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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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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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Published on: September 18, 2021

HCCDB v2.0: Decompose Expression Variations by Single-cell RNA-seq and Spatial Transcriptomics in HCC.

Ziming Jiang¹, Yanhong Wu², Yuxin Miao²

¹Eight-Year Program of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100006, China.

Genomics, Proteomics & Bioinformatics

|June 17, 2024

Summary

The updated Hepatocellular Carcinoma Database (HCCDB v2.0) integrates bulk, single-cell, and spatial transcriptomic data for comprehensive molecular analysis. This enhanced resource aids in understanding hepatocellular carcinoma (HCC) and identifying prognosis-associated cells and tumor microenvironments.

Keywords:

Database Hepatocellular carcinoma Integrative analysis Single-cell RNA sequencing Spatial transcriptomics

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Last Updated: Jun 23, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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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

Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Large-scale transcriptomic data are essential for understanding hepatocellular carcinoma (HCC) molecular features.
The initial HCC database (HCCDB v1.0) provided a systematic view of HCC heterogeneity using meta-analysis of 15 datasets.
Advancements in single-cell and spatial transcriptomics necessitate an updated database to incorporate new data and analytical capabilities.

Purpose of the Study:

To present HCCDB v2.0, an updated version integrating bulk, single-cell, and spatial transcriptomic data for hepatocellular carcinoma (HCC).
To expand the database with significantly more samples and cell data, enhancing the reliability of meta-analyses.
To introduce novel analytical metrics and visualization tools for exploring HCC at cellular and spatial levels.

Main Methods:

Integrated 11 new transcriptomic datasets, adding 1656 bulk samples to the existing 3917.
Incorporated single-cell (182,832 cells) and spatial transcriptomic (69,352 spots) data.
Developed a novel single-cell level 2-dimension (sc-2D) metric for analyzing cell type-specific gene expression patterns.

Main Results:

HCCDB v2.0 significantly expands the scale of transcriptomic data available for HCC research.
The database now includes comprehensive single-cell and spatial transcriptomic profiles, offering cellular-level resolution.
Demonstrated applications in identifying prognosis-associated cells and analyzing the tumor microenvironment.

Conclusions:

HCCDB v2.0 provides a robust, updated resource for studying hepatocellular carcinoma (HCC) molecular landscape.
The integration of diverse transcriptomic data types enables deeper insights into HCC heterogeneity and biology.
The user-friendly online portal facilitates data retrieval and exploration for cancer researchers.