| Title: | Integrating Heterogeneous Odorant Response Data into a Common Response Model: A DoOR to the Complete Olfactome |
|---|---|
| Description: | This is a function package providing functions to perform data manipulations and visualizations for DoOR.data. See the URLs for the original and the DoOR 2.0 publication. |
| Authors: | Daniel Münch [aut, cre], C. Giovanni Galizia [aut], Shouwen Ma [aut], Martin Strauch [aut], Anja Nissler [aut], Wolf Huetteroth [ctb] |
| Maintainer: | Daniel Münch <[email protected]> |
| License: | GPL-3 |
| Version: | 2.0.2 |
| Built: | 2024-08-29 22:59:05 UTC |
| Source: | https://github.com/ropensci/DoOR.functions |
project the model response values back into your scale of interest (spike, deltaF/F...)
back_project(template.data, responding.unit, response_matrix = door_default_values("door_response_matrix"))back_project(template.data, responding.unit, response_matrix = door_default_values("door_response_matrix"))
template.data |
data frame, the data that provides the scale to transform to, containing InChIKeys in a column called "odorants" and responses in a column called "responses" |
responding.unit |
character, the name of the receptor/OSN/glomerulus which responses should be transformed |
response_matrix |
DoOR response matrix, the source data is picked from here |
The process of back projection is the following:
1. rescale both data sets to [0,1],
2. find the best fitting model between "bp.data" and "cons.data" (lowest MD value),
3. project the consensus data onto the fitted curv, these are now our normalized, back projected responses
4. rescale all responses to the scale of the original data via a linear fit.
Output of back_project is a list containing a data frame with the back_projected data, the original data, the data used as a template and the data that resulted from fitting source and template (before rescaling to the template scale). additionaly the parameters of the linear fit between the source and template response scale is returned.
Daniel Münch <[email protected]>
Shouwen Ma <[email protected]>
# load some data sets data(Or22a) data(door_response_matrix) # create example data we are going to back project template.data <- data.frame(odorants = Or22a$InChIKey, responses = Or22a$Hallem.2004.EN) # run back_project and plot the results bp <- back_project(template.data, "Or22a") plot(bp$back_projected$original.data, bp$back_projected$back_projected.data, xlab = "DoOR consensus response", ylab = "back_projected data [spikes, Hallem.2004.EN]" )# load some data sets data(Or22a) data(door_response_matrix) # create example data we are going to back project template.data <- data.frame(odorants = Or22a$InChIKey, responses = Or22a$Hallem.2004.EN) # run back_project and plot the results bp <- back_project(template.data, "Or22a") plot(bp$back_projected$original.data, bp$back_projected$back_projected.data, xlab = "DoOR consensus response", ylab = "back_projected data [spikes, Hallem.2004.EN]" )
calculate_model is used to return the best model function that
represent the relationship between responses from study x and y.
calculate_model(x, y, select.MD = door_default_values("select.MD"))calculate_model(x, y, select.MD = door_default_values("select.MD"))
x, y
|
data vectors from study x and y (can contain NA) |
select.MD |
logical, if TRUE, only the best model function (in terms of MD) will be returned. |
calculate_model chooses the best model function from following: linear,
exponential function, sigmoid, asymptotic model with x intercept, asympototic
model with y intercept and their inverse versions. (If your are interested in
these functions please check the sources at
https://github.com/Dahaniel/DoOR.functions)
Shouwen Ma <[email protected]>
Daniel Münch <[email protected]>
# load a data set library(DoOR.data) data(Or35a) # pick 2 data sets for Or35a and rescale the data [0,1] x <- door_norm(Or35a[,6]) y <- door_norm(Or35a[,9]) # run calculate_model calM_xy <- calculate_model(x,y, select.MD = door_default_values("select.MD"))# load a data set library(DoOR.data) data(Or35a) # pick 2 data sets for Or35a and rescale the data [0,1] x <- door_norm(Or35a[,6]) y <- door_norm(Or35a[,9]) # run calculate_model calM_xy <- calculate_model(x,y, select.MD = door_default_values("select.MD"))
returns a matrix indiating how many studies have recorded individual receptor-odorant combinations
count_studies(ors = door_default_values("ORs"), odor_data = door_default_values("odor"), char.columns = door_default_values("num.charColumns"), ident = door_default_values("ident"))count_studies(ors = door_default_values("ORs"), odor_data = door_default_values("odor"), char.columns = door_default_values("num.charColumns"), ident = door_default_values("ident"))
ors |
data.frame containing all receptors exidting in DoOR. |
odor_data |
data.frame containing information about the odorants in DoOR. |
char.columns |
number of character columns in each receptor data.frame. |
ident |
odorant identifier to be used as rownames. |
matrix
# load some data library(DoOR.data) load_door_data(nointeraction = TRUE) #run count studies and plot the result count <- count_studies() image(count) head(count)# load some data library(DoOR.data) load_door_data(nointeraction = TRUE) #run count studies and plot the result count <- count_studies() image(count) head(count)
computes the complete response model for all receptors in the database (calls
model_response for all receptors). Overwrites response_matrix,
door_response_matrix_non_normalized and door_excluded_data.
create_door_database(tag = door_default_values("tag"), select.MDValue = door_default_values("select.MDValue"), overlapValues = door_default_values("overlapValues"), ...)create_door_database(tag = door_default_values("tag"), select.MDValue = door_default_values("select.MDValue"), overlapValues = door_default_values("overlapValues"), ...)
tag |
character string, format for rownames, possibilities: "InChIKey", CAS", "CID", "Name" |
select.MDValue |
a numeric, threshold on the MD, this is used to reject studies that do not align sufficiently well to the response model |
overlapValues |
numeric, a criterion using to refuse a data set that has not enough overlap value. |
... |
pass more parameters to |
Shouwen Ma <[email protected]>
Daniel Münch <[email protected]>
## Not run: # load DoOR data library(DoOR.data) load_door_data() # create a new consensus matrix create_door_database() ## End(Not run)## Not run: # load DoOR data library(DoOR.data) load_door_data() # create a new consensus matrix create_door_database() ## End(Not run)
door_default_values is used to return default values for DoOR
functions.
door_default_values(DoOR_default)door_default_values(DoOR_default)
DoOR_default |
a character string, indicating which argument is to be returned for DoOR functions. |
There are six categories for default value. real number, integer, logical, NULL, character string and character vector.
