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Series GSE206741 Query DataSets for GSE206741
Status Public on Jun 25, 2022
Title Drug combination sci-Plex Data
Organism Homo sapiens
Experiment type Expression profiling by high throughput sequencing
Summary Recent advances in multiplexed single-cell transcriptomics experiments are facilitating the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible, so computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA encodes and learns transcriptional drug responses across different cell type, dose, and drug combinations. The model produces easy-to-interpret embeddings for drugs and cell types, which enables drug similarity analysis and predictions for unseen dosage and drug combinations. We show that CPA accurately models single-cell perturbations across compounds, doses, species, and time. We further demonstrate that CPA predicts combinatorial genetic interactions of several types, implying that it captures features that distinguish different interaction programs. Finally, we demonstrate that CPA can generate in-silico 5,329 missing genetic combination perturbations ($97.6% of all possibilities) with diverse genetic interactions. We envision our model will facilitate efficient experimental design and hypothesis generation by enabling in-silico response prediction at the single-cell level, and thus accelerate therapeutic applications using single-cell technologies.
 
Overall design Drug treatments were performed in triplicate including controls matching prior study (Srivatsan, 2020)
 
Contributor(s) Srivatsan SR, Daza R
Citation(s) 37154091
Submission date Jun 22, 2022
Last update date Sep 08, 2023
Contact name Sanjay R Srivatsan
E-mail(s) sanjays@uw.edu, sanjayrsrivatsan@gmail.com
Organization name University of Washington
Department Genome Sciences
Lab Trapnell Lab
Street address 3720 15TH Ave NE Genome Sciences S Foefe Bldg
City Seattle
State/province WA
ZIP/Postal code 98195
Country USA
 
Platforms (1)
GPL18573 Illumina NextSeq 500 (Homo sapiens)
Samples (96)
GSM6261629 SciPlex Combo Library Well A01
GSM6261630 SciPlex Combo Library Well A02
GSM6261631 SciPlex Combo Library Well A03
Relations
BioProject PRJNA851906

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE206741_cell_metadata.tsv.gz 1.1 Mb (ftp)(http) TSV
GSE206741_count_matrix.mtx.gz 343.4 Mb (ftp)(http) MTX
GSE206741_gene_metadata.tsv.gz 420.4 Kb (ftp)(http) TSV
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Raw data are available in SRA
Processed data are available on Series record

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