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KoellenspergerLab edited this page Nov 28, 2023 · 1 revision

Pixel Notebook

This wiki aims to briefly explain the functions and or requirements for each cell of the Pixel Notebook. Note that the cell numbers in this wiki must not be confused with the execution count (in brackets left of a cell) which indicates the cell's position in the execution order.

Cell 1: Import Libraries and Set Up Environment

Functionality

This cell imports all the necessary Python libraries required for the notebook to function. These libraries support various operations like data manipulation, data visualization, clustering, and interactive widgets.

Libraries Imported

  • Standard Libraries: codecs, os for basic Python operations.
  • Data Manipulation: pandas (as pd) and numpy (as np) for data manipulation and mathematical operations.
  • Data Visualization: matplotlib (as mpl), matplotlib.pyplot (as plt), and seaborn (as sns) for plotting and data visualization.
  • Interactive Widgets: ipywidgets (as widgets), IPython.display for creating interactive user interfaces.
  • Clustering: KMeans from sklearn.cluster for machine learning clustering tasks.
  • Data Preprocessing: RobustScaler, MinMaxScaler from sklearn.preprocessing for data scaling.

Special Commands

  • %matplotlib widget: This magic command enables the interactive Matplotlib backend for Jupyter notebooks.

Additional Notes

  • Warning messages are optionally suppressed using the warnings library to make the notebook output cleaner.
  • The from io import StringIO line imports the StringIO class for reading and writing strings as file streams.

Cell 2: Define Widgets for Data Path and File Selection

Functionality

Initializes widgets for entering the working directory path and for selecting a CSV file from that directory. It also sets up a function to update the list of CSV files based on the given path.

Widgets

  • image_path: Text widget for entering the working directory path.
    • Type: Text
    • Placeholder: "Enter your working directory path"
  • csv_select: Radio buttons to select the CSV file containing single-cell data.
    • Type: Radio Buttons
    • Options: Populated based on the files in the provided directory path.

Function: update_csv_files(change)

Updates the list of available CSV files in the csv_select widget based on the directory path provided in image_path.

Output

Displays the image_path and csv_select widgets side by side in an HBox layout.

Additional Notes

The widgets are styled using a custom CSS style defined in the variable style.

Cell 3: Load and Clean Single-Cell Data

Functionality

Reads the selected single-cell CSV data into a pandas DataFrame and drops any columns with missing values.

Variables

  • PATH: Stores the value of the working directory path from image_path.
  • PX_DATA: Stores the name of the selected CSV file from csv_select.
  • file_path: Combines PATH and PX_DATA to form the complete file path.
  • image_df: DataFrame holding the loaded and cleaned single-cell data.

Output

The cell doesn't have a visual output but it updates the image_df DataFrame with the loaded data.

Additional Notes

Columns with missing or NaN values are dropped from image_df using dropna(axis=1).

Cell 4: Configure Clustering Parameters and Data Scaling Method

Functionality

Sets up widgets for entering the number of clusters, selecting channels for clustering, and choosing the scaling method for data standardization.

Widgets

  • number_clusters: Text box for entering the desired number of clusters.
    • Type: Text
    • Placeholder: "Desired number of clusters"
  • channels_cluster: Multiple selection box for choosing which channels to use for clustering.
    • Type: Multiple Select
    • Options: Columns of the image_df DataFrame
  • scaler_select: Radio buttons to choose the scaling method.
    • Type: Radio Buttons
    • Options: 'RobustScaler', 'MinMaxScaler'

Output

Displays the number_clusters, channels_cluster, and scaler_select widgets horizontally.

Additional Notes

For more information on data scaling methods, consult the Scikit-learn documentation and here.

Cell 5: Perform Clustering and Update DataFrame

Functionality

Performs KMeans clustering on the selected channels after scaling the data using the selected scaling method. The clustering labels are then added to the image_df DataFrame.

Variables

  • cluster_df: A subset of image_df containing only the selected channels.
  • cluster_std: The scaled version of cluster_df.
  • kmeans: KMeans object from scikit-learn.

