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RNA-seq

RNA-seq

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...

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Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

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splatPop: simulating population scale single-cell RNA sequencing data.

Christina B Azodi^1,2, Luke Zappia^3,4, Alicia Oshlack^2,5

¹St. Vincent's Institute of Medical Research, 9 Princes Street, Fitzroy, 3065, VIC, Australia.

|December 16, 2021

Summary

This summary is machine-generated.

We developed splatPop, a new simulation tool for population-scale single-cell RNA sequencing data. It models genetic effects and complex biological variations, aiding in method development for functional genomics.

Keywords:

Simulation Single-cell RNA-sequencing Software

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Last Updated: Oct 10, 2025

Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

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Published on: May 24, 2017

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Published on: June 23, 2012

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Population-scale single-cell RNA sequencing (scRNA-seq) offers high-resolution functional genomics.
Existing simulation frameworks lack population-scale data simulation with genetic effects.
Developing and testing new scRNA-seq analysis methods requires robust simulation tools.

Purpose of the Study:

To introduce splatPop, a novel simulation model for population-scale scRNA-seq data.
To enable simulation of genetic effects, batch effects, and complex biological variations.
To provide a flexible, reproducible, and well-documented tool for scRNA-seq method development.

Main Methods:

Developed splatPop, a simulation model incorporating expression quantitative trait loci (eQTLs).
Implemented simulation of complex batch, cell group, and conditional effects across cohorts.
Modeled genetically-driven co-expression patterns within the simulation framework.

Main Results:

splatPop successfully simulates population-scale scRNA-seq data with known genetic effects.
The model accounts for intricate biological variations, including batch and cohort differences.
Demonstrated the capability to simulate genetically-driven co-expression for robust method benchmarking.

Conclusions:

splatPop provides a powerful and flexible platform for simulating population-scale scRNA-seq data.
This tool facilitates the development, testing, and benchmarking of analysis methods for large-scale functional genomics studies.
Enables more accurate modeling of genetic influences on gene expression in complex populations.