ShinySOM: graphical SOM-based analysis of single-cell cytometry data

Abstract Summary ShinySOM offers a user-friendly interface for reproducible, high-throughput analysis of high-dimensional flow and mass cytometry data guided by self-organizing maps. The software implements a FlowSOM-style workflow, with improvements in performance, visualizations and data dissection possibilities. The outputs of the analysis include precise statistical information about the dissected samples, and R-compatible metadata useful for the batch processing of large sample volumes. Availability and implementation ShinySOM is free and open-source, available online at gitlab.com/exaexa/ShinySOM. Supplementary information Supplementary data are available at Bioinformatics online.

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CyTOFmerge: integrating mass cytometry data across multiple panels

Bioinformatics ◽

10.1093/bioinformatics/btz180 ◽

2019 ◽

Vol 35 (20) ◽

pp. 4063-4071 ◽

Cited By ~ 3

Author(s):

Tamim Abdelaal ◽

Thomas Höllt ◽

Vincent van Unen ◽

Boudewijn P F Lelieveldt ◽

Frits Koning ◽

...

Keyword(s):

Single Cell ◽

Biological Sample ◽

Supplementary Information ◽

High Dimensional ◽

Single Cell Level ◽

Supplementary Data ◽

Mass Cytometry ◽

Cell Level ◽

Cellular Markers

Abstract Motivation High-dimensional mass cytometry (CyTOF) allows the simultaneous measurement of multiple cellular markers at single-cell level, providing a comprehensive view of cell compositions. However, the power of CyTOF to explore the full heterogeneity of a biological sample at the single-cell level is currently limited by the number of markers measured simultaneously on a single panel. Results To extend the number of markers per cell, we propose an in silico method to integrate CyTOF datasets measured using multiple panels that share a set of markers. Additionally, we present an approach to select the most informative markers from an existing CyTOF dataset to be used as a shared marker set between panels. We demonstrate the feasibility of our methods by evaluating the quality of clustering and neighborhood preservation of the integrated dataset, on two public CyTOF datasets. We illustrate that by computationally extending the number of markers we can further untangle the heterogeneity of mass cytometry data, including rare cell-population detection. Availability and implementation Implementation is available on GitHub (https://github.com/tabdelaal/CyTOFmerge). Supplementary information Supplementary data are available at Bioinformatics online.

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dittoSeq: universal user-friendly single-cell and bulk RNA sequencing visualization toolkit

Bioinformatics ◽

10.1093/bioinformatics/btaa1011 ◽

2020 ◽

Author(s):

Daniel G Bunis ◽

Jared Andrews ◽

Gabriela K Fragiadakis ◽

Trevor D Burt ◽

Marina Sirota

Keyword(s):

Single Cell ◽

R Package ◽

Color Blindness ◽

Ease Of Use ◽

Supplementary Information ◽

Supplementary Data ◽

Rnaseq Data ◽

Visualization Toolkit ◽

User Friendly ◽

Publication Quality

Abstract Summary A visualization suite for major forms of bulk and single-cell RNAseq data in R. dittoSeq is color blindness-friendly by default, robustly documented to power ease-of-use and allows highly customizable generation of both daily-use and publication-quality figures. Availability and implementation dittoSeq is an R package available through Bioconductor via an open source MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

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CytoBackBone: an algorithm for merging of phenotypic information from different cytometric profiles

Bioinformatics ◽

10.1093/bioinformatics/btz212 ◽

2019 ◽

Vol 35 (20) ◽

pp. 4187-4189 ◽

Cited By ~ 3

Author(s):

Adrien Leite Pereira ◽

Olivier Lambotte ◽

Roger Le Grand ◽

Antonio Cosma ◽

Nicolas Tchitchek

Keyword(s):

Flow Cytometry ◽

Single Cell ◽

Nearest Neighbor ◽

Source Code ◽

Nearest Neighbors ◽

Supplementary Information ◽

Supplementary Data ◽

Mass Cytometry ◽

Phenotypic Information ◽

Nearest Neighbor Imputation

Abstract Motivation Flow and mass cytometry are experimental techniques used to measure the level of proteins expressed by cells at the single-cell resolution. Several algorithms were developed in flow cytometry to increase the number of simultaneously measurable markers. These approaches aim to combine phenotypic information of different cytometric profiles obtained from different cytometry panels. Results We present here a new algorithm, called CytoBackBone, which can merge phenotypic information from different cytometric profiles. This algorithm is based on nearest-neighbor imputation, but introduces the notion of acceptable and non-ambiguous nearest neighbors. We used mass cytometry data to illustrate the merging of cytometric profiles obtained by the CytoBackBone algorithm. Availability and implementation CytoBackBone is implemented in R and the source code is available at https://github.com/tchitchek-lab/CytoBackBone. Supplementary information Supplementary data are available at Bioinformatics online.

