SSBER: removing batch effect for single-cell RNA sequencing data

SSBER: removing batch effect for single-cell RNA sequencing data

Background: With the continuous maturity of sequencing technology, different laboratories or different sequencing platforms have generated a large amount of single-cell transcriptome sequencing data for the same or different tissues. Due to batch effects and high dimensions of scRNA data, downstream...

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Bibliographic Details
Main Authors: Wang, F. (Author), Zhang, Y. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
algorithm
Algorithms
article
Batch effect
cell composition
Cell culture
cell population
Cell populations
Cell proliferation
Cells
data integration
Data integration
genetics
High dimensions
Large amounts
Prior knowledge
RNA
Rna sequencing
sequence analysis
Sequence Analysis, RNA
single cell analysis
single cell RNA seq
Single cells
Single-Cell Analysis
Supervised cell type assignment
The shared cell type
transcriptome
Transcriptome
Transcriptomes
whole exome sequencing
Whole Exome Sequencing
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