2017
DOI: 10.1371/journal.pcbi.1005335
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Representing high throughput expression profiles via perturbation barcodes reveals compound targets

Abstract: High throughput mRNA expression profiling can be used to characterize the response of cell culture models to perturbations such as pharmacologic modulators and genetic perturbations. As profiling campaigns expand in scope, it is important to homogenize, summarize, and analyze the resulting data in a manner that captures significant biological signals in spite of various noise sources such as batch effects and stochastic variation. We used the L1000 platform for large-scale profiling of 978 representative genes… Show more

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Cited by 26 publications
(26 citation statements)
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References 40 publications
(54 reference statements)
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“…Other authors have used neural network models to build embeddings of gene expression data. In [14], the authors use a twin network architecture to represent gene expression profiles as 100 dimensional bar-codes. Comparing their representations to the standard representation (raw z-scores) and a representation based on gene set enrichment analysis (GSEA) they find that their perturbation barcodes consistently identify replicates, samples generated from perturbagens with shared targets, and…”
Section: Neural Representation Learningmentioning
confidence: 99%
“…Other authors have used neural network models to build embeddings of gene expression data. In [14], the authors use a twin network architecture to represent gene expression profiles as 100 dimensional bar-codes. Comparing their representations to the standard representation (raw z-scores) and a representation based on gene set enrichment analysis (GSEA) they find that their perturbation barcodes consistently identify replicates, samples generated from perturbagens with shared targets, and…”
Section: Neural Representation Learningmentioning
confidence: 99%
“…With one of these technologies, known as L1000™ Expression Profiling (profiling for 978 gene expressions) (De Wolf et al, 2016; ), thousands of compounds can be screened per day at lower costs than conventional microarray techniques (Subramanian et al, 2017). Merck reported the screening of a set of 3,699 compounds using the Genometry L1000 platform to unveil a new target for compounds (Filzen et al, 2017). Janssen announced (How library-scale gene-expression profiling is changing drug discovery; Pascale, 2015) that they will use Genometry’s L1000 platform to generate gene-expression profiles for 250,000 compounds from Janssen’s small-molecule screening library.…”
Section: Large-scale Compound Data In Pharmaceutical Industrymentioning
confidence: 99%
“…Among all query size we tested, our Bayesian method has higher TPR at the same FPR and the performance difference is the most significant when the query size is small. To better illustrate the performance of three methods under different query size, we show the median quantile of GSEA scores between bio replicates, 9 which is equivalent to the FPR at TPR = 0.5. As shown in Fig.…”
Section: Similarity Between Replicatesmentioning
confidence: 99%