Shouwen Ma <[email protected]>
# extract DoOR default values door_default_values(DoOR_default = "select.MD")# extract DoOR default values door_default_values(DoOR_default = "select.MD")
door_norm is used to normalize the data in values from 0 to 1.
door_norm(x)door_norm(x)
x |
a numeric vector |
Shouwen Ma <[email protected]>
Daniel Münch <[email protected]>
# create example data x <- rnorm(10) # run door_norm on it door_norm(x)# create example data x <- rnorm(10) # run door_norm on it door_norm(x)
Functions package providing manipulation and application of the DoOR.
| Package: | DoOR.functions |
| Type: | Package |
| Version: | 2.0.0 |
| Date: | 2016-01-25 |
| License: | GPL-3 |
| LazyLoad: | yes |
Type help(package = DoOR.functions) to see a complete list of
datasets and functions. Below is what you need for a quick start.
First, load the DoOR packages, data and function package:
library(DoOR.functions): |
|
library(DoOR.data): |
|
then, load all datasets including the precomputed response matrix:
load_door_data: |
Load all data into current active environment (function comes with DoOR.data) . |
or, load all odorant reseponse data into a list:
load2list: |
Load odorant response data only and compose them as a list. |
Try some visualizations (e.g. producing the plots from the paper):
dplot_al_map: |
response to a chemical mapped onto an image of the antennal lobe. |
dplot_compare_profiles:
|
compare the results of two studies. |
dplot_response_matrix: |
Dot Plot of Odorant Responses Across Receptors. |
dplot_response_profile: |
bar plot: one receptor, all chemicals. |
dplot_tuningCurve: |
pyramid diagram depicting a receptor's tuning breadth. |
Try some queries:
get_responses: |
given a chemical, get original responses from all studies in the database. |
get_normalized_responses: |
given a chemical, get normalised responses from all studies in the database. |
In case you wish to create your own response model (e.g. because you want to include your own datasets):
create_door_database:
|
compute the complete response model for all receptors in the database
(calls model_response for all receptors). |
model_response: |
run the DoOR algorithm, that merges all measurements for one receptor. |
Estimate odorant responses:
estimate_missing_value: |
estimate NA entries in a consensus response data. |
Project the model response values back to tested values:
back_project: |
project the model response values back to tested values. |
Introduce new data into DoOR and update the supported data sets:
import_new_data: |
import new data into DoOR, and update the weight, response range and receptor names. |
update_door_database: |
update response matrix by calculating new consensus response data for a given receptor. |
See the Vignettes and the help pages for more documentation.
C. Giovanni Galizia
Daniel Muench
Martin Strauch
Anja
Nissler
Shouwen Ma
Maintainer: Daniel Münch <[email protected]>
http://neuro.uni-konstanz.de/DoOR
DoOR.data
plot the activation patterns of one or several odorants across OSNs
dplot_across_osns(odorants, response_matrix = door_default_values("door_response_matrix"), odor_data = door_default_values("odor"), door_mappings = door_default_values("door_mappings"), zero = door_default_values("zero"), tag = "Name", sub, plot.type = 1, base_size = 12)dplot_across_osns(odorants, response_matrix = door_default_values("door_response_matrix"), odor_data = door_default_values("odor"), door_mappings = door_default_values("door_mappings"), zero = door_default_values("zero"), tag = "Name", sub, plot.type = 1, base_size = 12)
odorants |
character vector, one or several InChIKeys |
response_matrix |
DOOR response matrix, contains the data to plot |
odor_data |
data frame, contains the odorant information. |
door_mappings |
data frame, containing the mappings of response profiles to morphological structures. |
zero |
character, InChIKey of the odorant that should be set to 0 (e.g. SFR) |
tag |
character, the chemical identifier to use in the plot, one of
|
sub |
character vector, specify one or several classes of sensilla the plot should be restricted to. One or several of: "ab" "ac", "at", "ai", "pb", "sacI", "sacII" |
plot.type |
interger, 1 or 2, defining the type of plot (1: facet_grid of odorants * sensillae, 2: facet_wrap across OSNs) |
base_size |
numeric, the base font size for the ggplot2 plot |
DoOR response profiles will be selected and ordered according to the
OSNs they are related to. Several DoOR response profiles might exist for a
given OSN (e.g. one for the OSN itself and one for the OSNs misexpressed
receptor protein) but only one will be shown. Which DoOR profile is mapped
to which OSN is controlled via the "code.OSN" column in DoORmapings.
a ggplot2 object
Daniel Münch <[email protected]>
# load DoOR data & functions library(DoOR.data) library(DoOR.functions) # pick example odorants by name ans transform their ID to InChIKey odorants <- trans_id(c("1-butanol", "isopentyl acetate", "carbon dioxide", "water"), from = "Name", to = "InChIKey") # plot dplot_across_osns(odorants) # plot only across ac and at sensillae dplot_across_osns(odorants, sub = c("ac", "at")) # plot across sensory neurons dplot_across_osns(odorants, plot.type = 2)# load DoOR data & functions library(DoOR.data) library(DoOR.functions) # pick example odorants by name ans transform their ID to InChIKey odorants <- trans_id(c("1-butanol", "isopentyl acetate", "carbon dioxide", "water"), from = "Name", to = "InChIKey") # plot dplot_across_osns(odorants) # plot only across ac and at sensillae dplot_across_osns(odorants, sub = c("ac", "at")) # plot across sensory neurons dplot_across_osns(odorants, plot.type = 2)
barplot of DoOR responses of a set of odorant across all responding units in DoOR
dplot_across_ru(odorant, response_matrix = door_default_values("door_response_matrix"), odor_data = door_default_values("odor"), zero = door_default_values("zero"), tag = "Name", limits, base_size = 12)dplot_across_ru(odorant, response_matrix = door_default_values("door_response_matrix"), odor_data = door_default_values("odor"), zero = door_default_values("zero"), tag = "Name", limits, base_size = 12)
odorant |
character vecto, one or several InChIKeys |
response_matrix |
DoOR response matrix, a DoOR response matrix as data source |
odor_data |
data frame, contains the odorant information. |
zero |
character, an InChIKey of the odorant that should be set to 0 |
tag |
character, the chemical identifier to plot as odorant name (one of colnames(odor)) |
limits |
numeric of length 2, if provided the ylim will range accordingly |
base_size |
numeric, the base font size for the ggplot2 plot |
a ggplot object
Daniel Münch <[email protected]>
# load data library(DoOR.data) library(DoOR.functions) data(odor) # plot activation pattern of one or several odorants dplot_across_ru(trans_id("123-92-2"), tag = "CAS") dplot_across_ru(odor$InChIKey[4:10])# load data library(DoOR.data) library(DoOR.functions) data(odor) # plot activation pattern of one or several odorants dplot_across_ru(trans_id("123-92-2"), tag = "CAS") dplot_across_ru(odor$InChIKey[4:10])
Plot an antennal lobe map with color coded odorant responses.
