2010 Best Practices Competition IT & Informatics HPC

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Published Resources for the Life Sciences 250 First Avenue, Suite 300, Needham, MA 02494 | phone: 781‐972‐5400 | fax: 781‐972‐5425 Available Measurements Normalized data distribution Measurement name TargetActivationV3Well:SelectedObjectCount… TargetActivationV3Well:MEAN_ObjectAreaCh1 Run 47 Run 48 Run 55 TargetActivationV3Well:MEAN_ObjectShapeP2ACh1 TargetActivationV3Well:MEAN_ObjectTotalIntenCh1 100 TargetActivationV3Well:SelectedObjectCount TargetActivationV3Well:SelectedObjectCountPerValidF 80 TargetActivationV3Well:ValidObjectCount 60 Value PctInh 40 20 0 -20 -40 -60 Distribution of ALL normalized data across all plates Data table: SHADOW results Color by Status NEG POS SAMPLE Reference points Median Normalized data statistics Measurement name, Treatment role TargetActivationV3Well:Selecte dObjectCountPerValidField (Column Names) NEG POS SAMPLE UAV 34.45 102.24 103.86 Q3 9.26 100.60 63.69 Median -0.04 100.09 42.43 Q1 -9.05 99.49 23.39 LAV -35.25 97.84 -36.96 Mean + 3SD 40.94 103.30 126.89 Mean 0.00 100.00 43.18 Mean - 3SD -40.94 96.70 -40.54 Summary statistics for ALL normalized data across all plates. Data table: SHADOW res NEG POS SAM… NEG POS SAM… NEG POS SAM… Status UAV, Q3, Median, Q1, LAV, Mean + 3SD, Mean, Mea… Wells per treatment Normalized results for hits Measurement name, siRNA index Number of wells that match 6 current filters for each TargetActivationV3Well:SelectedObjectCountPerV… Grand Median treatment 1 2 3 4 (Empty) total normalized value for Data limited by: 5 Gene579 100.37 99.73 99.39 45.73 - - - 99.50 each siRNA Active measurement Gene59 83.49 66.61 89.87 58.68 84.88 84.87 for hits 4 Data table: Gene735 94.31 87.21 82.25 66.20 - - - 84.37 Data table: Results by well and measure Gene672 84.69 13.92 96.64 80.50 - - - 84.24 SHADOW r 3 Marking: Gene254 83.56 94.82 18.03 82.87 - - - 83.56 Color by Hit treatments Gene597 79.00 85.68 81.44 55.55 - - - 79.65 Median(Valu 2 Color by Gene694 81.13 72.30 86.55 53.80 - - - 79.30 Min (5.9 Treatment role Max (10 Gene195 75.64 71.84 95.17 69.35 - - - 77.26 SAMPLE 1 Gene536 77.07 87.20 78.93 27.19 - - - 77.02 0 Gene150 84.23 91.17 70.13 60.68 - - - 76.60 N NU NU NU NU NU NU NU NU NU NU NU NU NU NU… Gene109 81.08 62.99 85.96 75.22 - - - 76.29 S SA SA SA SA SA… SA SA SA SA SA SA SA SA SA Gene43 86.84 76.77 50.12 75.52 - - - 75.46 Treatment role, treatment,1 Value Gene98 89.32 76.14 71.90 5.93 - - - 75.15 Treatments per gene Gene406 80.80 25.61 77.28 75.79 - - - 74.33 Gene611 69.31 72.58 76.66 62.55 - - - 72.13 Gene550 79.76 70.80 71.10 40.41 - - - 71.99 Data limited by: 3 Gene707 74.31 70.90 92.75 28.29 - - - 71.83 Active measurement Hit treatments Gene180 71.11 62.81 96.32 71.82 - - - 71.58 Gene335 71.76 83.26 69.17 52.51 - - - 70.30 Data table: 2 Results by well and measure Gene433 63.75 92.22 41.97 70.78 - - - 66.79 Marking: Median(Value PctInh) Hit genes 1 Number of hits Color by Treatment role Count(Well id) 338 Data limited by: SAMPLE UniqueCount(treatment… 61 Active measurement 0 UniqueCount(Gene id) 20 Hit genes 5 230 100 597 285 115 167 555 232 185 381 139 687 844 5604 S SA SA SA SA SA SA SA SA SA SA SA SA SA SA… Data table: Treatment role, Gene id UniqueCount(Well id) UniqueCount(treatment,1 Value) Gene (Column N… Figure 3: TIBCO Spotfire workflow for hit selection from RNAi screens from HCS Road showing: (top left) table of available measurements; (top center) histograms of cell count percent inhibition for control and library wells across multiple runs; (top right) table of summary statistics for normalized cell count for control and library reagents; (middle left) bar chart of numbers of wells per RNAi reagent with normalized values above a user-defined threshold (blue shading indicates hit reagents where at least 4 of 6 replicate wells passed the threshold); (bottom left) bar chart of numbers of individual RNAi reagents per gene where 4 or more replicate wells passed the normalized value threshold (red shading indicates hit genes where 3 or more independent RNAi reagents for the same gene were selected as hits); (middle right) table of median cell count percent inhibition values for all hit genes; (bottom left) numbers of wells, RNAi reagents and genes selected as hits.
