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. 2015 Jun 9;5(6):1223–1232. doi: 10.1534/g3.115.017830

Table 2. The 60 binary classifiers used in the ensLOC framework.

Classifier ID Name of Binary Classifier No. of Positive Training Objects No. of Negative Training Objects- Validation Using 10-fold Cross-Validation Visual Inspection Recall
Recall Precision
Quality control
 1.1.1 DEAD 960 1541 0.986 0.995
 1.1.2 GHOST 1840 2398 0.995 1
Budded or Unbudded
 2.1.1 UNBUDDED 1095 1582 0.997 0.984
 2.1.2 SMALLBUDDED 434 733 0.952 0.948
 2.1.3 LARGEMEDIUMBUDDED 727 1508 0.985 0.986
3.1 Cytoplasm
 3.1.1 CYTOPLASM 3493 4285 0.979 0.966 ∼95%
 3.1.2 CYTOPLASMNOTNUCLEAR 2075 1419 0.915 0.842 >95%
3.2 Endosome
 3.2.1 ENDOSOME 2245 4730 0.826 0.912 <70%
 3.2.2 ENDOSOME_CYTOPLASM 2245 3493 0.977 0.995
 3.2.3 ENDOSOME_NUCLEI 2245 5612 0.995 0.999
 3.2.4 ENDOSOME_SPINDLEPOLE 2245 3397 0.963 0.986
 3.2.5 ENDOSOME_MITOCHONDRIA 2245 6315 0.899 0.967
3.3 ER
 3.3.1 ER 5274 4259 0.977 0.919 <80%
 3.3.2 ER_CYTOPLASM 5274 3493 0.97 0.965
 3.3.3 ER_VACUOLEVACUOLARMEMBRANE 5274 3893 0.976 0.958
 3.3.4 ER_CELLPERIPHERY 5274 4059 0.996 0.996
3.4 Golgi
 3.4.1 GOLGI 1994 1838 0.964 0.908 >80%
 3.4.2 GOLGI_MITOCHONDRIA 1994 6315 0.809 0.968
 3.4.3 GOLGI_ENDOSOME 1994 2245 0.919 0.934
 3.4.4 GOLGI_CYTOPLASM 1994 3493 0.996 0.999
3.5 Mitochondria
 3.5.1 MITOCHONDRIA 6315 7894 0.894 0.884 >85%
3.6 Nuclear Periphery
 3.6.1 NUCLEARPERIPHERY 2668 4367 0.94 0.96 ∼70%
3.7 Nucleus
 3.7.1 NUCLEI 5612 6881 0.977 0.956 >80%
 3.7.2 NUCLEINOTCYTOPLASM 1398 989 0.99 0.93 >80%
3.8 Nucleolus
 3.8.1 NUCLEOLUS 3882 5332 0.926 0.948 >85%
3.9 Peroxisome
 3.9.1 PEROXISOME 1256 2099 0.849 0.922 <70%
 3.9.2 PEROXISOME_GOLGI 1256 1993 0.928 0.971
 3.9.3 PEROXISOME_SPINDLEPOLE 1256 3397 0.965 0.995
 3.9.4 PEROXISOME_MITOCHONDRIA 1256 6315 0.814 0.981
3.10 Vacuole/Vacuolar Membrane
 3.10.1 VACUOLEVACUOLARMEMBRANE-COMBINED 3893 3352 0.926 0.898 >80%
 3.10.2 VACUOLE_VACUOLARMEMBRANE 2224 1846 0.92 0.845 >80% VAC, 65% VAC membrane
3.11 Cortical Patches
 3.11.1 CORTICALPATCHESUNBUDDED 1813 1279 0.964 0.877 ∼70%
 3.11.2 CORTICALPATCHESUNBUDDED_CYTOPLASM 1813 1661 0.994 0.996
 3.11.3 CORTICALPATCHESUNBUDDED_MITOCHONDRIA 1813 4440 0.95 0.984
 3.11.4 CORTICALPATCHESBUDDED 1345 2171 0.928 0.936 75%
 3.11.5 CORTICALPATCHESBUDDED_CELLPERIPHERY 1345 1059 0.994 0.988
 3.11.6 CORTICALPATCHESBUDDED_MITOCHONDRIA 1345 1875 0.981 0.986
 3.11.7 CORTICALPATCHESBUDDED_CYTOPLASM 1345 1022 0.987 0.988
3.12 Bud
 3.12.1 BUD 1619 1691 0.937 0.905 >70%
