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Glosensor Pipeline

This pipeline is designed for processing platereader CSV files, specifically for Glosensor data. It reads in files from a plate reader, consolidates the data based on user labeling, normalizes data, plots data values, and generates sigmoid dose curves.

Table of Contents

• Installation
• Usage
• Example
• Contributing

Installation

To use this pipeline, you need to have Python installed along with the necessary dependencies. You can create a conda environment and install the required packages using the following command:

conda create --name glosensor --file requirements.txt

After installation if nbformat error exists.

pip install --upgrade nbformat

Usage

1. Initialize the GlosensorData class:

   glo = GlosensorData(path)
   glo.ReadTableData()

2. Visualize the 96-well plate data:

   fig_list = glo.view_96well_curve(x_by='Time', y_by='value', facet_col_by='Column', facet_row_by='Row',
                                    facet_col_spacing=0, facet_row_spacing=0, n_facet_col_wrap=12)
   for fig in fig_list:
       fig.show()

3. Label your 96-well plate based on the transfected schema:

   glo.data.loc[(glo.data.Column.isin([1, 2, 3, 4])), 'Odor'] = 'Odor_1'
   glo.data.loc[(glo.data.Column.isin([5, 6, 7, 8, 9, 10, 11, 12])), 'Odor'] = 'Odor2'
   
   glo.data.loc[(glo.data.Column.isin([1])), 'Group'] = 'A'
   glo.data.loc[(glo.data.Column.isin([2])), 'Group'] = 'B'
   glo.data.loc[(glo.data.Column.isin([3])), 'Group'] = 'C'
   glo.data.loc[(glo.data.Column.isin([4])), 'Group'] = 'D'
   
   glo.data.loc[(glo.data.Row.isin(['A', 'B'])), 'Odor_conc'] = 100
   glo.data.loc[(glo.data.Row.isin(['C', 'D'])), 'Odor_conc'] = 10
   glo.data.loc[(glo.data.Row.isin(['E', 'F'])), 'Odor_conc'] = 1
   glo.data.loc[(glo.data.Row.isin(['G', 'H'])), 'Odor_conc'] = 0
   
   glo.data = glo.data.dropna()

4. Process and plot data:

   • Get melted, AUC, and slope data:

     melted_data, AUC_data, slope_data = glo.get_melted_AUC_slope_data(
         group_col=['Odor', 'Group', 'Odor_conc'],
         slope_AUC_time=[0, 3],
         data=glo.data
     )

   • Plot facet plots for AUC:

     plot_data = AUC_data[['Odor_conc', 'Group', 'AUC_mean', 'AUC_std', 'Plate']]
     plot_data = plot_data[plot_data.Plate.isin([1, 2])]
     plot_data = plot_data.sort_values('Group')
     plot_data = plot_data.drop('Plate', axis=1)
     plot_data = plot_data.drop_duplicates().reset_index(drop=True)
     
     plot_data['Group'] = pd.Categorical(plot_data['Group'], categories=list(glo.data.Group.unique()), ordered=True)
     plot_data = plot_data.sort_values(['Group', 'Odor_conc'], ascending=[True, False])
     plot_data['Odor_conc'] = plot_data['Odor_conc'].astype(str)
     
     fig = glo.plot_facet_bar(plot_data,
                              x_by="Odor_conc",
                              y_by="AUC_mean",
                              facet_col_by='Group',
                              error_y_by='AUC_std',
                              n_facet_col_wrap=4)
     fig.show()

     

   • Plot dose curves:

     group_col = ['Odor', 'Group']
     
     custom_color = [value for key, value in distinct_colors((['x'] * len(AUC_data.Group.unique())), category='pastel').items()]
     
     plot_data = AUC_data
     
     fig, stat = glo.plot_Dose_curves(plot_data.sort_values('AUC', ascending=False),
                                      groupby_col=group_col,
                                      figsize=(10, 7),
                                      log_conc_offset=-6,
                                      x_by='Odor_conc',
                                      point_size=12,
                                      std_capsize=10,
                                      std_linewidth=2.5,
                                      curve_width=5)
     fig.show()
     print(pd.DataFrame(stat).T)

     

Example

For more detailed examples follow Glosensor_demo.ipynb notebook

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.