scholarly journals The winning methods for predicting cellular position in the DREAM single cell transcriptomics challenge

2020 ◽  
Author(s):  
Vu VH Pham ◽  
Xiaomei Li ◽  
Buu Truong ◽  
Thin Nguyen ◽  
Lin Liu ◽  
...  
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Single Cells ◽  
Drosophila Embryo ◽  
Supplementary Information ◽  
Rna Seq ◽  
Link Type ◽  
Spatial Reconstruction ◽  
Spatial Environment ◽  
Supplementary Material

AbstractMotivationPredicting cell locations is important since with the understanding of cell locations, we may estimate the function of cells and their integration with the spatial environment. Thus, the DREAM Challenge on Single Cell Transcriptomics required participants to predict the locations of single cells in the Drosophila embryo using single cell transcriptomic data.ResultsWe have developed over 50 pipelines by combining different ways of pre-processing the RNA-seq data, selecting the genes, predicting the cell locations, and validating predicted cell locations, resulting in the winning methods for two out of three sub-challenges in the competition. In this paper, we present an R package, SCTCwhatateam, which includes all the methods we developed and the Shiny web-application to facilitate the research on single cell spatial reconstruction. All the data and the example use cases are available in the Supplementary material.AvailabilityThe scripts of the package are available at https://github.com/thanhbuu04/SCTCwhatateam and the Shiny application is available at https://github.com/pvvhoang/[email protected] informationSupplementary data are available at Briefings in Bioinformatics online.

scholarly journals The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge

2020 ◽  
Author(s):  
Vu V H Pham ◽  
Xiaomei Li ◽  
Buu Truong ◽  
Thin Nguyen ◽  
Lin Liu ◽  
...  
Keyword(s):  
Single Cell ◽  
Web Application ◽  
Single Cells ◽  
R Package ◽  
Drosophila Embryo ◽  
Supplementary Data ◽  
Rna Seq ◽  
Transcriptomic Data ◽  
Spatial Reconstruction ◽  
Spatial Environment

Abstract Motivation Predicting cell locations is important since with the understanding of cell locations, we may estimate the function of cells and their integration with the spatial environment. Thus, the DREAM challenge on single-cell transcriptomics required participants to predict the locations of single cells in the Drosophila embryo using single-cell transcriptomic data. Results We have developed over 50 pipelines by combining different ways of preprocessing the RNA-seq data, selecting the genes, predicting the cell locations and validating predicted cell locations, resulting in the winning methods which were ranked second in sub-challenge 1, first in sub-challenge 2 and third in sub-challenge 3. In this paper, we present an R package, SCTCwhatateam, which includes all the methods we developed and the Shiny web application to facilitate the research on single-cell spatial reconstruction. All the data and the example use cases are available in the Supplementary data.

scholarly journals PathScore: a web tool for identifying altered pathways in cancer data

2016 ◽  
Author(s):  
Stephen G. Gaffney ◽  
Jeffrey P. Townsend
Keyword(s):  
Web Application ◽  
Somatic Mutations ◽  
Supplementary Information ◽  
Web Tool ◽  
Cancer Data ◽  
Link Type ◽  
Novel Approach ◽  
Supplementary Material ◽  
User Friendly ◽  
Pathway Effect

ABSTRACTSummaryPathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects.Availability and ImplementationWeb application available at pathscore.publichealth.yale.edu. Site implemented in Python and MySQL, with all major browsers supported. Source code available at github.com/sggaffney/pathscore with a GPLv3 [email protected] InformationAdditional documentation can be found at http://pathscore.publichealth.yale.edu/faq.

scholarly journals DEsingle for detecting three types of differential expression in single-cell RNA-seq data

2017 ◽  
Author(s):  
Zhun Miao ◽  
Ke Deng ◽  
Xiaowo Wang ◽  
Xuegong Zhang
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Negative Binomial ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Binomial Model ◽  
Supplementary Data ◽  
Rna Seq ◽  
Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

scholarly journals Single cell network analysis with a mixture of Nested Effects Models

2018 ◽  
Author(s):  
Martin Pirkl ◽  
Niko Beerenwinkel
Keyword(s):  
Single Cell ◽  
New Technologies ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Data Sets ◽  
Cell Network ◽  
A Cell ◽  
Supplementary Material ◽  
Cell Data

