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Introduction to CellMiner Augustin Luna 21 January, 2016 Research - PowerPoint PPT Presentation

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Introduction to CellMiner Augustin Luna 21 January, 2016 Research Fellow Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute Topics to be Covered Introduction to CellMiner Introduction to rcellminer What is

  1. Introduction to CellMiner Augustin Luna 21 January, 2016 Research Fellow Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute

  2. Topics to be Covered Introduction to CellMiner Introduction to rcellminer

  3. What is CellMiner? Website: http://discover.nci.nih.gov/cellminer Retrieval and integration for NCI-60 datasets: molecular and pharmacological NCI-60 60 human cancer cell lines from 9 tissues of origin: breast, central nervous system, colon, leukemia, melanoma, non-small cell lung, ovarian, prostate, and renal Used by the Developmental Therapeutics Program of the National Cancer Institute to screen over 100,000 chemical compounds and natural products A subset of ~21,000 drugs is provided by CellMiner Drug activity levels expressed as 50% growth-inhibitory levels (GI50) were determined at 48 hours using the sulforhodamine B (SRB) assay Determines cell density based on the measurement of total cellular protein content

  4. What is rcellminer? Website and Tutorial (Vignette): https://www.bioconductor.org/packages/release/bioc/html/rcellminer.html Publication: http://www.ncbi.nlm.nih.gov/pubmed/26635141 Provides programmatic access to CellMiner NCI-60 data Data Types Gene and protein expression, copy number, whole exome mutations, etc Activity data for ~21K compounds and information on their structure, mechanism of action, and repeat screens Easy visualization of compound structures, activity patterns, and molecular feature profiles Embedded R Shiny applications allow interactive data exploration

  5. Pattern Comparison using rcellminer library(rcellminer) # Get drug and expression data drugAct <- exprs(getAct(rcellminerData::drugData)) expData <- getAllFeatureData(rcellminerData::molData)[["exp"]] # Create pattern of interest patternOfInterest <- expData["SLFN11", ] # Run pattern comparison to get correlated drugs and other gene expressions r1 <- patternComparison(patternOfInterest, drugAct) r2 <- patternComparison(patternOfInterest, expData) head(r1, 3) COR PVAL 639174 0.8343842 1.227589e-16 681636 0.7967239 1.243962e-13 34462 0.7950691 3.302182e-14 head(r2, 3) COR PVAL SLFN11 1.0000000 0.000000e+00 BCAT1 0.5847423 9.298535e-07 CCDC181 0.5766713 1.419268e-06

  6. Embedded Shiny Applications Simplified web applications to do common data exploration tasks Compare any two molecular and drug profiles Find related structures View information on repeat screening for drug compounds

  7. Getting Help CellMiner webadmin@discover.nci.nih.gov rcellminer Augustin Luna: aluna [AT] jimmy [DOT] harvard [DOT] edu Vinodh Rajapakse: vinodh [DOT] rajapakse [AT] nih [DOT] gov

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