Affymetrix GeneChip DNA Analysis Software

Transcription

1 Affymetrix GeneChip DNA Analysis Software User s Guide Version 3.0 For Research Use Only. Not for use in diagnostic procedures. P/N Rev. 3

2 Trademarks Affymetrix, GeneChip, EASI,,,, HuSNP, GenFlex, Jaguar, NetAffx, CustomExpress, Flying Objective, The Way Ahead, Tools to take you as far as your vision and CustomSeq are trademarks owned or used by Affymetrix, Inc. Adobe is a registered trademark of Adobe Corporation. Microsoft is a registered trademark of Microsoft Corporation. Oracle is a registered trademark of Oracle Corporation. GeneArray is a registered trademark of Agilent Technologies. Limited License EXCEPT AS EXPRESSLY SET FORTH HEREIN, NO RIGHT TO COPY, MODIFY, DISTRIBUTE, MAKE DERIVATIVE WORKS OF, PUBLICLY DISPLAY, MAKE, HAVE MADE, OFFER TO SELL, SELL, USE OR IMPORT PROBE ARRAYS OR ANY OTHER PRODUCT IS CONVEYED OR IMPLIED WITH THE PROBE ARRAYS, INSTRUMENTS, SOFTWARE, REAGENTS OR ANY OTHER ITEMS PROVIDED HEREUNDER. EXCEPT FOR CERTAIN ARRAYS AND REAGENTS DESIGNATED AS "ANALYTE SPECIFIC REAGENTS" (SEE APPLICABLE PACKAGE INSERT) WHICH ARE LICENSED FOR USE AS ANALYTE SPECIFIC REAGENTS OR RESEARCH USE, ALL PRODUCTS (INCLUDING THE PROBE ARRAYS, INSTRUMENTS, SOFTWARE, AND REAGENTS) DELIVERED HEREUNDER ARE LICENSED TO BUYER FOR RESEARCH USE ONLY. THIS LIMITED LICENSE PERMITS ONLY THE USE BY BUYER OF THE PARTICULAR PRODUCT(S), IN ACCORDANCE WITH THE WRITTEN INSTRUCTIONS PROVIDED THEREWITH, THAT BUYER PURCHASES FROM AFFYMETRIX OR ITS AUTHORIZED REPRESENTATIVE. THE PURCHASE OF ANY PRODUCT(S) DOES NOT BY ITSELF CONVEY OR IMPLY THE RIGHT TO USE SUCH PRODUCT(S) IN COMBINATION WITH ANY OTHER PRODUCT(S). IN PARTICULAR, (i) NO RIGHT TO MAKE, HAVE MADE OR DISTRIBUTE OTHER PROBE ARRAYS IS CONVEYED OR IMPLIED BY THE PROBE ARRAYS, (ii) NO RIGHT TO MAKE, HAVE MADE, IMPORT, DISTRIBUTE, OR USE PROBE ARRAYS IS CONVEYED OR IMPLIED BY THE INSTRUMENTS OR SOFTWARE, AND (iii) NO RIGHT TO USE PROBE ARRAYS IN COMBINATION WITH INSTRUMENTS OR SOFTWARE IS CONVEYED UNLESS ALL COMPONENT PARTS HAVE BEEN PURCHASED FROM AFFYMETRIX OR ITS AUTHORIZED REPRESENTATIVE. FURTHERMORE, PROBE ARRAYS DELIVERED HEREUNDER ARE LICENSED FOR ONE (1) TIME USE ONLY AND MAY NOT BE REUSED. THE PRODUCTS DO NOT HAVE FDA APPROVAL. NO PATENT LICENSE IS CONVEYED TO BUYER TO USE, AND BUYER AGREES NOT TO USE, THE PRODUCTS IN ANY SETTING REQUIRING FDA OR SIMILAR REGULATORY APPROVAL OR EXPLOIT THE PRODUCTS IN ANY MANNER NOT EXPRESSLY AUTHORIZED IN WRITING BY AFFYMETRIX IN ADVANCE. Patents Software products may be covered by one or more of the following patents: U.S. Patent No s. 5,733,729; 5,795,716; 5,974,164; 6,066,454; 6,090,555, 6,185,561 6,188,783, 6,223,127; 6,228,593; 6,229,911; 6,242,180; 6,308,170; 6,361,937; 6,420,108; 6,484,183; 6,505,125; 6510,391; 6,532,462; 6,546,340; 6,687,692; and other U.S. or foreign patents. Scanner products may be covered by one or more of the following patents: U.S. Patent Nos. 5,578,832; 5,631,734; 5,834,758; 5,936,324; 5,981,956; 6,025,601; 6,141,096; 6,171,793; 6,185,030; 6,201,639; 6,207,960; 6,218,803; 6,225,625; 6,252,236; 6,335,824; 6,403,320; 6,407,858; 6,472,671; and 6,490,533; and other U.S. or foreign patents. Copyright Affymetrix, Inc. All rights reserved.

