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   Articles with "batch effects" as a keyword



Embedding to reference t-SNE space addresses batch effects in single-cell classification

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recommendations! 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

Alleviating batch effects in cell type deconvolution with SCCAF-D

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recommendations! Published in 2024 at "Nature Communications"

DOI: 10.1038/s41467-024-55213-x

Abstract: Cell type deconvolution methods can impute cell proportions from bulk transcriptomics data, revealing changes in disease progression or organ development. But benchmarking studies often use simulated bulk data from the same source as the reference,… read more here.

Keywords: deconvolution; type deconvolution; batch effects; cell type ... See more keywords

Procrustes is a machine-learning approach that removes cross-platform batch effects from clinical RNA sequencing data

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recommendations! Published in 2024 at "Communications Biology"

DOI: 10.1038/s42003-024-06020-z

Abstract: With the increased use of gene expression profiling for personalized oncology, optimized RNA sequencing (RNA-seq) protocols and algorithms are necessary to provide comparable expression measurements between exome capture (EC)-based and poly-A RNA-seq. Here, we developed… read more here.

Keywords: batch effects; rna seq; seq; machine learning ... See more keywords

PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data

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recommendations! 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
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HDMC: a novel deep learning-based framework for removing batch effects in single-cell RNA-seq data

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recommendations! 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.

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recommendations! 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

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recommendations! 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

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recommendations! 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

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recommendations! 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
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Batch effects correction for microbiome data with Dirichlet-multinomial regression

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recommendations! 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

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recommendations! 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