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update response matrix

Source: R/update_door_database.R
update_door_database.Rd

update the globally response matrix and the unglobally normalized response matrix door_response_matrix_non_normalized by introducing new consensus response data of given receptor.

Usage

update_door_database(receptor, permutation = TRUE, perm,
  response_matrix_nn = door_default_values("door_response_matrix_non_normalized"),
  response_matrix = door_default_values("door_response_matrix"),
  responseRange = door_default_values("door_response_range"),
  weightGlobNorm = door_default_values("door_global_normalization_weights"),
  select.MDValue = door_default_values("select.MDValue"), strict = TRUE,
  overlapValues = door_default_values("overlapValues"),
  door_excluded_data = door_default_values("door_excluded_data"),
  plot = FALSE)

Arguments

receptor

character string, name of given odorant receptor.

permutation

logical, if TRUE, the sequence is chosen from permutation, if FALSE, sequence is chosen by the routine process.

perm

a matrix with one sequence of study names per row, if empty, all possible permutations of study names will be provided.

response_matrix_nn

data frame, response data that has not been globally normalized.

response_matrix

data frame, globally normalized response data.

responseRange

data frame, response range of studies.

weightGlobNorm

data frame, weight matrix for global normalization.

select.MDValue

the minimum mean distance between studies to perfom a merge (used if permutation == FALSE or if permutation == TRUE AND strict == TRUE)

strict

logical, if TRUE merging a permutation will be stopped once a single merge has a mean distance above select.MDValue (only valid if permutation == TRUE)

overlapValues

minimum overlap between studies to perfom a merge

door_excluded_data

the data frame that contains the list of excluded data sets.

plot

logical

Details

The merging sequence could be arranged by the routine process (using model_response or taking the optimized sequence that is chosen from permutations. The mean correlation between merged responses and each original recording will be computed for each permutation, the optimozed sequence is with the highest correlation.

See also

model_response,model_response_seq

Author

Shouwen Ma <shouwen.ma@uni-konstanz.de>

Shouwen Ma <daniel.muench@uni-konstanz.de>

Examples

if (FALSE) { # \dontrun{
# load data
library(DoOR.data)
load_door_data()
# update the entry "Or67b" of data "door_response_matrix" and
# "door_response_matrix_non_normalized" with permutations.
 update_door_database(receptor="Or67b", permutation = TRUE)
} # }