Cross-study normalization statistics

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Cross-study normalization specifications

Information


Unique identifier OMICS_26981
Name Cross-study normalization
Interface Web user interface
Restrictions to use None
Output data The downloadable transformed dataset resulting from the algorithmic combination of the two input data sets.
Computer skills Basic
Stability Stable
Maintained Yes

Maintainers


  • person_outline Enrico Glaab
  • person_outline Natalio Krasnogor
  • person_outline Jonathan Garibaldi

Publication for Cross-study normalization

Cross-study normalization citations

 (4)
library_books

Systematic assessment of cervical cancer initiation and progression uncovers genetic panels for deep learning based early diagnosis and proposes novel diagnostic and prognostic biomarkers

2017
Oncotarget
PMCID: 5752532
PMID: 29312619
DOI: 10.18632/oncotarget.22689

[…] Eventually, it is important to place more effort on discovering and validating potential biomarker candidates, individuals or panels, to improve diagnostic and prognostic assessment.Meta-analysis and cross-study normalization are two fundamental approaches to integrating data from different microarray data sets. Meta-analysis combines data at the “late stage”, while cross-study normalization combi […]

library_books

KIAA0101 is associated with human renal cell carcinoma proliferation and migration induced by erythropoietin

2015
Oncotarget
PMCID: 4924658
PMID: 26575329
DOI: 10.18632/oncotarget.5876

[…] o genomic instability, which in turn resulted in genomic rearrangements or even chromosomal fragment mismatching []. To overcome the problem of low sample sizes in typical microarray studies, various cross-study normalization algorithms such as empirical Bayes (EB) [], median rank scores (MRS) or quantile discretization (QD) [], NorDi [], Quantile discretization normalization (QDISC) [], XPN [] an […]

library_books

Layered Signaling Regulatory Networks Analysis of Gene Expression Involved in Malignant Tumorigenesis of Non Resolving Ulcerative Colitis via Integration of Cross Study Microarray Profiles

2013
PLoS One
PMCID: 3692446
PMID: 23825635
DOI: 10.1371/journal.pone.0067142

[…] arry out RMA analysis ( and S2). The output graphic plots including histogram, M-A plot and M-B plot for each study before and after within-array normalization were presented in . We then effectuated cross-study normalization and integration using ArrayMining, which averaged across individual probe expression values and the integrative data set were attached in and S4. pictured the density plot […]

call_split

Using Rule Based Machine Learning for Candidate Disease Gene Prioritization and Sample Classification of Cancer Gene Expression Data

2012
PLoS One
PMCID: 3394775
PMID: 22808075
DOI: 10.1371/journal.pone.0039932
call_split See protocol

[…] the same platform, cell type, environmental conditions and experimental procedure. However, as our classifiers support both continuous and discretized input data, they are compatible with most of the cross-study normalization methods that have been proposed in the literature to extend the applicability of machine learning models across different experimental platforms (we have previously developed […]

Citations

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Cross-study normalization institution(s)
School of Computer Science, Nottingham University, Nottingham, UK
Cross-study normalization funding source(s)
Supported by the Marie-Curie Early-Stage-Training programme (grant MEST-CT-2004-007597), by the UK Engineering and Physical Sciences Research Council (EP/E017215/1) and the Biotechnology and Biological Sciences Research Council (BB/F01855X/1).

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