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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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Updated: Jun 10, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based

Yunqing Liu¹, Ningshan Li^1,2,3, Ji Qi¹

¹Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

|October 14, 2024

Summary

This summary is machine-generated.

SDePER, a new method for spatial transcriptomics, accurately deconvolutes cellular data using machine learning. This improves tissue mapping by estimating cell types and gene expression with enhanced resolution.

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Last Updated: Jun 10, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Spatial transcriptomic (ST) data analysis requires deconvolution to identify cellular components.
Existing methods face challenges with platform effects, data sparsity, and spatial correlations.

Purpose of the Study:

To introduce SDePER, a novel hybrid machine learning and regression method for deconvoluting ST data.
To enable accurate cellular-level analysis of spatial transcriptomics by leveraging reference single-cell RNA sequencing (scRNA-seq) data.

Main Methods:

Developed SDePER, a hybrid approach combining machine learning and regression.
Addressed platform effects between ST and scRNA-seq data to ensure linear relationships.
Accounted for sparsity and spatial correlations in cell type distribution.

Main Results:

SDePER accurately estimates cell-type proportions in spatial transcriptomic data.
The method enables enhanced resolution tissue mapping through imputation of cell-type composition and gene expression.
Evaluations on simulated and real datasets demonstrated superior accuracy and robustness compared to existing methods.

Conclusions:

SDePER provides a robust and accurate solution for spatial transcriptomic deconvolution.
The method enhances the resolution of tissue mapping, offering deeper biological insights.
SDePER represents a significant advancement in analyzing spatial omics data.