GatingTree: Initialization Using CSV File Inputs

Dr. Masahiro Ono

2024-11-04

Step 1: Preparing Sample Data as CSV Files

GatingTree can import expression data from flow cytometric samples in CSV format. This method is versatile and compatible with various flow cytometric analysis software packages. Here is step-by-step guidance for sample data preparation.

1. Perform QC and clean flow cytometric data:
   • Remove any irregular data.
   • Identify target cells for analysis (e.g., lymphocytes identified by FSC/SSC and dead cell staining).
2. Export each sample’s data as a CSV file.
3. Place all sample files in a single directory.

Note that GatingTree performs all analyses using the entire set of sample data as a single batch.

1-1. Create Flow Object

Set your working directory to the folder that contains all the sample CSV files.

If you are using the Terminal, activate R and execute CreateFlowObject.

x <- CreateFlowObject()

By default, CreateFlowObject has the options path = '.', select = TRUE. This means that CreateFlowObject imports files from the current working directory, and a graphic device is automatically activated to enable user file selection.

If you have a character vector of all files to be imported, you can use the options select = TRUE, sample_file = filenames. For example, to import all files with the extension ‘csv’:

filenames <- list.files(pattern = "*.csv$")
x <- CreateFlowObject(select = TRUE, sample_file = filenames)

1-2. Defining Sample Grouping

The execution of the CreateFlowObject function generates a new folder named sampledef, which includes a new CSV file named sample.csv. This file is used for defining sample grouping.

Editing sample.csv: - First Column: Contains automatically generated file names. - Second Column (‘group’): Assign samples to experimental groups here.

Important Note: Ensure the last row of sample.csv ends with a newline character to avoid read errors in R. If R struggles to read the file, open it in a text editor and add a newline at the end of the last row.

Example of Edited sample.csv:

file	group
sample1.csv	control lymph node
sample2.csv	control lymph node
sample3.csv	control lymph node
sample4.csv	treated lymph node
sample5.csv	treated lymph node
sample6.csv	treated lymph node

Importing Sample Grouping

Once edited, import the grouping data back into R using the SampleDef function. The imported data is stored in the @sampledef slot of your FlowObject as a new SampleDef object. This object is crucial for facilitating any between-group comparisons in your downstream analysis.

# Define sample grouping
x <- SampleDef(x)

You can confirm the sample definitions using showSampleDef.

# Show sample grouping
showSampleDef(x)

This will display the data frame that is retained in the FlowObject.