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. 2023 Jun 22;16(4):573–605. doi: 10.1016/j.jcmgh.2023.06.010

Table 8.

Expected Abundance of Classes: Random Markers vs Observed

Cell Type No. Codes in the Class Expected Probability Cell Belongs to Class Expected No. Cells in Class (of 2596) Observed No. Cells Among the 2596
AIN1 5 .0012 3.2 54
AIN2 7 .0049 12.8 71
AIN3 5 .0129 33.6 126
DIN1 22 .0047 12.2 41
DIN2 8 .0014 3.5 15
DIN3 19 .1139 295.6 127
DIN4 12 .0941 244.3 62
DIN5 4 .0106 27.4 52
DIN6 6 .0524 136.0 22
EMN1 6 .0194 50.3 175
EMN2 3 .0471 122.2 192
EMN3 6 .0195 50.7 83
EMN4 7 .0556 144.3 328
IMN1 11 .0349 90.7 265
IMN2 6 .0566 147.0 110
IMN3 7 .0296 76.8 392
IMN4 7 .0555 144.1 314
Misc 10 .1230 319.2 48
SN1 10 .0008 2.1 75
SN2 3 .0054 14.0 44

Cells are not randomly distributed between classes. Each of the 20 classes comprised 3–22 codes (164 in total); each code is represented a unique combination of markers. We calculated the theoretic probability for each of the 164 codes by assuming that markers were assorted independently. As shown in Figure 2, the proportions of most codes were very different to those predicted by random assortment. These data were then used to calculate the proportion of each class that would be expected if markers assorted independently (shown above). As can be seen, in 15 of the 20 cases, there were more cells in a class than predicted by random assortment; in the other 5, there were fewer cells. Again, this reinforces that the chemical coding of classes is unlikely to arise from random assortment of markers.

AIN, ascending interneuron; DIN, descending interneuron; EMN, excitatory motor neuron; IMN, inhibitory motor neuron; SN, sensory neuron.