scholarly journals GranatumX: A community engaging and flexible software environment for single-cell analysis

2018 ◽  
Author(s):  
Xun Zhu ◽  
Breck Yunits ◽  
Thomas Wolfgruber ◽  
Yu Liu ◽  
Qianhui Huang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Programming Languages ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Software Environment ◽  
Software Developers ◽  
Software Ecosystem ◽  
Graphical Environment ◽  
Cell Data

AbstractWe present GranatumX, the next-generation software environment for single-cell data analysis. It enables biologists access to the latest single-cell bioinformatics methods in a graphical environment. It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines. The architecture of GranatumX allows for easy inclusion of plugin modules, named “Gboxes”, that wrap around bioinformatics tools written in various programming languages. GranatumX can be run in the cloud or private servers, and generate reproducible results. It is expected to become a community-engaging, flexible, and evolving software ecosystem for scRNA-Seq analysis, connecting developers with bench scientists. GranatumX is freely accessible at: http://garmiregroup.org/granatumx/app

scholarly journals User-friendly, scalable tools and workflows for single-cell analysis

2020 ◽  
Author(s):  
P. Moreno ◽  
N. Huang ◽  
J.R. Manning ◽  
S. Mohammed ◽  
A. Solovyev ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Programming Languages ◽  
Single Cell Analysis ◽  
Command Line ◽  
Cell Analysis ◽  
Rna Seq ◽  
Interactive Analysis ◽  
User Friendly ◽  
Analysis Environment

AbstractSingle-cell RNA-Seq (scRNA-Seq) data analysis requires expertise in command-line tools, programming languages and scaling on compute infrastructure. As scRNA-Seq becomes widespread, computational pipelines need to be more accessible, simpler and scalable. We introduce an interactive analysis environment for scRNA-Seq, based on Galaxy, with ~70 functions from major single-cell analysis tools, which can be run on compute clusters, cloud providers or single machines, to bring compute to the data in scRNA-Seq.

scholarly journals CoGAPS 3: Bayesian non-negative matrix factorization for single-cell analysis with asynchronous updates and sparse data structures

2019 ◽  
Author(s):  
Thomas D. Sherman ◽  
Tiger Gao ◽  
Elana J. Fertig
Keyword(s):  
Single Cell ◽  
Data Structures ◽  
Computational Efficiency ◽  
Matrix Factorization ◽  
Single Cell Analysis ◽  
Sparse Data ◽  
Data Sets ◽  
Cell Analysis ◽  
Gradient Based ◽  
Cell Data

AbstractMotivationBayesian factorization methods, including Coordinated Gene Activity in Pattern Sets (CoGAPS), are emerging as powerful analysis tools for single cell data. However, these methods have greater computational costs than their gradient-based counterparts. These costs are often prohibitive for analysis of large single-cell datasets. Many such methods can be run in parallel which enables this limitation to be overcome by running on more powerful hardware. However, the constraints imposed by the prior distributions in CoGAPS limit the applicability of parallelization methods to enhance computational efficiency for single-cell analysis.ResultsWe upgraded CoGAPS in Version 3 to overcome the computational limitations of Bayesian matrix factorization for single cell data analysis. This software includes a new parallelization framework that is designed around the sequential updating steps of the algorithm to enhance computational efficiency. These algorithmic advances were coupled with new software architecture and sparse data structures to reduce the memory overhead for single-cell data. Altogether, these updates to CoGAPS enhance the efficiency of the algorithm so that it can analyze 1000 times more cells, enabling factorization of large single-cell data sets.AvailabilityCoGAPS is available as a Bioconductor package and the source code is provided at github.com/FertigLab/CoGAPS. All efficiency updates to enable single-cell analysis available as of version [email protected]

scholarly journals 12 Grand Challenges in Single-Cell Data Science

2019 ◽  
Author(s):  
David Laehnemann ◽  
Johannes Köster ◽  
Ewa Szczurek ◽  
Davis J McCarthy ◽  
Stephanie C Hicks ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Science ◽  
Single Cell Analysis ◽  
State Of The Art ◽  
Cell Analysis ◽  
Open Problems ◽  
Single Cell Sequencing ◽  
Current State ◽  
Cell Data

The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology; analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis; together, they give rise to the new realm of 'Single-Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single-Cell Data Science' for the coming years.

