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Fast Batch Alignment of Single Cell Transcriptomes Unifies Multiple Mouse Cell Atlases into an Integrated Landscape [article]

Jong-Eun Park, Krzysztof Polanski, Kerstin Meyer, Sarah A Teichmann
2018 bioRxiv   pre-print
To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration method.  ...  Increasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration.  ...  Discussion BBKNN is a very fast and lightweight batch alignment tool that can be used immediately with SCANPY 15 , a scalable Python single cell RNA-Seq package capable of handling over a million cells  ... 
doi:10.1101/397042 fatcat:dlvhqfceyrawpfnn5tbymk6f6m

Strength in numbers: Large-scale integration of single-cell transcriptomic data reveals rare, transient muscle progenitor cell states in muscle regeneration [article]

David W. McKellar, Lauren D. Walter, Leo T. Song, Madhav Mantri, Michael F.Z. Wang, Iwijn De Vlaminck, Benjamin D. Cosgrove
2020 bioRxiv   pre-print
We created a large-scale single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 79 public single-cell  ...  Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.  ...  BBKNN: Fast batch alignment of single cell transcriptomes. Bioinformatics 36, 536 964-965 (2020). 537 36. Chen, B. & Shan, T.  ... 
doi:10.1101/2020.12.01.407460 fatcat:45w7yt6t6jajnigkawvptzozdq

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

FDA SEQC-2 Single-cell Sequencing Consortium, Wangqiu Chen, Yongmei Zhao, Xin Chen, Xiaojiang Xu, Zhaowei Yang, Yingtao Bi, Vicky Chen, Jing Li, Hannah Choi, Ben Ernest, Bao Tran (+9 others)
2020 bioRxiv   pre-print
that the samples evaluated each contain a shared portion of highly similar cells.  ...  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.  ...  The authors also would like to thank Sangeetha Anandakrishnan of Takara Bio USA, Inc. for the technical assistance in Takara Bio ICELL8 single cell capture and library preparation.  ... 
doi:10.1101/2020.03.27.010249 fatcat:ya2rjl6xhncxzmubfajppojf7i

Adversarial domain translation networks enable fast and accurate large-scale atlas-level single-cell data integration [article]

Jia Zhao, Gefei Wang, Jingsi Ming, Zhixiang Lin, Yang Wang, Angela Ruohao Wu, Can Yang, Tabula Microcebus Consortium
2021 bioRxiv   pre-print
The rapid emergence of large-scale atlas-level single-cell RNA-sequencing (scRNA-seq) datasets from various sources presents remarkable opportunities for broad and deep biological investigations through  ...  millions of cells in minutes with low memory consumption.  ...  Although integration methods for single-cell transcriptomics analysis have evolved along with single-cell sequencing technologies, the rapid accumulation of new and diverse single-cell datasets has introduced  ... 
doi:10.1101/2021.11.16.468892 fatcat:ob7mt3jkm5e7pghiwt3mo4gxz4

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

Tongxin Wang, Travis S. Johnson, Wei Shao, Zixiao Lu, Bryan R. Helm, Jie Zhang, Kun Huang
2019 Genome Biology  
To fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for identifying cell lineages and bona fide transcriptional signals, it is necessary to combine data from multiple experiments  ...  BERMUDA effectively combines different batches of scRNA-seq data with vastly different cell population compositions and amplifies biological signals by transferring information among batches.  ...  /datasets/2.1.0/t_3k for Pan T Cell batch).  ... 
doi:10.1186/s13059-019-1764-6 pmid:31405383 pmcid:PMC6691531 fatcat:i3uh5fe5p5f43e4wuivrpk5vfy

A benchmark of batch-effect correction methods for single-cell RNA sequencing data

Hoa Thi Nhu Tran, Kok Siong Ang, Marion Chevrier, Xiaomeng Zhang, Nicole Yee Shin Lee, Michelle Goh, Jinmiao Chen
2020 Genome Biology  
Large-scale single-cell transcriptomic datasets generated using different technologies contain batch-specific systematic variations that present a challenge to batch-effect removal and data integration  ...  We compare 14 methods in terms of computational runtime, the ability to handle large datasets, and batch-effect correction efficacy while preserving cell type purity.  ...  Additional information Peer review information: Yixin Yao was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.  ... 
doi:10.1186/s13059-019-1850-9 pmid:31948481 pmcid:PMC6964114 fatcat:mujbk5526jdare5sx2qsgdftma

Analysis of single-cell RNA sequencing data based on autoencoders

Andrea Tangherloni, Federico Ricciuti, Daniela Besozzi, Pietro Liò, Ana Cvejic
2021 BMC Bioinformatics  
Background Single-cell RNA sequencing (scRNA-Seq) experiments are gaining ground to study the molecular processes that drive normal development as well as the onset of different pathologies.  ...  of known or putatively novel cell-types.  ...  Leonardo Rundo (Department of Radiology, University of Cambridge) for his critical comments.  ... 
doi:10.1186/s12859-021-04150-3 pmid:34103004 pmcid:PMC8186186 fatcat:oswrlzlhongbdmfzecensqoaxe

A Robust and Scalable Graph Neural Network for Accurate Single Cell Classification [article]

