Documentation for HTS Data Processing Application

1. Overview

   This application is designed to process High-Throughput Screening (HTS) data using a web-based interface built with Dash, a Python framework for building analytical web applications. The application allows users to upload Excel files, define regions of interest, assign control regions, and perform calculations based on user-defined equations. The results can be saved back to Excel files.

2. Functional Description

   1. File Upload: Users can upload multiple Excel files and a template file (JSON format) to define the processing parameters.
   2. Region Selection: Users can specify start and end regions within the Excel files to focus on specific data ranges.
   3. Control Region Assignment: Users can define positive, negative, blank, and none control regions within the selected data range.
   4. Calculation: Users can input a custom equation to calculate results based on the control regions and the selected data.
   5. Result Display: The application displays the selected region, role assignments, and calculated results in tabular format.
   6. Save Functionality: Users can save the processing template and the processed results back to Excel files.

3. Architecture of this project(for python scripts)

   /project-root

   │

   ├── app.py # Dash application

   ├── requirements.txt # Python dependencies

   ├── README.md # Project documentation

   └── main.py # For terminal via python

4. Installation

   1. Set up a conda environment from requirements.txt conda env create -n HTSdata -f requirements.txt # Install dependencies
   2. Run the project conda activate HTSdata cd project-root/ python main.py # Run from terminal
   3. Process in browser

5. HTSData processing pipeline

   1. Uploading Files: (1) Upload Template File: Drag and drop or select a JSON template file that contains predefined processing parameters. (2) Upload Data Files: Drag and drop or select one or more Excel files containing the data to be processed.
   2. Defining Regions and Controls (1) Start and End Region: Enter the start and end regions (e.g., B2 to D10) to define the data range. (2) Control Regions: Define the positive, negative, blank, and none control regions using cell references or row/column ranges (e.g., A3-A21, column 1-5, row 2-4).
   3. Performing Calculations (1) Equation Input: Enter a custom equation using placeholders for control regions (e.g., (neg-sample)/(neg-pos)*100% or (1-sample/blank)*100%). (2) Calculate: Click the "Calculate" button to perform the calculation and display the results.
   4. Saving Results (1) Save Template: Save the current processing parameters as a JSON template file. (2) Save Results: Save the processed data to Excel files with _processed appended to the original filenames.

6. Control regions and abbreviations in the equation

   pos: positive control regions (averaged value in equation)

   neg: negative control regions(averaged value in equation)

   blank: blank control regions (averaged value in equation, often for DMSO or vehicle)

   none: irrelevant regions

   sample: cells in data range (including pos, neg, and blank without none)

7. Example Template File

   { "start_region": "B2", "end_region": "AV10", "positive_ctrl_cells": "A3-A21", "negative_ctrl_cells": "column 1-5", "blank_ctrl_cells": "row 2-4", "none_cells": "B5,row 7", "equation": "(neg-sample)/(neg-pos)*100%" }