Introduction to The Analysis of Microarray Data by dchip

Size: px

Start display at page:

Download "Introduction to The Analysis of Microarray Data by dchip"

Lesley Barber
7 years ago
Views:

1 Introduction to The Analysis of Microarray Data by dchip Guide for the First Use of The Program Dana Farber Cancer Institute Microarray Core Facility Outline Low Level Analysis Normalization Calculation of Model-Based Expression Intensities (MBEI) Outlier Detection Some Basic High Level Analysis Gene Filtering Hierarchical Clustering of Samples Hierarchical Clustering of Genes Gene-wise Comparison (Compare Samples) Use Anchor Genes to Find Candidate Genes Gene Annotation Analysis

2 Low level analyses 1. Normalization Purpose: To adjust the overall chip brightness of the arrays to a similar level Method: Invariant Set Nonlinear Normalization Before Normalization After Normalization

3 Before Normalization After Normalization Normalization Steps 1. Choose baseline array 2. Compare each array with the baseline array: 1. Plot probe fluorescence intensities in array A vs. array B 2. For each probe, calculate the rank of its intensity in array A and B, respectively. 3. Select an invariant set of probes based the rank difference in array A and B 4. Fit a smoothing curve based on invariant set 5. Induce normalized intensity values for each probe. Choose Baseline A B C D E Median intensity of all probes on an array Baseline

4 Baseline B C D E Probe fluorescence intensity in Baseline array ( array A) Flourecent intensity in Probe fluorescence intensity in B array (to be normalized) 1 2 Baseline array A Rank in array A: Rank in array B: invariant B array to be normalized

5 3 Baseline array A Invariant set B array to be normalized Diagonal line 4 Baseline array Smoothing curve * B array to be normalized Diagonal line 5 Baseline array y Normalized value yˆ = f( x ) Smoothing curve Normalization adjustment 0 y yˆ ŷ x 0 B array to be normalized

6 Baseline B C D E A B C D E A B Baseline B C D E

7 A B C D E A C Baseline B C D E A B C D E A D

8 Baseline B C D E Baseline B C D E A E Normalization - Result A B C D E A B C D E

9 Normalization Contrast with MAS4 Methods Probes used in normalization: dchip: use only invariant set ( exclude possibly differentially expressed genes) Microarray Suite: use all probes on a chip ( may include truly differentially expressed genes) normalization adjustment : dchip: not necessarily proportional to expression value (Nonlinear) Microarray Suite: proportional to expression value (Linear) 2. Model Based Expression Index (MBEI) Definition: MBEI = weighted average of probe intensity within one probe set Weighting scheme: Probe pairs with high variability (compared to other probes) are down weighted Why Use MBEI? Why use MBEI? - Reduces variablity for low expression estimates - Eliminates the cross-hybridizing probes and the consistently negative probes

10 2. Calculation of MBEI PM values of a probe set across 3 samples MBEI: Model = pattern * magnitude MBEI 2. Calculation of MBEI Types of models: -(PM MM):subtract away cross-hybridization may have negative values - PM only: background subtracted, always positive need other methods to detect possible cross-hybridization more often used ( under debate ) Two models give similar gene lists Expression intensities are only comparable within a gene between arrays 2. MBEI -- What s used by MAS 5.0? Weighted signal method (Robust average) Regard MM as a control for non-specific hybridization and subtracts it from PM Weighting scheme: Each probe pair, log(pm-mm) is weighted by its distance from the median value for the entire probe set expression values and high-level analysis results may be different from dchip

11 3. Outlier Detection Definition: single outlier Observed array outlier fitted PM-MM residual Global : 1) view Array summary file 2) view CEL image Gene specific: 3) view PM/MM data residual 3. Outlier Detection Array summary file Number Array File NMedian Intensity P call % Array outliersingle outliwarnin 1FANCD2+MMC#2L:\ww no treatment#2 (ml:\ww FANCD2#1 (A) L:\ww * 4FANCD2+MMC#1L:\ww MMC#1 (mislabellel:\ww MMC#2 L:\ww FANCD2#2 (A) L:\ww no treatment#1 L:\ww FANCD2#1 (B) L:\ww FANCD2#2 (B) L:\ww FANCD2#2 (C) L:\ww P call: present when PM is consistently higher than MM across probes in a probe set 3. Outlier Detection CEL image check the image after model-based expression calculation. - These images may have array-outlier (white bars) and single-outlier (pink dots) superimposed.

12 Higher level analyses 1. Filtering Genes Analysis will be more reliable if we first exclude genes that show little variation across the samples, or genes absent in the majority of the samples Criteria for Gene filtering (1) Standard Deviation / mean (check for variation across samples) (2) P call % in the arrays used ( e.g. make sure a gene is present in at least 50% of the samples ) 2. Hierarchical Clustering of Samples Advisable to always do this to validate the overall quality of experiment Biological replicates e.g. same celline grown in different plates Technical replicates e.g. multiple samples from the same plate Replicates should usually cluster together

13 Beware of mislabeling! 3. Hierarchical Clustering of Genes Cluster only the filtered genes Avoid erroneous result by absent /less variant genes» Clustering gene expressions fold change gene (absent) gene (maybe true) Find genes that have similar pattern across samples They may belong to similar functional group

14 4. Comparing Samples To identify genes that are reliably differentially expressed between two samples Creating a up/down regulated gene list Comparing the fold changes Example clustering diagram of 89 genes up/down regulated by MMC effect. These genes have at least a fold change of 1.9 between two samples compared 5. Use Anchor Genes to Find Candidate Genes Finding genes with a similar (time-course) expression pattern with a given gene - correlation coefficient

15 6. Gene Annotation Classification One can classify genes in a given gene list according to their functional category Information updated regularly (from Genbank) Map Genes to Chromosome Extensive Manual at Official dchip Website

1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number

1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number A. 3(x - x) B. x 3 x C. 3x - x D. x - 3x 2) Write the following as an algebraic expression