253 related articles for article (PubMed ID: 33840138)
1. Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre.
Ashhurst TM; Marsh-Wakefield F; Putri GH; Spiteri AG; Shinko D; Read MN; Smith AL; King NJC
Cytometry A; 2022 Mar; 101(3):237-253. PubMed ID: 33840138
[TBL] [Abstract][Full Text] [Related]
2. High-Dimensional Data Analysis Algorithms Yield Comparable Results for Mass Cytometry and Spectral Flow Cytometry Data.
Ferrer-Font L; Mayer JU; Old S; Hermans IF; Irish J; Price KM
Cytometry A; 2020 Aug; 97(8):824-831. PubMed ID: 32293794
[TBL] [Abstract][Full Text] [Related]
3. Multibatch Cytometry Data Integration for Optimal Immunophenotyping.
Ogishi M; Yang R; Gruber C; Zhang P; Pelham SJ; Spaan AN; Rosain J; Chbihi M; Han JE; Rao VK; Kainulainen L; Bustamante J; Boisson B; Bogunovic D; Boisson-Dupuis S; Casanova JL
J Immunol; 2021 Jan; 206(1):206-213. PubMed ID: 33229441
[TBL] [Abstract][Full Text] [Related]
4. Efficient cytometry analysis with FlowSOM in Python boosts interoperability with other single-cell tools.
Couckuyt A; Rombaut B; Saeys Y; Van Gassen S
Bioinformatics; 2024 Mar; 40(4):. PubMed ID: 38632080
[TBL] [Abstract][Full Text] [Related]
5. ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data.
Sidhom JW; Theodros D; Murter B; Zarif JC; Ganguly S; Pardoll DM; Baras A
J Vis Exp; 2019 Jan; (143):. PubMed ID: 30735162
[TBL] [Abstract][Full Text] [Related]
6. Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data.
Weber LM; Robinson MD
Cytometry A; 2016 Dec; 89(12):1084-1096. PubMed ID: 27992111
[TBL] [Abstract][Full Text] [Related]
7. diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering.
Weber LM; Nowicka M; Soneson C; Robinson MD
Commun Biol; 2019; 2():183. PubMed ID: 31098416
[TBL] [Abstract][Full Text] [Related]
8. SuperCellCyto: enabling efficient analysis of large scale cytometry datasets.
Putri GH; Howitt G; Marsh-Wakefield F; Ashhurst TM; Phipson B
Genome Biol; 2024 Apr; 25(1):89. PubMed ID: 38589921
[TBL] [Abstract][Full Text] [Related]
9. Analyzing high-dimensional cytometry data using FlowSOM.
Quintelier K; Couckuyt A; Emmaneel A; Aerts J; Saeys Y; Van Gassen S
Nat Protoc; 2021 Aug; 16(8):3775-3801. PubMed ID: 34172973
[TBL] [Abstract][Full Text] [Related]
10.
Opzoomer JW; Timms JA; Blighe K; Mourikis TP; Chapuis N; Bekoe R; Kareemaghay S; Nocerino P; Apollonio B; Ramsay AG; Tavassoli M; Harrison C; Ciccarelli F; Parker P; Fontenay M; Barber PR; Arnold JN; Kordasti S
Elife; 2021 Apr; 10():. PubMed ID: 33929322
[TBL] [Abstract][Full Text] [Related]
11. OPTIMAL: An OPTimized Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration.
Hunter B; Nicorescu I; Foster E; McDonald D; Hulme G; Fuller A; Thomson A; Goldsborough T; Hilkens CMU; Majo J; Milross L; Fisher A; Bankhead P; Wills J; Rees P; Filby A; Merces G
Cytometry A; 2024 Jan; 105(1):36-53. PubMed ID: 37750225
[TBL] [Abstract][Full Text] [Related]
12. PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells.
Stassen SV; Siu DMD; Lee KCM; Ho JWK; So HKH; Tsia KK
Bioinformatics; 2020 May; 36(9):2778-2786. PubMed ID: 31971583
[TBL] [Abstract][Full Text] [Related]
13. High-Dimensional Immune Monitoring for Chimeric Antigen Receptor T Cell Therapies.
Sharma S; Quinn D; Melenhorst JJ; Pruteanu-Malinici I
Curr Hematol Malig Rep; 2021 Feb; 16(1):112-116. PubMed ID: 33449291
[TBL] [Abstract][Full Text] [Related]
14. SPECTRE: a suite of phylogenetic tools for reticulate evolution.
Bastkowski S; Mapleson D; Spillner A; Wu T; Balvociute M; Moulton V
Bioinformatics; 2018 Mar; 34(6):1056-1057. PubMed ID: 29186450
[TBL] [Abstract][Full Text] [Related]
15. A Guide on Analyzing Flow Cytometry Data Using Clustering Methods and Nonlinear Dimensionality Reduction (tSNE or UMAP).
Ujas TA; Obregon-Perko V; Stowe AM
Methods Mol Biol; 2023; 2616():231-249. PubMed ID: 36715939
[TBL] [Abstract][Full Text] [Related]
16. FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data.
Van Gassen S; Callebaut B; Van Helden MJ; Lambrecht BN; Demeester P; Dhaene T; Saeys Y
Cytometry A; 2015 Jul; 87(7):636-45. PubMed ID: 25573116
[TBL] [Abstract][Full Text] [Related]
17. SPADEVizR: an R package for visualization, analysis and integration of SPADE results.
Gautreau G; Pejoski D; Le Grand R; Cosma A; Beignon AS; Tchitchek N
Bioinformatics; 2017 Mar; 33(5):779-781. PubMed ID: 27993789
[TBL] [Abstract][Full Text] [Related]
18. SCHNEL: scalable clustering of high dimensional single-cell data.
Abdelaal T; de Raadt P; Lelieveldt BPF; Reinders MJT; Mahfouz A
Bioinformatics; 2020 Dec; 36(Suppl_2):i849-i856. PubMed ID: 33381821
[TBL] [Abstract][Full Text] [Related]
19. Ultrafast clustering of single-cell flow cytometry data using FlowGrid.
Ye X; Ho JWK
BMC Syst Biol; 2019 Apr; 13(Suppl 2):35. PubMed ID: 30953498
[TBL] [Abstract][Full Text] [Related]
20. A cross entropy test allows quantitative statistical comparison of t-SNE and UMAP representations.
Roca CP; Burton OT; Neumann J; Tareen S; Whyte CE; Gergelits V; Veiga RV; Humblet-Baron S; Liston A
Cell Rep Methods; 2023 Jan; 3(1):100390. PubMed ID: 36814837
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]