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Items: 1 to 20 of 105

1.

Automated subset identification and characterization pipeline for multidimensional flow and mass cytometry data clustering and visualization.

Meehan S, Kolyagin GA, Parks D, Youngyunpipatkul J, Herzenberg LA, Walther G, Ghosn EEB, Orlova DY.

Commun Biol. 2019 Jun 20;2(1):229. doi: 10.1038/s42003-019-0467-6.

PMID:
31925064
2.

Automated subset identification and characterization pipeline for multidimensional flow and mass cytometry data clustering and visualization.

Meehan S, Kolyagin GA, Parks D, Youngyunpipatkul J, Herzenberg LA, Walther G, Ghosn EEB, Orlova DY.

Commun Biol. 2019 Jun 20;2:229. doi: 10.1038/s42003-019-0467-6. eCollection 2019.

3.

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. doi: 10.1002/cyto.a.23371. Epub 2018 Apr 17.

4.

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. doi: 10.1002/cyto.a.22626. Epub 2015 Apr 7.

5.

A multistage mathematical approach to automated clustering of high-dimensional noisy data.

Friedman A, Keselman MD, Gibb LG, Graybiel AM.

Proc Natl Acad Sci U S A. 2015 Apr 7;112(14):4477-82. doi: 10.1073/pnas.1503940112. Epub 2015 Mar 23.

6.

Misty Mountain clustering: application to fast unsupervised flow cytometry gating.

Sugár IP, Sealfon SC.

BMC Bioinformatics. 2010 Oct 9;11:502. doi: 10.1186/1471-2105-11-502.

7.

Feature-guided clustering of multi-dimensional flow cytometry datasets.

Zeng QT, Pratt JP, Pak J, Ravnic D, Huss H, Mentzer SJ.

J Biomed Inform. 2007 Jun;40(3):325-31. Epub 2006 Jun 27.

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. doi: 10.1002/cyto.a.23001. Epub 2016 Oct 18.

9.

caBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data.

Zhu Y, Li H, Miller DJ, Wang Z, Xuan J, Clarke R, Hoffman EP, Wang Y.

BMC Bioinformatics. 2008 Sep 18;9:383. doi: 10.1186/1471-2105-9-383.

10.
11.

A theorem proving approach for automatically synthesizing visualizations of flow cytometry data.

Raj S, Hussain F, Husein Z, Torosdagli N, Turgut D, Deo N, Pattanaik S, Chang CJ, Jha SK.

BMC Bioinformatics. 2017 Jun 7;18(Suppl 8):245. doi: 10.1186/s12859-017-1662-4.

12.

Automatically generate two-dimensional gating hierarchy from clustered cytometry data.

Yang X, Qiu P.

Cytometry A. 2018 Oct;93(10):1039-1050. doi: 10.1002/cyto.a.23577. Epub 2018 Sep 3.

13.

SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 2: biological evaluation.

Mosmann TR, Naim I, Rebhahn J, Datta S, Cavenaugh JS, Weaver JM, Sharma G.

Cytometry A. 2014 May;85(5):422-33. doi: 10.1002/cyto.a.22445. Epub 2014 Feb 14.

14.

Visual MRI: merging information visualization and non-parametric clustering techniques for MRI dataset analysis.

Castellani U, Cristani M, Combi C, Murino V, Sbarbati A, Marzola P.

Artif Intell Med. 2008 Nov;44(3):183-99. doi: 10.1016/j.artmed.2008.06.006. Epub 2008 Sep 4.

PMID:
18775655
15.

SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 1: algorithm design.

Naim I, Datta S, Rebhahn J, Cavenaugh JS, Mosmann TR, Sharma G.

Cytometry A. 2014 May;85(5):408-21. doi: 10.1002/cyto.a.22446. Epub 2014 Feb 14.

16.

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:S69-82. doi: 10.1002/cyto.b.20554.

17.

Visualization methods for statistical analysis of microarray clusters.

Hibbs MA, Dirksen NC, Li K, Troyanskaya OG.

BMC Bioinformatics. 2005 May 12;6:115.

18.

OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis.

Finak G, Frelinger J, Jiang W, Newell EW, Ramey J, Davis MM, Kalams SA, De Rosa SC, Gottardo R.

PLoS Comput Biol. 2014 Aug 28;10(8):e1003806. doi: 10.1371/journal.pcbi.1003806. eCollection 2014 Aug.

19.

Cytofast: A workflow for visual and quantitative analysis of flow and mass cytometry data to discover immune signatures and correlations.

Beyrend G, Stam K, Höllt T, Ossendorp F, Arens R.

Comput Struct Biotechnol J. 2018 Oct 24;16:435-442. doi: 10.1016/j.csbj.2018.10.004. eCollection 2018.

20.

Guiding biomedical clustering with ClustEval.

Wiwie C, Baumbach J, Röttger R.

Nat Protoc. 2018 Jun;13(6):1429-1444. doi: 10.1038/nprot.2018.038. Epub 2018 May 24.

PMID:
29844526

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