Here we show how to use a novel multifactor method on a diabetes data set to deduce important transcripts with respect to being diabetic. The data was obtained from the [Broad Insitute](http://www.gsea-msigdb.org/gsea/datasets.jsp) and contains gene expressions from a microarray hgu133a platform. We choose to employ the `Diabetes_collapsed_symbols.gct` file since it has already been collapsed down to useful transcripts. We have entered an `impetuous-gfa` ( version >= `0.50.0` ) environment and set up the a `diabetes.py` file with the following code content:
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@@ -497,13 +497,10 @@ The functions `select_from_distance_matrix` uses boolean indexing to select rows
# Notes
These examples were meant as illustrations of some of the codes implemented in the impetuous-gfa package. The impetuous visualisation codes requires [Bokeh](https://docs.bokeh.org/en/latest/index.html) and are still being migrated to work with the latest Bokeh versions.
These examples were meant as illustrations of some of the codes implemented in the impetuous-gfa package. The impetuous visualisation codes requires [Bokeh](https://docs.bokeh.org/en/latest/index.html) and are still being migrated to work with the latest Bokeh versions. For an example of the dynamic `triplot` routine (you can click on the lefthand and bottom scatter points) you can view it [here](https://bl.ocks.org/richardtjornhammar/raw/463e1aa3faceb95a4f894351f16b215a/?raw=true)(with[revision dates](https://gist.github.com/richardtjornhammar/463e1aa3faceb95a4f894351f16b215a/revisions) or download it [here](https://gist.github.com/richardtjornhammar/463e1aa3faceb95a4f894351f16b215a/) ).
# Manually updated code backups for this library :
