Difference between revisions of "Literature Studies"

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| 2018 || Mazoure B || [[Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening]]
 
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| 2018 || Li Z || Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection]]
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| 2018 || Li Z || [[Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection]]
 
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Revision as of 13:06, 25 February 2020

Page summary
Here outcomes of benchmarking studies from the literature are collected. The primary aim is a comprehensive overview about neutral benchmark studies, i.e. assessments which were performed independenty on publication of a new approach. Studies which are not neutral are put in brackets.

The focus is on computational methods for analyzing experimental data (instead of comparing experimental techniques or platforms).

Please extend this list by creating a new page and adding a link below.
Use the guidelines described here.

1 Results from Literature

1.1 Classification

2003

2005

2016

1.2 Selection of Differential Features and Regions

1.2.1 Identifying differential features

2006

2010

2017

2018

1.2.2 Identifying differential regions (e.g. DMRs)

2015

2016

2017

2018

1.2.3 Identifying sets of features (e.g. gene set analyses)

2009

2018

1.2.4 Dimension reduction

2008

2015

1.3 Imputation methods for missing values

Year First Author Title
1996 Schenker Partially parametric techniques for multiple imputation
1999 Imputing Missing Data for Gene Expression Arrays
2001 Troyanskaya Missing value estimation methods for DNA microarrays
2002 Imputation of missing longitudinal data: a comparison of methods
2003 Oba A Bayesian missing value estimation method for gene expression profile data
2005 Scholz Nonlinear PCA: a missing data approach
2007 Stacklies pcaMethods—a bioconductor package providing PCA methods for incomplete data
2007 Verboven Sequential imputation for missing values
2008 Shaffer GN Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes
2011 Templ Iterative stepwise regression imputation using standard and robust methods
2012 Hrydziuszko O Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline
2012 Stekhoven MissForest—non-parametric missing value imputation for mixed-type data
2013 Taylor Accounting for undetected compounds in statistical analyses of mass spectrometry ‘omic studies
2013 Waljee Comparison of imputation methods for missing laboratory data in medicine
2014 Shah Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study
2014 Rodwell Comparison of methods for imputing limited-range variables: a simulation study
2014 Morris Tuning multiple imputation by predictive mean matching and local residual draws
2014 Recursive partitioning for missing data imputation in the presence of interaction effects
2015 Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics
2016 Folch-Fortuny Assessment of maximum likelihood PCA missing data imputation
2016 Lazar C Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies
2016 Multiple imputation and analysis for high-dimensional incomplete proteomics data
2018 Wei Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data
2018 Poyatos Gap-filling a spatially explicit plant trait database: comparing imputation methods and different levels of environmental information
2018 O'Brien The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments

1.4 ODE-based Modelling

2001

2008

2011

2013

2017

2018

2019


2020

1.5 Omics Workflows

Year First Author Title
2013 Weisser H An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics
2014 Cox J Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ*
2015 ComparingVariant Call Files for Performance Benchmarkingof Next-Generation Sequencing Variant Calling Pipelines
2016 Tyanova S The MaxQuant computational platform for mass spectrometry–based shotgun proteomics
2017 A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies
2018 Välikangas T A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation
2019 A Systematic Evaluation of Single CellRNA-Seq Analysis Pipelines
2019 Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays

1.6 Preprocessing high-throughput data

Year First Author Title
2003 A comparison of normalization methods for high density oligonucleotide array data based on variance and bias
2005 Comparison of Affymetrix GeneChip Expression Measures
2005 Meleth S The case for well-conducted experiments to validate statistical protocols for 2D gels: different pre-processing = different lists of significant proteins
2005 Comparison of background correction and normalization procedures for high-density oligonucleotide microarrays
2006 Using RNA sample titrations to assess microarray platform performance and normalization techniques
2006 Wang P Normalization regarding non-random missing values in high-throughput mass spectrometry data
2007 Carvalho B Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data
2007 Cannataro M MS‐Analyzer: preprocessing and data mining services for proteomics applications on the Grid
2008 Comparison of preprocessing methods for the hgU133+2 chip from Affymetrix
2009 Comparison of Affymetrix data normalization methods using 6,926 experiments across five array generations
2010 Consistency of predictive signature genes and classifiers generated using different microarray platforms
2010 Detecting and correcting systematic variation in large-scale RNA sequencing data
2010 Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
2010 Normalization of RNA-seq data using factor analysis of control genes or samples
2011 Affymetrix GeneChip microarray preprocessing for multivariate analyses
2012 A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
2014 Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets
2014 Zhou X Prevention, diagnosis and treatment of high-throughput sequencing data pathologies
2014 Coble JB Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery
2015 Caraus I Detecting and overcoming systematic bias in high-throughput screening technologies: a comprehensive review of practical issues and methodological solutions
2015 Tam S Optimization of miRNA-seq data preprocessing
2015 Rafiei A Comparison of peak‐picking workflows for untargeted liquid chromatography/high‐resolution mass spectrometry metabolomics data analysis
2016 Yi L Chemometric methods in data processing of mass spectrometry-based metabolomics: A review
2018 Mazoure B Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening
2018 Li Z Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection