Related Experiment Video
Updated: Jun 20, 2025
10:12
Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
Published on: January 10, 2019
18.5K
TWCOM: an R package for inference of cell-cell communication on spatially resolved transcriptomics data
1Department of Biostatistics, University of Florida, Gainesville, FL 32603, United States.
Bioinformatics Advances
|July 23, 2024
Summary
TWCOM is a new R package for inferring cell-cell communication using a Tweedie distribution model. It accurately analyzes both single-cell and spatial transcriptomics data for biological insights.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Understanding cell-cell communication is crucial for deciphering complex biological processes.
- Molecular-level insights into cellular behaviors are essential for targeted interventions in diseases.
Purpose of the Study:
- To introduce TWCOM, an R package for accurate cell-cell communication inference.
- To provide a versatile tool for analyzing single-cell and spatially resolved transcriptomics data.
Main Methods:
- Implementation of a Tweedie distribution-based model.
- Utilizing a generalized additive model framework.
- Adaptability to both single-cell and spot-based spatial transcriptomics data.
Main Results:
- TWCOM enables accurate inference of cell-cell communication pathways.
- The package provides robust analysis for diverse biological samples.
- It offers a versatile approach for transcriptomics data interpretation.
Conclusions:
- TWCOM is a valuable R package for advancing cell-cell communication research.
- The tool enhances the understanding of biological processes through accurate data analysis.
- TWCOM supports robust biological sample analysis in various pathological contexts.
More Related Videos
Related Concept Videos
01:58Cell Specific Gene Expression
13.6K
Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
13.6K
01:21Cell-surface Signaling
51.8K
Hormones—or any molecule that binds to a receptor, known as a ligand—that are lipid-insoluble (water-soluble) are not able to diffuse across the cell membrane. In order to be able to affect a cell without entering it, these hormones bind to receptors on the cell membrane. When a first messenger, a hormone, binds to a receptor, a signal cascade is set off, causing second messengers, proteins inside the cell, to become activated, resulting in downstream effects.
51.8K
01:16Cell Lines
7.3K
A cell line is a population of cells grown in vitro that can be subcultured over several generations. Normal cells cease to divide after a certain number of cell divisions, a process known as replicative senescence. This number, called the Hayflick limit, was conceptualized by Leonard Hayflick in 1961 when he observed that fetal cells grown in culture could only divide 40-60 times. This limit is due to the shortening of the telomeres during each round of cell division, preventing cell division...
7.3K
03:21RNA-seq
9.9K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
9.9K