3 Dynamic Model Mapping Algorithm D The Dynamic Model Mapping algorithm analyzes the cell intensity data from an Affymetrix GeneChip Dynamic Model Mapping array, such as the arrays in the Affymetrix GeneChip Human Mapping 100K Set, to determine the alleles of the SNPs represented in the sample. This appendix describes: The Dynamic Model Mapping Array (see below) The Dynamic Model Mapping Algorithm (see page 319) The Dynamic Model Mapping Algorithm settings (see page 330) The Dynamic Model Mapping Array An Affymetrix GeneChip Dynamic Model Mapping array, such as the GeneChip Mapping 50K Array Xba and GeneChip Mapping 50K Array Hind arrays in the GeneChip Human Mapping 100K Set, consists of a number of probe cells or features. Each probe cell contains many copies of a unique 25-base oligonucleotide probe of defined sequence. The probe cells that are used to test for a SNP are grouped into probe sets. Probe cells are paired to test for the perfect match of the oligonucleotide and the mismatch. In the perfect match (P) cell, the oligonucleotide is the complement of the sequence being tested for. In the mismatch (M) cell, the central, thirteenth base is changed to a mismatch (Figure D.1). Mismatch probe cells help to factor cross-hybridization out of the data analysis. 317

4 318 APPENDIX D Dynamic Model Mapping Algorithm Reference Sequence...CCGGTGATTATG A G ACCTACTATAA... Probe Quartet Probe Pair Allele A Probe Pair Allele B GGCCACTAATAC GGCCACTAATAC GGCCACTAATAC GGCCACTAATAC A T C G TGGATGATATT TGGATGATATT TGGATGATATT TGGATGATATT MA PA PB MB Figure D.1 SNP Probe Quartet Two probe pairs, one for each allele, are used in a probe quartet (Figure D.1): Perfect match for the A allele (PA) Mismatch for the A allele (MA) Perfect match for the B allele (PB) Mismatch for the B allele (MB) Two probe quartets are paired to probe the sense and antisense strands of the sample. The SNP uses probe quartets with the center interrogation position placed in different sequence locations by moving the sequence position up and down stream of the SNP site (Figure D.2). SNP site Reference Sequence Probe Sequences...TAGGGGGTGATTATGAACCTACTATTTAGGA... CCCCACTAATACTTGGATGATAAAT CCCCCACTAATACTTGGATGATAAA CCACTAATACTTGGATGATAAATCC = changed base/interrogation position offset Figure D.2 Offset interrogation positions

5 Affymetrix GeneChip DNA Analysis Software User s Guide 319 Seven offset position options are tested for the pairs of probe quartet (-4, -2, -1, 0, +1, +3, +4). This results in fourteen probe quartets being tested. Five offsets are chosen, selected to give the best results for the particular SNP being tested by comparing their performance across a selection of samples as a built-in redundancy used to build confidence in a PM result. This results in ten probe quartets being used. The cells in a particular probe set are distributed on the Dynamic Model Mapping array and may not be physically located next to each other. The Dynamic Model Mapping Algorithm The Dynamic Model Mapping algorithm is a likelihood model based algorithm using Wilcoxon s signed rank test (see page 325). It provides a genotype call for each SNP, along with quality information for the call. The Dynamic Model Mapping algorithm compares the cell intensity values for all the probe quartets for a SNP to the following models (Figure D.3): Null: Intensities for both PM and MM are all background intensities. AA: The PM cell intensity for the A allele is high and the other three cells are background. AB: The PM cell intensities for the A and B alleles are high and the MM cell intensities are background. BB: The PM cell intensity for the B allele is high and the other three cells are background. AA Model BB Model AB Model Null Model PMA MMA PMB MMB PMA MMA PMB MMB PMA MMA PMB MMB PMA MMA PMB MMB Figure D.3 Examples of models

