Local two-sample testing: a new tool for analysing high-dimensional astronomical data

Freeman, Peter E.; Kim, I.; Lee, A. B.

doi:10.1093/mnras/stx1807

Cited by 7 publications

(6 citation statements)

References 54 publications

(66 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The objective of DA-seq is to identify regions in R m where f 0 is significantly larger than f 1 and vice versa, by analyzing the set of samples {x i , y i } n i=1 . One approach to find DA regions is based on local two-sample tests (49)(50)(51)(52). A global two-sample test determines whether two sets of samples were generated by the same distribution.…”

Section: Methodsmentioning

confidence: 99%

Detection of differentially abundant cell subpopulations in scRNA-seq data

Zhao

Jaffe

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

115

View full text Add to dashboard Cite

Comprehensive and accurate comparisons of transcriptomic distributions of cells from samples taken from two different biological states, such as healthy versus diseased individuals, are an emerging challenge in single-cell RNA sequencing (scRNA-seq) analysis. Current methods for detecting differentially abundant (DA) subpopulations between samples rely heavily on initial clustering of all cells in both samples. Often, this clustering step is inadequate since the DA subpopulations may not align with a clear cluster structure, and important differences between the two biological states can be missed. Here, we introduce DA-seq, a targeted approach for identifying DA subpopulations not restricted to clusters. DA-seq is a multiscale method that quantifies a local DA measure for each cell, which is computed from its k nearest neighboring cells across a range of k values. Based on this measure, DA-seq delineates contiguous significant DA subpopulations in the transcriptomic space. We apply DA-seq to several scRNA-seq datasets and highlight its improved ability to detect differences between distinct phenotypes in severe versus mildly ill COVID-19 patients, melanomas subjected to immune checkpoint therapy comparing responders to nonresponders, embryonic development at two time points, and young versus aging brain tissue. DA-seq enabled us to detect differences between these phenotypes. Importantly, we find that DA-seq not only recovers the DA cell types as discovered in the original studies but also reveals additional DA subpopulations that were not described before. Analysis of these subpopulations yields biological insights that would otherwise be undetected using conventional computational approaches.

show abstract

Section: Methodsmentioning

confidence: 99%

Detection of differentially abundant cell subpopulations in scRNA-seq data

Zhao

Jaffe

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

115

View full text Add to dashboard Cite

show abstract

“…Therefore, aside from detecting that a difference between the two distributions exists, it is of interest to localize it. Indeed, localizing the difference between two distributions is useful in various scientific applications, such as single-cell RNA sequencing [65], astronomy [25], and climate reanalysis [48].…”

Section: Scientific Discovery In Two-sample Datasetsmentioning

confidence: 99%

Local Two-Sample Testing over Graphs and Point-Clouds by Random-Walk Distributions

Landa,

Qu,

Chang

et al. 2020

Preprint

View full text Add to dashboard Cite

Two-sample testing is a fundamental tool for scientific discovery. Yet, aside from concluding that two samples do not come from the same probability distribution, it is often of interest to characterize how the two distributions differ. Specifically, given samples from two densities f 1 and f 0 , we consider the problem of localizing occurrences of the inequality f 1 > f 0 in the combined sample. We present a general hypothesis testing framework for this task, and investigate a special case of this framework where localization is achieved by a random walk over the sample. We derive a tractable testing procedure for this case employing a type of scan statistic, and provide non-asymptotic lower bounds on the power and accuracy of our test to detect whether f 1 > f 0 in a local sense. We also characterize the test's consistency according to a certain problem-hardness parameter, and show that our test achieves the minimax rate for this parameter. We conduct numerical experiments to validate our method, and demonstrate our approach on two real-world applications: detecting and localizing arsenic well contamination across the United States, and analyzing two-sample single-cell RNA sequencing data from melanoma patients.

show abstract

“…One approach to find DA regions is based on local two sample tests [8,10,19]. A global two sample test determines whether two sets of samples were generated by the same distribution.…”

Section: Problem Setupmentioning

confidence: 99%

“…al. [10]. These methods compute a test statistic in local neighborhoods around randomly selected cells {x i }.…”

Section: Problem Setupmentioning

confidence: 99%

Detection of differentially abundant cell subpopulations discriminates biological states in scRNA-seq data

Zhao

Jaffe

et al. 2019

Preprint

View full text Add to dashboard Cite

Comparing the transcriptomic landscapes of two biological conditions has become a key challenge in single cell RNA sequencing analysis. Often, we observe a strong deviation in the number of cells within certain cell subpopulations between the two conditions. Analysis of such differentially abundant (DA) subpopulations may uncover cellular processes that differentiate the biological conditions. Typical methods for identifying DA subpopulations strongly rely on known cell types or unsupervised clustering methods. Here, we develop DA-seq, a multiscale algorithm that detects DA subpopulations not restricted to well separated clusters or known cell types. We applied DA-seq to four scRNA-seq datasets as well as two simulated ones. For the former, we compare our results to previously published findings. We find that for some cases, DA-seq is able to reveal subpopulations undetected in the original studies.

show abstract

Local two-sample testing: a new tool for analysing high-dimensional astronomical data

Cited by 7 publications

References 54 publications

Detection of differentially abundant cell subpopulations in scRNA-seq data

Detection of differentially abundant cell subpopulations in scRNA-seq data

Local Two-Sample Testing over Graphs and Point-Clouds by Random-Walk Distributions

Detection of differentially abundant cell subpopulations discriminates biological states in scRNA-seq data

Contact Info

Product

Resources

About