scholarly journals Single-Cell Virtual Cytometer allows user-friendly and versatile analysis and visualization of multimodal single cell RNAseq datasets

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Frédéric Pont ◽  
Marie Tosolini ◽  
Qing Gao ◽  
Marion Perrier ◽  
Miguel Madrid-Mencía ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
Open Source ◽  
Open Source Software ◽  
Healthy Donor ◽  
Large Datasets ◽  
Integrated Analysis ◽  
Functional Maturation ◽  
User Friendly ◽  
Cell Data

Abstract The development of single-cell transcriptomic technologies yields large datasets comprising multimodal informations, such as transcriptomes and immunophenotypes. Despite the current explosion of methods for pre-processing and integrating multimodal single-cell data, there is currently no user-friendly software to display easily and simultaneously both immunophenotype and transcriptome-based UMAP/t-SNE plots from the pre-processed data. Here, we introduce Single-Cell Virtual Cytometer, an open-source software for flow cytometry-like visualization and exploration of pre-processed multi-omics single cell datasets. Using an original CITE-seq dataset of PBMC from an healthy donor, we illustrate its use for the integrated analysis of transcriptomes and epitopes of functional maturation in human peripheral T lymphocytes. So this free and open-source algorithm constitutes a unique resource for biologists seeking for a user-friendly analytic tool for multimodal single cell datasets.

scholarly journals Single-Cell Virtual Cytometer allows user-friendly and versatile analysis and visualization of multimodal single cell RNAseq datasets

2019 ◽  
Author(s):  
Frédéric Pont ◽  
Marie Tosolini ◽  
Qing Gao ◽  
Marion Perrier ◽  
Miguel Madrid-Mencía ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
Open Source ◽  
Open Source Software ◽  
Healthy Donor ◽  
Large Datasets ◽  
Integrated Analysis ◽  
Functional Maturation ◽  
Healthy Donors ◽  
User Friendly

ABSTRACTThe development of single cell transcriptomic technologies yields large datasets comprising multimodal informations such as transcriptomes and immunophenotypes. Currently however, there is no software to easily and simultaneously analyze both types of data. Here, we introduce Single-Cell Virtual Cytometer, an open-source software for flow cytometry-like visualization and exploration of multi-omics single cell datasets. Using an original CITE-seq dataset of PBMC from an healthy donor, we illustrate its use for the integrated analysis of transcriptomes and phenotypes of functional maturation in peripheral T lymphocytes from healthy donors. So this free and open-source algorithm constitutes a unique resource for biologists seeking for a user-friendly analytic tool for multimodal single cell datasets.

scholarly journals FlowCal: A User-Friendly, Open Source Software Tool for Automatically Converting Flow Cytometry Data from Arbitrary to Calibrated Units

2016 ◽  
Vol 5 (7) ◽  
pp. 774-780 ◽  
Author(s):  
Sebastian M. Castillo-Hair ◽  
John T. Sexton ◽  
Brian P. Landry ◽  
Evan J. Olson ◽  
Oleg A. Igoshin ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Open Source ◽  
Open Source Software ◽  
Software Tool ◽  
Flow Cytometry Data ◽  
User Friendly

scholarly journals Integrated analysis of multimodal single-cell data

Cell ◽  
2021 ◽  
Author(s):  
Yuhan Hao ◽  
Stephanie Hao ◽  
Erica Andersen-Nissen ◽  
William M. Mauck ◽  
Shiwei Zheng ◽  
...  
Keyword(s):  
Single Cell ◽  
Integrated Analysis ◽  
Cell Data

scholarly journals Integrated open-source software for multiscale electrophysiology

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Konstantinos Nasiotis ◽  
Martin Cousineau ◽  
François Tadel ◽  
Adrien Peyrache ◽  
Richard M. Leahy ◽  
...  
Keyword(s):  
Data Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Analysis Data ◽  
Open Science ◽  
Dense Array ◽  
Unrestricted Access ◽  
Research Transparency ◽  
Programming Skills ◽  
User Friendly

Abstract The methods for electrophysiology in neuroscience have evolved tremendously over the recent years with a growing emphasis on dense-array signal recordings. Such increased complexity and augmented wealth in the volume of data recorded, have not been accompanied by efforts to streamline and facilitate access to processing methods, which too are susceptible to grow in sophistication. Moreover, unsuccessful attempts to reproduce peer-reviewed publications indicate a problem of transparency in science. This growing problem could be tackled by unrestricted access to methods that promote research transparency and data sharing, ensuring the reproducibility of published results. Here, we provide a free, extensive, open-source software that provides data-analysis, data-management and multi-modality integration solutions for invasive neurophysiology. Users can perform their entire analysis through a user-friendly environment without the need of programming skills, in a tractable (logged) way. This work contributes to open-science, analysis standardization, transparency and reproducibility in invasive neurophysiology.

