Data Science & Machine Learning

.GCT v/s .TSV: Comparing File Formats for Storing Gene Expression Data

Pranav Divakar, Shrushti Joshi
November 23, 2023

In the field of bioinformatics, data management and analysis are fundamental for unraveling the complexities of biological systems. The choice of file format for storing and exchanging gene expression data is paramount, as it influences the efficiency and compatibility of bioinformatics workflows.

While the .tsv (Tab-Separated Values) format is a widely used and versatile option, the .gct (Gene Cluster Text) format offers distinctive advantages for bioinformaticians. In this blog, we delve into the reasons why opting for the .gct format over .tsv is beneficial when handling gene expression data.

What is the .gct File Format (Gene Cluster Text)? How is the Data Stored?

The .gct (Gene Cluster Text) format is a commonly used file format for storing gene expression data. Gene expression data represents the levels of gene activity (i.e., how genes are turned on or off) in different samples or conditions, such as different tissues, experimental treatments, or time points.

Let us breakdown what type of information is stored in a .gct file.

The .gct file, consists of a header section and a data section.

Here's the overview of the information that consists in these 2 sections

1. Header Section:

a. The header section contains annotated sample metadata and gene metadata. It includes tags like Gene ID, Sample name, GEO Accession etc.
b. The first line contains a version number, dimensions, and a description of the file.
c. The second line specifies the sample metadata like an organism, age, type, etc (rows) and gene metadata tags like ID, name, description of genes, etc (Columns) in the data matrix.

2. Data Section:

a. The data section is a matrix where each row represents a gene, and each column represents a sample or condition.
b. The data values in the matrix typically represent gene expression levels, such as mRNA expression values or signal intensity from microarray experiments.
c. The data values are tab-separated (hence the "text" in Gene Cluster Text) and can be in various numerical formats.

Here's a simplified example of a .gct file:

.GCT v/s .TSV: Comparing File Formats

In this example, there are five genes (Gene1 to Gene5) and three samples (Sample1, Sample2, and Sample3). The values in the data section represent the expression levels of these genes in each sample.

The .gct format is often used in gene expression analysis tools and software, making it a common way to share and store gene expression data for further analysis and visualization.

What is the .tsv file format?

The .tsv (Tab-Separated Values) file format is a plain text format used to represent tabular data. It is a common and simple way to store data in a structured form, where each row of the table represents a record, and columns are separated by tab characters. TSV files are similar to .csv (Comma-Separated Values) files, but they use tabs instead of commas to delimit the fields.

In a .tsv file:

  1. Tab character (ASCII code 9) is used as the field separator. Each tab character separates one column from the next.
  2. Each line represents a new record or row in the table.
  3. Columns contain data or values related to the records.

Here's a simple example of a .tsv file:

.GCT v/s .TSV: Comparing File Formats

In this example, the file represents a table with three columns: "Name," "Age," and "City." Each row corresponds to a different individual, and the tab character separates the values in each column.

Comparison of .gct and .tsv Formats for Gene Expression

Aspect .gct (Gene Cluster Text) .tsv (Tab-Separated Values)
File Purpose Specialized for gene expression data. General-purpose tabular format.
Data Structure Designed specifically for gene expression data. Versatile and can be used for various types of data.
Standardization Widely accepted and standardized in bioinformatics. While its popular amongst students, its not specialized for bioinformatics, less standard.
Annotations Supports gene and sample annotations. No built-in structure for annotations.
Metadata Includes metadata headers and descriptions. Limited metadata support; depends on conventions.
Ease of Use Straightforward for gene expression analysis. Requires manual management of annotations.
Software Support Many bioinformatics tools like cmapPy, cmapR, support .gct format. Widely recognized; support in various tools.
Data Sharing Useful for sharing gene expression data. Multiple files needed to be shared for detailed information on the experiment.
Versatility Not Limited to gene expression data. Can be used for various omics data storage. Can be used for various data types and purposes.
Examples of Use Bulk RNA-seq, gene expression analysis. General data storage, including non-bioinformatics.

Why Use .gct over .tsv for Gene Expression Data?

Gene Cluster Text (.gct) format offers several advantages, being specialized for gene expression data, ensuring structured and straightforward handling, particularly beneficial for RNA-seq analysis. It incorporates built-in support for gene and sample annotations, enjoys wide acceptance within the bioinformatics community, and is well-supported by numerous bioinformatics tools. However, its limitations include being tailored exclusively for gene expression data and limited flexibility in accommodating specific annotation needs.

On the other hand, the Tab-Separated Values (.tsv) format presents its own set of advantages and limitations. It boasts versatility, accommodating various data types beyond gene expression, and enjoys widespread recognition and support across multiple software and applications. Its tab-based delimiters avoid conflicts with data containing commas. However, .tsv lacks the specialized structure optimized for gene expression data, potentially requiring manual annotation management. Standardization and metadata handling may vary based on different conventions, impacting consistency and compatibility.

The choice between the two formats will largely depend on the analysis requirements, tools being used, and the need for standardized gene expression data structures when working extensively with RNA-seq data.


Polly's utilization of the .gct format for bulk RNA-seq data harnesses numerous advantages that streamline and enhance data analysis processes. The specialized nature of .gct facilitates efficient handling of gene expression data, ensuring structured organization and simplicity, which is pivotal for RNA-Seq analysis. With built-in support for gene and sample annotations, this format optimizes data organization, fostering ease of interpretation and analysis.

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