DoOR consists of two R packages and both are needed for DoOR to work properly.
One package, DoOR.data
contains all the Drosophila odor responses we
gathered from labs around the world or recorded ourselves. The other package
DoOR.functions
contains the DoOR framework for integrating heterogeneous data
sets as well as analysis tools and functions for visualization.
In this vignette we describe how to build, modify and update DoOR and introduce some helper functions. There are two other vignettes explaining the plotting functions and the analysis tools in detail.
import_new_data()
create_door_database()
](update_door_database()
](model_response()
and model_response_seq()
remove_study()
update_door_odorinfo()
](The first step after starting R is to attach both packages and to load the response data:
library(DoOR.data) library(DoOR.functions) load_door_data(nointeraction = TRUE)
load_door_data()
attaches the data from DoOR.data
.
DoOR comes with all the original data sets as well as with a pre-computed
version of the consensus matrix door_response_matrix
where all data was
integrated using the DoOR merging algorithms (see paper for details on how the
algorithm works). The values in door_response_matrix
are globally normalized
with values scaled [0,1]
. door_response_matrix_non_normalized
is a version
of the consensus data that is not globally normalized meaning that responses are
scaled [0,1]
within each responding unit (receptor, sensory neuron,
glomerulus...).
import_new_data()
{#import_new_data}It is easy to add new response data to DoOR, we only have to take care to provide it in the right format:
?read.table
for detailed specifications). * the filename corresponds to the
later name of the data set * if we add e.g. recordings obtained with different
methods, these should go into two data sets and thus into two different files
that we import * e.g. "Hallem.2004.EN" and "Hallem.2004.WT" are the "empty
neuron" and the "wildtype neuron" recordings from Elissa Hallem's 2004
publication * the file needs at least two columns: 1. one column named
"InChIKey" holding the InChIKey of the odorant 1. one column named after the
responding unit the recording comes from (e.g. "Or22a")A minimal example file could look like this:
tmp <- Or22a[c(1,3:5), c(3,6)] colnames(tmp)[2] <- "Or22a" knitr::kable(tmp)
We can provide more chemical identifiers:
tmp <- Or22a[c(1,3:5), c(1:6)] colnames(tmp)[6] <- "Or22a" knitr::kable(tmp)
Any of the following will be imported:
Class
: e.g. "ester"
: the chemical class an odorant belongs to
Name
: e.g. "isopentyl acetate"
InChIKey
: e.g. "MLFHJEHSLIIPHL-UHFFFAOYSA-N"
(details)
InChI
: e.g. "InChI=1S/C7H14O2/c1-6(2)4-5-9-7(3)8/h6H,4-5H2,1-3H3" (details)
CAS
: e.g. "123-92-2" (details)
CID
: e.g. "31276" (details)
SMILES
: e.g. "C(C(C)C)COC(=O)C"
(details)
See ?import_new_data
for more details. We can e.g. import data also based on
CAS or CID instead of InChIKey.
your data set, we recommend using the
webchem
package for automated
lookup or doing it manually via http://cactus.nci.nih.gov/chemical/structure
or any other chemical lookup service.
create_door_database()
{#create_door_database} Once we imported new data we can
use create_door_database()
in order to rebuild both response matrices. During
the merge process some data sets might be excluded because either their overlap
with other studies is too low or the fit against other studies is too bad; these
studies will be recorded in door_excluded_data
.
update_door_database()
{#update_door_database} If we imported new data only for
a few receptors, we can update the data base with update_door_database()
.
There are two ways to update the data base:
all data sets. All possible binary combinations of data sets will be merged using 10 different fitting functions on the odorants that were measured in both data sets. The two data sets yielding the "best merge" (i.e. lowest mean deviations of points from the fitted function) will be merged. The process of pairwise merges will be repeated with the "merged_data" against the remaining data sets until all of them are included:
update_door_database("Or92a", permutation = FALSE, plot = TRUE)
to test all possible sequences of data set merges, calculating the mean deviations from all original data sets and selecting the merge that produces the lowest mean deviations. This approach works well for responding units that contain a low number of recorded data sets. For responding units containing 5 data sets we have to calculate merges for 120 different sequences. With 6 it is already 720 sequences and with 10 data sets we have to test > 3.6 million different sequences.
