Required! Make sure to install Statistical Pattern Recognition Toolbox for Matlab.
CellClassifier was developed to enable cell biologists to apply supervised machine learning in their image-based experiments for the automated classification of single-cell phenotypes. The main features are:
- An intuitive and easy-to-use graphical user interface developed in MatLab to upload and visualize microscopy images. CellClassifier displays each original image interactively, so that each cell is recognized and made ‘clickable’.
- Three methods of supervised machine learning: Support Vector Machines (SVM), including multiclass SVMs, multilinear perceptrons, and k-nearest neighbors. Developers can easily add their own methods.
- Possibility to display fractions of correctly and incorrectly classified cells during the course of training in order to evaluate the classification performance and its improvement through training iterations.
- Export of classification results at the single-cell level to MatLab-readable files and at the population level to standard spreadsheet programs.
The user interface of CellClassifier. Cell and nucleus segmentation outlines (white lines) and single-cell class annotations (numbers) are shown. Moving the mouse over each cell centroid (white dot) and right-clicking allows manual classification or correction of automatically classified cells. Cells shown are HeLa cells stained with DAPI (blue) to visualize the nucleus and with an antibody against PolyA-binding protein (PABP) (red) to visualise RNA granules.
Publications in which CellClassifier was used:
- Wippich, F. et al. Dual Specificity Kinase DYRK3 Couples Stress Granule Condensation/Dissolution to mTORC1 Signaling. Cell 152, 791–805 (2013)
- Mercer, J. et al. RNAi Screening Reveals Proteasome- and Cullin3-Dependent Stages in Vaccinia Virus Infection. Cell Reports 2, 1036–1047 (2012)
- Snijder, B. et al. Single-cell analysis of population context advances RNAi screening at multiple levels. Molecular Systems Biology 8, (2012)
- Misselwitz, B. et al. RNAi screen of Salmonella invasion shows role of COPI in membrane targeting of cholesterol and Cdc42. Molecular Systems Biology 7, 474 (2011)
- Rämö, P., Sacher, R., Snijder, B., Begemann, B. & Pelkmans, L. CellClassifier: supervised learning of cellular phenotypes. Bioinformatics 25, 3028–3030 (2009)
- Snijder, B. et al. Population context determines cell-to-cell variability in endocytosis and virus infection. Nature 461, 520–523 (2009)