dplot_al_map(InChIKey, response_matrix = door_default_values("door_response_matrix"), odor_data = door_default_values("odor"), door_mappings = door_default_values("door_mappings"), zero = door_default_values("zero"), tag = door_default_values("tag.ALmap"), main = "Name", scalebar = door_default_values("scalebar"), door_AL_map = door_default_values("door_AL_map"), colors = door_default_values("colors"), legend = TRUE, limits, base_size = 12)dplot_al_map(InChIKey, response_matrix = door_default_values("door_response_matrix"), odor_data = door_default_values("odor"), door_mappings = door_default_values("door_mappings"), zero = door_default_values("zero"), tag = door_default_values("tag.ALmap"), main = "Name", scalebar = door_default_values("scalebar"), door_AL_map = door_default_values("door_AL_map"), colors = door_default_values("colors"), legend = TRUE, limits, base_size = 12)
InChIKey |
InChIKey specifying the odorant to plot |
response_matrix |
the input data (e.g. door_response_matrix or door_response_matrix_non_normalized) |
odor_data |
data frame, contains the odorant information. |
door_mappings |
the data frame containing the mapping information |
zero |
the odorant to set to zero (defaults to "SFR") |
tag |
the labels to plot on top of the glomeruli (one of the following
|
main |
the title, one column of |
scalebar |
whether or not to add a scalebar |
door_AL_map |
a list containing the AL model |
colors |
a vector containing 6 color values (2 for values below 0, 1 0 value and 3 steps between 0 and 1) |
legend |
logical, plot a legend? |
limits |
the limits for the color scale, if empty the range of the response matrix is taken (after setting “zero“ to 0) |
base_size |
numeric, the base font size for the ggplot plot |
Normalized, color coded odor responses across receptors are mapped onto a map of the Drosophila antennal lobe. The antennal lobe map was a kind gift from Veit Grabe.
a ggplot2 object
Daniel Münch [email protected]
Daniel Münch <[email protected]>
Grabe, V., Strutz, A., Baschwitz, A., Hansson, B.S., Sachse, S., 2014. A digital in vivo 3D atlas of the antennal lobe of Drosophila melanogaster. J. Comp. Neurol. n/a–n/a. doi:10.1002/cne.23697
get_normalized_responses, ggplot2, grid
# load data library(DoOR.data) library(DoOR.functions) # map responses on antennal lobe scheme dplot_al_map("MLFHJEHSLIIPHL-UHFFFAOYSA-N", scalebar = FALSE) # change colors dplot_al_map("MLFHJEHSLIIPHL-UHFFFAOYSA-N", tag = "Ors", color = c("magenta", "pink", "white", "yellow", "orange", "red")) # pass some ggplot2 theming parameters dplot_al_map(trans_id("123-92-2"), scalebar = FALSE) + ggplot2::theme(legend.position = "bottom", panel.background = ggplot2::element_rect(fill = "grey90", color = NA)) + ggplot2::ggtitle("responses elicited by isopentyl acetate") # export as pdf ## Not run: p <- dplot_al_map(trans_id("123-92-2")) ggplot2::ggsave("AL.response.pdf", p, width = 6, height = 2, scale = 2) ## End(Not run)# load data library(DoOR.data) library(DoOR.functions) # map responses on antennal lobe scheme dplot_al_map("MLFHJEHSLIIPHL-UHFFFAOYSA-N", scalebar = FALSE) # change colors dplot_al_map("MLFHJEHSLIIPHL-UHFFFAOYSA-N", tag = "Ors", color = c("magenta", "pink", "white", "yellow", "orange", "red")) # pass some ggplot2 theming parameters dplot_al_map(trans_id("123-92-2"), scalebar = FALSE) + ggplot2::theme(legend.position = "bottom", panel.background = ggplot2::element_rect(fill = "grey90", color = NA)) + ggplot2::ggtitle("responses elicited by isopentyl acetate") # export as pdf ## Not run: p <- dplot_al_map(trans_id("123-92-2")) ggplot2::ggsave("AL.response.pdf", p, width = 6, height = 2, scale = 2) ## End(Not run)
Orderdered bar plots for two studies, allowing for an easy comparison of the two studies / response profiles'.
