scholarly journals dropClust2: An R package for resource efficient analysis of large scale single cell RNA-Seq data

2019 ◽  
Author(s):  
Debajyoti Sinha ◽  
Pradyumn Sinha ◽  
Ritwik Saha ◽  
Sanghamitra Bandyopadhyay ◽  
Debarka Sengupta
Keyword(s):  
Single Cell ◽  
Programming Languages ◽  
Large Scale ◽  
Principal Component ◽  
Cell Types ◽  
R Package ◽  
Locality Sensitive Hashing ◽  
Rna Seq ◽  
Link Type ◽  
Component Selection

ABSTRACTDropClust leverages Locality Sensitive Hashing (LSH) to speed up clustering of large scale single cell expression data. It makes ingenious use of structure persevering sampling and modality based principal component selection to rescue minor cell types. Existing implementation of dropClust involves interfacing with multiple programming languagesviz. R, python and C, hindering seamless installation and portability. Here we present dropClust2, a complete R package that’s not only fast but also minimally resource intensive. DropClust2 features a novel batch effect removal algorithm that allows integrative analysis of single cell RNA-seq (scRNA-seq) datasets.Availability and implementationdropClust2 is freely available athttps://debsinha.shinyapps.io/dropClust/as an online web service and athttps://github.com/debsin/dropClustas an R package.

scholarly journals STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data

2020 ◽  
Author(s):  
Massimo Andreatta ◽  
Santiago J. Carmona
Keyword(s):  
Single Cell ◽  
Distance Measure ◽  
Cell Types ◽  
R Package ◽  
Rna Seq ◽  
Batch Effects ◽  
Link Type ◽  
Transcriptomics Data ◽  
Public Repositories ◽  
Cell Data

AbstractComputational tools for the integration of single-cell transcriptomics data are designed to correct batch effects between technical replicates or different technologies applied to the same population of cells. However, they have inherent limitations when applied to heterogeneous sets of data with moderate overlap in cell states or sub-types. STACAS is a package for the identification of integration anchors in the Seurat environment, optimized for the integration of datasets that share only a subset of cell types. We demonstrate that by i) correcting batch effects while preserving relevant biological variability across datasets, ii) filtering aberrant integration anchors with a quantitative distance measure, and iii) constructing optimal guide trees for integration, STACAS can accurately align scRNA-seq datasets composed of only partially overlapping cell populations. We anticipate that the algorithm will be a useful tool for the construction of comprehensive single-cell atlases by integration of the growing amount of single-cell data becoming available in public repositories.Code availabilityR package:https://github.com/carmonalab/STACASDocker image:https://hub.docker.com/repository/docker/mandrea1/stacas_demo

Improved dropClust R package with integrative analysis support for scRNA-seq data

2019 ◽  
Author(s):  
Debajyoti Sinha ◽  
Pradyumn Sinha ◽  
Ritwik Saha ◽  
Sanghamitra Bandyopadhyay ◽  
Debarka Sengupta
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
R Package ◽  
Integrative Analysis ◽  
Locality Sensitive Hashing ◽  
Supplementary Information ◽  
Expression Data ◽  
Rna Seq ◽  
Speed Up ◽  
Cell Expression

Abstract Summary DropClust leverages Locality Sensitive Hashing (LSH) to speed up clustering of large scale single cell expression data. Here we present the improved dropClust, a complete R package that is, fast, interoperable and minimally resource intensive. The new dropClust features a novel batch effect removal algorithm that allows integrative analysis of single cell RNA-seq (scRNA-seq) datasets. Availability and implementation dropClust is freely available at https://github.com/debsin/dropClust as an R package. A lightweight online version of the dropClust is available at https://debsinha.shinyapps.io/dropClust/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals scAIDE: clustering of large-scale single-cell RNA-seq data reveals putative and rare cell types

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Kaikun Xie ◽  
Yu Huang ◽  
Feng Zeng ◽  
Zehua Liu ◽  
Ting Chen
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Random Projection ◽  
Good Representation ◽  
Rna Seq ◽  
Unsupervised Deep Learning ◽  
High Level ◽  
Computational Resources

