The GCTx format and cmap{Py, R, M, J} packages: resources for optimized storage and integrated traversal of annotated dense matrices

Enache, Oana; Lahr, David L.; Natoli, Ted; Litichevskiy, Lev; Wadden, David; Flynn, Corey; Gould, Joshua; Asiedu, Jacob K.; Narayan, Rajiv; Subramanian, Aravind

doi:10.1093/bioinformatics/bty784

Cited by 39 publications

(25 citation statements)

References 13 publications

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“…In short, the L1000 is a highthroughput gene expression assay that measures the expression of 978 "landmark" genes from human cells [18] which can be used to computationally infer the expression of 11,350 genes. The data used in the present study was generated in GCTx format, which stored annotated data matrices [20]. The expression level analysis of HIF-1α, the landmark gene in the present study, was obtained from level 5moderated Z-score (MODZ).…”

Section: Analysis Of the Association Between Hdac Inhibitors And Exprmentioning

confidence: 99%

Investigating the potential effects of selective histone deacetylase 6 inhibitor ACY1215 on infarct size in rats with cardiac ischemia-reperfusion injury

Lin

Hsu

HuangFu

et al. 2020

BMC Pharmacol Toxicol

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Background: Despite the fact that histone deacetylase (HDAC) inhibitors have been tested to treat various cardiovascular diseases, the effects of selective HDAC6 inhibitor ACY1215 on infarct size during cardiac ischemiareperfusion (IR) injury still remain unknown. In the present study we aimed to investigate the effects of ACY1215 on infarct size in rats with cardiac IR injury, as well as to examine the association between HDAC6 inhibitors and the gene expression of hypoxia inducible factor-1α (HIF-1α), a key regulator of cellular responses to hypoxia. Methods: By using computational analysis of high-throughput expression profiling dataset, the association between HDAC inhibitors (pan-HDAC inhibitors panobinostat and vorinostat, and HDAC6 inhibitor ISOX) and their effects on HIF-1α geneexpression were evaluated. The male Wistar rats treated with ligation of left coronary artery followed by reperfusion were used as a cardiac IR model. ACY1215 (50 mg/kg), pan-HDAC inhibitor MPT0E028 (25 mg/kg), and vehicle were intraperitoneally injected within 5 min before reperfusion. The infarct size in rat myocardium was determined by 2,3,5-triphenyltetrazolium chloride staining. The serum levels of transforming growth factor-β (TGF-β) and C-reactive protein (CRP) were also determined. Results: The high-throughput gene expression assay showed that treatment of ISOX was associated with a more decreased gene expression of HIF-1α than that of panobinostat and vorinostat. Compared to control rats, ACY1215treated rats had a smaller infarct size (49.75 ± 9.36% vs. 19.22 ± 1.70%, p < 0.05), while MPT0E028-treated rats had a similar infarct size to control rats. ACY-1215-and MPT0E028-treated rats had a trend in decreased serum TGF-β levels, but not statistically significant. ACY1215-treated rats also had higher serum CRP levels compared to control rats (641.6 μg/mL vs. 961.37 ± 64.94 μg/mL, p < 0.05). Conclusions: Our research indicated that HDAC6 inhibition by ACY1215 might reduce infarct size in rats with cardiac IR injury possibly through modulating HIF-1α expression. TGF-β and CRP should be useful biomarkers to monitor the use of ACY1215 in cardiac IR injury.

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Section: Analysis Of the Association Between Hdac Inhibitors And Exprmentioning

confidence: 99%

Investigating the potential effects of selective histone deacetylase 6 inhibitor ACY1215 on infarct size in rats with cardiac ischemia-reperfusion injury

Lin

Hsu

HuangFu

et al. 2020

BMC Pharmacol Toxicol

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“…The compendia contain both microarray and RNA-seq data, quantile normalized and with missing values imputed using SVD-impute (Perry 2009) (430,119 human, 228,708 mouse samples). We extracted the microarray data (330,508 human, 123,279 mouse samples) and converted the data to gctx format to aid analysis (Enache et al 2019) ; no other transformations were applied to them. All RNA-seq libraries for human and mouse were downloaded from refine.bio (122,864 human, 125,652 mouse).…”

Section: Dataset Construction 1-expression Data Pre-processingmentioning

confidence: 99%

Large-Scale Labeling and Assessment of Sex Bias in Publicly Available Expression Data

Flynn

Chang

Altman

2020

Preprint

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Women are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we infer sex labels for 450,371 human and 245,107 mouse microarray and RNA-seq samples from refine.bio. Overall, we find slight female bias (52.1%) in human samples and (62.5%) male bias in mouse samples; this corresponds to a majority of single sex studies, split between female-only and male-only (33.3% vs 18.4% in human and 31.0% vs 30.4% in mouse respectively). In drug studies, we find limited evidence for sex-sampling bias overall; however, specific categories of drugs, including human cancer and mouse nervous system drugs, are enriched in female-only and male-only studies respectively. Our expression-based sex labels allow us to further examine the complexity of cell line sex and assess the frequency of metadata sex label misannotations (2-5%). We make our inferred and normalized labels, along with flags for misannotated samples, publicly available to catalyze the routine use of sex as a study variable in future analyses.

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“…We downloaded the 5th level of differential gene expression signatures released on the GEO website (GEO accession GSE70138) which includes 107,404 profiles corresponding to 1,768 different drugs in 41 cell lines, 83 concentrations, and four treatment durations. Data access was performed through the cmapR package 41 . Genes in each drug profile were ranked according to their expression, from the most up-regulated to the most down-regulated gene.…”

Section: Gene Expression Datamentioning

confidence: 99%

Automatic identification of small molecules that promote cell conversion and reprogramming

Napolitano

Rapakoulia

Annunziata

et al. 2020

Preprint

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Controlling cell fate has great potential for regenerative medicine, drug discovery, and basic research. Although numerous transcription factors have been discovered that are able to promote cell reprogramming and trans-differentiation, methods based on their up-regulation tend to show low efficiency. The identification of small molecules that can facilitate conversion between cell types can ameliorate this problem working through safe, rapid, and reversible mechanisms. Here we present DECCODE, an unbiased computational method for the identification of such molecules solely based on transcriptional data. DECCODE matches the largest available collection of druginduced profiles (the LINCS database) for drug treatments against the largest publicly available dataset of primary cell transcriptional profiles (FANTOM5), to identify drugs that either alone or in combination enhance cell reprogramming and cell conversion. Extensive in silico and in vitro validation of DECCODE in the context of human induced pluripotent stem cells (hIPSCs) generation shows that the method is able to prioritize drugs enhancing cell reprogramming. We also generated predictions for cell conversion with single drugs and drug combinations for 145 different cell types and made them available for further studies.

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The GCTx format and cmap{Py, R, M, J} packages: resources for optimized storage and integrated traversal of annotated dense matrices

Abstract: Software packages (available in Python, R, Matlab, and Java) are freely available at https://github.com/cmap. Additional instructions, tutorials, and datasets are available at clue.io/code.

Cited by 39 publications

References 13 publications

Investigating the potential effects of selective histone deacetylase 6 inhibitor ACY1215 on infarct size in rats with cardiac ischemia-reperfusion injury

Investigating the potential effects of selective histone deacetylase 6 inhibitor ACY1215 on infarct size in rats with cardiac ischemia-reperfusion injury

Large-Scale Labeling and Assessment of Sex Bias in Publicly Available Expression Data

Automatic identification of small molecules that promote cell conversion and reprogramming

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