dplot_compare_profiles(x, y, by.x, by.y, tag = "Name", base_size = 12)dplot_compare_profiles(x, y, by.x, by.y, tag = "Name", base_size = 12)
x |
the input data frame the first response profile will be taken from |
y |
the input data frame the second response profile will be taken from (x will be taken if y is missing) |
by.x |
character string, specifying a column in x |
by.y |
character string, specifying a column in y |
tag |
character, the chemical identifier that will be used as odorant label. |
base_size |
numeric, the base font size for the ggplot2 plot |
Daniel Münch <[email protected]>
# load data library(DoOR.data) library(DoOR.functions) data(Or22a) data(door_response_range) data(door_response_matrix) # compare the Hallem and the Pelz data set for Or22a dplot_compare_profiles(x = Or22a, y = Or22a, by.x = "Hallem.2006.EN", by.y = "Pelz.2006.AntEC50") # comparedata from two different sensory neurons and add odorant labels dplot_compare_profiles(x = cbind(door_response_matrix, InChIKey = rownames(door_response_matrix)), y = cbind(door_response_matrix, InChIKey = rownames(door_response_matrix)), by.x = "Or22a", by.y = "Or10a")# load data library(DoOR.data) library(DoOR.functions) data(Or22a) data(door_response_range) data(door_response_matrix) # compare the Hallem and the Pelz data set for Or22a dplot_compare_profiles(x = Or22a, y = Or22a, by.x = "Hallem.2006.EN", by.y = "Pelz.2006.AntEC50") # comparedata from two different sensory neurons and add odorant labels dplot_compare_profiles(x = cbind(door_response_matrix, InChIKey = rownames(door_response_matrix)), y = cbind(door_response_matrix, InChIKey = rownames(door_response_matrix)), by.x = "Or22a", by.y = "Or10a")
plot DoOR responses as a point matrix
dplot_response_matrix(data, odor_data = door_default_values("odor"), tag = door_default_values("tag"), colors = door_default_values("colors"), flip = FALSE, fix = TRUE, bw = FALSE, point = FALSE, limits, base_size = 12)dplot_response_matrix(data, odor_data = door_default_values("odor"), tag = door_default_values("tag"), colors = door_default_values("colors"), flip = FALSE, fix = TRUE, bw = FALSE, point = FALSE, limits, base_size = 12)
data |
a subset of e.g. door_response_matrix |
odor_data |
data frame, contains the odorant information. |
tag |
the chemical identfier to plot (one of colnames(odor)) |
colors |
the colors to use if negative values are supplied (range of 5 colors, 2 for negative values, 1 for 0 and 3 for positive values) |
flip |
logical, if TRUE the x and y axes will be flipped |
fix |
logical, whether to fix the ratio of the tiles when plotting as a heatmap |
bw |
logical, whether to plot b&w or colored |
point |
logical, if |
limits |
the limits of the scale, will be calculated if not set |
base_size |
numeric, the base font size for the ggplot2 plot |
a dotplot if limits[1] >= 0 or a heatmap if limits[1] < 0
Daniel Münch <[email protected]>
# load data library(DoOR.data) data(door_response_matrix) # reset the spontaneous firing rate to 0 tmp <- reset_sfr(door_response_matrix, "SFR") # plot heatmap / coloured tiles dplot_response_matrix(tmp[10:50,], tag = "Name", limits = range(tmp, na.rm = TRUE)) # plot dotplot dplot_response_matrix(door_response_matrix[10:50,], tag = "Name", limits = range(door_response_matrix, na.rm = TRUE))# load data library(DoOR.data) data(door_response_matrix) # reset the spontaneous firing rate to 0 tmp <- reset_sfr(door_response_matrix, "SFR") # plot heatmap / coloured tiles dplot_response_matrix(tmp[10:50,], tag = "Name", limits = range(tmp, na.rm = TRUE)) # plot dotplot dplot_response_matrix(door_response_matrix[10:50,], tag = "Name", limits = range(door_response_matrix, na.rm = TRUE))
create a barplot of a DoOR response profile
dplot_response_profile(receptor, response_matrix = door_default_values("door_response_matrix"), odor_data = door_default_values("odor"), tag = door_default_values("tag"), colored = TRUE, colors = door_default_values("colors"), limits, zero = door_default_values("zero"), scalebar = door_default_values("scalebar"), base_size = 12)dplot_response_profile(receptor, response_matrix = door_default_values("door_response_matrix"), odor_data = door_default_values("odor"), tag = door_default_values("tag"), colored = TRUE, colors = door_default_values("colors"), limits, zero = door_default_values("zero"), scalebar = door_default_values("scalebar"), base_size = 12)
receptor |
character, receptor name, any of colnames(door_response_matrix) |
response_matrix |
a DoOR door_response_matrix |
odor_data |
data frame, contains the odorant information. |
tag |
character, chemical identifier for annotation |
colored |
logical, color code the bars according to the response value? |
colors |
character vector, a vector of 5 colors (2 for values < 0, 1 value for 0 and 3 values > 0) |
limits |
numeric of length 2, the limits for the colorscale and the x axis, global range of data will be used if empty |
zero |
character, the odorant response that is set to 0, defaults to "SFR" |
scalebar |
logical, add or suppress scalebars |
base_size |
numeric, the base font size for the ggplot2 plot |
ggplot2 plot
Daniel Münch <[email protected]>
# load data library(DoOR.data) data(door_response_matrix) # plot with default parameters dplot_response_profile("Or22a", door_response_matrix) # plot wit odorant names dplot_response_profile("Or22a", door_response_matrix, tag = "Name")# load data library(DoOR.data) data(door_response_matrix) # plot with default parameters dplot_response_profile("Or22a", door_response_matrix) # plot wit odorant names dplot_response_profile("Or22a", door_response_matrix, tag = "Name")
plot a receptor or odorant tuning curve
dplot_tuningCurve(receptor, odorant, response.vector, response_matrix = door_default_values("door_response_matrix"), odor_data = door_default_values("odor"), zero = door_default_values("zero"), fill.receptor = door_default_values("color.receptor"), fill.odorant = door_default_values("color.odorant"), odor.main = "Name", limits, base_size = 12)dplot_tuningCurve(receptor, odorant, response.vector, response_matrix = door_default_values("door_response_matrix"), odor_data = door_default_values("odor"), zero = door_default_values("zero"), fill.receptor = door_default_values("color.receptor"), fill.odorant = door_default_values("color.odorant"), odor.main = "Name", limits, base_size = 12)
receptor |
character, a receptor name (one of ORS$OR) |
odorant |
character, an odorant name (InChIKey) |
response.vector |
numerical vector, a vector with responses, if empty this is taken from door_response_matrix |
response_matrix |
DoOR response matrix, response vector will be taken from here, not needed if response.vector is given |
odor_data |
data frame, contains the odorant information. |
zero |
InChIKey, will be set to zero, default is SFR, ignored when data is provided via response.vector, set to "" if you don't want to subtract anything |
fill.receptor |
color code, bar color for receptor tuning curve |
fill.odorant |
color code, bar color for odorant tuning curve |
odor.main |
the odor identifier to plot, one of colnamed(odor) |
limits |
the numerical vector of length 2, y limits for the tuning curve |
base_size |
numeric, the base font size for the ggplot2 plot |
a ggplot object
Daniel Münch <[email protected]>
# load data library(DoOR.data) data(door_response_matrix) data(odor) # plot a tuning curve for an odorant dplot_tuningCurve(odorant = odor$InChIKey[2]) # or for a receptor dplot_tuningCurve(receptor = "Or22a") # adjust the plotting range range <- range(reset_sfr(door_response_matrix, "SFR"), na.rm = TRUE) dplot_tuningCurve(receptor = "Or10a", limits = range, fill.receptor = "magenta") # plot with manual input data as receptor tuning curve dplot_tuningCurve(receptor = "receptor X", response.vector = c(1:100)) # or as odor tuning curve dplot_tuningCurve(odorant = "odor X", response.vector = rnorm(200))# load data library(DoOR.data) data(door_response_matrix) data(odor) # plot a tuning curve for an odorant dplot_tuningCurve(odorant = odor$InChIKey[2]) # or for a receptor dplot_tuningCurve(receptor = "Or22a") # adjust the plotting range range <- range(reset_sfr(door_response_matrix, "SFR"), na.rm = TRUE) dplot_tuningCurve(receptor = "Or10a", limits = range, fill.receptor = "magenta") # plot with manual input data as receptor tuning curve dplot_tuningCurve(receptor = "receptor X", response.vector = c(1:100)) # or as odor tuning curve dplot_tuningCurve(odorant = "odor X", response.vector = rnorm(200))
Estimate the missing entries in a response data
estimate_missing_value(data, nodor, method = "PC")estimate_missing_value(data, nodor, method = "PC")
data |
a data frame or matrix, contaning the consensus response values |
nodor |
a numeric value, specifying the number of the selected odors |
method |
character string, specifying the method ("PC" (Pearson's coefficient) and "Knn" (k nearest neighbors)) for estimation, the default is "PC". |
A wrapper programe for using Pearson Correlation or k-nearest neighbors to estimate the missing entries in a response matrix.
