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Results: 1 to 20 of 112

Similar articles for PubMed (Select 21182178)

1.

Rapid cell population identification in flow cytometry data.

Aghaeepour N, Nikolic R, Hoos HH, Brinkman RR.

Cytometry A. 2011 Jan;79(1):6-13. doi: 10.1002/cyto.a.21007.

2.

Critical assessment of automated flow cytometry data analysis techniques.

Aghaeepour N, Finak G; FlowCAP Consortium; DREAM Consortium, Hoos H, Mosmann TR, Brinkman R, Gottardo R, Scheuermann RH.

Nat Methods. 2013 Mar;10(3):228-38. doi: 10.1038/nmeth.2365. Epub 2013 Feb 10. Erratum in: Nat Methods. 2013 May;10(5):445.

3.

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.

4.

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.

5.

flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding.

Ge Y, Sealfon SC.

Bioinformatics. 2012 Aug 1;28(15):2052-8. doi: 10.1093/bioinformatics/bts300. Epub 2012 May 17.

6.

Data reduction for spectral clustering to analyze high throughput flow cytometry data.

Zare H, Shooshtari P, Gupta A, Brinkman RR.

BMC Bioinformatics. 2010 Jul 28;11:403. doi: 10.1186/1471-2105-11-403.

7.

On extensions of k-means clustering for automated gating of flow cytometry data.

Luta G.

Cytometry A. 2011 Jan;79(1):3-5. doi: 10.1002/cyto.a.20988. No abstract available.

8.

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. doi: 10.1002/cyto.a.20531.

9.

Computational methods for evaluation of cell-based data assessment--Bioconductor.

Le Meur N.

Curr Opin Biotechnol. 2013 Feb;24(1):105-11. doi: 10.1016/j.copbio.2012.09.003. Epub 2012 Oct 10. Review.

PMID:
23062230
10.

Optimizing transformations for automated, high throughput analysis of flow cytometry data.

Finak G, Perez JM, Weng A, Gottardo R.

BMC Bioinformatics. 2010 Nov 4;11:546. doi: 10.1186/1471-2105-11-546.

11.

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.

12.

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.

13.

Using flowViz to visualize flow cytometry data.

Sarkar D, Le Meur N, Gentleman R.

Bioinformatics. 2008 Mar 15;24(6):878-9. doi: 10.1093/bioinformatics/btn021. Epub 2008 Feb 1.

14.

Data quality assessment of ungated flow cytometry data in high throughput experiments.

Le Meur N, Rossini A, Gasparetto M, Smith C, Brinkman RR, Gentleman R.

Cytometry A. 2007 Jun;71(6):393-403.

15.

A flow cytometry-based workflow for detection and quantification of anti-plasmodial antibodies in vaccinated and naturally exposed individuals.

Ajua A, Engleitner T, Esen M, Theisen M, Issifou S, Mordmüller B.

Malar J. 2012 Nov 6;11:367. doi: 10.1186/1475-2875-11-367.

16.

Automated flow cytometric analysis across large numbers of samples and cell types.

Chen X, Hasan M, Libri V, Urrutia A, Beitz B, Rouilly V, Duffy D, Patin É, Chalmond B, Rogge L, Quintana-Murci L, Albert ML, Schwikowski B; Milieu Intérieur Consortium.

Clin Immunol. 2015 Apr;157(2):249-60. doi: 10.1016/j.clim.2014.12.009. Epub 2015 Jan 7.

17.

A software framework enabling analysis of plate-based flow cytometry data for high-throughput screening.

Stanton RA, Escobar S, Elliott GS.

Assay Drug Dev Technol. 2010 Apr;8(2):228-37. doi: 10.1089/adt.2009.0227.

PMID:
20035617
18.

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.

PMID:
25378466
19.

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.

20.

Sequential univariate gating approach to study the effects of erythropoietin in murine bone marrow.

Achuthanandam R, Quinn J, Capocasale RJ, Bugelski PJ, Hrebien L, Kam M.

Cytometry A. 2008 Aug;73(8):702-14. doi: 10.1002/cyto.a.20584.

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