The Database of Odor Responses - DoOR functions package • DoOR.functions

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. ##

Loading DoOR

The first step after starting R is to attach both packages and to load the response data:

library(DoOR.data)

## 
## Welcome to DoOR.data
## Version: 2.0.1
## Released: 2017-03-07
## 
## Please use load_door_data() to load all data into your workspace now.

library(DoOR.functions)

## 
## Welcome to DoOR.functions
## Version: 2.0.2
## Released: 
## 
## Please note that all function names have been changed to 'snake_case'
##        for consistency.
##       
## Old  'camelCase' function names will stop working in one of the next
##        versions.

load_door_data(nointeraction = TRUE)

load_door_data() attaches the data from DoOR.data.

Modifying, building and updating DoOR

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…).

Importing new data with `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:

either a .csv or a .txt file with fields separated by colons or tabs (see ?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:

	InChIKey	Or22a
1	SFR	4
3	VHUUQVKOLVNVRT-UHFFFAOYSA-N	17
4	KIDHWZJUCRJVML-UHFFFAOYSA-N	16
5	VHRGRCVQAFMJIZ-UHFFFAOYSA-N	17

We can provide more chemical identifiers:

	Class	Name	InChIKey	CID	CAS	Or22a
1	NA	sfr	SFR	SFR	SFR	4
3	amine	ammonium hydroxide	VHUUQVKOLVNVRT-UHFFFAOYSA-N	14923	1336-21-6	17
4	amine	putrescine	KIDHWZJUCRJVML-UHFFFAOYSA-N	1045	110-60-1	16
5	amine	cadaverine	VHRGRCVQAFMJIZ-UHFFFAOYSA-N	273	462-94-2	17

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.

Looking up InChIKeys If you do not know the InChIKeys of the odorants in

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.

Building the complete data base with

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.

Updating parts of the data base with

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:

Using the heuristic approach This is the faster way to perform a merge of

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)

## Not all datasets could be merged because they did not reach the
##                  criterion.

## door_response_matrix_non_normalized has been updated for Or92a

## door_response_matrix has been updated for Or92a

## door_excluded_data has been updated for Or92a

Trying all permutations The more exhaustive way to update the data base is

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)

## All possible permutations (6) have been calculated, now merging.

## Warning in getInitial.selfStart(func, data, mCall = as.list(match.call(func, :
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## [1/6] Hallem.2006.EN, Gabler.2013.AL, Hallem.2004.EN ------ Mean distance: 0.0282

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## [2/6] Hallem.2006.EN, Hallem.2004.EN, Gabler.2013.AL ------ Mean distance: 0.0251

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## [3/6] Gabler.2013.AL, Hallem.2006.EN, Hallem.2004.EN ------ Mean distance: 0.0282

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## [4/6] Gabler.2013.AL, Hallem.2004.EN, Hallem.2006.EN ------ Mean distance: 0.0288

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## [5/6] Hallem.2004.EN, Hallem.2006.EN, Gabler.2013.AL ------ Mean distance: 0.025

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## [6/6] Hallem.2004.EN, Gabler.2013.AL, Hallem.2006.EN ------ Mean distance: 0.0287

## --------------------------------------------------------

## The optimized sequence with the lowest mean MD 0.025 is:

## Hallem.2004.EN -> Hallem.2006.EN -> Gabler.2013.AL

## Warning in getInitial.selfStart(func, data, mCall = as.list(match.call(func, :
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## door_response_matrix_non_normalized has been updated for Or67a

## door_response_matrix has been updated for Or67a

## door_excluded_data has been updated for Or67a

`model_response()` and `model_response_seq()`

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.

Merging using the heuristic with `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))

Class	Name	InChIKey	CID	CAS	merged_data
NA	sfr	SFR	SFR	SFR	0.0401343
other	water	XLYOFNOQVPJJNP-UHFFFAOYSA-N	962	7732-18-5	NA
amine	ammonium hydroxide	VHUUQVKOLVNVRT-UHFFFAOYSA-N	14923	1336-21-6	0.0640151
amine	putrescine	KIDHWZJUCRJVML-UHFFFAOYSA-N	1045	110-60-1	0.0220159
amine	cadaverine	VHRGRCVQAFMJIZ-UHFFFAOYSA-N	273	462-94-2	0.0141344
amine	ammonia	QGZKDVFQNNGYKY-UHFFFAOYSA-N	222	7664-41-7	NA

Merging in a specific sequence with `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)

## Warning in getInitial.selfStart(func, data, mCall = as.list(match.call(func, :
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head(merge)

## [1] 0.09675994         NA 0.09047548 0.07062655 0.12658721         NA

Removing a study with `remove_study()` `remove_study()` will

remove 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")

