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Protocols

ACCENSE specifications

Information


Unique identifier OMICS_10529
Name ACCENSE
Alternative name Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding
Software type Toolkit/Suite
Interface Graphical user interface
Restrictions to use Academic or non-commercial use
Input data FCS or CSV
Output data CSV and figure file
Operating system Mac OS, Windows
Programming languages Python
License GNU General Public License version 2.0
Computer skills Medium
Version 0.4.3
Stability Stable
Maintained Yes

Versioning


No version available

Documentation


Maintainer


  • person_outline yang.chen

Publication for Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding

ACCENSE citations

 (6)
library_books

Limited immune surveillance in lymphoid tissue by cytolytic CD4+ T cells during health and HIV disease

2018
PLoS Pathog
PMCID: 5919077
PMID: 29652923
DOI: 10.1371/journal.ppat.1006973

[…] metric tests were conducted based on normal distribution of the data points (Shapiro-Wilk normality test). All analyses were performed using R studio, Graph Pad Prism v7.0 (GraphPad). FlowJo and Cell ACCENSE (Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding) analyses were used to conduct multivariate tSNE analysis on the single-cell flow data sets []. […]

call_split

HIV Specific CD8+ T Cells Exhibit Reduced and Differentially Regulated Cytolytic Activity in Lymphoid Tissue

2017
Cell Rep
PMCID: 5764192
PMID: 29262326
DOI: 10.1016/j.celrep.2017.11.075
call_split See protocol

[…] er of 8. Cell division “bins” were then applied to the CFSE dilution of each individual marker of interest in order to define modulation of specific markers with respect to cell division history.Cell ACCENSE (automatic classification of cellular expression by nonlinear stochastic embedding) analyses were performed to visualize phenotypic relationships within multivariate data obtained from resting […]

library_books

Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types

2017
Nat Commun
PMCID: 5700955
PMID: 29170529
DOI: 10.1038/s41467-017-01689-9

[…] xplore the data at lower (more detailed) levels.Next, we utilized Cytosplore+HSNE to analyze the complete dataset on 5.2 million cells, thus including the cells that were discarded in the SPADE-t-SNE-ACCENSE pipeline. The embeddings show by color coding that subsets of the same immune lineage clustered at all three levels (Fig. ). More interestingly, the cells removed during downsampling (shown in […]

library_books

Mass Cytometry Identifies Distinct Lung CD4+ T Cell Patterns in Löfgren’s Syndrome and Non Löfgren’s Syndrome Sarcoidosis

2017
Front Immunol
PMCID: 5601005
PMID: 28955342
DOI: 10.3389/fimmu.2017.01130

[…] n and study design: YK, AA, and JG. Sample collection: YK and AE. Mass cytometry panel development, optimization, and implementation: TL, JM, and PB. Data pre-processing and cluster analysis (Citrus, ACCENSE): YC and PB. Data visualization (t-SNE): YK. Interpretation and assessment of clinical parameters: YK, AA, and JG. Manuscript preparation: YK, AA, and JG. Critical reading and intellectual ass […]

library_books

Combining Flow and Mass Cytometry in the Search for Biomarkers in Chronic Graft versus Host Disease

2017
Front Immunol
PMCID: 5474470
PMID: 28674539
DOI: 10.3389/fimmu.2017.00717

[…] are () (http://www.cellaccense.com). Small adjustments to the Citrus software code were made to allow the export of single-cell data for calculation of cluster sizes and plotting within tSNE-maps for ACCENSE.Univariate statistical analysis was done with the Kruskal–Wallis test (KW), Mann–Whitney U test (MW), Pearson’s χ2 test (χ2), and Fisher’s exact test (FE) using IBM SPSS Statistics 23 (IBM, Ar […]

library_books

Mass cytometry as a platform for the discovery of cellular biomarkers to guide effective rheumatic disease therapy

2015
PMCID: 4436107
PMID: 25981462
DOI: 10.1186/s13075-015-0644-z

[…] Two t-distributed stochastic neighbor embedding (tSNE)-based algorithms are available to visualize high-dimensional single-cell data; namely, viSNE and ACCENSE [,]. tSNE is a non-linear dimensionality reduction approach to visualize CyTOF data. viSNE and ACCENSE generate two-dimensional maps, similar to a biaxial scatter plot, that reflect the proxim […]


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ACCENSE institution(s)
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

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