While this can be done via parallel computing, this is nothing you should try on your home PC. For the pre-computed response matrices we performed matches using the permutation approach for all responding units that contained a maximum of 10 different data sets on a computing cluster. For DoOR 2.0 these are all responding units except Or22a.
update_door_database("Or67a", permutation = TRUE, plot = FALSE)
model_response()
and model_response_seq()
{#model}update_door_database()
and createDatabse()
call model_response()
and
model_response_seq()
to perform the merges and update the different DoOR
objects. If we only want to perform a merge we can call them both directly.
model_response()
model_response()
returns a list containing the merged data, the names of the excluded data sets (if any) and the names of the included data sets (if any were excluded).
merge <- model_response(Or67a, plot = FALSE) knitr::kable(head(merge$model.response))
model_response_seq()
update_door_database()
with permutation = TRUE
calls model_response_seq()
.
Like model_response()
we can also call model_response_seq directly:
SEQ <- c("Hallem.2006.EN","Kreher.2008.EN","Hallem.2006.EN") merge <- model_response_seq(Or35a, SEQ = SEQ, plot = TRUE) head(merge)
remove_study()
{#remove_study} remove_study()
willremove a data set from all DoOR data objects. If we import a data set that
already exists with import_new_data()
, remove_study()
will automatically run
before the data is imported.
remove_study(study = "Hallem.2004.EN")
update_door_odorinfo()
{#update_door_odorinfo} If we edit the general odor
information in DoOR.data::odor
we need to update all other DoOR objects with
the new information. update_door_odorinfo()
overwrites the first 5 columns of
the DoOR responding units data frames (e.g. Or22a
), it does not add or remove
lines!
There are several small helper functions that belong to DoOR.functions
.
trans_id()
{#trans_id}Maybe the most important little function in DoOR. With trans_id()
we can
translate odorant identifiers, e.g. from CAS numbers to InChIKeys or to names.
The information is taken from DoOR.data::odor
, any colnames(odor)
can be
used to define input or output:
trans_id("123-92-2") trans_id("123-92-2", to = "Name") trans_id("carbon dioxide", from = "Name", to = "SMILES") odorants <- c("carbon dioxide", "pentanoic acid", "water", "benzaldehyde", "isopentyl acetate") trans_id(odorants, from = "Name", to = "InChI")
reset_sfr()
{#reset_sfr} reset_sfr()
subtracts the values of a specifiedodorant from a response vector or from the whole response matrix. It is usually
used to subtract the spontaneous firing rate of an odorant, thus setting it to
zero and restoring inhibitory responses. We treat SFR like a normal odorant
during the merging process, thus it becomes > 0 if negative values exist (as all
data gets rescaled [0,1]
before merging).
reset_sfr()
works either on the whole door_response_matrix
, then an odorant
InChIKey has to be specified for subtraction. Or it subtracts a value from a
response vector.
rm_sfrReset <- reset_sfr(x = door_response_matrix, sfr = "SFR") knitr::kable(rm_sfrReset[1:10,6:15], digits = 2)
reset_sfr(x = c(1:10), sfr = 4)
door_default_values()
{#door_default_values} door_default_values()
returnsdefault values for several parameters used by the DoOR functions, e.g. the default odor identifier of the colors used in plots.
door_default_values("ident") door_default_values("colors")
get_responses()
{#get_responses} get_responses()
returns the responsevalues of one or several odorants across individual data sets.
odorants <- trans_id(c("carbon dioxide", "isopentyl acetate"), from = "Name") responses <- get_responses(odorants) responses <- na.omit(responses) knitr::kable(head(responses))
get_normalized_responses()
{#get_normalized_responses}get_normalized_responses()
gathers responses to the specified odorants from
the door_response_matrix and resets the SFR via reset_sfr()
:
odorants <- trans_id(c("carbon dioxide", "isopentyl acetate"), from = "Name") responses <- get_normalized_responses(odorants) responses <- na.omit(responses) knitr::kable(head(responses))
countStudies()
{#countStudies} countStudies()
counts the number of studiesthat measured a given odorant-responding unit combination.
counts <- countStudies() knitr::kable(counts[1:10,6:15])
export_door_data()
{#export_door_data} export_door_data()
exports all orselected DoOR data objects in txt or csv format.
# export_door_data(".csv") # export all data as .csv files # export_door_data(".txt", all.data = FALSE) # export odorant responses data # only as .txt files
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