Embedding to reference t-SNE space addresses batch effects in single-cell classification
Sign Up to like & getrecommendations! Published in 2021 at "Machine Learning"
DOI: 10.1007/s10994-021-06043-1
Abstract: Dimensionality reduction techniques, such as t-SNE, can construct informative visualizations of high-dimensional data. When jointly visualising multiple data sets, a straightforward application of these methods often fails; instead of revealing underlying classes, the resulting visualizations… read more here.
Keywords: reference; batch effects; sne space; single cell ... See more keywords
PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data
Sign Up to like & getrecommendations! Published in 2023 at "Briefings in Bioinformatics"
DOI: 10.1093/bib/bbac622
Abstract: Abstract Microbial communities are highly dynamic and sensitive to changes in the environment. Thus, microbiome data are highly susceptible to batch effects, defined as sources of unwanted variation that are not related to and obscure… read more here.
Keywords: batch effects; microbiome data; variation; plsda batch ... See more keywords
HDMC: a novel deep learning-based framework for removing batch effects in single-cell RNA-seq data
Sign Up to like & getrecommendations! Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btab821
Abstract: MOTIVATION With the development of single-cell RNA sequencing (scRNA-seq) techniques, increasingly more large-scale gene expression datasets become available. However, to analyze datasets produced by different experiments, batch effects among different datasets must be considered. Although… read more here.
Keywords: framework; single cell; seq data; novel deep ... See more keywords
MultiBaC: An R package to remove batch effects in multi-omic experiments.
Sign Up to like & getrecommendations! Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btac132
Abstract: MOTIVATION Batch effects in omics datasets are usually a source of technical noise that masks the biological signal and hampers data analysis. Batch effect removal has been widely addressed for individual omics technologies. However, multi-omic… read more here.
Keywords: batch; multibac package; batch effect; batch effects ... See more keywords
SCIBER: a simple method for removing batch effects from single-cell RNA-sequencing data
Sign Up to like & getrecommendations! Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btac819
Abstract: Abstract Motivation Integrative analysis of multiple single-cell RNA-sequencing datasets allows for more comprehensive characterizations of cell types, but systematic technical differences between datasets, known as ‘batch effects’, need to be removed before integration to avoid… read more here.
Keywords: cell; batch effects; cell rna; single cell ... See more keywords
Detecting hidden batch factors through data-adaptive adjustment for biological effects
Sign Up to like & getrecommendations! Published in 2018 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btx635
Abstract: Motivation Batch effects are one of the major source of technical variations that affect the measurements in high-throughput studies such as RNA sequencing. It has been well established that batch effects can be caused by… read more here.
Keywords: hidden batch; batch factors; data adaptive; detecting hidden ... See more keywords
Batch effects correction for microbiome data with Dirichlet‐multinomial regression
Sign Up to like & getrecommendations! Published in 2019 at "Bioinformatics"
DOI: 10.1093/bioinformatics/bty729
Abstract: Motivation: Metagenomic sequencing techniques enable quantitative analyses of the microbiome. However, combining the microbial data from these experiments is challenging due to the variations between experiments. The existing methods for correcting batch effects do not… read more here.
Keywords: bdmma; multinomial regression; microbiome data; batch ... See more keywords
Batch effects correction for microbiome data with Dirichlet-multinomial regression
Sign Up to like & getrecommendations! Published in 2019 at "Bioinformatics"
DOI: 10.1093/bioinformatics/bty874
Abstract: Metagenomic sequencing techniques enable quantitative analyses of the microbiome. However, combining the microbial data from these experiments is challenging due to the variations between experiments. The existing methods for correcting batch effects do not consider… read more here.
Keywords: correction microbiome; data dirichlet; microbiome data; effects correction ... See more keywords
LAmbDA: label ambiguous domain adaptation dataset integration reduces batch effects and improves subtype detection
Sign Up to like & getrecommendations! Published in 2019 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btz295
Abstract: MOTIVATION Rapid advances in single cell RNA sequencing (scRNA-seq) have produced higher-resolution cellular subtypes in multiple tissues and species. Methods are increasingly needed across datasets and species to i) remove systematic biases, ii) model multiple… read more here.
Keywords: lambda; multiple datasets; cellular subtypes; cell ... See more keywords
A joint deep learning model enables simultaneous batch effect correction, denoising and clustering in single-cell transcriptomics.
Sign Up to like & getrecommendations! Published in 2021 at "Genome research"
DOI: 10.1101/gr.271874.120
Abstract: Recent development of single-cell RNA-seq (scRNA-seq) technologies has led to enormous biological discoveries. As the scale of scRNA-seq studies increases, a major challenge in analysis is batch effects, which are inevitable in studies involving human… read more here.
Keywords: scrna seq; space; batch; gene ... See more keywords
Scientific Regulatory Policy Committee Points to Consider*: Nuisance Factors, Block Effects, and Batch Effects in Nonclinical Safety Assessment Studies
Sign Up to like & getrecommendations! Published in 2020 at "Toxicologic Pathology"
DOI: 10.1177/0192623320906385
Abstract: Detection of test article–related effects and the determination of the adversity of those changes are the primary goals of nonclinical safety assessment studies for drugs and chemicals in development. During these studies, variables that are… read more here.
Keywords: safety assessment; safety; pathology; assessment studies ... See more keywords