DoOR.functions.package: DoOR Functions

In ropensci/DoOR.functions: Integrating Heterogeneous Odorant Response Data into a Common Response Model: A DoOR to the Complete Olfactome

DoOR Functions

Description

Functions package providing manipulation and application of the DoOR.

Details

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.

Author(s)

C. Giovanni Galizia
Daniel Muench
Martin Strauch
Anja Nissler
Shouwen Ma

Maintainer: Daniel Münch <[e-mail]>

References

http://neuro.uni-konstanz.de/DoOR

See Also

DoOR.data

ropensci/DoOR.functions documentation built on Feb. 22, 2024, 9:44 a.m.