Evalua&ng Methods. Tandy Warnow
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1 Evalua&ng Methods Tandy Warnow
2 You ve designed a new method! Now what? To evaluate a new method: Establish theore&cal proper&es. Evaluate on data. Compare the new method to other methods. How do you do this?
3 General Issues So far we have computed trees and we have computed alignments. How can we quan&fy accuracy or error? What datasets should we use? What are the issues?
4 Basic criteria Sensi&vity = true posi&ve rate = recall rate = TP/(TP+FN) Precision = posi&ve predic&ve value = TP/(TP+FP) Specificity = true nega&ve rate = TN/(TN+FP) False Discovery Rate = 1-PPV
5 True posi&ves, false posi&ves, etc. For these criteria, we need to understand the concepts of true posi&ve, false posi&ve, true nega&ve, and false nega&ve In other words, we need to have a yes/no classifier.
6 Simple example: HIV tes&ng Sample space: HIV tests (Eliza) True posi&ve: the test comes out posi&ve and the person does have HIV True nega&ve: the test comes out nega&ve and the person does not have HIV False posi&ve: the test comes out posi&ve but the person does not have HIV False nega&ve: the test comes out nega&ve and the person does have HIV
7 Hypothe&cal Example The popula&on is 1,000 samples 10 of them have the disease, 990 do not The test is posi&ve on 20: 9 of the 10 with the disease, and 11 of the 990 who do not have the disease TP = 9, FP = 11, TN = 979, FN = 1 Sensi&vity = TP/(TP+FN) = 9/10 = 90% Specificity = TN/(TN+FP) = 979/990 = 98.9% Precision = TP/(TP+FP) = 9/20 = 45%
8 Hypothe&cal Example The popula&on is 1,000 samples 10 of them have the disease, 990 do not The test is posi&ve on 20: 9 of the 10 with the disease, and 11 of the 990 who do not have the disease TP = 9, FP = 11, TN = 979, FN = 1 Sensi&vity = TP/(TP+FN) = 9/10 = 90% Specificity = TN/(TN+FP) = 979/990 = 98.9% Precision = TP/(TP+FP) = 9/20 = 45%
9 Hypothe&cal Example The popula&on is 1,000 samples 10 of them have the disease, 990 do not The test is posi&ve on 20: 9 of the 10 with the disease, and 11 of the 990 who do not have the disease TP = 9, FP = 11, TN = 979, FN = 1 Sensi&vity = TP/(TP+FN) = 9/10 = 90% Specificity = TN/(TN+FP) = 979/990 = 98.9% Precision = TP/(TP+FP) = 9/20 = 45%
10 Hypothe&cal Example The popula&on is 1,000 samples 10 of them have the disease, 990 do not The test is posi&ve on 20: 9 of the 10 with the disease, and 11 of the 990 who do not have the disease TP = 9, FP = 11, TN = 979, FN = 1 Sensi&vity = TP/(TP+FN) = 9/10 = 90% Specificity = TN/(TN+FP) = 979/990 = 98.9% Precision = TP/(TP+FP) = 9/20 = 45%
11 Hypothe&cal Example The popula&on is 1,000 samples 10 of them have the disease, 990 do not The test is posi&ve on 20: 9 of the 10 with the disease, and 11 of the 990 who do not have the disease TP = 9, FP = 11, TN = 979, FN = 1 Sensi&vity = TP/(TP+FN) = 9/10 = 90% Specificity = TN/(TN+FP) = 979/990 = 98.9% Precision = TP/(TP+FP) = 9/20 = 45%
12 Hypothe&cal Example The popula&on is 1,000 samples 10 of them have the disease, 990 do not The test is posi&ve on 20: 9 of the 10 with the disease, and 11 of the 990 who do not have the disease What is the false posi&ve rate? What is the false nega&ve rate?
