Evidence map›Paper›PMID 35046630›Full record

ArticleStatistica Sinica2021

STRUCTURED CORRELATION DETECTION WITH APPLICATION TO COLOCALIZATION ANALYSIS IN DUAL-CHANNEL FLUORESCENCE MICROSCOPIC IMAGING.

Shulei Wang, Jianqing Fan, Ginger Pocock, Ellen T Arena, Kevin W Eliceiri, Ming Yuan

Abstract read
In one paragraph

Article in Statistica Sinica, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Shulei WangUniversity of Pennsylvania.
Jianqing FanPrinceton University.
Ginger PocockUniversity of Wisconsin-Madison.
Ellen T ArenaUniversity of Wisconsin-Madison.
Kevin W EliceiriUniversity of Wisconsin-Madison.
Ming YuanColumbia University.

Funding

TrainingU54AI117924 · NIAID · UNIVERSITY OF WISCONSIN-MADISON · PI CRAVEN, MARK W. · 2014 to 2018
$11.3M
Statistical Methods for Ultrahigh-dimensional Biomedical DataR01GM072611 · NIGMS · PRINCETON UNIVERSITY · PI CATTANEO, MATIAS DAMIAN · 2006 to 2021
$4.2M
[PQC-3] A Metabolic Pathway Activation Marker for Prostate Cancer PrognosisR01CA185251 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI BASU, HIRAK S., BEEBE, DAVID J · 2014 to 2017
$2.9M
NCI NIH HHS R01 CA185251NIAID NIH HHS U54 AI117924NIGMS NIH HHS R01 GM072611
6 · The paper itself

Abstract

Current workflows for colocalization analysis in fluorescence microscopic imaging introduce significant bias in terms of the user's choice of region of interest (ROI). In this work, we introduce an automatic, unbiased structured detection method for correlated region detection between two random processes observed on a common domain. We argue that although intuitive, using the maximum log-likelihood statistic directly suffers from potential bias and substantially reduced power. Therefore, we introduce a simple size-based normalization to overcome this problem. We show that scanning using the proposed statistic leads to optimal correlated region detection over a large collection of structured correlation detection problems.

Indexed as

Colocalization analysisoptimal ratescan statisticssignal detectionstructured signal

Identifiers

PMID35046630
PMCPMC8765712

What OpenQuestion holds

Textmetadata
LicenceTDM
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.