Shouwen Ma <[email protected]>
Kim, H., Golub, G. H. & Park, H., Missing value estimation for DNA microarray gene expression data: local least squares imputation., 2005, Bioinformatics, 21, 187-198
## Not run: # load data library(DoOR.data) data(door_response_matrix) # pick an example subset subset <- door_response_matrix[1:100, 11:30] # estimate missing values est.data <- estimate_missing_value(data = subset, nodor = 6) ## End(Not run)## Not run: # load data library(DoOR.data) data(door_response_matrix) # pick an example subset subset <- door_response_matrix[1:100, 11:30] # estimate missing values est.data <- estimate_missing_value(data = subset, nodor = 6) ## End(Not run)
export odor response data and supported data
export_door_data(file.format, directory, odorantReceptors = door_default_values("ORs"), response_matrix = door_default_values("door_response_matrix"), responseRange = door_default_values("door_response_range"), unglobalNorm_RM = door_default_values("door_response_matrix_non_normalized"), weightGlobNorm = door_default_values("door_global_normalization_weights"), all.data = TRUE)export_door_data(file.format, directory, odorantReceptors = door_default_values("ORs"), response_matrix = door_default_values("door_response_matrix"), responseRange = door_default_values("door_response_range"), unglobalNorm_RM = door_default_values("door_response_matrix_non_normalized"), weightGlobNorm = door_default_values("door_global_normalization_weights"), all.data = TRUE)
file.format |
character string, the format of given file, either ".txt" or ".csv" |
directory |
character string, naming a directory for writing. If missing, the exported data are saved in current working directory. |
odorantReceptors |
data frame, receptor names and expressions |
response_matrix |
data matrix, an global unnormalized responses matrix |
responseRange |
data frame, response ranges for each study |
unglobalNorm_RM |
data matrix, an unnormalized responses matrix |
weightGlobNorm |
data frame, weight matrix for global normalizazion |
all.data |
logical, if TRUE, export odorant response data and supported data "door_response_matrix", "door_response_range", "door_response_matrix_non_normalized", "door_response_matrix", "door_global_normalization_weights" and "ORs". |
Please load ORs from data package DoOR.data by typing (data(ORs))
before use.
Shouwen Ma <[email protected]>
## Not run: # load data library(DoOR.data) library(DoOR.functions) load_door_data() # export odorant response data only export_door_data(".txt", all.data = FALSE) ## End(Not run)## Not run: # load data library(DoOR.data) library(DoOR.functions) load_door_data() # export odorant response data only export_door_data(".txt", all.data = FALSE) ## End(Not run)
aggregates original data from a given study
get_dataset(study, na.rm = FALSE)get_dataset(study, na.rm = FALSE)
study |
character, the name of the study you want to aggregate the dta from |
na.rm |
logical, whether or not to exclude odorants that were not measured in the study |
returns a data frame containing all the odorant responses measured in
study
Daniel Münch <[email protected]>
# load data library(DoOR.data) load_door_data(nointeraction = TRUE) # get all recordings from the Hallem.2004.EN data set get_dataset("Hallem.2004.EN", na.rm = TRUE)# load data library(DoOR.data) load_door_data(nointeraction = TRUE) # get all recordings from the Hallem.2004.EN data set get_dataset("Hallem.2004.EN", na.rm = TRUE)
given a chemical, get normalised receptor responses from all studies in the database.
get_normalized_responses(odors, zero = door_default_values("zero"), response_matrix = door_default_values("door_response_matrix"), round = 3, na.rm = FALSE)get_normalized_responses(odors, zero = door_default_values("zero"), response_matrix = door_default_values("door_response_matrix"), round = 3, na.rm = FALSE)
odors |
character vector, one or more odors provided as InChIKey. |
zero |
InChIKey of background that should be set to zero. The default is "SFR", i.e. the spontaneous firing rate. |
response_matrix |
a data frame, as e.g. "door_response_matrix" that is
loaded by |
round |
numeric, round to this amount of digits, set to NA if you do not want to round |
na.rm |
logical, remove NAs? |
Daniel Münch [email protected]
model_response,create_door_database
# load data library(DoOR.data) data(door_response_matrix) # define a list of odorants odors <- c("MLFHJEHSLIIPHL-UHFFFAOYSA-N", "OBNCKNCVKJNDBV-UHFFFAOYSA-N", "IKHGUXGNUITLKF-UHFFFAOYSA-N") # get the normalized responses for these odorants result <- get_normalized_responses(odors, response_matrix = door_response_matrix)# load data library(DoOR.data) data(door_response_matrix) # define a list of odorants odors <- c("MLFHJEHSLIIPHL-UHFFFAOYSA-N", "OBNCKNCVKJNDBV-UHFFFAOYSA-N", "IKHGUXGNUITLKF-UHFFFAOYSA-N") # get the normalized responses for these odorants result <- get_normalized_responses(odors, response_matrix = door_response_matrix)
given a chemical, get original receptor responses from all studies in the database.
get_responses(odorant, responseRange = door_default_values("door_response_range"), Or.list = load2list())get_responses(odorant, responseRange = door_default_values("door_response_range"), Or.list = load2list())
odorant |
a single odor provided as InChIKey |
responseRange |
data frame, response ranges of studies |
Or.list |
a list contains reponse data of all available receptors. It
can be loaded using |
output is a data frame containing response values of given odor across receptors from all available studies.