6 320 APPENDIX D Dynamic Model Mapping Algorithm For each model, Dynamic Model assumes that the background intensities are the same and smaller than the foreground intensities. The Dynamic Model Mapping algorithm uses the following steps: 1. Calculate four different quantitative log likelihoods (l-values), one for each model (null, AA, AB, BB), for each of the ten probe quartets in the SNP s probe set (see below). 2. Calculate log likelihood ratios (S-values) for each combination of model and probe quartet using the quantitative log likelihoods (see page 321). 3. Use one-sided Wilcoxon signed-rank test on the S-values to calculate reliability rank scores for each model; the SNP is assigned to the model with the lowest rank score (see page 322). The resulting output includes: Genotype call Confidence score (rank score for genotype call used) Rank scores for all genotype calls In a separate step, the software runs MPAM analysis on a selected set of SNPs on the Dynamic Model chips for contamination QC check (see page 324). Calculating Log Likelihood Values (l-values) The log likelihood value (l-value) provides an initial indication of which model is the best match to each of the ten individual probe quartets. Log likelihood equation is based on observed - estimated values for the mean. The log likelihood is calculated as: ln( l) v -- k + ( m k M k ) 2 = n 2 k ln( 2πV k ) k = 1 V 2 k Where for a given probe quartet: k = index for the probe cell in the quartet (1,2,3,4) n k = number of pixels

7 Affymetrix GeneChip DNA Analysis Software User s Guide 321 m k = cell intensity v k = standard deviation for intensity M k = estimated intensity for a given model V k = estimated standard deviation for a model The likelihood function for a probe quartet is calculated in the following steps: 1. Calculate maximum likelihood estimators from foreground and background data. To minimize the estimation error, for each of the four models we find maximum likelihood estimators for all parameters by using the assumptions and by differentiating the log likelihood equation with respect to all parameters and solving the corresponding system of equations. As an example, for model AA, the perfect match for AA is assumed as foreground, all the other three are assumed as background and evenly distributed, hence we have: M 1 = m 1 4 M 2 = M 3 = M 4 = n k m k V = v 1 = = = V2 2 V 2 3 V4 2 k = 2 4 k = 2 4 n k k = 2 2 n k [ v k + ( M k m k ) 2 ] 4 k = 2 n k 2. Calculate the log likelihood function for each model type using the estimators and the log likelihood equation. Calculating Log Likelihood Ratio (S-Values) The log likelihood ratios (LLRs), or S-values, are calculated by comparing the log likelihood value for a selected model of a probe quartet to the highest remaining log likelihood value for the quartet: S N = l N - max{l A, l B, l AB } S A = l A - max{l N, l B, l AB }

8 322 APPENDIX D Dynamic Model Mapping Algorithm S AB = l AB - max{l N, l A, l B } S B = l B - max{l N, l A, l AB } Each of the ten quartets has a set of four model LLRs, or S-values. One of the models will have a LLR (S-value) greater than zero, and the other three models will have negative values. The model with the positive score is the best-fitting model for that quartet (Figure D.4). AA AB BB NC q 1 S A S AB S B S N q 2 S A S AB S B S N q 3 S A S AB S B S N q 4 S A S AB S B S N q 5 S A S AB S B S N q 6 S A S AB S B S N q 7 S A S AB S B S N q 8 S A S AB S B S N q 9 S A S AB S B S N q 10 S A S AB S B S N Figure D.4 S-value matrix Genotype calls using Wilcoxon signed-rank test Next, the confidence of each model is calculated using a rank score. The rank score is a type of probability where the smaller the rank score, the larger the probability the model is correct. The Wilcoxon rank test (described later) calculates a rank score for each model based on the 10 quartet values for that model. For example, to calculate the AB rank score, the algorithm would use the S-values for the AB model for quartets 1 through 10 (Figure D.5). See One-sided Wilcoxon s Signed Rank Test, on page 325, for more information about the methods used to calculate the rank scores.

9 Affymetrix GeneChip DNA Analysis Software User s Guide 323 AA AB BB NC q 1 S A S AB S B S N q 2 S A S AB S B S N q 3 S A S AB S B S N q 4 S A S AB S B S N q 5 S A S AB S B S N q 6 S A S AB S B S N q 7 S A S AB S B S N q 8 S A S AB S B S N q 9 S A S AB S B S N q 10 S A S AB S B S N RS A RS AB RS B RS N Figure D.5 Calculating rank scores Most significant rank score among these will determine the call for the SNP The model with the most significant rank score (smallest value) is considered as the candidate model. The rank score is compared to a user adjustable threshold; a typical value for the threshold is If RS < threshold, assign the genotype call to the model else if RS threshold, assign genotype call to no call. See Dynamic Model Mapping Algorithm Settings, on page 330, for more information. Notice that even with RS < threshold the genotype call still could be no call since the Null model may be the most likely model. See One-sided Wilcoxon s Signed Rank Test, on page 325, for more information.

