Data analysis in cell-based functional assay
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1 Florian Hahne Molecular Genome Analysis Data analysis in cell-based functional assay Tools for automated pre-processing, analysis and visualization of high throughput FACS data
2 Overview Challenge and Concept Assay Design Data Analysis
3 Overview Challenge and Concept Assay Design Data Analysis
4 The Challenge: Identification of Disease Genes Candidate gene sets from systematic gene identification and microarray studies: dozens hundreds How close the gap? Capacity of in-vivo functional studies: few
5 The Concept: Functional Profiling ( 21,000+ human cdnas (~genes) microarray study (cancer vs. normal, in vitro) Genome-wide - disease-associated genes cellular assay (in vivo) hot candidates
6 Cancer relevance: challenging the cell cycle
7 Overview Challenge and Concept Assay Design Data Analysis
8 The design: manipulate gene expression system to willfully manipulate expression level of certain genes in cells up regulation (transfection of expression vectors) down regulation (RNA interference) means to monitor perturbation (beneficial but not mandatory) expression of fluorescence protein tag means to monitor effect of perturbation expression or activation state of key regulatory proteins (FACS, automated microscope)
9 ORF cloning: The Gateway System Full coding cdna clone PCR amplification attl1 attb1 attb1 attb1 ORF ORF ORF attb2 attb2 attb2 entry clone ORF attl2 N YFP ORF C N ORF YFP C N-terminal tag C-terminal tag
10 FACS: a quick reminder measures fluorescence intensities as well as morphological parameters on the basis of light emission offers single cell resolution robust, reliable, variable Laser light scatter detector Fluorescence detector (PMT3, PMT4 etc.)
11 Automation HTS Sampler for automated flow cytometry pipetting robot (liquid handling)
12 Workflow biology establishment of individual assays informatics Development of specialized software tools for data analysis adaptations and refinement of assays for high throughput High throughput screening Automated data analysis of individual experiments candidates validation in continuative experiments Comparison between experiments to identify candidates
13 Overview Challenge and Concept Assay Design Data Analysis
14 Keeping track of experiments: PACAT PACAT (proliferation assay clone administration tool) (Heiko Rosenfelder)
15 - package prada package prada contains functionalities for analysis of data derived from cell based assays modular framework data preprocessing data visualization data integration for statistical inference and modeling general purpose tools can be used linear and local regression hypothesis testing
16 Data import and maintenance FCS 3.0 files - standardized storage format for FACS data - contains fluorescence values in data segment, wealth of meta data in text segment - can be imported into R (function readfcs) cytoframe R internal representation of data from one FCS file generic functions cytoset R internal representation of data from several FCS files (e.g. one 96 well plate)
17 Data pre-processing: FSC vs. SSC plot granularity distinction on basis of morphological properties strong variation between experiments dynamic determination cell size
18 Data pre-processing: finding the main population assumption: bivariate normal distribution robust fitting discarding cells that do not lie within some given boundary of this distribution X midpoint of = distribution density of = distribution = discarded
19 Data pre-processing: finding the main population assumption: bivariate normal distribution robust fitting discarding cells that do not lie within some given boundary of this distribution X midpoint of = distribution density of = distribution = discarded shape and localization of main distribution can be used for quality control
20 Visualization: plate plots plate plots as graphical representation of experimental entities false color coding for concise display of numeric outcomes from statistical analyses HTML image map allows for hyper linking to include further information for each well visualization of results quantitative cell number
21 Visualization: plate plots plate plots as graphical representation of experimental entities false color coding for concise display of numeric outcomes from statistical analyses HTML image map allows for hyper linking to include further information for each well visualization of results qualitative
22 statistical analysis: mode of response different responses for different assays continuous response: concentration dependent (e.g. MAP kinase) theory FACS over expression discrete response: on/off mechanism (e.g. apoptosis, proliferation) theory FACS effect effect effect effect over expression over expression over expression
23 statistical analysis: continuous response robust fitting of smoothed local regression function z-score as measure of effect: * mˆ ( t ) ratio of estimated slope and its standard error z = * at YFP intensity t* ˆ σ m ( t ) z = 8.59 z = 0.88 z = t* t* t*
24 statistical analysis: mode of response discrete response: on/off mechanism (e.g. apoptosis, proliferation) theory FACS effect effect over expression over expression
25 statistical analysis: discrete response Fisher s exact test r = 1 a b effect untransfected positive (a) untransfected negative (b) transfected positive (c) transfected negative (d) r = 2 c d effect size transfection odds ratio = r r 1 2, p value significance
26 statistical analysis: discrete response no effect activator log(odds ratio) = 0.44 (p = 4.4e-03) -log(odds ratio) = 4.33 (p = 2.2e-16)
27 between well analysis: finding true effectors control inhibitor activator MAP kinase control activator apoptosis frequency -log odds ratio (p=0.83) frequency -log odds ratio (p=5.2e-05)
28 data integration PACAT ODBC SQL individual experiment assaydb ODBC assay 1 assay 2 ODBC ODBC assay 3 individual ORF
29 summary cellular assays help to close the gap between genome-wide large scale studies and analyses on the single molecule level association/correlation causal relationships FACS has proven to a capable tool for high throughput analyses with single cell resolution package prada provides a framework for integrating various analysis approaches of multiple assays modular structure
30 Annemarie Poustka Stefan Wiemann Wolfgang Huber Andreas Buneß Marcus Ruschhaupt Heiko Rosenfelder Alex Mehrle Dorit Arlt Meher Majety Mamatha Sauermann YOU for the invitation!
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