can be incorporated in the PMM to depict the quality of the siRNA. (F) The figure shows a schematic representation of the final output of PMM. The model estimates gene effects c _pg for each gene and pathogen and provides corresponding local False Discovery Rates q _pg .