SGAtools
Tools for the analysis of genetic screens
Image analysis
Fit a grid to a photograph of a plate and measure every colony on it, with
pitter — a Python
reimplementation of gitter. The result feeds normalization directly.
Start a run.
Normalization
Filtering and correction of the plate and experimental artifacts — spatial effects, row and column bias, jackknife, linkage — that otherwise pass straight through into your interaction scores. Start a run.
Scoring
Score normalized colony sizes against control screens to find the genetic interactions that mean something, with the epsilon, p-value and standard deviation behind each one. Part of the same run — tick Score when you submit.
67 array layouts ship with the pipeline, each one checked before a screen is scored against it. Browse them, or upload your own array for a single run.
The analysis is sga_score, a Python replacement for the original SGAtools R pipeline. It fixes several bugs the R shipped with, and keeps a compatibility mode that reproduces the old results exactly when you need to compare against an earlier run.