Overview
This package is designed for reading, processing, and visualizing of nematode morphology data extracted from images using CellProfiler’s WormToolbox.
Installation
easyXpress is specialized for use with image data produced by the cellprofiler-nf nextflow pipeline. To install easyXpress you will need the devtools package. You can install devtools and easyXpress using the commands below:
install.packages("devtools")
devtools::install_github("AndersenLab/easyXpress")OS X installations of easyXpress require XQuartz to be installed. Follow the instructions here to install XQuartz.
The functionality of the package can be broken down into three main goals:
Reading data generated from CellProfiler pipelines alongside information about experimental design.
Flagging and pruning anomalous data points.
Generating diagnostic images.
For more information about implementing cellprofiler-nf to generate data used by the easyXpress package, see AndersenLab/cellprofiler-nf.
Directory structure
The directory structure holding data is critically important. Below is an example of a correct project directory structure. The cp_data directory contains an .RData file output by cellprofiler-nf. The processed_images directory contains _overlay.png files output by cellprofiler-nf. There should be one .png file for each well included in your analysis. The design directory contains a .csv with all the variables necessary to describe your experiment (i.e. experiment names, drug names, drug concentrations, strain names, food types, etc.).
If you do not have condition information you do not need the design directory.
/projects/20200812_example
├── cp_data
│ ├── CellProfiler-Analysis_20191119_example_data.RData
└── processed_images
│ ├── 20191119-growth-p01-m2x_A01_overlay.png
│ ├── 20191119-growth-p01-m2x_A02_overlay.png
│ ├── 20191119-growth-p01-m2x_A03_overlay.png
│ ├── ...
├── design
└── 20191119_design.csvThis directory exhibits the minimal file content and naming for the easyXpress package to work.
Project directory
The project directory contains all of the files attached to a specific experiment conducted on a specific date. The naming convention for these folders should include the date in the format 4-digit year::2-digit month::2-digit day and experiment name separated by underscores.
# Example directory name
# Date is January 1st, 2020
# Experiment name is "ExperimentName"
20200101_ExperimentName/File naming
The processed image files should be formatted with the experiment data, name of the experiment, the plate number, the magnification used for imaging, and the well name. All processed image files must be saved as .png files. In the file named 20191119-growth-p01-m2x_A01_overlay.png the first section 20191119 is the experiment date, growth is the name of the experiment, p01 is the plate number, m2x is the magnification used for imaging, and A01 is the well name.
Package Overview
The easyXpress package consists of six function classes that work together to clean and process experimental data. The tidy functions will help pre-process raw images to get them ready for submission to the cellprofiler-nf pipeline. The ObjectFlag or OF functions help to flag problematic data output from cellprofiler-nf. The WellFlag or WF functions work to flag anomalous summary statistics for micro-plate wells. Throughout the data cleaning workflow, the check and view function classes are used to validate whether the flag functions are properly applied. All other functions serve to facilitate the cleaning process and do not have a standardized naming convention.
For more detailed information regarding use of these functions, see the article: Dose Response Processing.
Citation
Please cite the following in publications that use easyXpress: #### easyXpress: An R package to analyze and visualize high-throughput C. elegans microscopy data generated using CellProfiler #### Joy Nyaanga, Timothy A. Crombie, Samuel J. Widmayer, Erik C. Andersen #### (2021 August 12) PLoS ONE #### PLoS ONE PubMed