Create validation module

5.validate_model/README.md

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+# 5. Validate Model
+
+In this module, we validate the final ML model.
+
+### Validation Method 1
+
+The final model from [2.train_model](../2.train_model/) is used to classify nuclei images from the [Cell Health Dataset](https://github.com/WayScience/cell-health-data).
+The classification probabilities across CRISPR guide/cell line are then correlated to the Cell Health label for the the respective CRISPR perturbation/cell line.
+
+The Cell Health dataset has cell painting images across 119 CRISPR guide perturbations (~2 per gene perturbation) and 3 cell lines.
+More information regarding the generation of this dataset can be found at https://github.com/broadinstitute/cell-health.
+
+In [Cell-Health-Data/4.classify-features](https://github.com/WayScience/cell-health-data/tree/master/4.classify-features), we use the final model to determine phenotypic class probabilities for each of the Cell Health cells.
+These probabilities are averaged across CRISPR guide/cell line to create 357 *classifiction profiles* (119 CRISPR guides x 3 cell lines).
+
+As part of [Predicting cell health phenotypes using image-based morphology profiling](https://www.molbiolcell.org/doi/10.1091/mbc.E20-12-0784), Way et al derived cell health indicators.
+These indicators consist of 70 specific cell health phenotypes including proliferation, apoptosis, reactive oxygen species, DNA damage, and cell cycle stage.
+These indicators are averaged across across CRISPR guide/cell line to create 357 [*Cell Health label profiles*](https://github.com/broadinstitute/cell-health/blob/master/1.generate-profiles/data/consensus/cell_health_median.tsv.gz).
+
+We use [pandas.DataFrame.corr](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corr.html) to find the [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) between the *classifiction profiles* and the *Cell Health label profiles*.
+The Pearson correlation coefficient measures the linear relationship between two datasets, with correlations of -1/+1 implying exact linear inverse/direct relationships respectively.
+
+These correlations are interpreted with [seaborn.clustermap](https://seaborn.pydata.org/generated/seaborn.clustermap.html) to display the hierarchically-clustered correlation values.
+Searborn clustermap groups similar correlations into clusters that are broadly similar to each other.
+
+## Step 1: Validate Model
+
+Use the commands below to validate the final ML model:
+
+```sh
+# Make sure you are located in 5.validate_model
+cd 5.validate_model
+
+# Activate phenotypic_profiling conda environment
+conda activate phenotypic_profiling
+
+# Interpret model
+bash validate_model.sh
+```