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1 Statistics Achim Tresch UoC / MPIPZ Cologne treschgroup.de/OmicsModule1415.html

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Challenge You test plants/patients/… in two settings (or from different populations). You want to know which / how many genes are differentially expressed (alternate) You don’t want to make too many mistakes (declaring a gene to be alternate = differentially expressen when in fact they are null – not differentially expressed). Multiple Testing

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You choose a significance level, say You calculate p-values of the differences in expression. The p-value of g is the probability that if g is null (not differentially expressed), it would have a test statistic (e.g., t-statistic) at least that large. You say all genes that differ with p-value ≤ 0.05 are truly different. The Multiple Testing Problem What’s the problem?

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Suppose that you test 10,000 genes, but no genes are truly differentially expressed. You will conclude that about 5% of those you called significant are differentially expressed. You will find 500 “significant” genes. Bad. The Multiple Testing Problem You are testing many genes at the same time

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The Multiple Testing Problem

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Bonferroni Correction

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Bonferroni Correction (FWER control) Pr(at least one gene found diff.expr.) Bonferroni controls the probability by which our list of differentially expressed genes contains at least one mistake = Family-wise error rate (FWER). This is very strict.

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A Fundamental Insight All truly null genes (i.e. not truly differentially expressed) are equally likely to have any p-value. That is by construction of p-val: under the null hypothesis, 1% of the genes will be in the top 1 percentile, 1% will be in percentile between 89 and 90 th and so on. P-val is just a way of saying percentile in null condition. False Discovery Rate (FDR) estimation 0 1 p-value

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Idea: The observed p-value distribution is a mixture of null genes (light blue marbles) and truly different genes (red marbles). If the chosen test is appropriate, red marbles should be concentrated at the low p-values. False Discovery Rate (FDR) estimation 0 1 p-value Differential gene Non-Differential gene

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We don’t of course know the colors of the marbles/we don’t know which genes are true alternates. However, we know that null marbles are equally likely to have any p-value. So, at the p-value where the height of the marbles levels off, we have primarily light blue marbles/null genes. False Discovery Rate (FDR) estimation

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≈non- differential genes Because if all genes/marbles were null, the heights would be about uniform. Provided the reds are concentrated near the low p-values, the flat regions will be primarily light blues. Absolute frequency 0 1 p-value We estimate the baseline of null marbles

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False Discovery Rate (FDR) estimation ≈non- differential genes ≈ differential genes 0 1 p-value Subtracting the “baseline” of true null hypotheses, the remaining balls are primarily red, i.e., they are true alternative hypotheses Absolute frequency

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≈non- differential genes ≈ differential genes False Discovery Rate (FDR) estimation 0 1 p-value Given a p-value cutoff, we can estimate the rate of false discoveries (FDR) that pass this threshold. Absolute frequency p-value cutoff FDR(p-cut) = +

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Baseline of nulls Absolute frequency 0 1 p-value FDR-based p-value cutoff Given a desired FDR (e.g., 20%), we can find the largest p-value cutoff for which this FDR is achieved. FDR(p-cut 1 )= 9% p-cut 1 = 0.1

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Baseline of nulls Absolute frequency 0 1 p-value FDR-based p-value cutoff Given a desired FDR (e.g., 20%), we can find the largest p-value cutoff for which this FDR is achieved. FDR(p-cut 1 )= 9% FDR(p-cut 1 )= 20% p-cut 1 = 0.1 p-cut 1 = 0.2

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Baseline of nulls Absolute frequency 0 1 p-value FDR-based p-value cutoff Given a desired FDR (e.g., 20%), we can find the largest p-value cutoff for which this FDR is achieved. FDR(p-cut 1 )= 9% FDR(p-cut 1 )= 20% FDR(p-cut 3 )= 52% p-cut 1 = 0.1 p-cut 1 = 0.2 p-cut 1 = 0.7

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Baseline of nulls Absolute frequency 0 1 p-value FDR-based p-value cutoff Given a desired FDR (e.g., 20%), we can find the largest p-value cutoff for which this FDR is achieved. p-cut 1 = 0.1 FDR(p-cut 1 )= 9% FDR(p-cut 1 )= 20% FDR(p-cut 3 )= 52% p-cut 1 = 0.2 p-cut 1 = 0.7

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Consider the all null case (all marbles are blue). For any p-value cutoff, the estimated FDR will be close to 100%. For any sensible FDR (substantially below 100%), there will be no suitable p-value cutoff, and the method will not return any gene. Good. Example: All null 0 1 p-value

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Examples: All alternate 0 1 p-value Consider the all alternate case (all marbles are red). For a large range of p-value cutoffs, the estimated FDR will be close to 0. For sensible FDR cutoffs (e.g. 20%), the corresponding p-value cutoff will be high. The method will return many genes Good.

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A flat p-value distribution may force us to the far left in order to get a low False Discovery Rate. This may eliminate genes of interest. If subsequent validation experiments are not too expensive, we can accept a higher False Discovery Rate (e.g., 20%) FDR rate and significance level are entirely different things! Conclusions

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Gene Set Enrichment

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Fisher‘s exact test, once more

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Gene Ontology Example 559

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Gene Ontology Example (immune response) (macromolecule biosynthesis)

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Kolmogorov-Smirnov Test < Move 1/K up when you see a gene from group a Move 1/(N-K) down when you see a gene not in group a

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GO scoring: general problem

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GO Independence Assumption light yellow GO sets

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GO Independence Assumption light yellow

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The elim method

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Top 10 significant nodes (boxes) obtained with the elim method

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Algorithms Summary

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Evaluation: Top scoring GO term Significant GO terms in the ALL dataset

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Advantages & Disadvantages for ALL

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Simulation Study Introduce noise

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Simulation Study

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Quality of GO scoring methods 10% noise level 40% noise level

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Summary

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