S SG. Grier P Page, Ph.D. Associate Professor. ection. enetics. Department of Biostatistics School of Public Health. ON tatistical

Transcription

1 S SG ection ON tatistical enetics Grier P Page, Ph.D. Associate Professor Department of Biostatistics School of Public Health Quality Control of Microarray Studies

2 From Affymetrix.com mcd/research.html Microarrays

3 Quality Control In Microarray Studies

4 Which one is good or bad?

5 One view of the steps for a microarray study But essentially no quality checking of the data

6 From Drug Discov Today Sep 1;10(17):

7 The Myth That Data Mining has No Hypothesis There always needs to be a biological question in the experiment. If there is not even a question don t bother. The question could be nebulous: What happens to the gene expression of this tissue when I apply Drug A. The purpose of the question is to drive the experimental design. Make sure the samples answer the question: Cause vs. effect.

8 Design Issues - I Known sources of non-biological error (not exhaustive) that must be addressed Technician Chip lot Reagent lot Printer tip Time of printing Date Fluidics well/ Scanner Order of scanning Location Cage/ Field position Far and away the largest is cdna and crna labeling

9 cdna clones Plate effects printing (?) Sources of Artifacts excitation PCR product amplificatio n and purification RN A cdna Test sample Reference sample RNA Intensity effects cdna (labelling efficiency) green laser emission red laser overlay images scanning Intensity effects (quenching) Production Hybridize data: (R,G,...)

10 UMSA Analysis Day 1 Day 2 Insulin Resistant Insulin Sensitive

11 Example of Day or Batch Effect 800 PCA of muscle data Mean vs. Variance by Chip Average Intensity Phase1 U95A Phase 2 U95A Phase 3 U95A 0 0.E+00 1.E+06 2.E+06 3.E+06 4.E+06 5.E+06 6.E+06 Variance of Intensity

12 Design Issues II How to address these issues Make the experiment as uniform as possible Agree on exactly what defines the tissue to be used, use same technician, same chip lot, same reagents (always buy a little too much), same scanner, do sample extraction, labeling and hybridization on one day if possible, establish quality control Randomize when uniformity is not possible Don t do all of condition 1 on day 1 and condition 2 on day 2 Randomize the time a chips sits waiting to be scanned Randomize animal cage/plant field position Microarrays generate such a huge volume of data that is is possible to detect these issues, I suspect that northerns, Southerns, RT-PCR, westerns, and more have similar problems.

13 What is Orthogonalization vs Randomization? Orthogonalization- spreading the biological sources of error evenly across the nonbiological sources of error. Maximally powerful for known sources of error. Randomization spear the biological sources of error at random across the nonbiological sources of error. Useful for controlling for unknown sources of error

14 Examples of Orthogonalization and Randomization? Variety Treatment Sample # Sample Order Sample Order The experiment Orthogonalize Randomize

15 RNA Quality

16 Quality control of RNA Confirmation of RNA integrity, based on an 28S:18S ratio greater than 1.5 as quantified by Agilent BioAnalyzer and formaldehyde gel electrophoresis However, The Drosophila RNA has a split peak for the 28s ribosomal RNA on thebioanalyzer. Intact RNA Degraded RNA Images from Agilent

17 Be aware of what your specific Species should look like The Drosophila RNA has a split peak for the 28s ribosomal RNA on the Bioanalyzer. And no 18S peak

18 Affymetrix QC metrics The average background is calculated from the 2% probes with the weakest signal. The average background is an estimate of general nonspecific binding based on low-intensity features across an array. Bio B isa probe set designed to measure prelabeled bacterial nucleotides. Bio B is the signal from internal prelabeled standards and measures the efficacy of hybridization, washing, and scanning. Bio B is free of RNA, amplification, and labeling effects. The actin 3/5 ratio is a ratio of probe sets designed to detect the 3 and 5 regions of the actin mrna transcript and is reputed to detect RNA degradation. This ratio is thought to indicate RNA quality a well as the bias inherent in the Affymetrix labeling assay. The scale factor is a global normalization constant based on the trimmed mean of probe set signals or average differences and is inversely related to chip brightness. Percent present is an array level summary of the results of a statistical function designed to predict the presence or absence of each transcript. Percent present is a quality metric that is sensitive to any error source from RNA sampling to scanning and data extraction. Percent present is influenced by all stages in the microarray process including scanner brightness, background, RNA quality, algorithm, and chip design.

19 Relationship among metrics From Finkelstein. Mol Methods xxx

20 RNA digestion plot ' <-----> 3' Probe Number Mean Intensity : shifted and scaled

21 Does one size fit all?

22 The red/green ratios can be spatially biased. Top 2.5%of ratios red, bottom 2.5% of ratios green

23 Spatial plots: background from the two slides

24 Image and Data Quality Checking

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26 Y X An illustration of principle of a geography index: A full polynomial model is used to detect spatial patterns (e.g. upper right corner). Geography index (GEODEX). A full polynomial model was used to detect spatial patterns: z = x + y + x 2 + xy + y 2 + x 3 + x 2 y + xy 2 + y 3 + xy 3 + x 2 y 2 + x 3 y + x 2 y 3 + x 3 y 2 + x 3 y 3, where z expression level and x & y Cartesian coordinates of an array. Foreground and background readings for each channel were processed separately. Geography indices were determined as follows: GEODEX = 1 - R 2, where R 2 is a coefficient of determination for a polynomial model.

27 Distribution of the geography index (GEODEX) for individual chips GEODEX Image of chip Chip ID Image of the right corner on the bottom.