10 324 APPENDIX D Dynamic Model Mapping Algorithm Adjustment of thresholds Increasing the threshold can reduce the number of no calls, but may also reduce the confidence of the calls. Decreasing the threshold can increase the confidence of the calls, but may also increase the number of no calls. Displaying Rank Scores in the DM Scatter Plot The DM Scatter Plot (see Chapter 7, Dynamic Model Mapping Analysis Window, on page 155) displays four different values (rank scores for AA, AB, BB, and null genotype calls) on a two-dimensional graph. The following transformations are applied to the data to accomplish this and allow comparisons of genotype calls for different samples: The first transformation accentuates the areas of interests in the rank score. The scale is 0-1, but genotypes are only called for rank scores less than The first transformation is designed to magnify the important small rank score differences and lessen large rank score differences which aren t significant. A second transformation is used to convert to a range 0-1. The final transform maps the values to a two dimensional plot. Contamination QC Check Because of the importance of detecting mixed or contaminated samples, GDAS 3.0 has implemented an additional algorithm to supplement the Dynamic Model algorithm to help with sample contamination detection. The MPAM calling algorithm, used to make genotype calls for the MPAM Mapping arrays, is employed in this analysis to make genotype calls on a subset of ~ 8,000 SNPs chosen for each of the Mapping 50K Xba and 50K Hind arrays. These SNPs were chosen based upon robust performance and the ability to be a sensitive detector of sample contamination across large data sets. Using this subset also allows the software to quickly derive contamination metrics without the additional computational burden of having to make an extra set of calls on the entire array. This is done without losing the ability to detect sample contamination at low mixtures. In a pure sample, the proportion of labeled DNA target for two alleles of any SNP will typically be present at one of three allelic ratios, 100:0, 50:50 or 0:100. The models used to make genotype calls from probe hybridization

11 Affymetrix GeneChip DNA Analysis Software User s Guide 325 data apply this information to determine the genotype call for each SNP, and assign a no call if the observed data fall too far from these predicted ratios. If a mixed or contaminated DNA sample is genotyped, the assumption of only three possible allelic ratios will be violated for many SNPs, resulting in lower call rates. However, whether or not the sample is contaminated, the detection rate will remain high as it is based only on the extent to which PM probes are brighter than MM probes. Therefore, a reduction in call rate accompanied by no decrease in detection rate is a characteristic indicator of sample contamination. Note that for low levels of contamination, the decreased call rate is accompanied with only a minimal decrease in accuracy, whereas more dramatic sample contamination can result in much lower call rates and decreased accuracy. The analysis produces values for the MCR and MDR parameters in the Mapping report, which can be used to detect sample contamination. See Appendix C, MPAM Mapping Algorithm, on page 301, for more information about the algorithm. See Mapping Reports, on page 254, for more information on the report contents. See the GeneChip Mapping 100 K Assay Manual for more information about the QC checks. Gender Calls A gender call is made for the sample by comparing the heterozygous call ratio for X chromosome SNPs to a threshold value set in the software (see Determining Sample Gender, on page 309, for more information). One-sided Wilcoxon s Signed Rank Test This section describes the technique used by the Dynamic Model algorithm to calculate rank scores in more detail. The signed rank test was proposed by Wilcoxon in The basic procedure is well known, but there are different techniques used to handle ties and calculate rank scores. The signed rank test applies to two paired data sets: g = (g 1, g 2,, g n ) and h = (h 1, h 2,, h n ). It tests the null hypothesis H 0 : median(g i - h i ) = 0

12 326 APPENDIX D Dynamic Model Mapping Algorithm versus the alternative hypothesis H 1 : median(g i - h i ) > 0. Note that we use the one-sided test here. For the one-sided test, if the null hypothesis is true, the rank score should be close to 0.5. When the alternative hypothesis is true, the rank score should be close to 0. When median(g i - h i ) < 0 is true, the rank score should be close to 1. As a standard procedure of signed rank test, we first calculate the differences of all pairs of data: d i = g i - h i. In the Dynamic Model algorithm we are using the S values as the difference values for the paired data set. If all differences are zero, we output 0.5 as the one-sided rank score. If some of the differences are zero, we exclude them from further analysis and use only the nonzero differences for further analysis. For simplicity, we also denote the remaining nonzero differences as d i (i = 1,, n). We take their absolute values a i = d i (Table D.1). Table D.1 Absolute values Original position non-zero differences convert to absolute values 1 d1 = 2 a1 = 2 2 d2 = 1 a2 = 1 3 d3 = -2 a3 = 2 4 d4 = 0.5 a4 = d5 = 0.5 a5 = 0.5 Next, we sort the absolute values in ascending order and assign a rank to them. If all a i 's are different from each other, we rank them with integers from 1 to n, and assign the original signs to these ranks to form the signed ranks. We denote the ranks by r i and the signed rank of d i by s i. If there are

