scholarly journals Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies

2011 ◽  
Vol 27 (11) ◽  
pp. 1496-1505 ◽  
Author(s):  
Andrew E. Teschendorff ◽  
Joanna Zhuang ◽  
Martin Widschwendter
Keyword(s):  
Large Scale ◽  
Confounding Factors ◽  
Surrogate Variable Analysis ◽  
Surrogate Variable ◽  
Microarray Profiling ◽  
Variable Analysis

scholarly journals An improved and explicit surrogate variable analysis procedure by coefficient adjustment

Biometrika ◽  
2017 ◽  
Vol 104 (2) ◽  
pp. 303-316 ◽  
Author(s):  
Seunggeun Lee ◽  
Wei Sun ◽  
Fred A. Wright ◽  
Fei Zou
Keyword(s):  
Analysis Procedure ◽  
Surrogate Variable Analysis ◽  
Surrogate Variable ◽  
Variable Analysis

scholarly journals V-SVA: an R Shiny application for detecting and annotating hidden sources of variation in single-cell RNA-seq data

2020 ◽  
Vol 36 (11) ◽  
pp. 3582-3584
Author(s):  
Nathan Lawlor ◽  
Eladio J Marquez ◽  
Donghyung Lee ◽  
Duygu Ucar
Keyword(s):  
Single Cell ◽  
Gene Annotation ◽  
Supplementary Information ◽  
Surrogate Variable Analysis ◽  
Batch Correction ◽  
Surrogate Variable ◽  
R Shiny ◽  
Sources Of Variation ◽  
Shiny Application ◽  
Variable Analysis

Abstract Summary Single-cell RNA-sequencing (scRNA-seq) technology enables studying gene expression programs from individual cells. However, these data are subject to diverse sources of variation, including ‘unwanted’ variation that needs to be removed in downstream analyses (e.g. batch effects) and ‘wanted’ or biological sources of variation (e.g. variation associated with a cell type) that needs to be precisely described. Surrogate variable analysis (SVA)-based algorithms, are commonly used for batch correction and more recently for studying ‘wanted’ variation in scRNA-seq data. However, interpreting whether these variables are biologically meaningful or stemming from technical reasons remains a challenge. To facilitate the interpretation of surrogate variables detected by algorithms including IA-SVA, SVA or ZINB-WaVE, we developed an R Shiny application [Visual Surrogate Variable Analysis (V-SVA)] that provides a web-browser interface for the identification and annotation of hidden sources of variation in scRNA-seq data. This interactive framework includes tools for discovery of genes associated with detected sources of variation, gene annotation using publicly available databases and gene sets, and data visualization using dimension reduction methods. Availability and implementation The V-SVA Shiny application is publicly hosted at https://vsva.jax.org/ and the source code is freely available at https://github.com/nlawlor/V-SVA. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Practical impacts of genomic data “cleaning” on biological discovery using surrogate variable analysis

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Andrew E. Jaffe ◽  
Thomas Hyde ◽  
Joel Kleinman ◽  
Daniel R. Weinbergern ◽  
Joshua G. Chenoweth ◽  
...  
Keyword(s):  
Data Cleaning ◽  
Genomic Data ◽  
Surrogate Variable Analysis ◽  
Surrogate Variable ◽  
Biological Discovery ◽  
Variable Analysis

scholarly journals SVAw - a web-based application tool for automated surrogate variable analysis of gene expression studies

2013 ◽  
Vol 8 (1) ◽  
pp. 8 ◽  
Author(s):  
Mehdi Pirooznia ◽  
Fayaz Seifuddin ◽  
Fernando S Goes ◽  
Jeffrey T Leek ◽  
Peter P Zandi
Keyword(s):  
Gene Expression ◽  
Surrogate Variable Analysis ◽  
Web Based ◽  
Surrogate Variable ◽  
Expression Studies ◽  
Gene Expression Studies ◽  
Variable Analysis

scholarly journals Removing batch effects for prediction problems with frozen surrogate variable analysis

PeerJ ◽  
2014 ◽  
Vol 2 ◽  
pp. e561 ◽  
Author(s):  
Hilary S. Parker ◽  
Héctor Corrada Bravo ◽  
Jeffrey T. Leek
Keyword(s):  
Batch Effects ◽  
Surrogate Variable Analysis ◽  
Surrogate Variable ◽  
Prediction Problems ◽  
Variable Analysis

scholarly journals svaseq: removing batch effects and other unwanted noise from sequencing data

2014 ◽  
Author(s):  
Jeffrey Leek
Keyword(s):  
Count Data ◽  
Genomic Data ◽  
Data Matrix ◽  
Bioconductor Package ◽  
Batch Effects ◽  
Sequencing Data ◽  
Surrogate Variable Analysis ◽  
Surrogate Variable ◽  
Adjustment Factors ◽  
Variable Analysis

It is now well known that unwanted noise and unmodeled artifacts such as batch effects can dramatically reduce the accuracy of statistical inference in genomic experiments. We introduced surrogate variable analysis for estimating these artifacts by (1) identifying the part of the genomic data only affected by artifacts and (2) estimating the artifacts with principal components or singular vectors of the subset of the data matrix. The resulting estimates of artifacts can be used in subsequent analyses as adjustment factors. Here I describe an update to the sva approach that can be applied to analyze count data or FPKMs from sequencing experiments. I also describe the addition of supervised sva (ssva) for using control probes to identify the part of the genomic data only affected by artifacts. These updates are available through the surrogate variable analysis (sva) Bioconductor package.

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