Daniel Münch [email protected]
# load data library(DoOR.data) load_door_data(nointeraction = TRUE) # get raw responses for odorant MLFHJEHSLIIPHL-UHFFFAOYSA-N responses <- get_responses(odorant = 'MLFHJEHSLIIPHL-UHFFFAOYSA-N')# load data library(DoOR.data) load_door_data(nointeraction = TRUE) # get raw responses for odorant MLFHJEHSLIIPHL-UHFFFAOYSA-N responses <- get_responses(odorant = 'MLFHJEHSLIIPHL-UHFFFAOYSA-N')
correlates the result from a SSR recording of several odorants against all DoOR response profiles
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")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")
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
|
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 |
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
Daniel Münch <[email protected]>
# 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) identify_sensillum(recording, method = "dist", nshow = 5)# 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) identify_sensillum(recording, method = "dist", nshow = 5)
Import or update new data and update
door_global_normalization_weights, door_response_range,
odor, ORs and receptor data frames.
import_new_data(file.name, dataFormat = door_default_values("door_data_format"), odor_data = door_default_values("odor"), weightGlobNorm = door_default_values("door_global_normalization_weights"), responseRange = door_default_values("door_response_range"), receptors = door_default_values("ORs"), ident = door_default_values("ident"), round = 3)import_new_data(file.name, dataFormat = door_default_values("door_data_format"), odor_data = door_default_values("odor"), weightGlobNorm = door_default_values("door_global_normalization_weights"), responseRange = door_default_values("door_response_range"), receptors = door_default_values("ORs"), ident = door_default_values("ident"), round = 3)
file.name |
character string, the name of given file that contains response values of one or more odorant receptors, either a .csv or .txt file. |
dataFormat |
data frame, a data frame does not contain any response value but odorant information. |
odor_data |
data frame, contains the odorant information. |
weightGlobNorm |
data matrix, indicates whether given receptor has been measured by given study. |
responseRange |
data frame, contains the information about response range of each study and how many odors have been measured in each study. |
receptors |
data frame, contains the receptor and OSN names and their expression. |
ident |
the identifier used for matching, usually the InChIKey is used. |
round |
the number of digits the imported values are rounded to. |
import_new_data is used to import new data into database. If
the data contains a new receptor or ORN, then build a new data frame for this
receptor or ORN. If the data contains a receptor that is already present in
database, then merge the imported data into old data frame. The information
(e.g. response range, how many receptors and odors were measured from given
study) will be integrated into data door_response_range, odor,
ORs and door_global_normalization_weights. If an existing study
is imported, remove_study will be run first in order to perform
an update.
Shouwen Ma <[email protected]>
Daniel Münch <[email protected]>
## Not run: import new data named "odorantResponses_Orx.txt" into database and update the support data. library(DoOR.data) import_new_data(file.name = "odorantResponses_Orx.csv") ## End(Not run)## Not run: import new data named "odorantResponses_Orx.txt" into database and update the support data. library(DoOR.data) import_new_data(file.name = "odorantResponses_Orx.csv") ## End(Not run)
returns all original DoOR response data as a list
load2list()load2list()
a list
Daniel Münch <[email protected]>
# load DoOR.data library(DoOR.data) load_door_data(nointeraction = TRUE) # write the data into a list lst <- load2list()# load DoOR.data library(DoOR.data) load_door_data(nointeraction = TRUE) # write the data into a list lst <- load2list()
Identifying the source of unknown response data by correlating it agains all DoOR responding units.
map_receptor(data, response_matrix = door_default_values("door_response_matrix"), sub, threshold.p, threshold.cor, nshow)map_receptor(data, response_matrix = door_default_values("door_response_matrix"), sub, threshold.p, threshold.cor, nshow)
data |
data frame, containing two columns, one called "odorants" and one "responses" providing InChIKeys and odorant responses respectively. |
response_matrix |
output is a numeric vector that contains the Pearson Correlation Coefficient between given data and selected consensus data in |
sub |
character, a subset of responding units returned response matrix |
threshold.p |
numeric, a p-value threshold, only correlations below will be returned |
threshold.cor |
numeric, a correlation-coefficient threshold, only correlations above will be returned |
nshow |
numeric, if defined, only this number of results will be |
Shouwen Ma <[email protected]>
Daniel Münch <[email protected]>
# load data load_door_data(nointeraction = TRUE) # pick example data data <- data.frame(odorants = Or22a$InChIKey, responses = Or22a$Hallem.2004.EN) data <- na.omit(data) # find the corresponding receptor / responding unit map_receptor(data = data)# load data load_door_data(nointeraction = TRUE) # pick example data data <- data.frame(odorants = Or22a$InChIKey, responses = Or22a$Hallem.2004.EN) data <- na.omit(data) # find the corresponding receptor / responding unit map_receptor(data = data)
Runs the DoOR algorithm, that merges all measurements for one receptor into a common response model.
model_response(da, select.MDValue = door_default_values("select.MDValue"), overlapValues = door_default_values("overlapValues"), responseRange = door_default_values("door_response_range"), weightGlobNorm = door_default_values("door_global_normalization_weights"), glob.normalization = door_default_values("glob.normalization"), plot = door_default_values("plot"))model_response(da, select.MDValue = door_default_values("select.MDValue"), overlapValues = door_default_values("overlapValues"), responseRange = door_default_values("door_response_range"), weightGlobNorm = door_default_values("door_global_normalization_weights"), glob.normalization = door_default_values("glob.normalization"), plot = door_default_values("plot"))
da |
data frame, a selected receptor containing measured responses from studies. |
select.MDValue |
numeric, threshold on the MD for rejecting a fit. |
overlapValues |
numeric, a criterion using to refuse a data set that has not enough overlap value. |
responseRange |
data frame, contains response ranges for all studies. |
weightGlobNorm |
data frame, a binary data matrix, 1 indicates given odor has been measured in given study, NA indicates NOT. |
glob.normalization |
logical, default is |
plot |
logical, If |
Merging a data is processed by following:
Normalize all response data in value [0,1].
