RNA Detection

threshold_score<- seq(-2,2,1)

threshold_number<- seq(5,15,5)

# Compute the number of simulated cells meeting the requirements # for the selected

combinations of scores and the number of voxels # above that score:

number_cells_above¼matrix(nrow¼length(threshold_score),ncol¼length(threshold_-

number))

for(j in 1:length(threshold_number)){

print(j)

number_cells_above[,j]¼unlist(lapply(seq_along(threshold_score),function(i){

sup¼apply(mapping_simulated,2,function(x){

length(x[x>threshold_score[i]])

})

length(sup[sup>threshold_number[j]])

}))

}

number_cells_above¼data.frame(number_cells_above)

rownames(number_cells_above)¼as.character(threshold_score)

colnames(number_cells_above)¼as.character(threshold_number)

# Compute the proportions

proportion_cells_thresholds¼number_cells_above/(dim(mapping_simulated)[2])

proportion_cells_thresholds

7. Running this script should generate a table just as follows:

Voxels

Score^51015

 2 0.69 0.51 0.5

 1 0.39 0.31 0.25

0 0.25 0.25 0.19

1 0.17 0.17 0.17

2 0.15 0.07 0.07

Based on this table, we can define the values for the threshold-

ing of the mapping results for sequenced cells:

120 Kaia Achim et al.