Abstract Recent advancements in both single-cell RNA-sequencing technology and computational resources facilitate the study of cell types on global populations. Up to millions of cells can now be sequenced in one experiment; thus, accurate and efficient computational methods are needed to provide clustering and post-analysis of assigning putative and rare cell types. Here, we present a novel unsupervised deep learning clustering framework that is robust and highly scalable. To overcome the high level of noise, scAIDE first incorporates an autoencoder-imputation network with a distance-preserved embedding network (AIDE) to learn a good representation of data, and then applies a random projection hashing based k-means algorithm to accommodate the detection of rare cell types. We analyzed a 1.3 million neural cell dataset within 30 min, obtaining 64 clusters which were mapped to 19 putative cell types. In particular, we further identified three different neural stem cell developmental trajectories in these clusters. We also classified two subpopulations of malignant cells in a small glioblastoma dataset using scAIDE. We anticipate that scAIDE would provide a more in-depth understanding of cell development and diseases.

scholarly journals STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data

2020 ◽  
Author(s):  
Massimo Andreatta ◽  
Santiago J Carmona
Keyword(s):  
Single Cell ◽  
Distance Measure ◽  
Source Code ◽  
Cell Types ◽  
R Package ◽  
Computational Method ◽  
Biological Variability ◽  
Rna Seq ◽  
Batch Effects ◽  
Guide Trees

Abstract Summary STACAS is a computational method for the identification of integration anchors in the Seurat environment, optimized for the integration of single-cell (sc) RNA-seq datasets that share only a subset of cell types. We demonstrate that by (i) correcting batch effects while preserving relevant biological variability across datasets, (ii) filtering aberrant integration anchors with a quantitative distance measure and (iii) constructing optimal guide trees for integration, STACAS can accurately align scRNA-seq datasets composed of only partially overlapping cell populations. Availability and implementation Source code and R package available at https://github.com/carmonalab/STACAS; Docker image available at https://hub.docker.com/repository/docker/mandrea1/stacas_demo.

scholarly journals Linear-time cluster ensembles of large-scale single-cell RNA-seq and multimodal data

2020 ◽  
Author(s):  
Van Hoan Do ◽  
Francisca Rojas Ringeling ◽  
Stefan Canzar
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Linear Time ◽  
Cell Types ◽  
Substantial Improvement ◽  
Rna Seq ◽  
Sampling Step ◽  
Protein Marker ◽  
Cluster Ensembles ◽  
Sequencing Technologies

AbstractA fundamental task in single-cell RNA-seq (scRNA-seq) analysis is the identification of transcriptionally distinct groups of cells. Numerous methods have been proposed for this problem, with a recent focus on methods for the cluster analysis of ultra-large scRNA-seq data sets produced by droplet-based sequencing technologies. Most existing methods rely on a sampling step to bridge the gap between algorithm scalability and volume of the data. Ignoring large parts of the data, however, often yields inaccurate groupings of cells and risks overlooking rare cell types. We propose method Specter that adopts and extends recent algorithmic advances in (fast) spectral clustering. In contrast to methods that cluster a (random) subsample of the data, we adopt the idea of landmarks that are used to create a sparse representation of the full data from which a spectral embedding can then be computed in linear time. We exploit Specter’s speed in a cluster ensemble scheme that achieves a substantial improvement in accuracy over existing methods and that is sensitive to rare cell types. Its linear time complexity allows Specter to scale to millions of cells and leads to fast computation times in practice. Furthermore, on CITE-seq data that simultaneously measures gene and protein marker expression we demonstrate that Specter is able to utilize multimodal omics measurements to resolve subtle transcriptomic differences between subpopulations of cells. Specter is open source and available at https://github.com/canzarlab/Specter.

scholarly journals rPanglaoDB: an R package to download and merge labeled single-cell RNA-seq data from the PanglaoDB database

2021 ◽  
Author(s):  
Daniel Osorio ◽  
Marieke Lydia Kuijjer ◽  
James J. Cai
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
R Package ◽  
Rna Seq ◽  
Cell Type ◽  
Sequencing Data ◽  
Single Experiment ◽  
Tissue Samples ◽  
Molecular Phenotypes ◽  
Public Datasets

