GAC: Gene Associations with Clinical, a web based application

We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC. Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data. In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC.

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GAC: Gene Associations with Clinical, a web based application

F1000Research ◽

10.12688/f1000research.11840.1 ◽

2017 ◽

Vol 6 ◽

pp. 1039

Author(s):

Xinyan Zhang ◽

Manali Rupji ◽

Jeanne Kowalski

Keyword(s):

High Dimensional Data ◽

Interactive Visualization ◽

High Dimensional ◽

Forest Plot ◽

Event Data ◽

Time To Event ◽

Web Based ◽

Link Type ◽

Time To Event Data ◽

Clinical Associations

We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC. Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data. In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC.

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GAC: Gene Associations with Clinical, a web based application

F1000Research ◽

10.12688/f1000research.11840.4 ◽

2018 ◽

Vol 6 ◽

pp. 1039 ◽

Cited By ~ 1

Author(s):

Xinyan Zhang ◽

Manali Rupji ◽

Jeanne Kowalski

Keyword(s):

High Dimensional Data ◽

Interactive Visualization ◽

High Dimensional ◽

Forest Plot ◽

Event Data ◽

Time To Event ◽

Web Based ◽

Link Type ◽

Time To Event Data ◽

Clinical Associations

We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC. Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data. In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC.

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Variable Selection Methods for Right-Censored Time-to-Event Data with High-Dimensional Covariates

Journal of Quality and Reliability Engineering ◽

10.1155/2015/795154 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Keivan Sadeghzadeh ◽

Nasser Fard

Keyword(s):

Failure Time ◽

High Dimensional ◽

Complex Data ◽

Redundant Information ◽

Event Data ◽

Selection Methods ◽

Time To Event ◽

Time To Event Data ◽

Preventive Actions ◽

Censored Observations

Advancement in technology has led to greater accessibility of massive and complex data in many fields such as quality and reliability. The proper management and utilization of valuable data could significantly increase knowledge and reduce cost by preventive actions, whereas erroneous and misinterpreted data could lead to poor inference and decision making. On the other side, it has become more difficult to process the streaming high-dimensional time-to-event data in traditional application approaches, specifically in the presence of censored observations. This paper presents a multipurpose analytic model and practical nonparametric methods to analyze right-censored time-to-event data with high-dimensional covariates. In order to reduce redundant information and to facilitate practical interpretation, variable inefficiency in failure time is determined for the specific field of application. To investigate the performance of the proposed methods, these methods are compared with recent relevant approaches through numerical experiments and simulations.

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Proposals on Kaplan–Meier plots in medical research and a survey of stakeholder views: KMunicate

BMJ Open ◽

10.1136/bmjopen-2019-030215 ◽

2019 ◽

Vol 9 (9) ◽

pp. e030215 ◽

Cited By ~ 4

Author(s):

Tim P Morris ◽

Christopher I Jarvis ◽

William Cragg ◽

Patrick P J Phillips ◽

Babak Choodari-Oskooei ◽

...

Keyword(s):

Outcome Measures ◽

Event Data ◽

Time To Event ◽

Web Based ◽

Time To Event Data ◽

Kaplan Meier ◽

Starting Point ◽

Wide Range ◽

International Participation ◽

Over Time

ObjectivesTo examine reactions to the proposed improvements to standard Kaplan–Meier plots, the standard way to present time-to-event data, and to understand which (if any) facilitated better depiction of (1) the state of patients over time, and (2) uncertainty over time in the estimates of survival.DesignA survey of stakeholders’ opinions on the proposals.SettingA web-based survey, open to international participation, for those with an interest in visualisation of time-to-event data.Participants1174 people participated in the survey over a 6-week period. Participation was global (although primarily Europe and North America) and represented a wide range of researchers (primarily statisticians and clinicians).Main outcome measuresTwo outcome measures were of principal importance: (1) participants’ opinions of each proposal compared with a ‘standard’ Kaplan–Meier plot; and (2) participants’ overall ranking of the proposals (including the standard).ResultsMost proposals were more popular than the standard Kaplan–Meier plot. The most popular proposals in the two categories, respectively, were an extended table beneath the plot depicting the numbers at risk, censored and having experienced an event at periodic timepoints, and CIs around each Kaplan–Meier curve.ConclusionsThis study produced a high response number, reflecting the importance of graphics for time-to-event data. Those producing and publishing Kaplan–Meier plots—both authors and journals—should, as a starting point, consider using the combination of the two favoured proposals.

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A surrogate ℓ 0 sparse Cox's regression with applications to sparse high‐dimensional massive sample size time‐to‐event data

Statistics in Medicine ◽

10.1002/sim.8438 ◽

2019 ◽

Vol 39 (6) ◽

pp. 675-686 ◽

Cited By ~ 3

Author(s):

Eric S. Kawaguchi ◽

Marc A. Suchard ◽

Zhenqiu Liu ◽

Gang Li

Keyword(s):

Sample Size ◽

High Dimensional ◽

Event Data ◽

Time To Event ◽

Time To Event Data ◽

Massive Sample ◽

Cox's Regression

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A fast algorithm for the accelerated failure time model with high-dimensional time-to-event data

Journal of Statistical Computation and Simulation ◽

10.1080/00949655.2021.1927034 ◽

2021 ◽

pp. 1-19

Author(s):

Taehwa Choi ◽

Sangbum Choi

Keyword(s):

Failure Time ◽

Accelerated Failure Time Model ◽

High Dimensional ◽

Accelerated Failure Time ◽

Event Data ◽

Time To Event ◽

Time Model ◽

Time To Event Data ◽

Accelerated Failure ◽

Failure Time Model

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Combining techniques for screening and evaluating interaction terms on high-dimensional time-to-event data

BMC Bioinformatics ◽

10.1186/1471-2105-15-58 ◽

2014 ◽

Vol 15 (1) ◽

Cited By ~ 3

Author(s):

Murat Sariyar ◽

Isabell Hoffmann ◽

Harald Binder

Keyword(s):

High Dimensional ◽

Event Data ◽

Time To Event ◽

Interaction Terms ◽

Time To Event Data

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BPG: Seamless, Automated and Interactive Visualization of Scientific Data

10.1101/156067 ◽

2017 ◽

Cited By ~ 12

Author(s):

Christine P’ng ◽

Jeffrey Green ◽

Lauren C. Chong ◽

Daryl Waggott ◽

Stephenie D. Prokopec ◽

...

Keyword(s):

Open Source ◽

Interactive Visualization ◽

Scientific Data ◽

High Dimensional ◽

Seamless Integration ◽

Web Based ◽

Link Type ◽

Interactive Tool ◽

High Dimensional Datasets ◽

Publication Quality

AbstractWe introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg

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