AbstractMotivationNew technologies allow for the elaborate measurement of different traits of single cells. These data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.ResultsWe developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular sub-populations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq.AvailabilityThe mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbgethz/mnem/[email protected], [email protected] informationSupplementary data are available.online.

scholarly journals starmapVR: immersive visualisation of single cell spatial omic data

2020 ◽  
Author(s):  
Andrian Yang ◽  
Yu Yao ◽  
Xiunan Fang ◽  
Jianfu Li ◽  
Yongyan Xia ◽  
...  
Keyword(s):  
Single Cell ◽  
Low Cost ◽  
Single Cells ◽  
Three Dimensional ◽  
Supplementary Information ◽  
Phenotypic Properties ◽  
Histological Image ◽  
Link Type ◽  
Molecular Expression ◽  
Omic Data

AbstractMotivationAdvances in high throughput single-cell and spatial omic technologies have enabled the profiling of molecular expression and phenotypic properties of hundreds of thousands of individual cells in the context of their two dimensional (2D) or three dimensional (3D) spatial endogenous arrangement. However, current visualisation techniques do not allow for effective display and exploration of the single cell data in their spatial context. With the widespread availability of low-cost virtual reality (VR) gadgets, such as Google Cardboard, we propose that an immersive visualisation strategy is useful.ResultsWe present starmapVR, a light-weight, cross-platform, web-based tool for visualising single-cell and spatial omic data. starmapVR supports a number of interaction methods, such as keyboard, mouse, wireless controller and voice control. The tool visualises single cells in a 3D space and each cell can be represented by a star plot (for molecular expression, phenotypic properties) or image (for single cell imaging). For spatial transcriptomic data, the 2D single cell expression data can be visualised alongside the histological image in a 2.5D format. The application of starmapVR is demonstrated through a series of case studies. Its scalability has been carefully evaluated across different platforms.Availability and implementationstarmapVR is freely accessible at https://holab-hku.github.io/starmapVR, with the corresponding source code available at https://github.com/holab-hku/starmapVR under the open source MIT license.Supplementary InformationSupplementary data are available at Bioinformatics online.

scholarly journals SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning

2017 ◽  
Author(s):  
Bo Wang ◽  
Daniele Ramazzotti ◽  
Luca De Sano ◽  
Junjie Zhu ◽  
Emma Pierson ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Single Cell Analysis ◽  
R Package ◽  
Supplementary Information ◽  
Cell Analysis ◽  
Rna Seq ◽  
A Cell ◽  
Supplementary Material ◽  
Public Datasets

AbstractMotivationWe here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell RNA-seq data. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of cells. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization.Availability and ImplementationSIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on [email protected] or [email protected] InformationSupplementary data are available at Bioinformatics online.

scholarly journals scater: pre-processing, quality control, normalisation and visualisation of single-cell RNA-seq data in R

2016 ◽  
Author(s):  
Davis J. McCarthy ◽  
Kieran R. Campbell ◽  
Aaron T. L. Lun ◽  
Quin F. Wills
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Sequence Data ◽  
Supplementary Information ◽  
Processing Quality ◽  
Rna Seq ◽  
Study Gene Expression ◽  
Supplementary Material ◽  
Downstream Analysis ◽  
Cell Data

AbstractMotivationSingle-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts, and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalisation.ResultsWe have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalisation and visualisation of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development.AvailabilityThe open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater.Supplementary informationSupplementary material is available online at bioRxiv accompanying this manuscript, and all materials required to reproduce the results presented in this paper are available at dx.doi.org/10.5281/zenodo.60139.

scholarly journals SAFE-clustering: Single-cell Aggregated (From Ensemble) Clustering for Single-cell RNA-seq Data

2017 ◽  
Author(s):  
Yuchen Yang ◽  
Ruth Huh ◽  
Houston W. Culpepper ◽  
Yuan Lin ◽  
Michael I. Love ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Supplementary Information ◽  
Adjusted Rand Index ◽  
Ensemble Clustering ◽  
Rna Seq ◽  
Clustering Methods ◽  
Cluster Assignment ◽  
Absolute Deviation ◽  
Number Of Clusters

ABSTRACTMotivationAccurately clustering cell types from a mass of heterogeneous cells is a crucial first step for the analysis of single-cell RNA-seq (scRNA-Seq) data. Although several methods have been recently developed, they utilize different characteristics of data and yield varying results in terms of both the number of clusters and actual cluster assignments.ResultsHere, we present SAFE-clustering, Single-cell Aggregated (From Ensemble) clustering, a flexible, accurate and robust method for clustering scRNA-Seq data. SAFE-clustering takes as input, results from multiple clustering methods, to build one consensus solution. SAFE-clustering currently embeds four state-of-the-art methods, SC3, CIDR, Seurat and t-SNE + k-means; and ensembles solutions from these four methods using three hypergraph-based partitioning algorithms. Extensive assessment across 12 datasets with the number of clusters ranging from 3 to 14, and the number of single cells ranging from 49 to 32,695 showcases the advantages of SAFE-clustering in terms of both cluster number (18.9 - 50.0% reduction in absolute deviation to the truth) and cluster assignment (on average 28.9% improvement, and up to 34.5% over the best of the four methods, measured by adjusted rand index). Moreover, SAFE-clustering is computationally efficient to accommodate large datasets, taking <10 minutes to process 28,733 cells.Availability and implementationSAFE-clustering, including source codes and tutorial, is free available on the web at http://yunliweb.its.unc.edu/safe/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data

2019 ◽  
Author(s):  
Héctor Climente-González ◽  
Chloé-Agathe Azencott ◽  
Samuel Kaski ◽  
Makoto Yamada
Keyword(s):  
Single Cell ◽  
Synthetic Data ◽  
Real Data ◽  
Supplementary Information ◽  
Rna Seq ◽  
Link Type ◽  
Model Free ◽  
Computational Overhead ◽  
Expression Microarrays ◽  
And Function

AbstractMotivationFinding nonlinear relationships between biomolecules and a biological outcome is computationally expensive and statistically challenging. Existing methods have crucial drawbacks, among others lack of parsimony, non-convexity, and computational overhead. Here we present the block HSIC Lasso, a nonlinear feature selector that does not present the previous drawbacks.ResultsWe compare the block HSIC Lasso to other state-of-the-art feature selection techniques in synthetic data and real data, including experiments over three common types of genomic data: gene-expression microarrays, single-cell RNA-seq, and GWAS. In all the cases, we observe that features selected by block HSIC Lasso retain more information about the underlying biology than features of other techniques. As a proof of concept, we applied the block HSIC Lasso to a single-cell RNA-seq experiment on mouse hippocampus. We discovered that many genes linked in the past to brain development and function are involved in the biological differences between the types of neurons.AvailabilityBlock HSIC Lasso is implemented in the Python 2/3 package pyHSICLasso, available in Github (https://github.com/riken-aip/pyHSICLasso) and PyPi (https://pypi.org/project/pyHSICLasso)[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals ascend: R package for analysis of single cell RNA-seq data

2017 ◽  
Author(s):  
Anne Senabouth ◽  
Samuel W Lukowski ◽  
Jose Alquicira Hernandez ◽  
Stacey Andersen ◽  
Xin Mei ◽  
...  
Keyword(s):  
Single Cell ◽  
R Package ◽  
Computational Genomics ◽  
Supplementary Information ◽  
Rna Seq ◽  
Software Packages ◽  
Wide Range ◽  
Flexible Framework ◽  
Supplementary Material ◽  
Data Objects

AbstractSummaryascend is an R package comprised of fast, streamlined analysis functions optimized to address the statistical challenges of single cell RNA-seq. The package incorporates novel and established methods to provide a flexible framework to perform filtering, quality control, normalization, dimension reduction, clustering, differential expression and a wide-range of plotting. ascend is designed to work with scRNA-seq data generated by any high-throughput platform, and includes functions to convert data objects between software packages.AvailabilityThe R package and associated vignettes are freely available at https://github.com/IMB-Computational-Genomics-Lab/[email protected] informationAn example dataset is available at ArrayExpress, accession number E-MTAB-6108

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