Despite advances in the modeling and understanding of colorectal cancer development, the dynamics of the progression from benign adenomatous polyp to colorectal carcinoma are still not fully resolved.To take advantage of adenoma size and prevalence data in the National Endoscopic Database of the Clinical Outcomes Research Initiative (CORI) as well as colorectal cancer incidence and size data from the Surveillance Epidemiology and End Results (SEER) database, we construct a two-type branching process model with compartments representing adenoma and carcinoma cells.To perform parameter inference we present a new large-size approximation to the size distribution of the cancer compartment and validate our approach on simulated data.By fitting the model to the CORI and SEER data, we learn biologically relevant parameters, including the transition rate from adenoma to cancer.The inferred parameters allow us to predict the individualized risk of the presence of cancer cells for each screened patient.We provide a web application which allows the user to calculate these individual probabilities at https://ccrc-eth.shinyapps.io/CCRC/.For example, we find a 1 in 100 chance of cancer given the presence of an adenoma between 10 and 20mm size in an average risk patient at age 50.We show that our two-type branching process model recapitulates the early growth dynamics of colon adenomas and cancers and can recover epidemiological trends such as adenoma prevalence and cancer incidence while remaining mathematically and computationally tractable. Author summaryColorectal cancer is a major public health burden.The development of colorectal cancer starts with the mutational initiation of non-cancerous growths in the form of benign adenomatous polyps.These adenomas grow over time with the potential to develop into carcinomas.Many mathematical and simulation-based models have been used to gain insight into this process.We aimed to understand rates of adenoma growth and transition

MOTIVATION: Genome-wide RNA interference (RNAi) experiments are becoming a widely used approach for identifying intracellular molecular pathways of specific functions. However, detecting all relevant genes involved in a biological process is challenging, because typically only few samples per gene knock-down are available and readouts tend to be very noisy. We investigate the reliability of top scoring hit lists obtained from RNAi screens, compare the performance of different ranking methods, and propose a new ranking method to improve the reproducibility of gene selection. RESULTS: The performance of different ranking methods is assessed by the size of the stable sets they produce, i.e. the subsets of genes which are estimated to be re-selected with high probability in independent validation experiments. Using stability selection, we also define a new ranking method, called stability ranking, to improve the stability of any given base ranking method. Ranking methods based on mean, median, t-test and rank-sum test, and their stability-augmented counterparts are compared in simulation studies and on three microscopy image RNAi datasets. We find that the rank-sum test offers the most favorable trade-off between ranking stability and accuracy and that stability ranking improves the reproducibility of all and the accuracy of several ranking methods. AVAILABILITY: Stability ranking is freely available as the R/Bioconductor package staRank at http://www.cbg.ethz.ch/software/staRank.

BACKGROUND: Light microscopy is of central importance in cell biology. The recent introduction of automated high content screening has expanded this technology towards automation of experiments and performing large scale perturbation assays. Nevertheless, evaluation of microscopy data continues to be a bottleneck in many projects. Currently, among open source software, CellProfiler and its extension Analyst are widely used in automated image processing. Even though revolutionizing image analysis in current biology, some routine and many advanced tasks are either not supported or require programming skills of the researcher. This represents a significant obstacle in many biology laboratories. RESULTS: We have developed a tool, Enhanced CellClassifier, which circumvents this obstacle. Enhanced CellClassifier starts from images analyzed by CellProfiler, and allows multi-class classification using a Support Vector Machine algorithm. Training of objects can be done by clicking directly "on the microscopy image" in several intuitive training modes. Many routine tasks like out-of focus exclusion and well summary are also supported. Classification results can be integrated with other object measurements including inter-object relationships. This makes a detailed interpretation of the image possible, allowing the differentiation of many complex phenotypes. For the generation of the output, image, well and plate data are dynamically extracted and summarized. The output can be generated as graphs, Excel-files, images with projections of the final analysis and exported as variables. CONCLUSION: Here we describe Enhanced CellClassifier which allows multiple class classification, elucidating complex phenotypes. Our tool is designed for the biologist who wants both, simple and flexible analysis of images without requiring programming skills. This should facilitate the implementation of automated high-content screening.