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 Open-source software for mouse-tracking in Qualtrics to measure category competition

2018 ◽  
Author(s):  
Maya B Mathur ◽  
David Reichling
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Real Time ◽  
Cognitive Processes ◽  
Open Source Software ◽  
Online Survey ◽  
Mouse Tracking ◽  
Crowdsourced Data ◽  
Programming Skills ◽  
User Friendly

Mouse-tracking is a sophisticated tool for measuring rapid, dynamic cognitive processes in real time, particularly in experiments investigating competition between perceptual or cognitive categories. We provide user-friendly, open-source software (https://osf.io/st2ef/) for designing and analyzing such experiments online using the Qualtrics survey platform. The software consists of a Qualtrics template with embedded Javascript and CSS along with R code to clean, parse, and analyze the data. No special programming skills are required to use this software. As we discuss, this software could be readily modified for use with other online survey platforms that allow the addition of custom Javascript. We empirically validate the provided software by benchmarking its performance on previously tested stimuli in a standard category-competition experiment with realistic crowdsourced data collection.

Traffic Simulation with Open-Source and Commercial Traffic Microsimulators: A Case Study

2022 ◽  
Vol 24 (2) ◽  
pp. E49-E62
Author(s):  
Nelson Baza-Solares ◽  
Ruben Velasquez-Martínez ◽  
Cristian Torres-Bohórquez ◽  
Yerly Martínez-Estupiñán ◽  
Cristian Poliziani
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Transport Modeling ◽  
Graphical Interface ◽  
Urban Centers ◽  
Output Data ◽  
Computational Tools ◽  
Input And Output ◽  
User Friendly

The analysis of traffic problems in large urban centers often requires the use of computational tools, which give the possibility to make a more detailed analysis of the issue, suggest solutions, predict behaviors and, above all, support efficient decision-making. Transport microsimulation software programs are a handy set of tools for this type of analysis. This research paper shows a case study where functions and limitations of Aimsun version 8.2.0, a commercial-like European software and Sumo version 1.3.1, a European open-source software, are presented. The input and output data are similar in both software and the interpretation of results is quite intuitive for both, as well. However, Aimsun's graphical interface interprets results more user-friendly, because Sumo is an open-access software presented as an effective alternative tool for transport modeling.

scholarly journals New Capabilities of EvoBot: A Modular, Open-Source Liquid-Handling Robot

2017 ◽  
Vol 22 (5) ◽  
pp. 500-506 ◽  
Author(s):  
Farzad Nejatimoharrami ◽  
Andres Faina ◽  
Kasper Stoy
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Low Cost ◽  
Integrated Analysis ◽  
Experiment Data ◽  
Time Interaction ◽  
Modeling Software ◽  
Offline Processing ◽  
Liquid Handling Robot

We introduce a robot developed to perform feedback-based experiments, such as droplet experiments, a common type of experiments in artificial chemical life research. These experiments are particularly well suited for automation because they often stretch over long periods of time, possibly hours, and often require that the human takes action in response to observed events such as changes in droplet size, count, shape, or clustering or declustering of multiple droplets. Our robot is designed to monitor long-term experiments and, based on the feedback from the experiment, interact with it. The combination of precise automation, accurately collected experiment data, and integrated analysis and modeling software makes real-time interaction with the experiment feasible, as opposed to traditional offline processing of experiments. Last but not least, we believe the low cost of our platform can promote artificial life research. Furthermore, prevalently, findings from an experiment will inspire redesign for novel experiments. In addition, the robot’s open-source software enables easy modification of experiments. We will cover two case studies for application of our robot in feedback-based experiments and demonstrate how our robot can not only automate these experiments, collect data, and interact with the experiments intelligently but also enable chemists to perform formerly infeasible experiments.

scholarly journals Fast searches of large collections of single cell data using scfind

2019 ◽  
Author(s):  
Jimmy Tsz Hang Lee ◽  
Nikolaos Patikas ◽  
Vladimir Yu Kiselev ◽  
Martin Hemberg
Keyword(s):  
Single Cell ◽  
Language Processing ◽  
Housekeeping Genes ◽  
Mouse Cell ◽  
Marker Genes ◽  
Transcriptome Data ◽  
Optimization Routine ◽  
Cell Type Specific ◽  
User Friendly ◽  
Cell Data

Single cell technologies have made it possible to profile millions of cells, but for these resources to be useful they must be easy to query and access. To facilitate interactive and intuitive access to single cell data we have developed scfind, a search engine for cell atlases. Using transcriptome data from mouse cell atlases we show how scfind can be used to evaluate marker genes, to perform in silico gating, and to identify both cell-type specific and housekeeping genes. Moreover, we have developed a subquery optimization routine to ensure that long and complex queries return meaningful results. To make scfind more user friendly and accessible, we use indices of PubMed abstracts and techniques from natural language processing to allow for arbitrary queries. Finally, we show how scfind can be used for multi-omics analyses by combining single-cell ATAC-seq data with transcriptome data.

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