Commit 7559796d authored by Michaela Olson's avatar Michaela Olson
Browse files

update individual READMEs

parent fff2e8f7
......@@ -9,7 +9,7 @@ Run the script classification_trials.m to run a set of 70 classification trials.
### Before you run
In order to run a classification trial, you need to download Hanchuan Peng's mRMR feature selection algorithm and compile it to work on your operating system. It can be found here:
[mRMR Feature Selection] (https://www.mathworks.com/matlabcentral/fileexchange/14608-mrmr-feature-selection-using-mutual-information-computation?s_tid=prof_contriblnk). Place the resulting mRMR_0.9_compiled folder in the classification_trials directory.
[mRMR Feature Selection](https://www.mathworks.com/matlabcentral/fileexchange/14608-mrmr-feature-selection-using-mutual-information-computation?s_tid=prof_contriblnk). Place the resulting mRMR_0.9_compiled folder in the classification_trials directory.
### Workspaces for current presets
......@@ -22,6 +22,8 @@ High dose: full_3x_with_redos_no_A22_drug_name_only.mat
Low dose: full_025x_with_redos_v3_drug_names_only.mat
Condition data: all_condition_dat_with_unt.mat
## Examining the data
To look through a set of classification trials, run cKNN_creator.m and select the desired workspace.
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......@@ -6,7 +6,9 @@ Image segmentation process with data from MicrobeJ.
### How to run
Create a folder with a name that is meaningful to the data contained. Inside this folder, create a folder called 'data', where you will place your csvs named with the following naming scheme:
This script is run after using MocrbeJ to segment a 3 channel image (one brightfield, two flourescent) and exporting their output as csv files. The process of using MicrobeJ to segment an image is more closely detailed in the documentation linked above.
Once you have your results from segmentation, create a folder with a name that is meaningful to the data contained. Inside this folder, create a folder called 'data', where you will place your csvs named with the following naming scheme:
all_bacteria.csv
all_feature1.csv
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