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Abstract Numerous single‐cell transcriptomic datasets from identical tissues or cell lines are generated from different laboratories or single‐cell RNA sequencing (scRNA‐seq) protocols. The denoising of these datasets to eliminate batch effects is crucial for data integration, ensuring accurate interpretation and comprehensive analysis of biological questions. Although many scRNA‐seq data integration methods exist, most are inefficient and/or not conducive to downstream analysis. Here, DeepBID, a novel deep learning‐based method for batch effect correction, non‐linear dimensionality reduction, embedding, and cell clustering concurrently, is introduced. DeepBID utilizes a negative binomial‐based autoencoder with dual Kullback–Leibler divergence loss functions, aligning cell points from different batches within a consistent low‐dimensional latent space and progressively mitigating batch effects through iterative clustering. Extensive validation on multiple‐batch scRNA‐seq datasets demonstrates that DeepBID surpasses existing tools in removing batch effects and achieving superior clustering accuracy. When integrating multiple scRNA‐seq datasets from patients with Alzheimer's disease, DeepBID significantly improves cell clustering, effectively annotating unidentified cells, and detecting cell‐specific differentially expressed genes.
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ComBat-seq: batch effect adjustment for RNA-seq count data
The benefit of integrating batches of genomic data to increase statistical power is often hindered by batch effects, or unwanted variation in data caused by differences in technical factors across batches. It is therefore critical to effectively address batch effects in genomic data to overcome these challenges. Many existing methods for batch effects adjustment assume the data follow a continuous, bell-shaped Gaussian distribution. However in RNA-seq studies the data are typically skewed, over-dispersed counts, so this assumption is not appropriate and may lead to erroneous results. Negative binomial regression models have been used previously to better capture the properties of counts. We developed a batch correction method, ComBat-seq, using a negative binomial regression model that retains the integer nature of count data in RNA-seq studies, making the batch adjusted data compatible with common differential expression software packages that require integer counts. We show in realistic simulations that the ComBat-seq adjusted data results in better statistical power and control of false positives in differential expression compared to data adjusted by the other available methods. We further demonstrated in a real data example that ComBat-seq successfully removes batch effects and recovers the biological signal in the data.
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- Award ID(s):
- 1810829
- PAR ID:
- 10293518
- Date Published:
- Journal Name:
- Nucleic acids research
- Volume:
- 2
- Issue:
- 3
- ISSN:
- 0305-1048
- Page Range / eLocation ID:
- lqaa078
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The performance of computational methods and software to identify differentially expressed features in single‐cell RNA‐sequencing (scRNA‐seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA‐seq expression features. To model the technological variability in cross‐platform scRNA‐seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA‐seq expression profiles across experimental platforms induced by platform‐ and gene‐specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero‐inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero‐inflated scRNA‐seq data with excessive zero counts. Using both synthetic and published plate‐ and droplet‐based scRNA‐seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state‐of‐the‐art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open‐source software (R/Bioconductor package) is available athttps://github.com/himelmallick/Tweedieverse.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/nargab/lqaa078
