Research graph
References from Perspectives for better batch effect correction in mass-spectrometry-based proteomics. Local targets link to admitted publications; unresolved targets remain external evidence.
Dealing with confounders in omics analysis
10.1016/j.tibtech.2018.01.013 · 2018 · External reference
10.1093/biostatistics/kxv027
10.1093/biostatistics/kxv027 · External reference
Examining the practical limits of batch effect-correction algorithms: When should you care about batch effects?
10.1016/j.jgg.2019.08.002 · 2019 · External reference
Are batch effects still relevant in the age of big data?
2022 · External reference
Computational mass spectrometry-based proteomics
10.1371/journal.pcbi.1002277 · 2011 · External reference
Protein inference: a review
10.1093/bib/bbs004 · 2012 · External reference
10.1038/nrg2825
10.1038/nrg2825 · External reference
10.1093/biostatistics/kxj037
10.1093/biostatistics/kxj037 · External reference
Risk-conscious correction of batch effects: maximising information extraction from high-throughput genomic datasets
10.1186/s12859-016-1212-5 · 2016 · External reference
10.1371/journal.pgen.0030161
10.1371/journal.pgen.0030161 · External reference
svaseq: removing batch effects and other unwanted noise from sequencing data
10.1093/nar/gku864 · 2014 · External reference
Practical impacts of genomic data ‘cleaning’ on biological discovery using surrogate variable analysis
10.1186/s12859-015-0808-5 · 2015 · External reference
BatchI: Batch effect Identification in high-throughput screening data using a dynamic programming algorithm
10.1093/bioinformatics/bty900 · 2019 · External reference
10.1038/tpj.2010.57
10.1038/tpj.2010.57 · External reference
10.1186/s13059-019-1850-9
10.1186/s13059-019-1850-9 · External reference
Protein complex-based analysis is resistant to the obfuscating consequences of batch effects –- a case study in clinical proteomics
10.1186/s12864-017-3490-3 · 2017 · External reference
Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial
10.15252/msb.202110240 · 2021 · External reference
GFS: fuzzy preprocessing for effective gene expression analysis
10.1186/s12859-016-1327-8 · 2016 · External reference
10.1016/j.heliyon.2022.e09502
10.1016/j.heliyon.2022.e09502 · External reference
10.1016/j.drudis.2017.01.005
10.1016/j.drudis.2017.01.005 · External reference
10.1016/s0375-9601(98)00570-2
10.1016/s0375-9601(98)00570-2 · External reference
Why batch effects matter in omics data, and how to avoid them
10.1016/j.tibtech.2017.02.012 · 2017 · External reference
proteiNorm – A user-friendly tool for normalization and analysis of TMT and label-free protein quantification
10.1021/acsomega.0c02564 · 2020 · External reference
Multibatch TMT reveals false positives, batch effects and missing values
10.1074/mcp.ra119.001472 · 2019 · External reference
Why batch sensitization is important for missing value imputation
2022 · External reference
Visualizing data using t-SNE
2008 · External reference
10.32614/cran.package.uwot
10.32614/cran.package.uwot · External reference
Can peripheral blood-derived gene expressions characterize individuals at ultra-high risk for psychosis?
10.1162/cpsy_a_00007 · 2017 · External reference
10.1002/9780470685983.ch12
10.1002/9780470685983.ch12 · 2009 · External reference
A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis
10.1093/bioinformatics/btt480 · 2013 · External reference
10.1093/nargab/lqaa078
10.1093/nargab/lqaa078 · External reference
10.1016/j.isci.2022.104788
10.1016/j.isci.2022.104788 · External reference
10.1101/2021.01.24.428009
10.1101/2021.01.24.428009 · External reference
10.1093/nar/gkn276
10.1093/nar/gkn276 · External reference
10.1093/bioinformatics/btz447
10.1093/bioinformatics/btz447 · External reference
WikiPathways App for Cytoscape: making biological pathways amenable to network analysis and visualization
10.12688/f1000research.4254.2 · 2014 · External reference
10.12688/f1000research.4410.2
10.12688/f1000research.4410.2 · External reference
10.1016/j.cels.2021.09.005
10.1016/j.cels.2021.09.005 · External reference
pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods
2020 · External reference