28 A. Foreground - high GEODEX values C. Background - high GEODEX values B. Foreground - low GEODEX values D. Background - low GEODEX values X-axis and Y-axis - Cartesian coordinates of an array; Z-axis - foreground or background signal readings. In this analysis lower GEODEX is considered a worse outcome.

29 These are OK

30 These are not OK

31 Quality Control Using Deleted Residuals Page GP, Edwards JW, Yelisetti P, & Allison DB Assessing Quality of Microarrays Using Studentized Deleted Residuals using normative data from 4358 arrays from a public database. In preparation

32 Using Deleted Residual Plots & Statistics to Assess Array Quality Traditional tools for detecting outliers do not work when n is small. Deleted residuals can work when n is small. Within a single gene, the distribution of the deleted residuals is t with n-2 degrees of freedom. By looking at all the genes on a chip, we may be able to use apparent departure from t n-2 among the deleted residuals as a measure of array quality. Compare are the observed deleted residuals to the expected t with a Kolmogorov-Smirnov test with genes-1 df.

33 Presumed Bad Chip Presumed Good Chip

34 Empirical percentile cut offs derived from 10,447 comparisons using the 4358 chips taken from GEO. Percentile Mean Stdev Skewness Kurtosis K-S D 1% % % % % % % % % % % % %

35 Histogram of p-values

36 Potentially Bad Chip

37 Histogram of p-values with bad chip removed

38 Quality Control in Data sources

39 Understand What Databases Include, don t include, and assumptions Just because a database says something does not mean it is right. Read the evidence. Databases are biased. Databases are incomplete Databases have lots of data Understand data before you use it

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41 TCA cycle from Ingenuity

42 TCA from GeneMAPP

43 TCA cycle from Ingenuity

44 Issues in the Annotation of Genes

45 Annotation is inconsistent across sources Gene Symbol p-value fc 50/21 Gene Ontology Biological Process Gene Ontology Cellular ComponentPathway Aco Krebs-TCA_Cycle // Pdk // acetyl-coa biosynthesis from pyru5739 // mitochondrion // Krebs-TCA_Cycle // Pdk // acetyl-coa biosynthesis from pyru5739 // mitochondrion // Krebs-TCA_Cycle // Pdha // glycolysis 5739 // mitochondrion Krebs-TCA_Cycle // Idh // tricarboxylic acid cycle 5829 // cytosol --- Acly // tricarboxylic acid cycle 5622 // intracellular Fatty_Acid_Synthes Aco2 1.22E Krebs-TCA_Cycle // Fh1 6.76E // tricarboxylic acid cycle // 5739 // mitochondrion Krebs-TCA_Cycle // Atp5g3 1.53E // tricarboxylic acid cycle // 5739 // mitochondrion --- Suclg1 8.87E // tricarboxylic acid cycle // 5739 // mitochondrion Krebs-TCA_Cycle // Mdh1 5.92E // tricarboxylic acid cycle // --- Krebs-TCA_Cycle // Mor1 4.24E // tricarboxylic acid cycle // 5739 // mitochondrion Krebs-TCA_Cycle // Idh1 2.36E // tricarboxylic acid cycle // 5829 // cytosol // --- Idh3g 2.19E // tricarboxylic acid cycle // 5739 // mitochondrion Krebs-TCA_Cycle // Dlst 2.49E Sdhd 5.13E // mitochondrial electron transport, s5749 // respiratory chain complex I Krebs-TCA_Cycle // Sdhc 1.82E RGD: E // dihydrolipoyl dehydrogenas --- Cs 1.56E // mitochondrion Krebs-TCA_Cycle // RGD: E // tricarboxylic acid cycle // 5829 // cytosol --- Idh3B 2.57E Krebs-TCA_Cycle // Mdh1 1.08E // tricarboxylic acid cycle // --- Krebs-TCA_Cycle // Pc 1.91E // gluconeogenesis // 5739 // mitochondrion Krebs-TCA_Cycle // RGD: // cell-cell adherens junction Krebs-TCA_Cycle // RGD: // cell-cell adherens junction Krebs-TCA_Cycle // Dlat 4.76E // acetyl-coa biosynthesis from pyru5739 // mitochondrion // Krebs-TCA_Cycle // Sdhd 1.3E // mitochondrial electron transport, s5749 // respiratory chain complex I Krebs-TCA_Cycle // Sdha 7.85E // tricarboxylic acid cycle // 5739 // mitochondrion // Krebs-TCA_Cycle // Idh3a // tricarboxylic acid cycle // 5739 // mitochondrion // Krebs-TCA_Cycle // Pdk // acetyl-coa biosynthesis from pyru5739 // mitochondrion // Krebs-TCA_Cycle // Cs 1.36E // mitochondrion // Krebs-TCA_Cycle // Acly // acetyl-coa biosynthesis 5622 // intracellular // Fatty_Acid_Synthes

46 Nucleic Acids Research, 2005, Vol. 33, No. 3 e31

47 Nucleic Acids Research, 2005, Vol. 33, No. 3 e31

48 Nucleic Acids Research, 2005, Vol. 33, No. 3 e31

49 Mapping of Plant Arrays AG ATH1 Operon(75%) Operon(98%) CATMA(75%) CATMA(98%) AFGC(75%) AFGC(98%) Vendor Mapping Numbers Nil Entries from vendor (No Mapping for these probes) Exact Match * Present-Vendor Absent-Blast Absent-Vendor Present-Blast One-Vendor Many-Blast Many-Vendor One-Blast

50 MAQC Nature Biotechnololgy Oct 2006

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53 Summary Design your experiment well Control for non-biological sources of error Know what is good and bad quality data at each stage including RNA, image, data, and annotation If you are aware of these issues and control for them highly powerful and reproducible microarray experimentation is possible.

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