Removing unwanted variation from large-scale RNA sequencing data with PRPS

Molania, Ramyar; Foroutan, Momeneh; Gagnon-Bartsch, Johann A.; Gandolfo, Luke C.; Jain, Aryan; Sinha, Abhishek; Olshansky, Gavriel; Dobrovic, Alexander; Papenfuss, Anthony T.; Speed, T. P.

doi:10.1038/s41587-022-01440-w

Cited by 29 publications

(23 citation statements)

References 68 publications

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“…The general use of the term “batch effect” has also included, sometimes explicitly (25) but often implicitly (26–28), biological variations that are deemed ignorable within the scope of a study. For example, consider a hypothetical study where one cannot control the time of day of sample collection.…”

Section: Resultsmentioning

confidence: 99%

Signal recovery in single cell batch integration

Zhang

Mathew

Lim

et al. 2023

Preprint

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Data integration to align cells across batches has become a cornerstone of most single cell analysis pipelines, critically affecting downstream analyses. Yet, how much signal is erased from data during integration? Currently, there are no guidelines for when biological signals are separable from batch effects in single cell studies, and thus, studies usually take a black-box, trial-and-error approach towards batch integration. We show evidence that current paradigms for single cell data integration are unnecessarily aggressive, removing biologically meaningful variation. To remedy this, we present a novel statistical model and computationally scalable algorithm, CellANOVA, to recover biological signal that is lost during single cell data integration. CellANOVA utilizes a “pool-of-controls” design concept, applicable across diverse settings, to separate unwanted variation from biological variation of interest. When applied with existing integration methods, CellANOVA allows the recovery of subtle biological signals and corrects, to a large extent, the data distortion introduced by integration. Further, CellANOVA explicitly estimates cell- and gene-specific batch effect terms which can be used to identify the cell types and pathways exhibiting the largest batch variations, providing clarity as to which biological signals can be recovered.

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Section: Resultsmentioning

confidence: 99%

Signal recovery in single cell batch integration

Zhang

Mathew

Lim

et al. 2023

Preprint

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“…The standard method involves within-sample normalization using positive control reporters and housekeeping genes, as well as the removal of endogenous genes found to be at or below the level of ‘noise’, which is calculated using the observed values of the negative control genes ( Waggott et al , 2012 ). Alternatively, the Removing Unwanted Variation-III (RUV-III) method has been demonstrated to provide improved performance for datasets including technical replicates—particularly when those technical replicates span multiple batches—and it can also be used for normalization without true replicates by generating pseudoreplicates from pseudosamples ( Molania et al , 2019 , 2022 ). Finally, the RUVg method has been demonstrated on multiple NanoString datasets to provide improved normalization performance using housekeeping genes ( Bhattacharya et al , 2020 ; Risso et al , 2014 ).…”

Section: Methods and Featuresmentioning

confidence: 99%

Easy NanoString nCounter data analysis with the NanoTube

et al. 2022

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Summary The NanoTube is an open-source pipeline that simplifies the processing, quality control, normalization, and analysis of NanoString nCounter gene expression data. It is implemented in an extensible R library, which performs a variety of gene expression analysis techniques and contains additional functions for integration with other R libraries performing advanced NanoString analysis techniques. Additionally, the NanoTube web application is available as a simple tool for researchers without programming expertise. Availability The NanoTube R package is available on Bioconductor under the GPL-3 license (https://www.bioconductor.org/packages/NanoTube/). The R-Shiny application can be downloaded at https://github.com/calebclass/Shiny-NanoTube, or a simplified version of this application can be run on all major browsers, at https://research.butler.edu/nanotube/. Supplementary information Supplementary data are available at Bioinformatics online.

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“…These undesirable variations fall into four categories: variabilities from notorious batch effects (batch variation) 10 , sequencing platforms (platform variation) 11, 12 , heterogeneous bio-samples (purity variation) 13, 14 and other unknown technical differences (unknown variation), substantially compromising downstream discoveries. Batch variations arising in different runs at different time points represent the prevailing technical factors 15 .…”

Section: Introductionmentioning

confidence: 99%

A self-adaptive and versatile tool for eliminating multiple undesirable variations from transcriptome

Zhang,

Yan,

Wang

et al. 2024

Preprint

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Accurate identification of true biological signals from diverse undesirable variations in large-scale transcriptomes is essential for downstream discoveries. Herein, we develop a universal deep neural network, called DeepAdapter, to eliminate various undesirable variations from transcriptomic data. The innovation of our approach lies in automatic learning of the corresponding denoising strategies to adapt to different situations. The data-driven strategies are flexible and highly attuned to the transcriptomic data that requires denoising, yielding significant improvement in reducing undesirable variation originating from batches, sequencing platforms, and bio-samples with varied purity beyond manually designed schemes. Comprehensive evaluations across multiple batches, different RNA measurement technologies and heterogeneous bio-samples demonstrate that DeepAdapter can robustly correct diverse undesirable variations and accurately preserve biological signals. Our findings indicate that DeepAdapter can act as a versatile tool for the comprehensive denoising of the large and heterogeneous transcriptome across a wide variety of application scenarios.

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Removing unwanted variation from large-scale RNA sequencing data with PRPS

Cited by 29 publications

References 68 publications

Signal recovery in single cell batch integration

Signal recovery in single cell batch integration

Easy NanoString nCounter data analysis with the NanoTube

A self-adaptive and versatile tool for eliminating multiple undesirable variations from transcriptome

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