## removed Hallem.2004.EN from Or2a .

## removed Hallem.2004.EN from Or7a .

## removed Hallem.2004.EN from Or9a .

## removed Hallem.2004.EN from Or10a .

## removed Hallem.2004.EN from Or19a .

## removed Hallem.2004.EN from Or22a .

## removed Hallem.2004.EN from Or23a .

## removed Hallem.2004.EN from Or33b .

## removed Hallem.2004.EN from Or35a .

## removed Hallem.2004.EN from Or43a .

## removed Hallem.2004.EN from Or43b .

## removed Hallem.2004.EN from Or47a .

## removed Hallem.2004.EN from Or47b .

## removed Hallem.2004.EN from Or49b .

## removed Hallem.2004.EN from Or59b .

## removed Hallem.2004.EN from Or65a .

## removed Hallem.2004.EN from Or67a .

## removed Hallem.2004.EN from Or67c .

## removed Hallem.2004.EN from Or82a .

## removed Hallem.2004.EN from Or85a .

## removed Hallem.2004.EN from Or85b .

## removed Hallem.2004.EN from Or85f .

## removed Hallem.2004.EN from Or88a .

## removed Hallem.2004.EN from Or98a .

## removed Hallem.2004.EN from 'door_response_range'

## removed Hallem.2004.EN from 'door_global_normalization_weights'

Updating the odor information with

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!

Helper functions

There are several small helper functions that belong to DoOR.functions.

`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")

## [1] "MLFHJEHSLIIPHL-UHFFFAOYSA-N"

trans_id("123-92-2", to = "Name")

## [1] "isopentyl acetate"

trans_id("carbon dioxide", from = "Name", to = "SMILES")

## [1] "O=C=O"

odorants <- c("carbon dioxide", "pentanoic acid", "water", "benzaldehyde", 
              "isopentyl acetate")
trans_id(odorants, from = "Name", to = "InChI")

## [1] "InChI=1S/CO2/c2-1-3"                                
## [2] "InChI=1S/C5H10O2/c1-2-3-4-5(6)7/h2-4H2,1H3,(H,6,7)" 
## [3] "InChI=1S/H2O/h1H2"                                  
## [4] "InChI=1S/C7H6O/c8-6-7-4-2-1-3-5-7/h1-6H"            
## [5] "InChI=1S/C7H14O2/c1-6(2)4-5-9-7(3)8/h6H,4-5H2,1-3H3"

`reset_sfr()` `reset_sfr()` subtracts the values of a specified

odorant 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)

	ac3B	ac4	Or1a	Or2a	Or7a	Or9a	Or10a	Or13a	Or19a	Or22a
SFR	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
XLYOFNOQVPJJNP-UHFFFAOYSA-N	0.02	0.01	0.02	NA	0.07	0.00	0.01	0.12	NA	0.28
VHUUQVKOLVNVRT-UHFFFAOYSA-N	0.33	0.06	NA	0.01	0.04	0.04	0.00	0.04	0.00	0.10
KIDHWZJUCRJVML-UHFFFAOYSA-N	0.37	0.00	NA	0.02	0.03	0.03	0.01	0.07	-0.02	0.09
VHRGRCVQAFMJIZ-UHFFFAOYSA-N	0.02	-0.01	NA	0.00	-0.01	0.08	-0.01	NA	-0.01	0.01
QGZKDVFQNNGYKY-UHFFFAOYSA-N	0.06	0.08	NA	NA	0.06	NA	0.05	0.04	NA	0.00
HZAXFHJVJLSVMW-UHFFFAOYSA-N	NA	0.01	NA	NA	0.06	NA	0.02	0.05	NA	0.20
WJYIASZWHGOTOU-UHFFFAOYSA-N	0.41	0.00	NA	NA	NA	NA	NA	NA	-0.06	NA
BMFVGAAISNGQNM-UHFFFAOYSA-N	0.32	-0.01	NA	NA	NA	NA	NA	NA	NA	NA
ROSDSFDQCJNGOL-UHFFFAOYSA-N	0.19	0.09	NA	NA	NA	NA	NA	NA	NA	NA

reset_sfr(x = c(1:10), sfr = 4)

##  [1] -3 -2 -1  0  1  2  3  4  5  6

`door_default_values()` `door_default_values()` returns

default 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")

## [1] "InChIKey"

door_default_values("colors")

## [1] "#0570b0" "#74a9cf" "#ffffff" "#fdcc8a" "#fc8d59" "#d7301f"

`get_responses()` `get_responses()` returns the response

values 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))

	ORs	studies	Odor	Response
25	ac3B	Marshall.2010.WT	CURLTUGMZLYLDI-UHFFFAOYSA-N	13
29	ac4	Marshall.2010.WT	CURLTUGMZLYLDI-UHFFFAOYSA-N	-1
30	ac4	Yao.2005.WT	MLFHJEHSLIIPHL-UHFFFAOYSA-N	1
33	Or1a	Kreher.2008.EN	CURLTUGMZLYLDI-UHFFFAOYSA-N	4
34	Or1a	Kreher.2008.EN	MLFHJEHSLIIPHL-UHFFFAOYSA-N	21
36	Or2a	Kreher.2008.EN	MLFHJEHSLIIPHL-UHFFFAOYSA-N	73

