scholarly journals Surrogate variable analysis using partial least squares (SVA-PLS) in gene expression studies

2012 ◽  
Vol 28 (6) ◽  
pp. 799-806 ◽  
Author(s):  
Sutirtha Chakraborty ◽  
Somnath Datta ◽  
Susmita Datta
Keyword(s):  
Gene Expression ◽  
Least Squares ◽  
Partial Least Squares ◽  
Surrogate Variable Analysis ◽  
Surrogate Variable ◽  
Expression Studies ◽  
Gene Expression Studies ◽  
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 A statistical framework for the robust detection of hidden variation in single cell transcriptomes

2017 ◽  
Author(s):  
Donghyung Lee ◽  
Anthony Cheng ◽  
Mohan Bolisetty ◽  
Duygu Ucar
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Marker Genes ◽  
Correlated Sources ◽  
Surrogate Variable Analysis ◽  
Surrogate Variable ◽  
Sources Of Variation ◽  
Hidden Variation ◽  
Variable Analysis

AbstractSingle cell RNA-sequencing (scRNA-seq) precisely characterize gene expression levels and dissect variation in expression associated with the state (technical or biological) and the type of the cell, which is averaged out in bulk measurements. Multiple and correlated sources contribute to gene expression variation in single cells, which makes their estimation difficult with the existing methods developed for bulk measurements (e.g., surrogate variable analysis (SVA)) that estimate orthogonal transformations of these sources. We developed iteratively adjusted surrogate variable analysis (IA-SVA) that can estimate hidden and correlated sources of variation by identifying a set of genes affected with each hidden factor in an iterative manner. Analysis of scRNA-seq data from human cells showed that IA-SVA could accurately capture hidden variation arising from technical (e.g., stacked doublet cells) or biological sources (e.g., cell type or cell-cycle stage). Furthermore, IA-SVA delivers a set of genes associated with the detected hidden source to be used in downstream data analyses. As a proof of concept, IA-SVA recapitulated known marker genes for islet cell subsets (e.g., alpha, beta), which improved the grouping of subsets into distinct clusters. Taken together, IA-SVA is an effective and novel method to dissect multiple and correlated sources of variation in scRNA-seq data.

scholarly journals Assessment of common housekeeping genes as reference for gene expression studies using RT-qPCR in mouse choroid plexus

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kim Hoa Ho ◽  
Annarita Patrizi
Keyword(s):  
Gene Expression ◽  
Choroid Plexus ◽  
Sensory Deprivation ◽  
Housekeeping Genes ◽  
Housekeeping Gene ◽  
Experimental Conditions ◽  
Brain Repair ◽  
Expression Studies ◽  
Testing Algorithms ◽  
Gene Expression Studies

AbstractChoroid plexus (ChP), a vascularized secretory epithelium located in all brain ventricles, plays critical roles in development, homeostasis and brain repair. Reverse transcription quantitative real-time PCR (RT-qPCR) is a popular and useful technique for measuring gene expression changes and also widely used in ChP studies. However, the reliability of RT-qPCR data is strongly dependent on the choice of reference genes, which are supposed to be stable across all samples. In this study, we validated the expression of 12 well established housekeeping genes in ChP in 2 independent experimental paradigms by using popular stability testing algorithms: BestKeeper, DeltaCq, geNorm and NormFinder. Rer1 and Rpl13a were identified as the most stable genes throughout mouse ChP development, while Hprt1 and Rpl27 were the most stable genes across conditions in a mouse sensory deprivation experiment. In addition, Rpl13a, Rpl27 and Tbp were mutually among the top five most stable genes in both experiments. Normalisation of Ttr and Otx2 expression levels using different housekeeping gene combinations demonstrated the profound effect of reference gene choice on target gene expression. Our study emphasized the importance of validating and selecting stable housekeeping genes under specific experimental conditions.

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