BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes

AbstractTo fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for cell lineation and identifyingbona fidetranscriptional signals, it is necessary to combine data from multiple experiments. We presentBERMUDA(Batch-Effect ReMoval Using Deep Autoencoders) — a novel transfer-learning-based method for batch-effect correction in scRNA-seq data.BERMUDAeffectively combines different batches of scRNA-seq data with vastly different cell population compositions and amplifies biological signals by transferring information among batches. We demonstrate thatBERMUDAoutperforms existing methods for removing batch effects and distinguishing cell types in multiple simulated and real scRNA-seq datasets.

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EnTSSR: a weighted ensemble learning method to impute single-cell RNA sequencing data

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2021.3110850 ◽

2021 ◽

pp. 1-1

Author(s):

Fan Lu ◽

Yilong Lin ◽

Chongbin Yuan ◽

Xiao-Fei Zhang ◽

Le Ou-Yang

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Ensemble Learning ◽

Learning Method ◽

Sequencing Data ◽

Single Cell Rna Sequencing

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Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing

Communications Biology ◽

10.1038/s42003-020-01463-6 ◽

2020 ◽

Vol 3 (1) ◽

Author(s):

Patrick S. Stumpf ◽

Xin Du ◽

Haruka Imanishi ◽

Yuya Kunisaki ◽

Yuichiro Semba ◽

...

Keyword(s):

Machine Learning ◽

Bone Marrow ◽

Single Cell ◽

Rna Sequencing ◽

Transfer Learning ◽

Biomedical Research ◽

Human Cell ◽

Cell Types ◽

Single Cell Rna Sequencing ◽

Using Data

AbstractBiomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. Here we show that transfer learning—the branch of machine learning that concerns passing information from one domain to another—can be used to efficiently map bone marrow biology between species, using data obtained from single-cell RNA sequencing. We first trained a multiclass logistic regression model to recognize different cell types in mouse bone marrow achieving equivalent performance to more complex artificial neural networks. Furthermore, it was able to identify individual human bone marrow cells with 83% overall accuracy. However, some human cell types were not easily identified, indicating important differences in biology. When re-training the mouse classifier using data from human, less than 10 human cells of a given type were needed to accurately learn its representation. In some cases, human cell identities could be inferred directly from the mouse classifier via zero-shot learning. These results show how simple machine learning models can be used to reconstruct complex biology from limited data, with broad implications for biomedical research.

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deepMNN: Deep Learning-Based Single-Cell RNA Sequencing Data Batch Correction Using Mutual Nearest Neighbors

Frontiers in Genetics ◽

10.3389/fgene.2021.708981 ◽

2021 ◽

Vol 12 ◽

Author(s):

Bin Zou ◽

Tongda Zhang ◽

Ruilong Zhou ◽

Xiaosen Jiang ◽

Huanming Yang ◽

...

Keyword(s):

Deep Learning ◽

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Cell Types ◽

Batch Effect ◽

Batch Effects ◽

Batch Correction ◽

Single Cell Rna Sequencing ◽

Identical Cell

It is well recognized that batch effect in single-cell RNA sequencing (scRNA-seq) data remains a big challenge when integrating different datasets. Here, we proposed deepMNN, a novel deep learning-based method to correct batch effect in scRNA-seq data. We first searched mutual nearest neighbor (MNN) pairs across different batches in a principal component analysis (PCA) subspace. Subsequently, a batch correction network was constructed by stacking two residual blocks and further applied for the removal of batch effects. The loss function of deepMNN was defined as the sum of a batch loss and a weighted regularization loss. The batch loss was used to compute the distance between cells in MNN pairs in the PCA subspace, while the regularization loss was to make the output of the network similar to the input. The experiment results showed that deepMNN can successfully remove batch effects across datasets with identical cell types, datasets with non-identical cell types, datasets with multiple batches, and large-scale datasets as well. We compared the performance of deepMNN with state-of-the-art batch correction methods, including the widely used methods of Harmony, Scanorama, and Seurat V4 as well as the recently developed deep learning-based methods of MMD-ResNet and scGen. The results demonstrated that deepMNN achieved a better or comparable performance in terms of both qualitative analysis using uniform manifold approximation and projection (UMAP) plots and quantitative metrics such as batch and cell entropies, ARI F1 score, and ASW F1 score under various scenarios. Additionally, deepMNN allowed for integrating scRNA-seq datasets with multiple batches in one step. Furthermore, deepMNN ran much faster than the other methods for large-scale datasets. These characteristics of deepMNN made it have the potential to be a new choice for large-scale single-cell gene expression data analysis.

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A Multi-center Cross-platform Single-cell RNA Sequencing Reference Dataset

10.1101/2020.09.20.305474 ◽

2020 ◽

Cited By ~ 1

Author(s):

Xin Chen ◽

Zhaowei Yang ◽

Wanqiu Chen ◽

Yongmei Zhao ◽

Andrew Farmer ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Whole Genome Sequencing Data ◽

Differential Analysis ◽

Sequencing Data ◽

Reference Dataset ◽

Batch Correction ◽

Distinct Cell ◽

Single Cell Rna Sequencing ◽

Benchmark Datasets

AbstractSingle-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-seq platforms and bioinformatics methods. To be broadly applicable, these should be generated from renewable, well characterized reference samples and processed in multiple centers across different platforms. Here we present a benchmarking scRNA-seq dataset that includes 20 scRNA-seq datasets acquired either as a mixtures or as individual samples from two biologically distinct cell lines for which a large amount of multi-platform whole genome sequencing data are also available. These scRNA-seq datasets were generated from multiple popular platforms across four sequencing centers. Our benchmark datasets provide a resource that we believe will have great value for the single-cell community by serving as a reference dataset for evaluating various bioinformatics methods for scRNA-seq analyses, including but not limited to data preprocessing, imputation, normalization, clustering, batch correction, and differential analysis.

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High−resolution phenotyping of spermatogenic defects using single−cell RNA sequencing

Fertility and Sterility ◽

10.1016/j.fertnstert.2017.07.035 ◽

2017 ◽

Vol 108 (3) ◽

pp. e6-e7

Author(s):

M. Jung ◽

J. Rusch ◽

A. Usmani ◽

S. Ahmad ◽

D. Conrad

Keyword(s):

High Resolution ◽

Single Cell ◽

Rna Sequencing ◽

Single Cell Rna Sequencing

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Single‐cell RNA sequencing reveals a high‐resolution cell atlas of xylem in Populus

Journal of Integrative Plant Biology ◽

10.1111/jipb.13159 ◽

2021 ◽

Author(s):

Hui Li ◽

Xinren Dai ◽

Xiong Huang ◽

Mengxuan Xu ◽

Qiao Wang ◽

...

Keyword(s):

High Resolution ◽

Single Cell ◽

Rna Sequencing ◽

Single Cell Rna Sequencing ◽

Resolution Cell

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A multi-center cross-platform single-cell RNA sequencing reference dataset

Scientific Data ◽

10.1038/s41597-021-00809-x ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Xin Chen ◽

Zhaowei Yang ◽

Wanqiu Chen ◽

Yongmei Zhao ◽

Andrew Farmer ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Whole Genome Sequencing Data ◽

Differential Analysis ◽

Sequencing Data ◽

Batch Correction ◽

Distinct Cell ◽

Single Cell Rna Sequencing ◽

Cross Platform ◽

Reference Samples

AbstractSingle-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-seq platforms and bioinformatics methods. To be broadly applicable, these should be generated from renewable, well characterized reference samples and processed in multiple centers across different platforms. Here we present a benchmark scRNA-seq dataset that includes 20 scRNA-seq datasets acquired either as mixtures or as individual samples from two biologically distinct cell lines for which a large amount of multi-platform whole genome sequencing data are also available. These scRNA-seq datasets were generated from multiple popular platforms across four sequencing centers. We believe the datasets we describe here will provide a resource that meets this need by allowing evaluation of various bioinformatics methods for scRNA-seq analyses, including but not limited to data preprocessing, imputation, normalization, clustering, batch correction, and differential analysis.

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A Comprehensive Multi-Center Cross-platform Benchmarking Study of Single-cell RNA Sequencing Using Reference Samples

10.1101/2020.03.27.010249 ◽

2020 ◽

Author(s):

Wanqiu Chen ◽

Yongmei Zhao ◽

Xin Chen ◽

Xiaojiang Xu ◽

Zhaowei Yang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Batch Effect ◽

Batch Effects ◽

Data Set ◽

Batch Correction ◽

Single Cell Rna Sequencing ◽

Cross Platform ◽

Reference Samples

AbstractSingle-cell RNA sequencing (scRNA-seq) has become a very powerful technology for biomedical research and is becoming much more affordable as methods continue to evolve, but it is unknown how reproducible different platforms are using different bioinformatics pipelines, particularly the recently developed scRNA-seq batch correction algorithms. We carried out a comprehensive multi-center cross-platform comparison on different scRNA-seq platforms using standard reference samples. We compared six pre-processing pipelines, seven bioinformatics normalization procedures, and seven batch effect correction methods including CCA, MNN, Scanorama, BBKNN, Harmony, limma and ComBat to evaluate the performance and reproducibility of 20 scRNA-seq data sets derived from four different platforms and centers. We benchmarked scRNA-seq performance across different platforms and testing sites using global gene expression profiles as well as some cell-type specific marker genes. We showed that there were large batch effects; and the reproducibility of scRNA-seq across platforms was dictated both by the expression level of genes selected and the batch correction methods used. We found that CCA, MNN, and BBKNN all corrected the batch variations fairly well for the scRNA-seq data derived from biologically similar samples across platforms/sites. However, for the scRNA-seq data derived from or consisting of biologically distinct samples, limma and ComBat failed to correct batch effects, whereas CCA over-corrected the batch effect and misclassified the cell types and samples. In contrast, MNN, Harmony and BBKNN separated biologically different samples/cell types into correspondingly distinct dimensional subspaces; however, consistent with this algorithm’s logic, MNN required that the samples evaluated each contain a shared portion of highly similar cells. In summary, we found a great cross-platform consistency in separating two distinct samples when an appropriate batch correction method was used. We hope this large cross-platform/site scRNA-seq data set will provide a valuable resource, and that our findings will offer useful advice for the single-cell sequencing community.

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