Motivation: Characterizing cells with rare molecular phenotypes is one of the promises of high throughput single-cell RNA sequencing (scRNA-seq) techniques. However, collecting enough cells with the desired molecular phenotype in a single experiment is challenging, requiring several samples preprocessing steps to filter and collect the desired cells experimentally before sequencing. Data integration of multiple public single-cell experiments stands as a solution for this problem, allowing the collection of enough cells exhibiting the desired molecular signatures. By increasing the sample size of the desired cell type, this approach enables a robust cell type transcriptome characterization. Results: Here, we introduce rPanglaoDB, an R package to download and merge the uniformly processed and annotated scRNA-seq data provided by the PanglaoDB database. To show the potential of rPanglaoDB for collecting rare cell types by integrating multiple public datasets, we present a biological application collecting and characterizing a set of 157 fibrocytes. Fibrocytes are a rare monocyte-derived cell type, that exhibits both the inflammatory features of macrophages and the tissue remodeling properties of fibroblasts. This constitutes the first fibrocytes' unbiased transcriptome profile report. We compared the transcriptomic profile of the fibrocytes against the fibroblasts collected from the same tissue samples and confirm their associated relationship with healing processes in tissue damage and infection through the activation of the prostaglandin biosynthesis and regulation pathway. Availability and Implementation: rPanglaoDB is implemented as an R package available through the CRAN repositories https://CRAN.R-project.org/package=rPanglaoDB.

scholarly journals scDIOR: single cell RNA-seq data IO software

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Huijian Feng ◽  
Lihui Lin ◽  
Jiekai Chen
Keyword(s):  
Single Cell ◽  
Programming Languages ◽  
Large Scale ◽  
Developmental Trajectories ◽  
Rapid Development ◽  
Data Transformation ◽  
Rna Seq ◽  
Data Types ◽  
User Friendly ◽  
Cell Data

Abstract Background Single-cell RNA sequencing is becoming a powerful tool to identify cell states, reconstruct developmental trajectories, and deconvolute spatial expression. The rapid development of computational methods promotes the insight of heterogeneous single-cell data. An increasing number of tools have been provided for biological analysts, of which two programming languages- R and Python are widely used among researchers. R and Python are complementary, as many methods are implemented specifically in R or Python. However, the different platforms immediately caused the data sharing and transformation problem, especially for Scanpy, Seurat, and SingleCellExperiemnt. Currently, there is no efficient and user-friendly software to perform data transformation of single-cell omics between platforms, which makes users spend unbearable time on data Input and Output (IO), significantly reducing the efficiency of data analysis. Results We developed scDIOR for single-cell data transformation between platforms of R and Python based on Hierarchical Data Format Version 5 (HDF5). We have created a data IO ecosystem between three R packages (Seurat, SingleCellExperiment, Monocle) and a Python package (Scanpy). Importantly, scDIOR accommodates a variety of data types across programming languages and platforms in an ultrafast way, including single-cell RNA-seq and spatial resolved transcriptomics data, using only a few codes in IDE or command line interface. For large scale datasets, users can partially load the needed information, e.g., cell annotation without the gene expression matrices. scDIOR connects the analytical tasks of different platforms, which makes it easy to compare the performance of algorithms between them. Conclusions scDIOR contains two modules, dior in R and diopy in Python. scDIOR is a versatile and user-friendly tool that implements single-cell data transformation between R and Python rapidly and stably. The software is freely accessible at https://github.com/JiekaiLab/scDIOR.

scholarly journals SELINA: Single-cell Assignment using Multiple-Adversarial Domain Adaptation Network with Large-scale References

2022 ◽  
Author(s):  
Chenfei Wang ◽  
Pengfei Ren ◽  
Xiaoying Shi ◽  
Xin Dong ◽  
Zhiguang Yu ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Cell ◽  
Large Scale ◽  
Domain Adaptation ◽  
Cell Types ◽  
Rna Seq ◽  
Batch Effects ◽  
Cell Type ◽  
Data Annotation ◽  
One Stop