Yuansong Zeng, Xiang Zhou, Zixiang Pan, Yutong Lu, Yuedong Yang
2021 bioRxiv   pre-print
With the launch of several large-scale single-cell projects, millions of sequenced cells have been annotated and it is promising to transfer labels from the annotated datasets to newly generated datasets  ...  for a high speed and scalability on millions of cells.  ...  BBKNN [44] provides an extremely fast and scalable neighbourhood construction method across all batches.  ... 
doi:10.1101/2021.06.24.449752 fatcat:y47otzsq35gupcya64lrglk4lu

Fast, sensitive, and accurate integration of single cell data with Harmony [article]

Ilya Korsunsky, Jean Fan, Kamil Slowikowski, Fan Zhang, Kevin Wei, Yuriy Baglaenko, Michael Brenner, Po-Ru Loh, Soumya Raychaudhuri
2018 bioRxiv   pre-print
The rapidly emerging diversity of single cell RNAseq datasets allows us to characterize the transcriptional behavior of cell types across a wide variety of biological and clinical conditions.  ...  In a meta-analysis of 14,746 cells from 5 studies of human pancreatic islet cells, Harmony accounts for variation among technologies and donors to successfully align several rare subpopulations.  ...  Integrating single-cell transcriptomic data across different 555 conditions, technologies, and species. Nat. Biotechnol. (2018). URL 556 nbt.4096. 557 33 Blondel, V.  ... 
doi:10.1101/461954 fatcat:bbvlaw3torfzhhel6o7xc7lkpm

Construction of continuously expandable single-cell atlases through integration of heterogeneous datasets in a generalized cell-embedding space [article]

Lei Xiong, Kang Tian, Yuzhe Li, Qiangfeng Cliff Zhang
2021 bioRxiv   pre-print
For integrative single-cell data analysis, we have developed SCALEX, a deep generative framework that maps cells into a generalized, batch-invariant cell-embedding space.  ...  Single-cell RNA-seq and ATAC-seq analyses have been widely applied to decipher cell-type and regulation complexities.  ...  BBKNN: fast batch alignment of single cell transcriptomes. 416 Bioinformatics 36, 964-965, doi:10.1093/bioinformatics/btz625 (2020). 417 13 Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija  ... 
doi:10.1101/2021.04.06.438536 fatcat:h2cku4qe5jfipmpwwzx3liqf24

Deep learning applications in single-cell omics data analysis [article]

Nafiseh Erfanian, A. Ali Heydari, Pablo Ianez, Afshin Derakhshani, Mohammad Ghasemigol, Mohsen Farahpour, Saeed Nasseri, Hossein Safarpour, Amirhossein Sahebkar
2021 bioRxiv   pre-print
We examine DL applications in a variety of single-cell omics (genomics, transcriptomics, proteomics, metabolomics and multi-omics integration) and address whether DL techniques will prove to be advantageous  ...  However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) but lacking the needed biological interpretability in many  ...  (Polański, Young et al. 2020 ) developed BBKNN, a fast graph-based algorithm that removes batch effects through linking analogous cells in different batches.  ... 
doi:10.1101/2021.11.26.470166 fatcat:3bmpecoza5dedbmwm62jwhfm4e

Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species [article]

Mohammad Lotfollahi, F. Alexander Wolf, Fabian J. Theis
2018 bioRxiv   pre-print
Here, we present scGen, a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data.  ...  In benchmarks across a broad range of examples, we show that scGen accurately models dose and infection response of cells across cell types, studies and species.  ...  Fast Batch Alignment of Single Cell 548 Transcriptomes Unifies Multiple Mouse Cell Atlases into an Integrated Landscape. bioRxiv 549 397042 (2018). 550 [42] Haghverdi, L., Lun, A.  ... 
doi:10.1101/478503 fatcat:kskrjmawsvetrllj27gigf2sku

Mapping single-cell atlases throughout Metazoa unravels cell type evolution

Alexander J Tarashansky, Jacob M Musser, Margarita Khariton, Pengyang Li, Detlev Arendt, Stephen R Quake, Bo Wang
2021 eLife  
Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases.  ...  Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019).  ...  Kebschull for their critical reading of the manuscript. 811 AJT is a Bio-X Stanford Interdisciplinary Graduate Fellow.  ... 
doi:10.7554/elife.66747 pmid:33944782 pmcid:PMC8139856 fatcat:b7heeteybvhqtoekssfp4xooyq

JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation [article]

Mohit Goyal, Guillermo Serrano, Ilan Shomorony, Mikel Hernaez, Idoia Ochoa
2020 bioRxiv   pre-print
To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need  ...  However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic  ...  ... 
doi:10.1101/2020.10.06.327601 fatcat:3arizo75jrhavn7mdt4errfizq

Single-cell transcriptomics reveals temporal dynamics of critical regulators of germ cell fate during mouse sex determination [article]

Chloé Mayere, Yasmine Neirijnck, Pauline Sararols, Isabelle Stévant, Françoise Kühne, Anne-Amandine Chassot, Marie-Christine Chaboissier, Emmanouil Dermitzakis, Serge Nef
2019 bioRxiv   pre-print
Our study provides a molecular roadmap of germ cell sex determination at single-cell resolution that will serve as a valuable resource for future studies of gonad development, function and disease.  ...  Here, we describe a comprehensive characterization of gene expression dynamics during sex determination based on single-cell RNA sequencing on 14,750 XX and XY mouse germ cells between embryonic days 10.5  ...  To assess for batch effect, we built a nearest neighbor graph using BBKNN function (BBKNN package).  ... 
doi:10.1101/747279 fatcat:wsp4fwuyejhhxd5ft4qrvja6uu
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