`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))

	ORs	Odor	Response
11	ac3B	CURLTUGMZLYLDI-UHFFFAOYSA-N	-0.043
13	ac4	CURLTUGMZLYLDI-UHFFFAOYSA-N	-0.001
14	ac4	MLFHJEHSLIIPHL-UHFFFAOYSA-N	0.008
15	Or1a	CURLTUGMZLYLDI-UHFFFAOYSA-N	0.007
16	Or1a	MLFHJEHSLIIPHL-UHFFFAOYSA-N	0.068
17	Or2a	CURLTUGMZLYLDI-UHFFFAOYSA-N	-0.046

`countStudies()` `countStudies()` counts the number of studies

that measured a given odorant-responding unit combination.

counts <- countStudies()
knitr::kable(counts[1:10,6:15])

	ac3B	ac4	Or1a	Or2a	Or7a	Or9a	Or10a	Or13a	Or19a	Or22a
SFR	3	3	1	3	9	2	6	7	3	9
XLYOFNOQVPJJNP-UHFFFAOYSA-N	2	2	1	1	2	1	1	2	0	1
VHUUQVKOLVNVRT-UHFFFAOYSA-N	2	2	0	1	2	2	2	1	2	2
KIDHWZJUCRJVML-UHFFFAOYSA-N	3	3	0	1	4	2	3	2	2	3
VHRGRCVQAFMJIZ-UHFFFAOYSA-N	1	1	0	1	2	1	1	0	2	1
QGZKDVFQNNGYKY-UHFFFAOYSA-N	1	1	0	0	2	0	1	1	0	1
HZAXFHJVJLSVMW-UHFFFAOYSA-N	0	1	0	0	1	0	1	1	0	1
WJYIASZWHGOTOU-UHFFFAOYSA-N	1	2	0	0	0	0	0	0	1	0
BMFVGAAISNGQNM-UHFFFAOYSA-N	1	2	0	0	0	0	0	0	0	0
ROSDSFDQCJNGOL-UHFFFAOYSA-N	2	2	0	0	0	0	0	0	0	0

`export_door_data()` `export_door_data()` exports all or

selected 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

The Database of Odor Responses - DoOR functions package

Daniel Münch

2024-11-27

Content

Loading DoOR

Modifying, building and updating DoOR

Importing new data with `import_new_data()`

Looking up InChIKeys If you do not know the InChIKeys of the odorants in

Building the complete data base with

Updating parts of the data base with

Using the heuristic approach This is the faster way to perform a merge of

Trying all permutations The more exhaustive way to update the data base is

`model_response()` and `model_response_seq()`

Merging using the heuristic with `model_response()` `model_response()`

Merging in a specific sequence with `model_response_seq()`

Removing a study with `remove_study()` `remove_study()` will

Updating the odor information with

Helper functions

`trans_id()`

`reset_sfr()` `reset_sfr()` subtracts the values of a specified

`door_default_values()` `door_default_values()` returns

`get_responses()` `get_responses()` returns the response

`get_normalized_responses()`

`countStudies()` `countStudies()` counts the number of studies

`export_door_data()` `export_door_data()` exports all or

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The Database of Odor Responses - DoOR functions package

Daniel Münch

2024-11-27

Content

Loading DoOR

Modifying, building and updating DoOR

Importing new data with import_new_data()

Looking up InChIKeys If you do not know the InChIKeys of the odorants in

Building the complete data base with

Updating parts of the data base with

Using the heuristic approach This is the faster way to perform a merge of

Trying all permutations The more exhaustive way to update the data base is

model_response() and model_response_seq()

Merging using the heuristic with model_response() model_response()

Merging in a specific sequence with model_response_seq()

Removing a study with remove_study() remove_study() will

Updating the odor information with

Helper functions

trans_id()

reset_sfr() reset_sfr() subtracts the values of a specified

door_default_values() door_default_values() returns

get_responses() get_responses() returns the response

get_normalized_responses()

countStudies() countStudies() counts the number of studies

export_door_data() export_door_data() exports all or

Importing new data with `import_new_data()`

`model_response()` and `model_response_seq()`

Merging using the heuristic with `model_response()` `model_response()`

Merging in a specific sequence with `model_response_seq()`

Removing a study with `remove_study()` `remove_study()` will

`trans_id()`

`reset_sfr()` `reset_sfr()` subtracts the values of a specified

`door_default_values()` `door_default_values()` returns

`get_responses()` `get_responses()` returns the response

`get_normalized_responses()`

`countStudies()` `countStudies()` counts the number of studies

`export_door_data()` `export_door_data()` exports all or