Abstract The rapid accumulation of single-cell RNA-seq data has provided rich resources to characterize various human cell types. Cell type annotation is the critical step in analyzing single-cell RNA-seq data. However, accurate cell type annotation based on public references is challenging due to the inconsistent annotations, batch effects, and poor characterization of rare cell types. Here, we introduce SELINA (single cELl identity NAvigator), an integrative annotation transferring framework for automatic cell type annotation. SELINA optimizes the annotation for minority cell types by synthetic minority over-sampling, removes batch effects among reference datasets using a multiple-adversarial domain adaptation network (MADA), and fits the query data with reference data using an autoencoder. Finally, SELINA affords a comprehensive and uniform reference atlas with 1.7 million cells covering 230 major human cell types. We demonstrated the robustness and superiority of SELINA in most human tissues compared to existing methods. SELINA provided a one-stop solution for human single- cell RNA-seq data annotation with the potential to extend for other species.

scholarly journals Addressing the looming identity crisis in single cell RNA-seq

2017 ◽  
Author(s):  
Megan Crow ◽  
Anirban Paul ◽  
Sara Ballouz ◽  
Z. Josh Huang ◽  
Jesse Gillis
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Ad Hoc ◽  
Meta Analysis ◽  
High Specificity ◽  
Cell Types ◽  
Marker Genes ◽  
Rna Seq ◽  
Cortical Interneuron ◽  
Large Sets

AbstractSingle cell RNA-sequencing technology (scRNA-seq) provides a new avenue to discover and characterize cell types, but the experiment-specific technical biases and analytic variability inherent to current pipelines may undermine the replicability of these studies. Meta-analysis of rapidly accumulating data is further hampered by the use of ad hoc naming conventions. Here we demonstrate our replication framework, MetaNeighbor, that allows researchers to quantify the degree to which cell types replicate across datasets, and to rapidly identify clusters with high similarity for further testing. We first measure the replicability of neuronal identity by comparing more than 13 thousand individual scRNA-seq transcriptomes, sampling with high specificity from within the data to define a range of robust practices. We then assess cross-dataset evidence for novel cortical interneuron subtypes identified by scRNA-seq and find that 24/45 cortical interneuron subtypes have evidence of replication in at least one other study. Identifying these putative replicates allows us to re-analyze the data for differential expression and provide lists of robust candidate marker genes. Across tasks we find that large sets of variably expressed genes can identify replicable cell types and subtypes with high accuracy, suggesting a general route forward for large-scale evaluation of scRNA-seq data.

scholarly journals A descriptive marker gene approach to single-cell pseudotime inference

2016 ◽  
Author(s):  
Kieran R Campbell ◽  
Christopher Yau
Keyword(s):  
Single Cell ◽  
Marker Gene ◽  
Cell Types ◽  
R Package ◽  
Estimation Methods ◽  
Marker Genes ◽  
Peak Time ◽  
Transient Behaviour ◽  
Link Type ◽  
Cell Gene Expression

AbstractPseudotime estimation from single-cell gene expression allows the recovery of temporal information from otherwise static profiles of individual cells. This pseudotemporal information can be used to characterise transient events in temporally evolving biological systems. Conventional algorithms typically emphasise an unsupervised transcriptome-wide approach and use retrospective analysis to evaluate the behaviour of individual genes. Here we introduce an orthogonal approach termed “Ouija” that learns pseudotimes from a small set of marker genes that might ordinarily be used to retrospectively confirm the accuracy of unsupervised pseudotime algorithms. Crucially, we model these genes in terms of switch-like or transient behaviour along the trajectory, allowing us to understand why the pseudotimes have been inferred and learn informative parameters about the behaviour of each gene. Since each gene is associated with a switch or peak time the genes are effectively ordered along with the cells, allowing each part of the trajectory to be understood in terms of the behaviour of certain genes. In the following we introduce our model and demonstrate that in many instances a small panel of marker genes can recover pseudotimes that are consistent with those obtained using the entire transcriptome. Furthermore, we show that our method can detect differences in the regulation timings between two genes and identify “metastable” states - discrete cell types along the continuous trajectories - that recapitulate known cell types. Ouija therefore provides a powerful complimentary approach to existing whole transcriptome based pseudotime estimation methods. An open source implementation is available at http://www.github.com/kieranrcampbell/ouija as an R package and at http://www.github.com/kieranrcampbell/ouijaflow as a Python/TensorFlow package.

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