Microarray Analysis Using R/Bioconductor

Transcription

1 Microarray Analysis Using R/Bioconductor Reddy Gali, Ph.D. h"p://catalyst.harvard.edu

2 Agenda Introduction to microarrays Workflow of a gene expression microarray experiment Publishing microarray data (MIAME format) Microarray experimental design Public microarray databases Microarray preprocessing - Quality control and Diagnostic analysis 1

3 Agenda Introduction to R/Bioconductor Installation of R and Bioconductor Packages General data analysis and strategies Data analysis using R/Bioconductor and OneChannelGUI 2

4 cdna Microarrays

5 Antibody Arrays

6 Protein Arrays

7 Affymetrix array design PM MM 11 Probe pairs / Probe Set Multiple Probe Sets / Gene Lipshutz et al; 1999; Nature Genetics, 21(1):

8 Microarray Applications Analyze and compare patterns of gene expression - before and after an intervention - between tissue types - between transgenic strains - in neighboring cells (laser capture microdissection) Find DNA copy-number variations SNP detection Tool for genotyping High throughput screening tool for drug discovery Elucidate gene function (RNAi microarrays; Silva et al., PNAS 2004) Investigate interactions between DNA and protein (ChIP on Chip) 7

9 Workflow of Gene Expression Biological question Experimental design QC Tissue / sample preparation Extraction of Total RNA QC Probe amplification & labeling QC Microarray hybridization & processing Image analysis QC Biological Verification Data analysis Expression measures - Normalization - Statistical Filtering - Clustering - Pathway analysis QC 8

10 Pitfalls of Microarray Experiment Gene expression changes detected by microarray analysis cannot be validated by other methods - Inadequate design - Data quality is low - Statistical approach is not adequate - Expression level of gene is below detection limit - Change in gene expression is small - Microarray detection probe is not specific or not sensitive 9

11 Two color vs Single color Homemade Microarray Affymetrix GeneChip Tissue normal diseased normal diseased Tissue Total RNA Total RNA Cy3 or Cy5 labeled cdna First-strand cdna synthesis Cy5 Mixing Hybridization Cy3 cdna synthesis in vitro transcription Hybridization and Staining Double-stranded cdna Biotin-labeled crna Raw Data Output Raw Data Output Expression Ratio to Absolute Expression Values 10

12 Questions usually asked What kind of technology or microarrays I have to use How many replicates do I need What is a real replicate Do I need statistical advice Should I do technical replicate Should I do dye swap Should I pool my samples How do I analyze my dataset What software should I use 11

13 Design of Microarray Experiment Replicates Goal, resources, technology, quality, design and analysis Two fold change 3 replicates Smaller change 5 replicates Technical replicates and Biological replicates Sample pooling Amount of sample Replicates of pooled sample No way to find variance between samples 12

14 Two-color arrays Reference design Universal reference design Loop design Dye Swap for cdna arrays Design of Microarray Experiment 13

15 MIAME Standards Minimum Information About a Microarray Experiment (MIAME)- 14

16 MIAME Check list Type of experiment: for example, is it a comparison of normal vs. diseased tissue, a time course, or is it designed to study the effects of a gene knock-out? Experimental factors: the parameters or conditions tested, such as time, dose, or genetic variation. The number of hybridizations performed in the experiment. The type of reference used for the hybridizations, if any. Hybridization design: if applicable, a description of the comparisons made in each hybridization, whether to a standard reference sample, or between experimental samples. An accompanying diagram or table may be useful. Quality control steps taken: for example, replicates or dye swaps. 15

17 MIAME Check list The origin of the biological sample (for instance, name of the organism, the provider of the sample) and its characteristics: for example, gender, age, developmental stage, strain, or disease state. Manipulation of biological samples and protocols used: for example, growth conditions, treatments, separation techniques. Protocol for preparing the hybridization extract: for example, the RNA or DNA extraction and purification protocol. 16

18 MIAME Check list Type of scanning hardware and software used: this information is appropriate for a materials and methods section. Type of image analysis software used: specifications should be stated in the materials and methods. A description of the measurements produced by the image-analysis software and a description of which measurements were used in the analysis. The complete output of the image analysis before data selection and transformation. Data selection and transformation procedures. Final gene expression data table(s) used by the authors to make their conclusions after data selection and transformation (gene expression data matrices). 17

19 Gene Expression Omnibus- GEO 18

20 Public Microarray Databases BodyMap - SMD - RIKEN - MGI - GEO - CIBEX - ArrayExpress

21 Microarray Platforms Agilent Microarrays 60-mer format Codelink Bioarrays 30-mer format Affymetrix GeneChips 25-mer format Illumina Beadchips NimbleGen 60-mer format 20

