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Series GSE141982 Query DataSets for GSE141982
Status Public on Mar 01, 2020
Title Ensemble learning for classifying single-cell data and projection across reference atlases
Organism Homo sapiens
Experiment type Expression profiling by high throughput sequencing
Summary Single-cell data are being generated at an accelerating pace. How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types. By comparing novel and published data from distinct scRNA-seq modalities that were acquired from the same tissues, we show that this approach preserves cell-type labels when mapping across diverse platforms.
Overall design We developed an ensemble classifier of scRNA-seq, single-nuclei RNA-sequencing (snRNA-seq), and bulk-extraction RNA-sequencing (RNA-seq) data: Ensemble Learning for classifying Single-cell data and projection across reference Atlases (ELSA; We trained ELSA on public atlases and tested it on published single-cell data, novel scRNA-seq and snRNA-seq of human glioma tissues (4 patients, >11K cells, Table S1 and S2).
Note: Submitter did not submit the raw data files due to privacy concerns for patients.
Contributor(s) Wang L, Catalan FL, Babikir H, Shamardani K, Diaz A
Citation(s) 32105316
Submission date Dec 13, 2019
Last update date Jun 01, 2020
Contact name Aaron Diaz
Organization name University of California, San Francisco
Department Neurological Surgery
Lab Diaz Lab
Street address 1450 3rd St
City San Francisco
State/province CA
ZIP/Postal code 94158
Country USA
Platforms (1)
GPL24676 Illumina NovaSeq 6000 (Homo sapiens)
Samples (4)
GSM4217362 SF12199
GSM4217363 Mixing
GSM4217364 S10432
BioProject PRJNA595499

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Supplementary file Size Download File type/resource
GSE141982_RAW.tar 39.1 Mb (http)(custom) TAR (of MTX, TSV)
Processed data provided as supplementary file
Raw data not provided for this record

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