TY - GEN
T1 - Toward exhaustive gating of flow cytometry data
AU - Qiu, Peng
PY - 2012
Y1 - 2012
N2 - Flow cytometry is a high-throughput technology that measures protein expressions at the single-cell level. A typical flow cytometry experiment on one biological sample provides measurements of several protein markers on or inside hundreds of thousands of individual cells in that sample. Analysis of such data often aims to identify subpopulations of cells with distinct phenotypes. Currently, the most widely used analysis in the flow cytometry community is manual gating on a sequence of biaxial plots, which is highly subjective and labor intensive. To address those issues, the majority of efforts in the literature have been devoted to automate the gating analysis using clustering algorithms. However, completely removing the subjectivity can be quite challenging. This paper describes an opposite approach. Instead of automating the analysis, we aim to develop novel visualizations to facilitate manual gating. The proposed method views a flow cytometry data of one biological sample as a high-dimensional point cloud of cells, derives the skeleton of the cloud, and unfolds the skeleton to generate a 2D visualization.
AB - Flow cytometry is a high-throughput technology that measures protein expressions at the single-cell level. A typical flow cytometry experiment on one biological sample provides measurements of several protein markers on or inside hundreds of thousands of individual cells in that sample. Analysis of such data often aims to identify subpopulations of cells with distinct phenotypes. Currently, the most widely used analysis in the flow cytometry community is manual gating on a sequence of biaxial plots, which is highly subjective and labor intensive. To address those issues, the majority of efforts in the literature have been devoted to automate the gating analysis using clustering algorithms. However, completely removing the subjectivity can be quite challenging. This paper describes an opposite approach. Instead of automating the analysis, we aim to develop novel visualizations to facilitate manual gating. The proposed method views a flow cytometry data of one biological sample as a high-dimensional point cloud of cells, derives the skeleton of the cloud, and unfolds the skeleton to generate a 2D visualization.
UR - http://www.scopus.com/inward/record.url?scp=84877794229&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84877794229&partnerID=8YFLogxK
U2 - 10.1109/GENSIPS.2012.6507759
DO - 10.1109/GENSIPS.2012.6507759
M3 - Conference contribution
AN - SCOPUS:84877794229
SN - 9781467352369
T3 - Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
SP - 183
EP - 186
BT - Proceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012
T2 - 2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012
Y2 - 2 December 2012 through 4 December 2012
ER -