Functions

  • Scaling: Uses either RobustScaler or MinMaxScaler based on user selection.
  • KMeans Clustering: Uses the scikit-learn KMeans algorithm.

Output

Prints a message indicating that clustering has been completed.

Additional Notes

If only one channel is selected, cluster_df is reshaped to be 2D, as required by scikit-learn's scaling and clustering functions.

Cell 6: Input Image Dimensions

Functionality

Sets up widgets for entering the dimensions of the image, specifically its width and height in pixels.

Widgets

  • img_width: Text box for entering the image width in pixels.
    • Type: Text
    • Placeholder: "Enter your image width in pixels"
  • img_height: Text box for entering the image height in pixels.
    • Type: Text
    • Placeholder: "Enter your image height in pixels"

Output

Displays the img_width and img_height widgets horizontally.

Cell 7: Generate and Display Clustered Image

Functionality

Reshapes the clustering labels to match the original image dimensions and then displays this image.

Variables

  • labels: Array containing the KMeans cluster labels extracted from image_df.
  • image: 2D array formed by reshaping the labels array, representing the clustered image.

Output

Displays the clustered image in a 10x10 figure.

Additional Notes

  • The labels array is reshaped based on the user-provided image dimensions (img_width and img_height).
  • The Matplotlib parameter savefig.pad_inches is set to 0 to remove padding around the saved figure.

Cell 8: Save Clustered Image (Optional)

Functionality

Contains code for saving the generated clustered image as a PNG file.

Output

No output unless the code is uncommented and executed.

Additional Notes

To save the clustered image, uncomment the line and execute the cell.

Cell 9: Generate Cluster Statistics

Functionality

Calculates and prints the average, minimum, and maximum pixel values for each selected channel in each cluster. This information is useful for understanding the distribution of pixel values within each cluster.

Function: generate_info(cluster_label, channel, df)

  • Calculates the mean, minimum, and maximum pixel values for a given channel in a specified cluster.
  • Returns a string containing these statistics.

Variables

  • info_strings: List to store all the generated information strings for each cluster and channel.

Output

Prints the statistics for each channel in each cluster.

Additional Notes

This cell uses the labels array generated from previous cells and iterates over the range of unique cluster labels. For each cluster and channel, it calls the generate_info function to compute and print the statistics.

Cell 10: Save Cluster Statistics to File (Optional)

Functionality

Contains code for saving the calculated statistics to a text file.

Output

No output unless the code is uncommented and executed.

Additional Notes

To save the statistics, uncomment the lines and execute the cell. The statistics will be saved in a text file named kmeans_cluster_statistics.txt in the specified path.

Cell 11: Channel Selection for Correlation Analysis

Functionality

Initiates widgets for selecting the channels and the type of correlation method (Spearman or Pearson) for which the correlation matrix will be computed.

Widgets

  • correlation_channels: Allows multiple selection of channels for which to calculate the correlation coefficients.
    • Type: SelectMultiple (Options are DataFrame columns)
  • correlation_select: Allows selection of the correlation method to be used (Spearman or Pearson).
    • Type: RadioButtons

Output

Displays the widgets for channel and correlation method selection.

Additional Notes

The selected channels and correlation method will be used in the next cell to plot the correlation heatmap.

Cell 12: Generate Correlation Heatmap

Functionality

Generates a heatmap to visualize the correlations between the selected channels. The heatmap provides insight into how the channels are related to each other.

Functions

  • plot_correlation_heatmap(df, channels, method):
    • Plots a correlation heatmap for the given DataFrame, channels, and correlation method.

Variables

  • transformed_data: Transforms the original data by applying the square root.
  • scaled_data: Scales the transformed data using the previously selected scaler.

Output

Displays the correlation heatmap.

Additional Notes

The cell uses the square root transformation and scaling to prepare the data for correlation analysis.

Cell 13: Save Correlation Heatmap to File (Optional)

Functionality

Contains code for saving the generated correlation heatmap to a PNG file.

Output

No output unless the code is uncommented and executed.

Additional Notes

To save the heatmap, uncomment the lines and execute the cell. The heatmap will be saved as a PNG file in the specified path.

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