Calculates correlation between a cell and a matrix of the same dimensions extracted from the cell's associated region.

comparison_cor(
  cellHeightValues,
  regionHeightValues,
  fft_ccf_df,
  use = "pairwise.complete.obs"
)

Arguments

cellHeightValues

list/tibble column of x3p objects containing a reference scan's cells (as returned by comparison_cellDivision)

regionHeightValues

list/tibble column of x3p objects containing a target scan's regions (as returned by comparison_getTargetRegions)

fft_ccf_df

data frame/tibble column containing the data frame of (x,y) and CCF values returned by comparison_fft_ccf

use

argument for stats::cor

Value

A vector of the same length as the input containing correlation values at the estimated alignment between each reference cell and its associated target region

Examples

data(fadul1.1_processed,fadul1.2_processed) cellTibble <- fadul1.1_processed %>% comparison_cellDivision(numCells = 64) %>% dplyr::mutate(regionHeightValues = comparison_getTargetRegions(cellHeightValues = cellHeightValues, target = fadul1.2_processed)) %>% dplyr::mutate(cellPropMissing = comparison_calcPropMissing(heightValues = cellHeightValues), regionPropMissing = comparison_calcPropMissing(heightValues = regionHeightValues)) %>% dplyr::filter(cellPropMissing <= .85 & regionPropMissing <= .85) %>% dplyr::mutate(cellHeightValues = comparison_standardizeHeights(heightValues = cellHeightValues), regionHeightValues = comparison_standardizeHeights(heightValues = regionHeightValues)) %>% dplyr::mutate(cellHeightValues = comparison_replaceMissing(heightValues = cellHeightValues), regionHeightValues = comparison_replaceMissing(heightValues = regionHeightValues)) %>% dplyr::mutate(fft_ccf_df = comparison_fft_ccf(cellHeightValues, regionHeightValues)) %>% dplyr::mutate(pairwiseCompCor = comparison_cor(cellHeightValues, regionHeightValues, fft_ccf_df)) head(cellTibble)
#> # A tibble: 6 x 7 #> cellIndex cellHeightValues regionHeightValues cellPropMissing regionPropMissi~ #> <chr> <named list> <named list> <dbl> <dbl> #> 1 8, 6 <x3p> <x3p> 0.833 0.808 #> 2 7, 7 <x3p> <x3p> 0.657 0.700 #> 3 7, 8 <x3p> <x3p> 0.834 0.757 #> 4 6, 8 <x3p> <x3p> 0.353 0.638 #> 5 5, 8 <x3p> <x3p> 0.153 0.576 #> 6 4, 1 <x3p> <x3p> 0.117 0.767 #> # ... with 2 more variables: fft_ccf_df <named list>, pairwiseCompCor <dbl>