Compute the correlation between studies
and selected the best pair using select_model.
Merge the
first pair using function project_points.
Add other datasets if the correlation between the growing model response and the new dataset is below the correlation threshold (select.MDValue). Datasets excluded based on this criterion will be appended in a separate list.
Shouwen Ma <[email protected]>
# load data library(DoOR.data) data(Or35a) data(door_global_normalization_weights) data(door_response_range) # merge all existing data sets for Or35a into a consensus model response model_response_Or35a <- model_response(Or35a, plot = TRUE)# load data library(DoOR.data) data(Or35a) data(door_global_normalization_weights) data(door_response_range) # merge all existing data sets for Or35a into a consensus model response model_response_Or35a <- model_response(Or35a, plot = TRUE)
generates a model response and merge data in given sequence
model_response_seq(data, SEQ, overlapValues = door_default_values("overlapValues"), select.MDValue = door_default_values("select.MDValue"), strict = TRUE, plot = FALSE)model_response_seq(data, SEQ, overlapValues = door_default_values("overlapValues"), select.MDValue = door_default_values("select.MDValue"), strict = TRUE, plot = FALSE)
data |
data frame, odorant response data, e.g. Or22a. |
SEQ |
character vector, containing the names of studies indicating given sequence for merging data. |
overlapValues |
minimum overlap between studies to perfom a merge |
select.MDValue |
the minimum mean distance between studies to perfom a merge |
strict |
logical, if TRUE merging a permutation will be stopped once a single merge has a mean distance above select.MDValue |
plot |
logical |
# model_response_seq.R: #################
# merges studies in a given sequence (determined by the user or by exhaustive enumeration and choosing the optimal sequence)
# input parameters: ####################
# data : data frame, odorant response data for a given receptor, e.g. Or22a # SEQ : character vector, contains the names of studies that measured this receptor in a specific order (the merging sequence)
# output is a numeric vector: response values
Shouwen Ma <[email protected]>
# load data library(DoOR.data) data(Or35a) data(door_response_range) # specify a sequence of merging SEQ <- c("Hallem.2006.EN","Kreher.2008.EN","Hallem.2006.EN") # perform the merging selected_merg <- model_response_seq(Or35a, SEQ = SEQ, plot = TRUE)# load data library(DoOR.data) data(Or35a) data(door_response_range) # specify a sequence of merging SEQ <- c("Hallem.2006.EN","Kreher.2008.EN","Hallem.2006.EN") # perform the merging selected_merg <- model_response_seq(Or35a, SEQ = SEQ, plot = TRUE)
return an odorant that activates the receptor of interest exclusively
private_odorant(receptor, sensillum = FALSE, response_matrix = door_default_values("door_response_matrix"), door_mappings = door_default_values("door_mappings"), zero = door_default_values("zero"), nshow = 5, tag)private_odorant(receptor, sensillum = FALSE, response_matrix = door_default_values("door_response_matrix"), door_mappings = door_default_values("door_mappings"), zero = door_default_values("zero"), nshow = 5, tag)
receptor |
character, name of a DoOR responding unit (one of
|
sensillum |
logical, restrict the search to the sensillum the receptor is expressed in? |
response_matrix |
DoOR response matrix, the input data to perform the search on |
door_mappings |
the data frame containing the mapping information |
zero |
character, an odorant that should be set to 0 |
nshow |
numeric, the number of private odorants to return |
tag |
character, the chemical identifier to give the odorant names in
(on of |
a data.frame containing odorants and the response in the receptor of interest as well as the maximum response of the remaining receptors and their difference
# load data library(DoOR.data) # find a private odorant for Gr21a.Gr63a (the carbon dioxide receptor) # private_odorant("Gr21a.Gr63a", tag = "Name") # now find an odorant that within the ab3 sensillum specifically activates # Or22a private_odorant("Or22a", tag = "Name", sensillum = TRUE)# load data library(DoOR.data) # find a private odorant for Gr21a.Gr63a (the carbon dioxide receptor) # private_odorant("Gr21a.Gr63a", tag = "Name") # now find an odorant that within the ab3 sensillum specifically activates # Or22a private_odorant("Or22a", tag = "Name", sensillum = TRUE)
projects data points onto the curve defined by the model function
project_points(x, y, xylim, best.model, plot = door_default_values("plot"), points_cex = door_default_values("points.cex"), title = door_default_values("title"), ...)project_points(x, y, xylim, best.model, plot = door_default_values("plot"), points_cex = door_default_values("points.cex"), title = door_default_values("title"), ...)
x, y
|
numeric vectors of data values, coordinate vectors of points to
plot, the coordinates can contain |
xylim |
numeric vectors, x, y limits of the plot. |
best.model |
a list, containing the parameters, function, inverse function, Leibniz's notation for distance calculation and MD value. if missing, the best model will be generated automatically. |
plot |
logical, If |
points_cex |
a numerical value, giving the magnification level of symbols relative to the default size. |
title |
logical, If |
... |
further graphical parameters |
For internal use in the merging process (see also
model_response). The model function is choosen by
calculate_model. project_points then projects the
data points from the datasets to be merged onto the curve defined by the
model function. It computes the closest distance from a data point to a point
on the curve by numerical optimisation.
A list with two data frames "double.observations" and "single.observations"
is returned, which give the coordinates of double observations (defined as
(x,y)) and coordinates of single observation (defined as (x,NA) or (NA,y)).
Both data frames contain seven columns: "ID" indicating the original position
of data x and y, "x", "y" indicating the coordinate of observation, "X", "Y"
indicating the coordinate of projected point on the function, "distance"
indicating the distances between (xmin, f(xmin)) and all points on the
functional line, "NDR" indicating the normalized distances across all the
distance values.