scholarly journals 12 Grand challenges in single-cell data science

2019 ◽  
Author(s):  
David Laehnemann ◽  
Johannes Köster ◽  
Ewa Szcureck ◽  
Davis McCarthy ◽  
Stephanie C Hicks ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Science ◽  
Single Cell Analysis ◽  
State Of The Art ◽  
Cell Analysis ◽  
Open Problems ◽  
Single Cell Sequencing ◽  
Current State ◽  
Cell Data

The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology; analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis; together, they give rise to the new realm of 'Single Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single Cell Data Science' for the coming years.

scholarly journals 12 Grand Challenges in Single-Cell Data Science

2019 ◽  
Author(s):  
David Laehnemann ◽  
Johannes Köster ◽  
Ewa Szczurek ◽  
Davis J McCarthy ◽  
Stephanie C Hicks ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Science ◽  
Single Cell Analysis ◽  
State Of The Art ◽  
Cell Analysis ◽  
Open Problems ◽  
Single Cell Sequencing ◽  
Current State ◽  
Cell Data

The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology; analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis; together, they give rise to the new realm of 'Single-Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single-Cell Data Science' for the coming years.

scholarly journals singlecellVR: interactive visualization of single-cell data in virtual reality

2020 ◽  
Author(s):  
David F. Stein ◽  
Huidong Chen ◽  
Michael E. Vinyard ◽  
Luca Pinello
Keyword(s):  
Virtual Reality ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Data Conversion ◽  
Cell Populations ◽  
Complex Data ◽  
Cell Analysis ◽  
Cell Assays ◽  
User Friendly ◽  
Cell Data

ABSTRACTSingle-cell assays have transformed our ability to model heterogeneity within cell populations and tissues. Virtual Reality (VR) has recently emerged as a powerful technology to dynamically explore complex data. However, expensive hardware or advanced data preprocessing skills are required to adapt such technology to single-cell data. To address current shortcomings, we present singlecellVR, a user-friendly website for visualizing single-cell data, designed for cheap and easily available virtual reality hardware (e.g., Google Cardboard, ∼$8). We provide a companion package, scvr to streamline data conversion from the most widely-adopted single-cell analysis tools and a database of pre-analyzed datasets to which users can contribute.

scholarly journals CoGAPS 3: Bayesian non-negative matrix factorization for single-cell analysis with asynchronous updates and sparse data structures

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Thomas D. Sherman ◽  
Tiger Gao ◽  
Elana J. Fertig
Keyword(s):  
Single Cell ◽  
Data Structures ◽  
Computational Efficiency ◽  
Matrix Factorization ◽  
Single Cell Analysis ◽  
Sparse Data ◽  
Data Sets ◽  
Cell Analysis ◽  
Gradient Based ◽  
Cell Data

Abstract Background Bayesian factorization methods, including Coordinated Gene Activity in Pattern Sets (CoGAPS), are emerging as powerful analysis tools for single cell data. However, these methods have greater computational costs than their gradient-based counterparts. These costs are often prohibitive for analysis of large single-cell datasets. Many such methods can be run in parallel which enables this limitation to be overcome by running on more powerful hardware. However, the constraints imposed by the prior distributions in CoGAPS limit the applicability of parallelization methods to enhance computational efficiency for single-cell analysis. Results We developed a new software framework for parallel matrix factorization in Version 3 of the CoGAPS R/Bioconductor package to overcome the computational limitations of Bayesian matrix factorization for single cell data analysis. This parallelization framework provides asynchronous updates for sequential updating steps of the algorithm to enhance computational efficiency. These algorithmic advances were coupled with new software architecture and sparse data structures to reduce the memory overhead for single-cell data. Conclusions Altogether our new software enhance the efficiency of the CoGAPS Bayesian matrix factorization algorithm so that it can analyze 1000 times more cells, enabling factorization of large single-cell data sets.

scholarly journals 12 Grand challenges in single-cell data science

2019 ◽  
Author(s):  
David Laehnemann ◽  
Johannes Köster ◽  
Ewa Szczurek ◽  
Davis J McCarthy ◽  
Stephanie C Hicks ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Science ◽  
Single Cell Analysis ◽  
State Of The Art ◽  
Cell Analysis ◽  
Open Problems ◽  
Single Cell Sequencing ◽  
Current State ◽  
Cell Data

The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology; analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis; together, they give rise to the new realm of 'Single Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single Cell Data Science' for the coming years.

Author response for "Immunocyte single cell analysis of vaccine‐induced narcolepsy"

2020 ◽  
Author(s):  
Alexander Lind ◽  
Falastin Salami ◽  
Anne‐Marie Landtblom ◽  
Lars Palm ◽  
Åke Lernmark ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Author Response ◽  
Cell Analysis
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