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MyelinJ: an ImageJ macro for high throughput analysis of myelinating cultures

Bioinformatics ◽

10.1093/bioinformatics/btz403 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4528-4530 ◽

Cited By ~ 1

Author(s):

Michael J Whitehead ◽

George A McCanney ◽

Hugh J Willison ◽

Susan C Barnett

Keyword(s):

Statistical Analysis ◽

High Throughput ◽

Supplementary Information ◽

Local Contrast ◽

High Throughput Analysis ◽

Throughput Analysis ◽

Neurite Density ◽

Supplementary Material ◽

User Friendly

Abstract Summary MyelinJ is a free user friendly ImageJ macro for high throughput analysis of fluorescent micrographs such as 2D-myelinating cultures and statistical analysis using R. MyelinJ can analyse single images or complex experiments with multiple conditions, where the ggpubr package in R is automatically used for statistical analysis and the production of publication quality graphs. The main outputs are percentage (%) neurite density and % myelination. % neurite density is calculated using the normalize local contrast algorithm, followed by thresholding, to adjust for differences in intensity. For % myelination the myelin sheaths are selected using the Frangi vesselness algorithm, in conjunction with a grey scale morphology filter and the removal of cell bodies using a high intensity mask. MyelinJ uses a simple graphical user interface and user name system for reproducibility and sharing that will be useful to the wider scientific community that study 2D-myelination in vitro. Availability and implementation MyelinJ is freely available at https://github.com/BarnettLab/MyelinJ. For statistical analysis the freely available R and the ggpubr package are also required. MyelinJ has a user guide (Supplementary Material) and has been tested on both Windows (Windows 10) and Mac (High Sierra) operating systems. Supplementary information Supplementary data are available at Bioinformatics online.

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VeriNA3d: an R package for nucleic acids data mining

Bioinformatics ◽

10.1093/bioinformatics/btz553 ◽

2019 ◽

Vol 35 (24) ◽

pp. 5334-5336 ◽

Cited By ~ 1

Author(s):

Diego Gallego ◽

Leonardo Darré ◽

Pablo D Dans ◽

Modesto Orozco

Keyword(s):

Data Mining ◽

Nucleic Acids ◽

High Throughput ◽

Structural Data ◽

R Package ◽

Supplementary Information ◽

Supplementary Data ◽

Rna Structures ◽

High Throughput Analysis ◽

Single Structure

Abstract Summary veriNA3d is an R package for the analysis of nucleic acids structural data, with an emphasis in complex RNA structures. In addition to single-structure analyses, veriNA3d also implements functions to handle whole datasets of mmCIF/PDB structures that could be retrieved from public/local repositories. Our package aims to fill a gap in the data mining of nucleic acids structures to produce flexible and high throughput analysis of structural databases. Availability and implementation http://mmb.irbbarcelona.org/gitlab/dgallego/veriNA3d. Supplementary information Supplementary data are available at Bioinformatics online.

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Correction to: High-dimensional single cell mass cytometry analysis of the murine hematopoietic system reveals signatures induced by ageing and physiological pathogen challenges

Immunity & Ageing ◽

10.1186/s12979-021-00235-y ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Christos Nikolaou ◽

Kerstin Muehle ◽

Stephan Schlickeiser ◽

Alberto Sada Japp ◽

Nadine Matzmohr ◽

...

Keyword(s):

Single Cell ◽

Cell Mass ◽

High Dimensional ◽

Mass Cytometry ◽

Hematopoietic System

An amendment to this paper has been published and can be accessed via the original article.

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High-Throughput Analysis of Selected Urinary Hydroxy Polycyclic Aromatic Hydrocarbons by an Innovative Automated Solid-Phase Microextraction

Molecules ◽

10.3390/molecules23081869 ◽

2018 ◽

Vol 23 (8) ◽

pp. 1869 ◽

Cited By ~ 9

Author(s):

Stefano Dugheri ◽

Alessandro Bonari ◽

Matteo Gentili ◽

Giovanni Cappelli ◽

Ilenia Pompilio ◽

...

Keyword(s):

Polycyclic Aromatic Hydrocarbons ◽

High Throughput ◽

Aromatic Hydrocarbons ◽

High Throughput Screening ◽

Solid Phase Microextraction ◽

Solid Phase ◽

User Friendliness ◽

High Throughput Analysis ◽

Polycyclic Aromatic ◽

User Friendly

High-throughput screening of samples is the strategy of choice to detect occupational exposure biomarkers, yet it requires a user-friendly apparatus that gives relatively prompt results while ensuring high degrees of selectivity, precision, accuracy and automation, particularly in the preparation process. Miniaturization has attracted much attention in analytical chemistry and has driven solvent and sample savings as easier automation, the latter thanks to the introduction on the market of the three axis autosampler. In light of the above, this contribution describes a novel user-friendly solid-phase microextraction (SPME) off- and on-line platform coupled with gas chromatography and triple quadrupole-mass spectrometry to determine urinary metabolites of polycyclic aromatic hydrocarbons 1- and 2-hydroxy-naphthalene, 9-hydroxy-phenanthrene, 1-hydroxy-pyrene, 3- and 9-hydroxy-benzoantracene, and 3-hydroxy-benzo[a]pyrene. In this new procedure, chromatography’s sensitivity is combined with the user-friendliness of N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide on-fiber SPME derivatization using direct immersion sampling; moreover, specific isotope-labelled internal standards provide quantitative accuracy. The detection limits for the seven OH-PAHs ranged from 0.25 to 4.52 ng/L. Intra-(from 2.5 to 3.0%) and inter-session (from 2.4 to 3.9%) repeatability was also evaluated. This method serves to identify suitable risk-control strategies for occupational hygiene conservation programs.

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High-throughput and high-dimensional single-cell analysis of antigen-specific CD8+ T cells

Nature Immunology ◽

10.1038/s41590-021-01073-2 ◽

2021 ◽

Author(s):

Ke-Yue Ma ◽

Alexandra A. Schonnesen ◽

Chenfeng He ◽

Amanda Y. Xia ◽

Eric Sun ◽

...

Keyword(s):

T Cells ◽

Single Cell ◽

High Throughput ◽

Cd8 T Cells ◽

Single Cell Analysis ◽

High Dimensional ◽

Cell Analysis

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

10.1101/173997 ◽

2017 ◽

Cited By ~ 1

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.

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