Shouwen Ma <[email protected]>
calculate_model, optimize,
integrate
# load data library(DoOR.data) data(Or23a) # normalize two example data sets x <- door_norm(Or23a[,'Hallem.2004.EN']) y <- door_norm(Or23a[,'Hallem.2006.EN']) # find the best fitting function and project the remaining points (measured # only in one of the data sets) onto the fit. project_points(x = x, y = y, plot = TRUE)# load data library(DoOR.data) data(Or23a) # normalize two example data sets x <- door_norm(Or23a[,'Hallem.2004.EN']) y <- door_norm(Or23a[,'Hallem.2006.EN']) # find the best fitting function and project the remaining points (measured # only in one of the data sets) onto the fit. project_points(x = x, y = y, plot = TRUE)
Use this function to remove a study from the DoOR database.
import_new_data.R uses this function when it detects an existing study
during the import process (e.g. because you imported updated data).
remove_study(study, receptors = door_default_values("ORs"), responseRange = door_default_values("door_response_range"), weightGlobNorm = door_default_values("door_global_normalization_weights"))remove_study(study, receptors = door_default_values("ORs"), responseRange = door_default_values("door_response_range"), weightGlobNorm = door_default_values("door_global_normalization_weights"))
study |
a string containing the name of the study you want to remove (e.g. 'Bruyne.2001.WT') |
receptors |
a vector of all the receptors to be checked. Defaults to all receptors exidting in DoOR. |
responseRange |
the dataframe containing the info about the response
ranges of all studies ( |
weightGlobNorm |
the dataframe containing the info about the relative
weights between receptors ( |
Daniel Münch <[email protected]>
# load data library(DoOR.data) load_door_data(nointeraction = TRUE) # remove Bruyne.2001.WT from DoOR remove_study('Bruyne.2001.WT')# load data library(DoOR.data) load_door_data(nointeraction = TRUE) # remove Bruyne.2001.WT from DoOR remove_study('Bruyne.2001.WT')
A function for reseting SFR to zero
reset_sfr(x, sfr)reset_sfr(x, sfr)
x |
numeric or DoOR response matrix, input values |
sfr |
numeric or character, either a value to subtract if x is a vector or an InChIKey if x is a DoOR response matrix |
Performs a simple subtraction of the SFR value.
Daniel Münch <[email protected]>
# load data library(DoOR.data) data(door_response_matrix) # create a response matrix with the SFR reset to 0 door_response_matrix_SFRreset <- reset_sfr(door_response_matrix, "SFR")# load data library(DoOR.data) data(door_response_matrix) # create a response matrix with the SFR reset to 0 door_response_matrix_SFRreset <- reset_sfr(door_response_matrix, "SFR")
compute the data pairwise using function calculate_model and
selects a pair with the lowest "MD" value.
select_model(candidate, data_candidate, merged_data, overlapValues = door_default_values("overlapValues"), merged = door_default_values("merged"))select_model(candidate, data_candidate, merged_data, overlapValues = door_default_values("overlapValues"), merged = door_default_values("merged"))
candidate |
a character vector, contains the names of studies. |
data_candidate |
a data frame, odorant response data that only contains value columns. |
merged_data |
numeric vector, merged data |
overlapValues |
numeric, a criterion using to refuse a data set that has not enough overlap value. |
merged |
logical, if merged is |
This function is used in model_response to select the first
pair or next data set for merging. The output is a list containing
"selected.x" and "selected.y" that specify which data plots against another,
and "best.model" that is used in function project_points.
Shouwen Ma <[email protected]>
# load data library(DoOR.data) data(ac3B) # split into data and header studies <- names(ac3B)[c(7:8)] data_candidate <- ac3B[,c(7:8)] # rescale data norm_data_candidate <- apply(data_candidate, 2, door_norm) # find the best fitting model select_model(candidate = studies, data_candidate = norm_data_candidate, merged = FALSE)# load data library(DoOR.data) data(ac3B) # split into data and header studies <- names(ac3B)[c(7:8)] data_candidate <- ac3B[,c(7:8)] # rescale data norm_data_candidate <- apply(data_candidate, 2, door_norm) # find the best fitting model select_model(candidate = studies, data_candidate = norm_data_candidate, merged = FALSE)
Sparseness can be calculated as lifetime kurtosis (LTK, Willmore and Tolhurst, 2001) or as lifetime sparseness (LTS, Bhandawat et al., 2007).
sparse(x, method = "ltk")sparse(x, method = "ltk")
x |
numerical input vector |
method |
type of sparseness measure, either 'ltk' for lifetime kurtosis or 'lts' for lifetime sparseness (see references). |
LTS scales between \[0,1\] while LTK is not restricted. LTS only takes positive values.
Daniel Münch <[email protected]>
Bhandawat, V., Olsen, S.R., Gouwens, N.W., Schlief, M.L., Wilson, R.I., 2007. Sensory processing in the Drosophila antennal lobe increases reliability and separability of ensemble odor representations. Nature neuroscience 10, 1474–82. doi:10.1038/nn1976
Willmore, B., Tolhurst, D.J., 2001. Characterizing the sparseness of neural codes. Network 12, 255–270. doi:10.1080/net.12.3.255.270
Translate chemical identifiers from one to the other.
trans_id(x, from = "CAS", to = "InChIKey", odor_data = door_default_values("odor"))trans_id(x, from = "CAS", to = "InChIKey", odor_data = door_default_values("odor"))
x |
character vector, one or many chemical identifiers |
from |
character, the type of identifier to translate from (one of the column names of “odor“) |
to |
character, the type of identifier to translate from (one of the column names of “odor“) |
odor_data |
the data frame containing the odor information (defaults to “odor“). |
character vector of translated chemical identifiers
# load data library(DoOR.data) # transform CAS to InChIKey trans_id("123-92-2") # transform Name to InChIKey trans_id("isopentyl acetate", "Name") # transform SMILE to InChIKey trans_id("C(C(C)C)COC(=O)C", "SMILES", "Name")# load data library(DoOR.data) # transform CAS to InChIKey trans_id("123-92-2") # transform Name to InChIKey trans_id("isopentyl acetate", "Name") # transform SMILE to InChIKey trans_id("C(C(C)C)COC(=O)C", "SMILES", "Name")
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.
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)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)
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 |
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.
Shouwen Ma <[email protected]>
Shouwen Ma <[email protected]>
model_response,model_response_seq
## Not run: # 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) ## End(Not run)## Not run: # 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) ## End(Not run)
Update the DoOR odor data with info from odor. For the function to
work, all DoOR data has to be loaded to the current environment.
update_door_odorinfo()update_door_odorinfo()
Daniel Münch, [email protected]
# load data load_door_data(nointeraction = TRUE) # modify odor odor[1,1] <- "acid" # run update_door_odorinfo() # check that data sets have been updated head(Or22a)# load data load_door_data(nointeraction = TRUE) # modify odor odor[1,1] <- "acid" # run update_door_odorinfo() # check that data sets have been updated head(Or22a)