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Items: 1 to 20 of 143

1.

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.

2.

Identification and visualization of multidimensional antigen-specific T-cell populations in polychromatic cytometry data.

Lin L, Frelinger J, Jiang W, Finak G, Seshadri C, Bart PA, Pantaleo G, McElrath J, DeRosa S, Gottardo R.

Cytometry A. 2015 Jul;87(7):675-82. doi: 10.1002/cyto.a.22623. Epub 2015 Apr 23.

3.

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 15;31(4):606-7. doi: 10.1093/bioinformatics/btu677. Epub 2014 Oct 16.

4.

flowClust: a Bioconductor package for automated gating of flow cytometry data.

Lo K, Hahne F, Brinkman RR, Gottardo R.

BMC Bioinformatics. 2009 May 14;10:145. doi: 10.1186/1471-2105-10-145.

5.

flowCore: a Bioconductor package for high throughput flow cytometry.

Hahne F, LeMeur N, Brinkman RR, Ellis B, Haaland P, Sarkar D, Spidlen J, Strain E, Gentleman R.

BMC Bioinformatics. 2009 Apr 9;10:106. doi: 10.1186/1471-2105-10-106.

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. doi: 10.1002/cyto.a.23030. Epub 2016 Dec 19.

7.

Development of an automated analysis system for data from flow cytometric intracellular cytokine staining assays from clinical vaccine trials.

Shulman N, Bellew M, Snelling G, Carter D, Huang Y, Li H, Self SG, McElrath MJ, De Rosa SC.

Cytometry A. 2008 Sep;73(9):847-56. doi: 10.1002/cyto.a.20600.

8.

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. doi: 10.1002/eji.201545774. Epub 2015 Nov 30. Review.

9.

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.

10.

Scalable clustering algorithms for continuous environmental flow cytometry.

Hyrkas J, Clayton S, Ribalet F, Halperin D, Armbrust EV, Howe B.

Bioinformatics. 2016 Feb 1;32(3):417-23. doi: 10.1093/bioinformatics/btv594. Epub 2015 Oct 17.

PMID:
26476780
11.

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.

12.

"Virtual flow cytometry" of immunostained lymphocytes on microscopic tissue slides: iHCFlow tissue cytometry.

Cualing HD, Zhong E, Moscinski L.

Cytometry B Clin Cytom. 2007 Jan 15;72(1):63-76.

13.

Standardization and quality control for high-dimensional mass cytometry studies of human samples.

Kleinsteuber K, Corleis B, Rashidi N, Nchinda N, Lisanti A, Cho JL, Medoff BD, Kwon D, Walker BD.

Cytometry A. 2016 Oct;89(10):903-913. doi: 10.1002/cyto.a.22935. Epub 2016 Aug 30.

14.

A multidimensional classification approach for the automated analysis of flow cytometry data.

Pedreira CE, Costa ES, Arroyo ME, Almeida J, Orfao A.

IEEE Trans Biomed Eng. 2008 Mar;55(3):1155-62. doi: 10.1109/TBME.2008.915729.

PMID:
18334408
15.

QUAliFiER: an automated pipeline for quality assessment of gated flow cytometry data.

Finak G, Jiang W, Pardo J, Asare A, Gottardo R.

BMC Bioinformatics. 2012 Sep 28;13:252. doi: 10.1186/1471-2105-13-252.

16.

Flow cytometry bioinformatics.

O'Neill K, Aghaeepour N, Spidlen J, Brinkman R.

PLoS Comput Biol. 2013;9(12):e1003365. doi: 10.1371/journal.pcbi.1003365. Epub 2013 Dec 5.

17.

Comparative exploration of multidimensional flow cytometry software: a model approach evaluating T cell polyfunctional behavior.

Spear TT, Nishimura MI, Simms PE.

J Leukoc Biol. 2017 Aug;102(2):551-561. doi: 10.1189/jlb.6A0417-140R. Epub 2017 May 26.

PMID:
28550117
18.

GenePattern flow cytometry suite.

Spidlen J, Barsky A, Breuer K, Carr P, Nazaire MD, Hill BA, Qian Y, Liefeld T, Reich M, Mesirov JP, Wilkinson P, Scheuermann RH, Sekaly RP, Brinkman RR.

Source Code Biol Med. 2013 Jul 3;8(1):14. doi: 10.1186/1751-0473-8-14.

19.

A computational framework to emulate the human perspective in flow cytometric data analysis.

Ray S, Pyne S.

PLoS One. 2012;7(5):e35693. doi: 10.1371/journal.pone.0035693. Epub 2012 May 1.

20.

Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells.

Pedersen NW, Chandran PA, Qian Y, Rebhahn J, Petersen NV, Hoff MD, White S, Lee AJ, Stanton R, Halgreen C, Jakobsen K, Mosmann T, Gouttefangeas C, Chan C, Scheuermann RH, Hadrup SR.

Front Immunol. 2017 Jul 26;8:858. doi: 10.3389/fimmu.2017.00858. eCollection 2017.

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