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Series GSE175525 Query DataSets for GSE175525
Status Public on Aug 04, 2022
Title Resolution of the curse of dimensionality in single-cell RNA-sequencing data analysis
Organism Homo sapiens
Experiment type Expression profiling by high throughput sequencing
Summary Single-cell RNA sequencing (scRNA-seq) can determine gene expression in numerous individual cells simultaneously, promoting progress in the biomedical sciences. However, scRNA-seq data are high-dimensional with substantial technical noise, including dropouts. During analysis of scRNA-seq data, such noise engenders a statistical problem known as the curse of dimensionality (COD). Based on high-dimensional statistics, we herein formulate a noise reduction method, RECODE (resolution of the curse of dimensionality), for high-dimensional data with random sampling noise. We show that RECODE consistently resolves COD in relevant scRNA-seq data with unique molecular identifiers. RECODE does not involve dimension reduction and recovers expression values for all genes, including lowly expressed genes, realizing precise delineation of cell-fate transitions and identification of rare cells with all gene information. Compared to representative imputation methods, RECODE employs different principles and exhibits superior overall performance in cell-clustering, expression-value recovery, and single-cell level analysis. The RECODE algorithm is parameter-free, data-driven, deterministic, and high-speed, and its applicability can be predicted based on the variance normalization performance. We propose RECODE as a powerful strategy for preprocessing noisy high-dimensional data.
Overall design Single cell transcriptome analysis of human primodial germ cell like cell (PGCLC) induction process using 10x chromium Single Cell Gene expression system.
Contributor(s) Imoto Y, Nakamura T, Escolar EG, Yoshiwaki M, Kojima Y, Yabuta Y, Katou Y, Yamamoto T, Hiraoka Y, Saitou M
Citation(s) 35944930
Submission date May 25, 2021
Last update date Nov 03, 2022
Contact name Yukihiro Yabuta
Organization name Kyoto University, Graduate school of medicine
Department Anatomy and Cell Biology
Street address Yoshida-Konoe-cho, Sakyo-ku
City Kyoto
State/province Kyoto
ZIP/Postal code 606-8501
Country Japan
Platforms (1)
GPL24676 Illumina NovaSeq 6000 (Homo sapiens)
Samples (1)
GSM5340861 mix of hiPSC and day4 hPGCLC [BTAG+]
BioProject PRJNA732704
SRA SRP321418

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Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE175525_RAW.tar 143.2 Mb (http)(custom) TAR (of MTX, TSV)
Raw data are available in SRA
Processed data provided as supplementary file

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