13 Hypothe&cal Example The popula&on is 1,000 samples 10 of them have the disease, 990 do not The test is posi&ve on 20: 9 of the 10 with the disease, and 11 of the 990 who do not have the disease What is the false posi&ve rate? FP rate = # false posi&ves divided by the number of total posi&ves, so FP/(FP+TP) = 11/20 = 55%
14 Hypothe&cal Example The popula&on is 1,000 samples 10 of them have the disease, 990 do not The test is posi&ve on 20: 9 of the 10 with the disease, and 11 of the 990 who do not have the disease What is the false nega&ve rate? FN rate = # false nega&ves divided by the number of total nega&ves, so FN/(FN+TN) = 1/990 = 0.1%
15 Hypothe&cal Example The popula&on is 1,000 samples 10 of them have the disease, 990 do not The test is posi&ve on 20: 9 of the 10 with the disease, and 11 of the 990 who do not have the disease What is the false nega&ve rate? FN rate = # false nega&ves divided by the number of total nega&ves, so FN/(FN+TN) = 1/990 = 0.1%
16 General Issues So far we have computed trees and we have computed alignments. How can we quan&fy accuracy or error? What datasets should we use? What are the issues?
17 Performance criteria Running time Space Statistical performance issues (e.g., statistical consistency and sequence length requirements) Topological accuracy with respect to the underlying true tree, typically studied in simulation. Accuracy with respect to a mathematical score (e.g. tree length or likelihood score) on real data
18 Sta&s&cal Consistency error Data
19 FN FN: false negative (missing edge) FP: false positive (incorrect edge) FP 50% error rate
20 Alignment Error/Accuracy SPFN: percentage of homologies in the true alignment that are not recovered (false nega&ve homologies) SPFP: percentage of homologies in the es&mated alignment that are false (false posi&ve homologies) TC: total number of columns correctly recovered SP-score: percentage of homologies in the true alignment that are recovered Pairs score: 1-(avg of SP-FN and SP-FP)
21 Other Alignment Es&ma&on Criteria Tree topology error Tree branch length error Gap length distribu&on Inser&on/dele&on ra&o Alignment length Number of indels
22 Studying Methods The point is to evaluate a new method in comparison to prior methods. You need to do this on data, not just using theorems. How do you do this?
23 Benchmarks Simula&ons: can control everything, and true alignment is not disputed Different simulators Biological: can t control anything, and reference alignment and reference tree might not be correct. Alignment benchmarks are also somewhat problema&c, for various reasons: BAliBASE, HomFam, Prefab CRW (Compara&ve Ribosomal Website)
24 Simulation Studies S1 = AGGCTATCACCTGACCTCCA S2 = TAGCTATCACGACCGC S3 = TAGCTGACCGC S4 = TCACGACCGACA Unaligned Sequences S1 = -AGGCTATCACCTGACCTCCA S2 = TAG-CTATCAC--GACCGC-- S3 = TAG-CT GACCGC-- S4 = TCAC--GACCGACA S1 S2 S4 S3 True tree and alignment Compare S1 = -AGGCTATCACCTGACCTCCA S2 = TAG-CTATCAC--GACCGC-- S3 = TAG-C--T-----GACCGC-- S4 = T---C-A-CGACCGA----CA S1 S4 S2 S3 Estimated tree and alignment
25 Designing a simula&on study Consider the realism of the simulator. Consider whether the condi&ons are too easy or too difficult to be helpful. Consider the compe&ng methods to explore. Consider sta&s&cal significance. Be concerned with repeatability.
26 Data Biological data: How reliable are the reference alignments and trees? Simulated data: How realis&c are the simula&on condi&ons?
27 Simulators Sequence evolu&on down a tree: Indels? If so, what lengths? Subs&tu&ons under what model? How many subs&tu&ons? How many indels? How is the tree topology and set of branch lengths defined? Is the tree ultrametric? How many leaves in the tree (i.e., # sequences)? How long are the sequences?
28 Methods Are you picking the best compe&ng methods? Are you running them in the best way?
29 Criteria Are you using criteria that are considered appropriate by the research community? If you are using new criteria, jus&fy these criteria (and probably use the standard criteria anyway).
30 Repeatability Provide full details about how you ran the analyses so that the same experiment could be done by the person reading the paper. Save your data and make them available to the readers.
31 Wri&ng Papers Read Appendix C in Computa&onal Phylogene&cs for guidelines about wri&ng papers about computa&onal methods. How to write your first paper on my homepage Commonly encountered challenges in research ethics on my homepage
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