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The simplicity, flexibility and brevity of SET could make it an invaluable tool for marker identification in clinical research. This tool provides a filtration function for signature identification and lies between clinical analyses and class prediction (or feature selection) tools. Here we present SET to evaluate and visualize the sample-discrimination ability of a given gene expression signature. Information obtained from the above applications could be useful in prognostic analyses or clinical management decisions.

Here, we demonstrated the application of SET for several purposes: (1) for signatures consisting of a large number of genes, SET offers the ability to rapidly narrow down the number of genes (2) for a given signature (from third party analyses or user-defined), SET can re-evaluate and re-adjust its discrimination power by selecting/de-selecting genes repeatedly (3) for multiple microarray datasets, SET can evaluate the classification capability of a signature among datasets and (4) by providing a module to visualize the prediction strength for each sample, SET allows users to re-evaluate the discrimination power on mis-grouped or less-certain samples. SET provides a flexible and easy-to-follow platform to evaluate the discrimination power of a gene signature. Signature Evaluation Tool (SET) is a Java-based tool adopting the Golub's weighted voting algorithm as well as incorporating the visual presentation of prediction strength for each array sample. A tool to evaluate the discrimination power of candidate genes is therefore in high demand by clinical researchers.

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Furthermore, experimental verification is sometimes limited by the availability of wet-lab materials such as antibodies and reagents. Although a number of tools have been developed to identify optimal gene expression signatures, the number of signature genes obtained is often overly large to be applied clinically. So here’s the “good enough” solution: write programs that are actually Java, but ‘skinned’ to look like C++.The identification of specific gene expression signature for distinguishing sample groups is a dominant field in cancer research. The bad news is that the implementations in Python and Java are heavily dependent on modern features, such as a debugging API and reflection, which C++ doesn’t readily offer. I was very used to having a visualizer to show new concepts like for loops, function calls, object-oriented programming and recursion… I’d used it both in Python for CS Circles and for the Java course. Eric Grimson had an antique visualizer running at MIT when I was a TA for their course 6.001, but the great and novel aspects of Philip’s one are that it was freely available online, could be embedded in other web pages, had a much nicer interface and output, and used a language that has fewer scary parentheses (and is for this reason more widely taught).Ī couple of years ago, I scavenged the frontend of that visualizer and wrote a Java backend for it, creating the Java visualizer. This was very helpful during my stint at Princeton, where the introductory programming course I taught used Java.įlash-forward to the most recent academic year, teaching C++ at the University of Southern California.

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His visualizer takes an arbitrary Python program as input, and then creates an animated HTML illustration showing which lines of code are executed in what order, and also showing the values of the variables as they change. This is based off of the Python visualizer at by Philip Guo. Today I pushed some code that I used last semester for C++ visualization to github. The bad news is that it’s smoke and mirrors, but the good news is that it did the trick: it helped to convey important ideas about flow control and programming semantics to my students.