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Table 4 Parameters and performance (as measured by (10) and manual gating as ground truth) of the best results obtained by optimalFlowTemplates + optimalFlowClassification on \(\mathcal {TS}\)

From: optimalFlow: optimal transport approach to flow cytometry gating and population matching

 

\({\mathcal {C}}^2\)

\({\mathcal {C}}^{5}\)

\({\mathcal {C}}^{7}\)

Median F-measure

0.9441931

0.8530806

0.957045

Database Clustering

Complete linkage

HDBSCAN

Complete linkage

Template Formation

Pooling

Pooling

HDBSCAN

Assigned Cluster

1

6

2

Sample Clustering

tclust

tclust

tclust

Supervised Classification

QDA

QDA from template

Random forest

Assigned Cytometry

\({\mathcal {C}}^{1}\)

 

\({\mathcal {C}}^{8}\)

 

\({\mathcal {C}}^{9}\)

\({\mathcal {C}}^{14}\)

\({\mathcal {C}}^{15}\)

Median F-measure

0.9458429

0.9254252

0.8807339

Database Clustering

HDBSCAN

HDBSCAN

HDBSCAN

Template Formation

Pooling

k-barycenter

k-barycenter

Assigned Cluster

9

1

1

Sample Clustering

tclust

tclust

tclust

Supervised Classification

QDA

Label transfer with (6)

Random forest

Assigned Cytometry

\({\mathcal {C}}^{13}\)

 

\({\mathcal {C}}^{1}\)

 

\({\mathcal {C}}^{17}\)

\({\mathcal {C}}^{18}\)

\({\mathcal {C}}^{26}\)

Median F-measure

0.9679446

0.9575489

0.8316279

Database Clustering

HDBSCAN

HDBSCAN

Complete linkage

Template Formation

HDBSCAN

HDBSCAN

HDBSCAN

Assigned Cluster

7

7

4

Sample Clustering

tclust

flowMeans

tclust

Supervised Classification

Random forest

Random forest

Random forest

Assigned Cytometry

\({\mathcal {C}}^{20}\)

\({\mathcal {C}}^{20}\)

\({\mathcal {C}}^{24}\)

 

\({\mathcal {C}}^{27}\)

\({\mathcal {C}}^{29}\)

\({\mathcal {C}}^{31}\)

Median F-measure

0.9312977

0.9259644

0.931515

Database Clustering

Complete linkage

Complete linkage

HDBSCAN

Template Formation

Pooling

k-barycenter

Pooling

Assigned Cluster

5

6

4

Sample Clustering

tclust

flowMeans

tclust

Supervised Classification

Random forest

Random forest

QDA from template

Assigned Cytometry

\({\mathcal {C}}^{28}\)

\({\mathcal {C}}^{33}\)

 
 

\({\mathcal {C}}^{40}\)

Median F-measure

0.8240522

Database Clustering

Complete linkage

Template Formation

Pooling

Assigned Cluster

6

Sample Clustering

tclust

Supervised Classification

Random forest

Assigned Cytometry

\({\mathcal {C}}^{30}\)

  1. Database Clustering refers to the clustering method used in line 17 in Algorithm 1. Template Formation refers to the method used in line 19 in Algorithm 1. Assigned Cluster refers to the label of the cluster as given in Table 2 to which the new cytometry is assigned. Sample Clustering refers to how we obtain \({\mathcal {C}}^u\) in Algorithm 5. Supervised Classification refers to the method used in line 13 in Algorithm 5. Assigned Cytometry refers to the optimal cytometry in the respective cluster that is used for learning (when applicable)