Published Resources for the Life Sciences 250 First Avenue, Suite 300, Needham, MA 02494 | phone: 781‐972‐5400 | fax: 781‐972‐5425 7. REFERENCES/testimonials/supporting internal documents (If necessary; 5 pages max.) 1. Agler M, Prack M, Zhu Y, Kolb J, Nowak K, Ryseck R, Shen D, Cvijic ME, Somerville J, Nadler S, Chen T: A high-content glucocorticoid receptor translocation assay for compound mechanism-of-action evaluation. J Biomol Screen 2007; 12:1029-1041. 2. Ross-Macdonald P, de Silva H, Guo Q, Xiao H, Hung CY, Penhallow B, Markwalder J, He L, Attar RM, Lin TA, Seitz S, Tilford C, Wardwell-Swanson J, Jackson D: Identification of a nonkinase target mediating cytotoxicity of novel kinase inhibitors. Molecular cancer therapeutics 2008; 7:3490-3498. 3. Zock JM: Applications of high content screening in life science research. Combinatorial chemistry & high throughput screening 2009; 12:870-876. 4. Dunlay RT, Czekalski WJ, Collins MA: Overview of informatics for high content screening. Methods in molecular biology (Clifton, NJ 2007; 356:269-280. 5. Goldberg IG, Allan C, Burel JM, Creager D, Falconi A, Hochheiser H, Johnston J, Mellen J, Sorger PK, Swedlow JR: The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging. Genome biology 2005; 6:R47. 6. Miaca Draft Specification Retrieved from http://cdnetworks-us- 2.dl.sourceforge.net/project/miaca/Documentation/MIACA_080404/MIACA_080404.pdf. 7. Palmer M, Kremer A, Terstappen GC: A primer on screening data management. J Biomol Screen 2009; 14:999-1007. 8. Ling XB: High throughput screening informatics. Combinatorial chemistry & high throughput screening 2008; 11:249-257. 9. Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, Golland P, Sabatini DM: CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome biology 2006; 7:R100. 10. Garfinkel LS: Large-scale data management for high content screening. Methods in molecular biology (Clifton, NJ 2007; 356:281-291. 11. Zhang JH, Chung TD, Oldenburg KR: A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays. J Biomol Screen 1999; 4:67-73. 12. Malo N, Hanley JA, Cerquozzi S, Pelletier J, Nadon R: Statistical practice in high-throughput screening data analysis. Nature biotechnology 2006; 24:167-175. 13. Giuliano KA, Chen YT, Taylor DL: High-content screening with siRNA optimizes a cell biological approach to drug discovery: defining the role of P53 activation in the cellular response to anticancer drugs. J Biomol Screen 2004; 9:557-568. 14. Perlman ZE, Slack MD, Feng Y, Mitchison TJ, Wu LF, Altschuler SJ: Multidimensional drug profiling by automated microscopy. Science (New York, NY 2004; 306:1194-1198. 15. Low J, Huang S, Blosser W, Dowless M, Burch J, Neubauer B, Stancato L: High-content imaging characterization of cell cycle therapeutics through in vitro and in vivo subpopulation analysis. Molecular cancer therapeutics 2008; 7:2455-2463. 16. Collins MA: Generating 'omic knowledge': the role of informatics in high content screening. Combinatorial chemistry & high throughput screening 2009; 12:917-925.
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