13 Affymetrix GeneChip DNA Analysis Software User s Guide 327 ties among a i, all differences in a tie group are assigned to a rank equal to the average of the integer ranks. Table D.2 Assigning rank values to the sorted absolute values original positions Rank position (used to assign ranks) Sorted in increasing order Assign Ranks Signed Ranks 4 1 a4 = 0.5 Rank position r4 = 1.5 s4 = 1.5 (1 +2)/2 5 2 a5 = 0.5 r5 = 1.5 s5 = a2 = 1 3 r2 = 3 s2 = a1 = 2 Rank position r1 = 4.5 s1 = 4.5 (4 + 5)/2 3 5 a3 = 2 r3 = 4.5 s3 = Ties are assigned a rank equal to the average of the rank positions that the tied values occupy Then we form the sum of positive signed ranks, S = sum(u(s i ) s i, i = 1:n), where u(s i )=1 if s i > 0, and u(s i ) = 0 if s i < 0. In our example, S = s 1 + s 2 + s 4 + s 5 = Small Probe Sets When n is small (we use n < 12), we can assign signs to ranks r i (i=1, 2,, n) in every possible way, calculate the sum of positive ranks and denote this sum by S j (j=1,, 2 n ). We define p(s) = 2 -n sum(u(s j > S) u(s j = S), j = 1:2 n ), 2 n ps ( ) = 2 n us ( j > S) + 0.5u( S j =S) j = 1

14 328 APPENDIX D Dynamic Model Mapping Algorithm where: if S j > S then u(s j > S) is 1 if Sj < S then u(s j > S) is 0 if S j equals S then u(s j = S) is 1 if S j does not equal S then u(s j = S) is 0 In our example, all possible signed ranks and the sum of positive ranks S j are list in the table below (Table D.3). Since the order of these ranks does not matter, we use the ascending order of their absolute values in the table and denote them by s i.

15 Affymetrix GeneChip DNA Analysis Software User s Guide 329 Table D.3 Random Signed Ranks for rank score evaluation j s 1 s 2 s 3 s 4 s 5 S j Values = 10.5 displayed in italic Values > 10.5 displayed in Bold All signed ranks above 10.5 (a total of 5) are given a weight of 1 and signed ranks equal to 10.5 (a total of 4) are given a weight of 0.5.

16 330 APPENDIX D Dynamic Model Mapping Algorithm In our example: ρ( 10.5) Dynamic Model Mapping Algorithm Settings ( 1 5) + ( 0.5 4) = = The Dynamic Model Mapping algorithm incorporates user modifiable settings which influence call rate and call accuracy. The default settings are set to allow high call rates with better than 99% accuracy. Care should be taken when changing these settings as adjusting the parameters to increase call rates may decrease the accuracy. (Table D.4)The table below explains the settings and how changing a value affects the analysis. 1. To view the user-modifiable algorithm settings, click the Mapping Algorithm Settings button in the Settings shortcut bar, or select Tools Mapping Algorithm Settings from the menu bar. The Mapping Algorithm Settings dialog box opens (Figure D.6). Select array type here Enter a new value for the selected item here Figure D.6 Mapping Algorithm Settings dialog box, Dynamic Model Mapping settings deposed 2. Select the array type you are analyzing from the Probe Array Type drop-down box. The parameters for that array type are displayed.

17 Affymetrix GeneChip DNA Analysis Software User s Guide To change a setting: a. Click the item you want to change. The current value for the item is displayed (Figure D.6). b. Enter a new value for the item. 4. To return all settings to the defaults, click Default. Table D.4 Dynamic Model Mapping calling algorithm, user-adjustable parameters Item Description Effect of Changing the Settings Setting Change Number of Calls Homozygote Call Threshold The homozygote calls with confidence values below the threshold are set to no calls. Increasing the value Increase the number of calls Heterozygote Call Threshold The heterozygote calls with confidence values below the threshold are set to no calls. Increasing the value Increases the number of calls N/A = Not Applicable You can change the displayed threshold for all calls in the DM Scatter Plot (see Controlling the Display of Call Thresholds, on page 161, for more information).