2014
DOI: 10.1016/j.ygeno.2014.03.001
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YuGene: A simple approach to scale gene expression data derived from different platforms for integrated analyses

Abstract: Gene expression databases contain invaluable information about a range of cell states, but the question "Where is my gene of interest expressed?" remains one of the most difficult to systematically assess when relevant data is derived on different platforms. Barriers to integrating this data include disparities in data formats and scale, a lack of common identifiers, and the disproportionate contribution of a platform to the 'batch effect'. There are few purpose-built cross-platform normalization strategies, a… Show more

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Cited by 67 publications
(54 citation statements)
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“…Additionally, those genes were determined which were identified as DEG in most treatments (across all treatments and subgroups). (2) In order to identify genes with a common trend of differential transcription, common significance testing was applied: Each array was normalized by cumulative proportion transformation using the R-package “YuGene”, a normalization method specifically designed for cross-platform normalization considering different dynamic ranges for different platforms (Lê Cao et al , 2014). Then, random effect models were used to determine a summary effect size for each gene based on the fold-change and significance was estimated using a permutation analysis (1000 permutations) based on Significance Analysis of Microarrays (Tusher et al , 2001) applying the R-packages “MAMA “(Ihnatova, 2013) in combination with “GeneMeta” (Choi et al , 2003; Lusa et al , 2015).…”
Section: Methodsmentioning
confidence: 99%
“…Additionally, those genes were determined which were identified as DEG in most treatments (across all treatments and subgroups). (2) In order to identify genes with a common trend of differential transcription, common significance testing was applied: Each array was normalized by cumulative proportion transformation using the R-package “YuGene”, a normalization method specifically designed for cross-platform normalization considering different dynamic ranges for different platforms (Lê Cao et al , 2014). Then, random effect models were used to determine a summary effect size for each gene based on the fold-change and significance was estimated using a permutation analysis (1000 permutations) based on Significance Analysis of Microarrays (Tusher et al , 2001) applying the R-packages “MAMA “(Ihnatova, 2013) in combination with “GeneMeta” (Choi et al , 2003; Lusa et al , 2015).…”
Section: Methodsmentioning
confidence: 99%
“…Following annotation of each sample for which microarray data was available, the raw microarray data was downloaded from public databases, and normalized for within-study datasets (using BioC packages "affy" [6] and "lumi" [7] for Affymetrix and Illumina arrays, respectively, in R version 3.1 or greater [8]) and normalized with YuGene [9] for inter-study comparisons. Differential gene expression was calculated in studies where there were at least two cell types and three samples for each cell type, using both the within-study normalization data values and the cross-normalization data values.…”
Section: Data Processing and Storagementioning
confidence: 99%
“…Analysis of such small datasets is very challenging [25,26] because it is prone to over-fitting even if all of the samples are collected in the same laboratory under the same conditions. A direct comparison of gene expression data obtained in different laboratories is notoriously hard due to the nature of experimental procedures, leading to batch effects requiring, but not necessarily cured by, extensive normalization [27][28][29].…”
Section: A Selection Of Long-lived Strains and Life-extending Intervmentioning
confidence: 99%
“…For individual genes represented by multiple probesets, the probeset with the largest signal was used. Gene expressions in all datasets were normalized using the YuGene [27] algorithm. The final MetaWorm dataset represents a 3724 ⇥ 4861 matrix (samples-x-genes) and incorporates more than 400 transcriptomic experiments (see Electronic Supplementary Materials).…”
Section: G Metaworm Transcriptomementioning
confidence: 99%