Upcoming Seminar Presentations
All seminars take place Thursdays at 8:30 am PT / 11:30 am ET / 4:30 pm London / 6:30 pm Tel Aviv. Past seminar presentations are posted here
Thursday, December 3, 2020 [Link to join]
Speaker: Jingyi Jessica Li (UCLA)
Title: Clipper: p-value-free FDR control on high-throughput data from two conditions
Abstract: High-throughput biological data analysis commonly involves the identification of “interesting” features (e.g., genes, genomic regions, and proteins), whose values differ between two conditions, from numerous features measured simultaneously. To ensure the reliability of such analysis, the most widely-used criterion is the false discovery rate (FDR), the expected proportion of uninteresting features among the identified ones. Existing bioinformatics tools primarily control the FDR based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions, two requirements that are often unmet in biological studies. To address this issue, we propose Clipper, a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper is applicable to identifying both enriched and differential features from high-throughput biological data of diverse types. In comprehensive simulation and real-data benchmarking, Clipper outperforms existing generic FDR control methods and specific bioinformatics tools designed for various tasks, including peak calling from ChIP-seq data, differentially expressed gene identification from RNA-seq data, differentially interacting chromatin region identification from Hi-C data, and peptide identification from mass spectrometry data. Notably, our benchmarking results for peptide identification are based on the first mass spectrometry data standard that has a realistic dynamic range. Our results demonstrate Clipper’s flexibility and reliability for FDR control, as well as its broad applications in high-throughput data analysis.
Discussant: Nikos Ignatiadis (Stanford University)
Links: [Relevant paper]
Thursday, December 10, 2020 [Link to join]
Speaker: Toru Kitagawa (University College London)
Title: Inference on Winners
Abstract: Many empirical questions concern target parameters selected through optimization. For example, researchers may be interested in the effectiveness of the best policy found in a randomized trial, or the best-performing investment strategy based on historical data. Such settings give rise to a winner’s curse, where conventional estimates are biased and conventional confidence intervals are unreliable. This paper develops optimal confidence intervals and median-unbiased estimators that are valid conditional on the target selected and so overcome this winner’s curse. If one requires validity only on average over targets that might have been selected, we develop hybrid procedures that combine conditional and projection confidence intervals to offer further performance gains relative to existing alternatives. This is joint work with Isaiah Andrews and Adam McCloskey.
Discussant: Kenneth Hung (Facebook)
Links: [Relevant paper]
Thursday, January 7, 2021 [Link to join]
Speaker: Yuval Benjamini (Hebrew University of Jerusalem)
Thursday, February 18, 2021 [Link to join]
Speaker: Tijana Zrnic (UC Berkeley)
Title: Title: Post-Selection Inference via Algorithmic Stability
Abstract: Modern approaches to data analysis make extensive use of data-driven model selection. The resulting dependencies between the selected model and data used for inference invalidate statistical guarantees derived from classical theories. The framework of post-selection inference (PoSI) has formalized this problem and proposed corrections which ensure valid inferences. Yet, obtaining general principles that enable computationally-efficient, powerful PoSI methodology with formal guarantees remains a challenge. With this goal in mind, we revisit the PoSI problem through the lens of algorithmic stability. Under an appropriate formulation of stability---one that captures closure under post-processing and compositionality properties---we show that stability parameters of a selection method alone suffice to provide non-trivial corrections to classical z-test and t-test intervals. Then, for several popular model selection methods, including the LASSO, we show how stability can be achieved through simple, computationally efficient randomization schemes. Our algorithms offer provable unconditional simultaneous coverage and are computationally efficient; in particular, they do not rely on MCMC sampling. Importantly, our proposal explicitly relates the magnitude of randomization to the resulting confidence interval width, allowing the analyst to tune interval width to the loss in utility due to randomizing selection. This is joint work with Michael I. Jordan.
The seminars are held on Zoom and last 60 minutes:
45 minutes of presentation
15 minutes of discussion, led by an invited discussant
Moderators collect questions using the Q&A feature during the seminar.
How to join
You can attend by clicking the link to join (there is no need to register in advance).
More instructions for attendees can be found here.
What is selective inference?
Broadly construed, selective inference means searching for interesting patterns in data, usually with inferential guarantees that account for the search process. It encompasses:
Multiple testing: testing many hypotheses at once (and paying disproportionate attention to rejections)
Post-selection inference: examining the data to decide what question to ask, or what model to use, then carrying out one or more appropriate inferences
Adaptive / interactive inference: sequentially asking one question after another of the same data set, where each question is informed by the answers to preceding questions
Cheating: cherry-picking, double dipping, data snooping, data dredging, p-hacking, HARKing, and other low-down dirty rotten tricks; basically any of the above, but done wrong!