CancerSCEM 2.0: an updated data resource of single-cell expression map across various human cancers.

Jingyao Zeng, Zhi Nie, Yunfei Shang, Jialin Mai, Yadong Zhang, Yuntian Yang, Chenle Xu, Jing Zhao, Zhuojing Fan, Jingfa Xiao
Author Information
  1. Jingyao Zeng: National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China. ORCID
  2. Zhi Nie: National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China.
  3. Yunfei Shang: National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China.
  4. Jialin Mai: National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China.
  5. Yadong Zhang: National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China. ORCID
  6. Yuntian Yang: Huazhong University of Science and Technology, Wuhan 430074, China.
  7. Chenle Xu: National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China.
  8. Jing Zhao: National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China.
  9. Zhuojing Fan: National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China.
  10. Jingfa Xiao: National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China. ORCID

Abstract

The field of single-cell RNA sequencing (scRNA-seq) has advanced rapidly in the past decade, generating significant amounts of valuable data for researchers to study complex tumor profiles. This data is crucial for gaining innovative insights into cancer biology. CancerSCEM (https://ngdc.cncb.ac.cn/cancerscem) is a public resource that integrates, analyzes and visualizes scRNA-seq data related to cancer, and it provides invaluable support to numerous cancer-related studies. With CancerSCEM 2.0, scRNA-seq data have increased from 208 to 1466 datasets, covering tumor, matching-normal and peripheral blood samples across 127 research projects and 74 cancer types. The new version of this resource enhances transcriptome analysis by adding copy number variation evaluation, transcription factor enrichment, pseudotime trajectory construction, and diverse biological feature scoring. It also introduces a new cancer metabolic map at the single-cell level, providing an intuitive understanding of metabolic regulation across different cancer types. CancerSCEM 2.0 has a more interactive analysis platform, including four modules and 14 analytical functions, allowing researchers to perform cancer scRNA-seq data analyses in various dimensions. These enhancements are expected to expand the usability of CancerSCEM 2.0 to a broader range of cancer research and clinical applications, potentially revolutionizing our understanding of cancer mechanisms and treatments.

Grants

  1. 2022098/Youth Innovation Promotion Association of the Chinese Academy of Sciences
  2. XDB38030400/Chinese Academy of Sciences
  3. 32300542/National Natural Science Foundation of China

MeSH Term

Humans
Single-Cell Analysis
Neoplasms
Software
Gene Expression Profiling
Sequence Analysis, RNA
RNA-Seq
DNA Copy Number Variations
Gene Expression Regulation, Neoplastic
Databases, Genetic
Transcriptome

Links to CNCB-NGDC Resources

Database Commons: DBC007430 (CancerSCEM)

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