Loading…
This event has ended. Visit the official site or create your own event on Sched.
Click here to return to main conference site. For a one page, printable overview of the schedule, see this.
View analytic
Wednesday, June 29 • 2:30pm - 3:30pm
Partition-Assisted Clustering: Application to High-Dimensional Multi-Sample Single-Cell Data Analysis

Log in to save this to your schedule and see who's attending!

Poster #29

Cytometry advances the study of cellular phenomena at the single-cell level by providing the necessary information to find subpopulation complexity of a heterogeneous sample. The analysis of cytometry datasets is becoming more challenging as cytometry advances increase the data size and dimension. Flow cytometry typically monitors at most 12 genes per cell; however, in recent years, mass cytometry (CyTOF) has taken the routine upper limit to 45 genes per cell. Biologists traditionally analyze the data by manually drawing polygon enclosures around subpopulations on a series of 2D projections of the data; this procedure becomes exponentially harder and time-consuming with increasing dimensions. To aid both experts and non-experts in finding the subpopulation complexity of cytometry samples, we introduce and apply partition-assisted clustering (PAC), which is implemented as an R package called PAC, to enable consistently accurate and efficient analysis of low and high-dimensional single-cell datasets. PAC utilizes the data density implicitly and enables the discovery of cell subpopulations in the dataset without computational bias due to systematic elimination of data points in the analysis.

Speakers
YL

Ye Li

Stanford University


Wednesday June 29, 2016 2:30pm - 3:30pm
Sponsor Pavilion 326 Galvez Street Stanford, CA 94305-6105

Attendees (8)