22 Hybridization 21

23 OD 260/ Electropherograms: degradation, rrna peaks RNA quality

24 Microarray data Mining Biological question Experimental design Microarray experiment Image analysis Pre-processing Expression quantification Normalization Estimation/Testing Biological verification/ interpretation Data analysis Classification/Prediction Clustering 23

25 Microarray data Mining CDF / CEL Gpr / Gal / Spot Quality assessment Background correction probe level normalization probe set summary Quality assessment Background correction Within and between array normalization Log ratios Log intensities Identify genes Clustering etc 24

26 Scanning Steps Prescan Determines the area of hybridization to perform a final scan on only that area. Final Scan Performs a final scan in only the area identified above. QA/QC Adjustments to the final scan in terms of: Image manipulation (i.e. rotation) Spot Checking, i.e. identifying and determining troubled spots such as:» Spots with low or below-background hybridization or» Locations with significant dust and/or debris 25

27 Scanning steps- Quality Control Dust spots These are spots where dust particles have covered part of the spot, so much so that not enough of the spot can be considered a good hybridization Undetectable spots These are spots where the hybridization might be below background or so close to background that they cannot be realistically quantified Spots with failure in hybridization These spots are similar to the undetectable spots since their hybridization is so low that they are very difficult to determine their hybridization intensity NOTE: Replicates of the chips especially reverse-fluors can become very informative. 26

28 Microarrays Image Inspection Microarray: - Visual inspection of the chip Scratches, bubbles, uneven hybridization dchip outlier detection 27

29 Image Analysis two color 28

30 Diagnostic plots-rna degradation 29

31 Box Plots of unnormalized data 30

32 Raw vs Normalized data Raw Data Normalized Data 31

33 Histograms of unnormalized data 32

34 QC stats 33

35 Why Normalize 34

36 Free Software Data analysis 35

37 R / Bioconductor R and Bioconductor packages R ( )is a comprehensive statistical environment and programming language for professional data analysis and graphical display. Bioconductor ( is an open source and open development software project for the analysis of microarray, sequence and genome data. More 300 Bioconductor packages. R_BioCondManual.html 36

38 R / Bioconductor - Installation 37

39 OneChannelGUI A graphical interface (GUI) for Bioconductor libraries to be used for quality control, normalization, filtering, statistical validation and data mining for single channel microarrays Affymetrix IVT, Human Gene 1.0 ST and exon arrays are implemented OneChannelGUI is an add-on Bioconductor package providing a new set of functions extending the capability of the affylmgui package. 38

40 TCL and Tk pacakges ActiveTcl is ActiveState's distribution of Tcl. It is most commonly used for rapid prototyping, scripted applications and GUIs. Install Tcl - Tcl/Tk packages, BWidget and Tktable Install in C:\Tcl Directory 39

41 Installing R/ Active Tcl 40

42 Installing AffylmGUI packages for Affymetrix data install.packages("affylmgui",contriburl=" bioinf.wehi.edu.au/affylmgui") source(" bioclite("affylmgui", dependencies=true) bioclite("affylmgui") bioclite("tkrplot") bioclite("affyplm") bioclite("r2html") bioclite("xtable") library(affylmgui) 41

43 AffylmGUI Browser 42

44 OneChannelGUI Installation source(" bioclite("onechannelgui") bioclite("onechannelgui ", dependencies=true) library(onechannelgui) Note: Some onechannelgui slides are from

45 OneChannelGUI 44

46 Target File Creation 45

47 Target File- Affymetrix 46

48 Working with OnechannelGUI B 47

49 Working with OnechannelGUI A Click on File to start a new project B C Click on New to start a new project Selected 3 IVT arrays Select working directory that has the.cel files and targets.txt file D 48

50 Working with OnechannelGUI

51 Working with OnechannelGUI 50

52 Quality Control plots Click on Quality Control menu 51

53 Working with OnechannelGUI 52

54 Probe set summary A Click on probe set menu and select the probe set summary and normalization option. B 53

55 Normalization 54

56 Working with OnechannelGUI 55

57 Filtering - OnechannelGUI 56

58 Linear Modeling (Limma) 57

59 Differential Expression Computer contrasts builds differential expression

60 MA and Valcano plots 59

61 Expression values Gene Description Average intensity P-values AffyID Gene Symbol Log2 FC T statistics Log-odd statistics 60

62 Thank you Reddy Gali, Ph.D. Phone: