A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation

Lakkis, Justin; Schroeder, Amelia; Su, Kenong; Lee, Michelle Ying Ya; Bashore, Alexander C.; Reilly, Muredach P; Li, Mingyao

doi:10.1038/s42256-022-00545-w

Cited by 45 publications

(46 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The popularity of single-cell multi-omics analysis in biological research has developed our comprehension of cellular heterogeneity. For example, proteins are known to be much more abundant than RNA and functionally directly participate in the process of cell–cell interactions and cell signalling [31] . The incorporation of cell surface proteins data has the potential to reveal cellular heterogeneity missed by single-modality scRNA-seq data and greatly expand the upper limit of our performance.…”

Section: Resultsmentioning

confidence: 99%

scEMAIL: Universal and Source-Free Annotation Method for scRNA-Seq Data with Novel Cell-Type Perception

Wan¹,

Chen²,

Deng³

2022

Genomics, Proteomics &Amp; Bioinformatics

View full text Add to dashboard Cite

Current cell-type annotation tools for single-cell RNA sequencing (scRNA-seq) data mainly utilize well-annotated source data to help identify cell types in target data. However, on account of privacy preservation, their requirements for raw source data may not always be satisfied. In this case, achieving feature alignment between source and target data explicitly is impossible. Additionally, these methods are barely able to discover the presence of novel cell types. A subjective threshold is often selected by users to detect novel cells. We propose a universal annotation framework for scRNA-seq data called scEMAIL, which automatically detects novel cell types without accessing source data during adaptation. For new cell-type identification, a novel cell-type perception module is designed with three steps. First, an expert ensemble system measures uncertainty of each cell from three complementary aspects. Second, based on this measurement, bimodality tests are applied to detect the presence of new cell types. Third, once assured of their presence, an adaptive threshold via manifold mixup partitions target cells into “known” and “unknown” groups. Model adaptation is then conducted to alleviate the batch effect. We gather multi-order neighborhood messages globally and impose local affinity regularizations on “known” cells. These constraints mitigate wrong classifications of the source model via reliable self-supervised information of neighbors. scEMAIL is accurate and robust under various scenarios in both simulation and real data. It is also flexible to be applied to challenging single-cell ATAC-seq data without loss of superiority. The source code of scEMAIL can be accessed at https://github.com/aster-ww/scEMAIL and https://ngdc.cncb.ac.cn/biocode/tools/BT007335/releases/v1.0.

show abstract

Section: Resultsmentioning

confidence: 99%

scEMAIL: Universal and Source-Free Annotation Method for scRNA-Seq Data with Novel Cell-Type Perception

Wan¹,

Chen²,

Deng³

2022

Genomics, Proteomics &Amp; Bioinformatics

View full text Add to dashboard Cite

show abstract

“…Recently, various microfluidics platforms (whether droplet or microwell) have been proposed with multimodal analysis, 267,268 and the resulting big-data framework necessitates the implementation of deep learning on bioinformatics that computationally urges the advent of the multi-omics era. 269–271 On the horizon, it is also anticipated that multilayer microfluidics can be designed to accommodate multiple functional modules for conducting either the multi-omics or dynamic cell biology experiments.…”

Section: Future Perspectives and Conclusionmentioning

confidence: 99%

Recent advances in microfluidics for single-cell functional proteomics

et al. 2023

View full text Add to dashboard Cite

show abstract

“…We applied the sciPENN from the python package sciPENN to cross modal generation [25]. The training dataset multiplies element-wise with an input-mask matrix.…”

Section: Scipennmentioning

confidence: 99%

InClust+: the multimodal version of inClust for multimodal data integration, imputation, and cross modal generation

Wang

Nie

Cai

et al. 2023

Preprint

View full text Add to dashboard Cite

With the development of single-cell technology, many cell traits (e.g. gene expression, chromatin accessibility, DNA methylation) can be measured. Furthermore, the multi-omic profiling technology could jointly measure two or more traits in a single cell simultaneously. In order to process the various data accumulated rapidly, computational methods for multimodal data integration are needed. Previously, we developed inClust, a flexible all-in deep generative framework for transcriptome data. Here, we extend the applicability of inClust into the realm of multimodal data by adding two mask modules: an input-mask module in front of the encoder and an output-mask module behind the decoder. We call this augmented model inClust+, and apply it to various multimodal data. InClust+ was first used to integrate scRNA and MERFISH data from similar cell populations and to impute MERFISH data based on scRNA data. Then, inClust+ is shown to have the capability to integrate a multimodal data contain scRNA and scATAC or two multimodal CITE datasets with batch effect. Finally, inClust+ is used to integrate a monomodal scRNA dataset and two multimodal CITE datasets, and generate the missing modality of surface protein in monomodal scRNA data. In the above examples, the performance of inClust+ is better than or comparable to the most recent tools to the corresponding task, which prove inClust+ is a suitable framework for handling multimodal data. Meanwhile, the successful implementation of mask in inClust+ means that it can be applied to other deep learning methods with similar encoder-decoder architecture to broaden the application scope of these models.

show abstract

A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation

Cited by 45 publications

References 26 publications

scEMAIL: Universal and Source-Free Annotation Method for scRNA-Seq Data with Novel Cell-Type Perception

scEMAIL: Universal and Source-Free Annotation Method for scRNA-Seq Data with Novel Cell-Type Perception

Recent advances in microfluidics for single-cell functional proteomics

InClust+: the multimodal version of inClust for multimodal data integration, imputation, and cross modal generation

Contact Info

Product

Resources

About