3.13 Budneck
 3.13.1 BUDNECK 2170 3095 0.947 0.942 >70%
 3.13.2 BUDNECK_BUD 2170 1619 0.962 0.946
 3.13.3 BUDNECK_CELLPERIPHERY 2170 1059 1 0.994
 3.13.4 BUDNECK_MITOCHONDRIA 2170 1875 0.99 0.98
 3.13.5 BUDNECK_CYTOPLASM 2170 1022 0.987 0.98
 3.13.6 BUDNECK_NUCLEI 2170 1313 1 0.996
3.14 Budsite
 3.14.1 BUDSITE 453 637 0.982 0.961 >80%
 3.14.2 BUDSITE_CYTOPLASM 453 4955 0.943 0.992
 3.14.3 BUDSITE_CELLPERIPHERY 453 359 0.996 0.992
3.15 Cell Periphery
 3.15.1 CELLPERIPHERYUNBUDDED 2269 858 0.989 0.98 >95%
 3.15.2 CELLPERIPHERYBUDDED 1059 1688 0.981 0.991 >85%
3.16 Spindle Pole
 3.16.1 SPINDLEPOLETWODOTFARBUDDED 416 966 0.938 0.965 >70%
 3.16.2 SPINDLEPOLETWODOTFARBUDDED_BUDNECK 416 2170 0.913 0.997
 3.16.3 SPINDLEPOLETWODOTFARBUDDED_NUCLEARPERIPHERY 416 492 1 0.996
 3.16.4 SPINDLEPOLETWODOTFARBUDDED_NUCLEOLUS 416 1109 0.99 0.995
 3.16.5 SPINDLEPOLETWODOTCLOSEBUDDED 306 1016 0.905 0.97 ∼80%
 3.16.6 SPINDLEPOLETWODOTCLOSEBUDDED_BUDNECK 306 2170 0.899 0.995
 3.16.7 SPINDLEPOLETWODOTCLOSEBUDDED_MITOCHONDRIA 306 1875 0.974 0.996
 3.16.8 SPINDLEPOLETWODOTCLOSEBUDDED_NUCLEARPERIPHERY 306 492 0.993 0.996
 3.16.9 SPINDLEPOLETWODOTCLOSEBUDDED_NUCLEOLUS 306 1109 0.98 0.988
 3.16.10 SPINDLEPOLEONEDOT 2675 3676 0.974 0.983 70%

In total, approximately 70K handpicked cell images (objects) were used to train the classifiers. “No. of positive training objects” refers to cells which belong to the targeted class and “No. of negative training objects” refer to cells not belonging to the targeted class. For example, to construct the “DEAD” cells classifier, 960 images of dead cells were used as positive training objects and 1541 images of non-dead cells from across all 16 localization classes were used as negative training objects. The first number of the classifier ID reflects the level and therefore the sequence at which the classifier was applied. For instance, all cell images were first tested using the “DEAD” cells classifier to eliminate dead cells from further classification to the 16 localization classes, and only cells that were tested positive in the level 2 “SMALLBUDDED” and “LARGEMEDIUMBUDDED” classifiers would be further classified by the “BUDNECK” classifier. The accuracy of the classifiers was validated computationally using 10-fold cross-validation and manually using visual inspection of 500 random positive cells. Recall = True positives/(True positives + False negatives); Precision = True positives/(True positives + False positives). ER, endoplasm reticulum.