217 related articles for article (PubMed ID: 30176185)
1. Automatically generate two-dimensional gating hierarchy from clustered cytometry data.
Yang X; Qiu P
Cytometry A; 2018 Oct; 93(10):1039-1050. PubMed ID: 30176185
[TBL] [Abstract][Full Text] [Related]
2. Toward deterministic and semiautomated SPADE analysis.
Qiu P
Cytometry A; 2017 Mar; 91(3):281-289. PubMed ID: 28234411
[TBL] [Abstract][Full Text] [Related]
3. Unfold High-Dimensional Clouds for Exhaustive Gating of Flow Cytometry Data.
Qiu P
IEEE/ACM Trans Comput Biol Bioinform; 2014; 11(6):1045-51. PubMed ID: 26357042
[TBL] [Abstract][Full Text] [Related]
4. DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data.
Lee AJ; Chang I; Burel JG; Lindestam Arlehamn CS; Mandava A; Weiskopf D; Peters B; Sette A; Scheuermann RH; Qian Y
Cytometry A; 2018 Jun; 93(6):597-610. PubMed ID: 29665244
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. A computational approach for phenotypic comparisons of cell populations in high-dimensional cytometry data.
Platon L; Pejoski D; Gautreau G; Targat B; Le Grand R; Beignon AS; Tchitchek N
Methods; 2018 Jan; 132():66-75. PubMed ID: 28917725
[TBL] [Abstract][Full Text] [Related]
7. immunoClust--An automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets.
Sörensen T; Baumgart S; Durek P; Grützkau A; Häupl T
Cytometry A; 2015 Jul; 87(7):603-15. PubMed ID: 25850678
[TBL] [Abstract][Full Text] [Related]
8. Cluster stability in the analysis of mass cytometry data.
Melchiotti R; Gracio F; Kordasti S; Todd AK; de Rinaldis E
Cytometry A; 2017 Jan; 91(1):73-84. PubMed ID: 27754590
[TBL] [Abstract][Full Text] [Related]
9. Analysis of Mass Cytometry Data.
Pedersen CB; Olsen LR
Methods Mol Biol; 2019; 1989():267-279. PubMed ID: 31077111
[TBL] [Abstract][Full Text] [Related]
10. Identifying Cell Populations in Flow Cytometry Data Using Phenotypic Signatures.
Pouyan MB; Nourani M
IEEE/ACM Trans Comput Biol Bioinform; 2017; 14(4):880-891. PubMed ID: 27076456
[TBL] [Abstract][Full Text] [Related]
11. Validation of a hybrid approach to standardize immunophenotyping analysis in large population studies: The Health and Retirement Study.
Hunter-Schlichting D; Lane J; Cole B; Flaten Z; Barcelo H; Ramasubramanian R; Cassidy E; Faul J; Crimmins E; Pankratz N; Thyagarajan B
Sci Rep; 2020 May; 10(1):8759. PubMed ID: 32472068
[TBL] [Abstract][Full Text] [Related]
12. Elucidation of seventeen human peripheral blood B-cell subsets and quantification of the tetanus response using a density-based method for the automated identification of cell populations in multidimensional flow cytometry data.
Qian Y; Wei C; Eun-Hyung Lee F; Campbell J; Halliley J; Lee JA; Cai J; Kong YM; Sadat E; Thomson E; Dunn P; Seegmiller AC; Karandikar NJ; Tipton CM; Mosmann T; Sanz I; Scheuermann RH
Cytometry B Clin Cytom; 2010; 78 Suppl 1(Suppl 1):S69-82. PubMed ID: 20839340
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. The end of gating? An introduction to automated analysis of high dimensional cytometry data.
Mair F; Hartmann FJ; Mrdjen D; Tosevski V; Krieg C; Becher B
Eur J Immunol; 2016 Jan; 46(1):34-43. PubMed ID: 26548301
[TBL] [Abstract][Full Text] [Related]
15. Misty Mountain clustering: application to fast unsupervised flow cytometry gating.
Sugár IP; Sealfon SC
BMC Bioinformatics; 2010 Oct; 11():502. PubMed ID: 20932336
[TBL] [Abstract][Full Text] [Related]
16. DGCyTOF: Deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data.
Cheng L; Karkhanis P; Gokbag B; Liu Y; Li L
PLoS Comput Biol; 2022 Apr; 18(4):e1008885. PubMed ID: 35404970
[TBL] [Abstract][Full Text] [Related]
17. Automated gating of flow cytometry data via robust model-based clustering.
Lo K; Brinkman RR; Gottardo R
Cytometry A; 2008 Apr; 73(4):321-32. PubMed ID: 18307272
[TBL] [Abstract][Full Text] [Related]
18. cytometree: A binary tree algorithm for automatic gating in cytometry analysis.
Commenges D; Alkhassim C; Gottardo R; Hejblum B; Thiébaut R
Cytometry A; 2018 Nov; 93(11):1132-1140. PubMed ID: 30277649
[TBL] [Abstract][Full Text] [Related]
19. flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification.
Malek M; Taghiyar MJ; Chong L; Finak G; Gottardo R; Brinkman RR
Bioinformatics; 2015 Feb; 31(4):606-7. PubMed ID: 25378466
[TBL] [Abstract][Full Text] [Related]
20. NetFCM: a semi-automated web-based method for flow cytometry data analysis.
Frederiksen J; Buggert M; Karlsson AC; Lund O
Cytometry A; 2014 Nov; 85(11):969-77. PubMed ID: 25044796
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
