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correlates the result from a SSR recording of several odorants against all DoOR response profiles

Usage

identify_sensillum(recording,
  response_matrix = door_default_values("door_response_matrix"),
  odor_data = door_default_values("odor"),
  door_mappings = door_default_values("door_mappings"), tag = "Name",
  min.cor = 0.9, nshow = 10, method = "cor", sub, base_size = 12,
  plot = TRUE, use = "everything")

Arguments

recording

data frame, a data frame with the following columns "odorants" containing InChIKeys of the tested odorrant, and one column called "unit1" etc. for each unit, containing responses (or estimates) scaled between 0 and 1 (see examples)

response_matrix

DoOR response matrix, the data to compair against

odor_data

data frame, contains the odorant information.

door_mappings

the data frame containing the mapping information

tag

character, the chemical identifier to use in plots, one of colnames(odor)

min.cor

numeric, a minimum correlation value, the function will check wether there is a higher correlation for all units within a single sensillum

nshow

numeric, the number of plots to nshow, plot e.g. only the top 10 matches

method

character, the method for similarity calculations: correlation ("cor") or Euclidean distances ("dist")

sub

character, if you know the class of sensillum you were recording from you can restrict the search to this subset here ("ab", "ac", "at", "pb", "sac")

base_size

numeric, the base font size of the ggplot plots

plot

logical, if TRUE returns the plot, else returns the data frame with the correlations/distances

use

character, the "use" option from the cor function, "all" returns NA when pairs are incomplete, "na.or.complete" only uses complete observations to calculate correlations; see cor for details

Value

eithe& Carolin G.(†27)r a plot (gtable) with responses sorted by highest correlations or lowest distances, or a data frame containing all calculated correlations or Euclidean distances

Author

Daniel Münch <daniel.muench@uni-konstanz.de>

Examples

# load data
library(DoOR.data)

# create an example recording
recording <- data.frame(
   odorants = c(trans_id(c("BEDN", "ETAS"), "Code"),
   trans_id("carbon dioxide", "Name")),
   unit1 = c(.9,.1,.1),
   unit2 = c(0, .1, 1)
)

# run the identification
identify_sensillum(recording)
#> found correlations above 0.9 for all 2 units in: ab1

identify_sensillum(recording, method = "dist", nshow = 5)
#> Warning: Removed 3 rows containing missing values (`geom_bar()`).
#> Warning: Removed 5 rows containing missing values (`geom_bar()`).