the adventure of the ten Arcs
Three answers to peruse and compare for this week's problem set, as Jupyter Notebook pages for download:
conda. Simulation is properly wrapped in a function, so it can be re-run with different conditions. Reads
kallistooutput with Pandas, and uses Pandas to analyze the difference between
kallistoresults and the true abundance parameters of the simulation. In part 5, explores the effect of circularity and of different amounts of overlap.
Michael: Simulation is neatly modularized into functions. Uses shell commands to view and
numpy.loadtxtto load kallisto output for analysis. In part 5, tests the effect of circularity.
kallistoby building from source. Simulation is lazily controlled by global variables in the notebook, so I have to rerun the whole notebook if I change my simulation conditions in part 5. Reads
kallistooutput with basic Python and with unix command line calls. Includes using matplotlib to make some simple graphs, comparing kallisto results to the true TPM parameters and to Moriarty's result.