marker shift
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2018 ◽  
Author(s):  
Tyler J. Burns ◽  
Garry P. Nolan ◽  
Nikolay Samusik

In high-dimensional single cell data, comparing changes in functional markers between conditions is typically done across manual or algorithm-derived partitions based on population-defining markers. Visualizations of these partitions is commonly done on low-dimensional embeddings (eg. t-SNE), colored by per-partition changes. Here, we provide an analysis and visualization tool that performs these comparisons across overlapping k-nearest neighbor (KNN) groupings. This allows one to color low-dimensional embeddings by marker changes without hard boundaries imposed by partitioning. We devised an objective optimization of k based on minimizing functional marker KNN imputation error. Proof-of-concept work visualized the exact location of an IL-7 responsive subset in a B cell developmental trajectory on a t-SNE map independent of clustering. Per-condition cell frequency analysis revealed that KNN is sensitive to detecting artifacts due to marker shift, and therefore can also be valuable in a quality control pipeline. Overall, we found that KNN groupings lead to useful multiple condition visualizations and efficiently extract a large amount of information from mass cytometry data. Our software is publicly available through the Bioconductor package Sconify.



2015 ◽  
Vol 21 (3) ◽  
pp. 314-321 ◽  
Author(s):  
Graham G. Walmsley ◽  
Yuval Rinkevich ◽  
Michael S. Hu ◽  
Daniel T. Montoro ◽  
David D. Lo ◽  
...  
Keyword(s):  


Calphad ◽  
2012 ◽  
Vol 36 ◽  
pp. 94-99 ◽  
Author(s):  
Yajun Liu ◽  
Jiang Wang ◽  
Yong Du ◽  
Guang Sheng ◽  
Zhaohui Long ◽  
...  


1991 ◽  
Vol 66-69 ◽  
pp. 1281-1286
Author(s):  
Toshitada Shimozaki ◽  
N. Saita ◽  
Y. Wakamatsu ◽  
M. Onishi
Keyword(s):  


1989 ◽  
Vol 30 (4) ◽  
pp. 258-264 ◽  
Author(s):  
T. Shimozaki ◽  
N. Saita ◽  
M. Onishi ◽  
Y. Wakamatsu


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