Add classifications preview

5.validate_model/README.md

@@ -4,13 +4,13 @@ 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.
+We use the models from [2.train_model](../2.train_model/) 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 in [cell_health_correlations.ipynb](cell_health_correlations.ipynb) 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.
+In [Cell-Health-Data/4.classify-features](https://github.com/WayScience/cell-health-data/tree/master/4.classify-features), we use the trained models 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.
@@ -20,10 +20,19 @@ These indicators are averaged across across CRISPR guide/cell line to create 357
 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.
+We also derive the [Clustermatch Correlation Coefficient (CCC)](https://github.com/greenelab/ccc) introduced in [Pividori et al, 2022](https://www.biorxiv.org/content/10.1101/2022.06.15.496326v1).
+This is a not-only-linear coefficient based on machine learning models and gives an idea of how correlated the feature coefficients are (where 0 is no relationship and 1 is a perfect relationship).
+
+These correlations are briefly interpreted in [preview_CH_correlations.ipynb](preview_CH_correlations.ipynb) and [preview_CH_correlations.ipynb](preview_CH_correlation_differences.ipynb) 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
+## Step 1: Define Folder Paths
+
+Inside the notebook [cell_health_correlations.ipynb](cell_health_correlations.ipynb), the variable `classification_profiles_save_dir` needs to be set to specify where the classficiation profiles are saved.
+We used an external harddrive and therefore needed to use specific paths.
+The classification profiles are the output of [cell-health-data/4.classify-single-cell-phenotypes](https://github.com/roshankern/cell-health-data/tree/derive-classification-profiles/4.classify-single-cell-phenotypes).
+
+## Step 2: Validate Model
 
 Use the commands below to validate the final ML model: