scholarly journals Accurate sub-population detection and mapping across single cell experiments with PopCorn

2018 ◽  
Author(s):  
Yijie Wang ◽  
Jan Hoinka ◽  
Teresa M Przytycka
Keyword(s):  
Comparative Analysis ◽  
Single Cell ◽  
Single Cells ◽  
Novel Method ◽  
Cell Data

The identification of sub-populations of cells present in a sample and the comparison of such sub-populations across samples are among the most frequently performed analyzes of single-cell data. Current tools for these kinds of data, however, fall short in their ability to adequately perform these tasks. We introduce a novel method, PopCorn (single cell sub-Populations Comparison), allowing for the identification of sub-populations of cells present within individual experiments while simultaneously performing sub-populations mapping across these experiments. PopCorn utilizes several novel algorithmic solutions enabling the execution of these tasks with unprecedented precision. As such, PopCorn provides a much-needed tool for comparative analysis of populations of single cells.

scholarly journals Single Cell Viewer (SCV): An interactive visualization data portal for single cell RNA sequence data

2019 ◽  
Author(s):  
Shuoguo Wang ◽  
Constance Brett ◽  
Mohan Bolisetty ◽  
Ryan Golhar ◽  
Isaac Neuhaus ◽  
...  
Keyword(s):  
Single Cell ◽  
Sequence Data ◽  
Single Cells ◽  
Link Type ◽  
Technological Advances ◽  
R Shiny ◽  
Data Volume ◽  
Exploratory Data ◽  
Cell Data ◽  
Shiny Application

AbstractMotivationThanks to technological advances made in the last few years, we are now able to study transcriptomes from thousands of single cells. These have been applied widely to study various aspects of Biology. Nevertheless, comprehending and inferring meaningful biological insights from these large datasets is still a challenge. Although tools are being developed to deal with the data complexity and data volume, we do not have yet an effective visualizations and comparative analysis tools to realize the full value of these datasets.ResultsIn order to address this gap, we implemented a single cell data visualization portal called Single Cell Viewer (SCV). SCV is an R shiny application that offers users rich visualization and exploratory data analysis options for single cell datasets.AvailabilitySource code for the application is available online at GitHub (http://www.github.com/neuhausi/single-cell-viewer) and there is a hosted exploration application using the same example dataset as this publication at http://periscopeapps.org/[email protected]; [email protected]

scholarly journals CIM-seq

2021 ◽  
Author(s):  
Nathanael Andrews ◽  
Martin Enge
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Likelihood Estimation ◽  
Cell Types ◽  
Data Sets ◽  
Target Tissue ◽  
Data Set ◽  
Rnaseq Data ◽  
The Given ◽  
Cell Data

Abstract CIM-seq is a tool for deconvoluting RNA-seq data from cell multiplets (clusters of two or more cells) in order to identify physically interacting cell in a given tissue. The method requires two RNAseq data sets from the same tissue: one of single cells to be used as a reference, and one of cell multiplets to be deconvoluted. CIM-seq is compatible with both droplet based sequencing methods, such as Chromium Single Cell 3′ Kits from 10x genomics; and plate based methods, such as Smartseq2. The pipeline consists of three parts: 1) Dissociation of the target tissue, FACS sorting of single cells and multiplets, and conventional scRNA-seq 2) Feature selection and clustering of cell types in the single cell data set - generating a blueprint of transcriptional profiles in the given tissue 3) Computational deconvolution of multiplets through a maximum likelihood estimation (MLE) to determine the most likely cell type constituents of each multiplet.

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 Massively multiplex single-cell Hi-C

2016 ◽  
Author(s):  
Vijay Ramani ◽  
Xinxian Deng ◽  
Kevin L Gunderson ◽  
Frank J Steemers ◽  
Christine M Disteche ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Cell Heterogeneity ◽  
Proof Of Concept ◽  
Chromosome Conformation ◽  
Large Numbers ◽  
Conformational Properties ◽  
Novel Method ◽  
Different Cell Types

AbstractWe present combinatorial single cell Hi-C, a novel method that leverages combinatorial cellular indexing to measure chromosome conformation in large numbers of single cells. In this proof-of-concept, we generate and sequence combinatorial single cell Hi-C libraries for two mouse and four human cell types, comprising a total of 9,316 single cells across 5 experiments. We demonstrate the utility of single-cell Hi-C data in separating different cell types, identify previously uncharacterized cell-to-cell heterogeneity in the conformational properties of mammalian chromosomes, and demonstrate that combinatorial indexing is a generalizable molecular strategy for single-cell genomics.

scholarly journals Cellsnp-lite: an efficient tool for genotyping single cells

2021 ◽  
Author(s):  
Xianjie Huang ◽  
Yuanhua Huang
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Basic Research ◽  
Substantial Improvement ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Memory Efficiency ◽  
Computational Speed ◽  
Cell Data

AbstractSummarySingle-cell sequencing is an increasingly used technology and has promising applications in basic research and clinical translations. However, genotyping methods developed for bulk sequencing data have not been well adapted for single-cell data, in terms of both computational parallelization and simplified user interface. Here we introduce a software, cellsnp-lite, implemented in C/C++ and based on well supported package htslib, for genotyping in single-cell sequencing data for both droplet and well based platforms. On various experimental data sets, it shows substantial improvement in computational speed and memory efficiency with retaining highly concordant results compared to existing methods. Cellsnp-lite therefore lightens the genetic analysis for increasingly large single-cell data.AvailabilityThe source code is freely available at https://github.com/single-cell-genetics/[email protected]

scholarly journals Modeling latent flows on single-cell data using the Hodge decomposition

2019 ◽  
Author(s):  
Kazumitsu Maehara ◽  
Yasuyuki Ohkawa
Keyword(s):  
Diffusion Process ◽  
Single Cell ◽  
Trajectory Analysis ◽  
Single Cells ◽  
Hodge Decomposition ◽  
Biological Data ◽  
Graph Representation ◽  
Specific Cell ◽  
Sparse Graph ◽  
Cell Data

AbstractSingle-cell analysis is a powerful technique used to identify a specific cell population of interest during differentiation, aging, or oncogenesis. Individual cells occupy a particular transient state in the cell cycle, circadian rhythm, or during cell death. An appealing concept of pseudo-time trajectory analysis of single-cell RNA sequencing data was proposed in the software Monocle, and several methods of trajectory analysis have since been published to date. These aim to infer the ordering of cells and enable the tracing of gene expression profile trajectories in cell differentiation and reprogramming. However, the methods are restricted in terms of time structure because of the pre-specified structure of trajectories (linear, branched, tree or cyclic) which contrasts with the mixed state of single cells.Here, we propose a technique to extract underlying flows in single-cell data based on the Hodge decomposition (HD). HD is a theorem of vector fields on a manifold which guarantees that any given flow can decompose into three types of orthogonal component: gradient-flow (acyclic), curl-, and harmonic-flow (cyclic). HD is generalized on a simplicial complex (graph) and the discretized HD has only a weak assumption that the graph is directed. Therefore, in principle, HD can extract flows from any mixture of tree and cyclic time flows of observed cells. The decomposed flows provide intuitive interpretations about complex flow because of their linearity and orthogonality. Thus, each extracted flow can be focused on separately with no need to consider crosstalk.We developed ddhodge software, which aims to model the underlying flow structure that implies unobserved time or causal relations in the hodge-podge collection of data points. We demonstrated that the mathematical framework of HD is suitable to reconstruct a sparse graph representation of diffusion process as a candidate model of differentiation while preserving the divergence of the original fully-connected graph. The preserved divergence can be used as an indicator of the source and sink cells in the observed population. A sparse graph representation of the diffusion process transforms data analysis of the non-linear structure embedded in the high-dimensional space of single-cell data into inspection of the visible flow using graph algorithms. Hence, ddhodge is a suitable toolkit to visualize, inspect, and subsequently interpret large data sets including, but not limited to, high-throughput measurements of biological data.The beta version of ddhodge R package is available at:https://github.com/kazumits/ddhodge

scholarly journals propeller: testing for differences in cell type proportions in single cell data

2021 ◽  
Author(s):  
Belinda Phipson ◽  
Choon Boon Sim ◽  
Enzo R. Porrello ◽  
Alex W Hewitt ◽  
Joseph Powell ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
R Package ◽  
Cell Type ◽  
Experimental Conditions ◽  
Cell Type Composition ◽  
Type Composition ◽  
Biological Replication ◽  
Cell Data ◽  
Different Sources

Single cell RNA Sequencing (scRNA-seq) has rapidly gained popularity over the last few years for profiling the transcriptomes of thousands to millions of single cells. To date, there are more than a thousand software packages that have been developed to analyse scRNA-seq data. These focus predominantly on visualization, dimensionality reduction and cell type identification. Single cell technology is now being used to analyse experiments with complex designs including biological replication. One question that can be asked from single cell experiments which has not been possible to address with bulk RNA-seq data is whether the cell type proportions are different between two or more experimental conditions. As well as gene expression changes, the relative depletion or enrichment of a particular cell type can be the functional consequence of disease or treatment. However, cell type proportions estimates from scRNA-seq data are variable and statistical methods that can correctly account for different sources of variability are needed to confidently identify statistically significant shifts in cell type composition between experimental conditions. We present propeller, a robust and flexible method that leverages biological replication to find statistically significant differences in cell type proportions between groups. The propeller method is publicly available in the open source speckle R package (https://github.com/Oshlack/speckle).

scholarly journals Automated population identification and sorting algorithms for high-dimensional single-cell data

2016 ◽  
Author(s):  
Benedict Anchang ◽  
Sylvia K. Plevritis
Keyword(s):  
Single Cell ◽  
Cell Sorting ◽  
Intracellular Signaling ◽  
Expert Knowledge ◽  
Single Cells ◽  
Cell Type ◽  
Experimental Conditions ◽  
Cell Type Specific ◽  
Cell Subpopulations ◽  
Cell Data

AbstractCell sorting or gating homogenous subpopulations from single-cell data enables cell-type specific characterization, such as cell-type genomic profiling as well as the study of tumor progression. This highlight summarizes recently developed automated gating algorithms that are optimized for both population identification and sorting homogeneous single cells in heterogeneous single-cell data. Data-driven gating strategies identify and/or sort homogeneous subpopulations from a heterogeneous population without relying on expert knowledge thereby removing human bias and variability. We further describe an optimized cell sorting strategy called CCAST based on Clustering, Classification and Sorting Trees which identifies the relevant gating markers, gating hierarchy and partitions that define underlying cell subpopulations. CCAST identifies more homogeneous subpopulations in several applications compared to prior sorting strategies and reveals simultaneous intracellular signaling across different lineage subtypes under different experimental conditions.

scholarly journals Benchmarking PSM identification tools for single cell proteomics

2021 ◽  
Author(s):  
Daisha Van Der Watt ◽  
Hannah Boekweg ◽  
Thy Truong ◽  
Amanda J Guise ◽  
Edward D Plowey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Single Cells ◽  
Peptide Identification ◽  
Machine Learning Algorithms ◽  
Cellular Heterogeneity ◽  
Proteomics Data ◽  
Improve Performance ◽  
False Discovery ◽  
Cell Data

AbstractSingle cell proteomics is an emerging sub-field within proteomics with the potential to revolutionize our understanding of cellular heterogeneity and interactions. Recent efforts have largely focused on technological advancements in sample preparation, chromatography and instrumentation to enable measuring proteins present in these ultra-limited samples. Although advancements in data acquisition have rapidly improved our ability to analyze single cells, the software pipelines used in data analysis were originally written for traditional bulk samples and their performance on single cell data has not been investigated. We benchmarked five popular peptide identification tools on single cell proteomics data. We found that MetaMorpheus achieved the greatest number of peptide spectrum matches at a 1% false discovery rate. Depending on the tool, we also find that post processing machine learning can improve spectrum identification results by up to ∼40%. Although rescoring leads to a greater number of peptide spectrum matches, these new results typically are generated by 3rd party tools and have no way of being utilized by the primary pipeline for quantification. Exploration of novel metrics for machine learning algorithms will continue to improve performance.

scholarly journals SCINA: Semi-Supervised Analysis of Single Cells in Silico

Genes ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 531 ◽  
Author(s):  
Zhang ◽  
Luo ◽  
Zhong ◽  
Choi ◽  
Ma ◽  
...  
Keyword(s):  
Single Cell ◽  
Expectation Maximization ◽  
Immune Cell ◽  
Single Cells ◽  
Intermediate Stage ◽  
Immune Cell Population ◽  
Knock Out ◽  
Wide Range ◽  
Cell Data ◽  
Human Inspection

Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore the prior knowledge of transcriptomes and of the probable structures of the data. Moreover, cell identification heavily relies on subjective and inaccurate human inspection afterwards. To address these analytical challenges, we developed the Semi-supervised Category Identification and Assignment (SCINA) algorithm, a semi-supervised model, for analyses of scRNA-Seq and flow cytometry/CyTOF data, and other data of similar format, by automatically exploiting previously established gene signatures using an expectation–maximization (EM) algorithm. We applied SCINA on a wide range of datasets, and showed its accuracy, stableness and efficiency exceeded most popular unsupervised approaches. SCINA discovered an intermediate stage of oligodendrocyte from mouse brain scRNA-Seq data. SCINA also detected immune cell population shifting in Stk4 knock-out -knockoutmouse cytometry data. Finally, SCINA identified a new kidney tumor clade with similarity to FH-deficient tumors from bulk tumor data. Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.

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