DoOR: Database of Odorant Receptors User's Guide Preliminary
Contents
1. DoOR function mkdir man Edit a file called DESCRIPTION you can write this file by following the extension 42 manual at http cran r project org doc manuals R exts pdf Then put the function files such as default val R projectPoints R etc into the directory R The man directory contains the Rd files that share the same names with function such as default val Rd projectPoints Rd etc If you want to know how to write an Rd file in detail please see the extension manual or follow the instruction template created by package sekeleton which will be described in the Windows section To check whether the function and the help files Rd have been correctly written go back to home directory type S R CMD check DoOR function R CMD check can also detect the locations of mistakes If everything is fine you can build a package by typing R CMD build DoOR function then a package called DoOR function 0 1 1 tar gz will be created For Windows Because R was designed in a Unix environment there are some components such as compilers and programs that are missing in Windows so that you need to download and install those components We build the package for Windows by following the instruction at http www maths bris ac uk maman computerstuff Rhelp Rpackages html Win Win User can find the link to download those components including Perl cygwin mingwin and hhc exe2. Rs lt NA next if lm y x Nissler coe Scoef 2 if the two data are fitted horizontally or vertically then return NA and run next loop 2 0 is na lm y x_Nissler Rs lt NA next else Rs lt cor test x x Nissler y y Sestimat Rss lt Rs names Rss i res Nissler lt Gc res Nissler Rss 35 sort the data in decreasing order gt sorted res Nissler lt sort res Nissler decreasing TRUE Plot the data after specifying the margin size gt par mar c 9 4 5 3 gt barplot sorted res Nissler las 2 ylim c 0 3 1 main Mapping response profiles of study Nissler 2007 nmr to antennal receptors and ORNs cex main 0 8 ylab c Pearson correlation coefficient Mapping response profiles of study Nissler 2007 nmr to antennal receptors and ORNs 1 0 0 8 0 6 0 4 a Illis Pearson correlation coefficient I o N lt L UUA UUU UM C G Q O n Cm oam OL o oa oa o ona oA oa C GADNO NOM 0 co MOI NOINWI AN e P L0 P7 AAS 02 00 LO 00 00 O T QY T O99 FT AVON FNP OPEV OPES FT 5OCP TOOT YY 5000 000 000000 9606 66 6 SOO00000008 Figure 10 Mapping response profile of sdataset Nissler 2007 nmr to antennal receptors and ORNs 36 5 Extension 5 1Importing new data The general format of odorant response data is shown in following table The first four columns are set for odorant class amine acid etc name chemical iden
3. gt bp data lt Or22a c 1 4 13 first columns of bp data contain odorant information the 13th column is the recording values from Pelz 2006 AntEC50 gt dec50 lt backProject cons data RP Or22a bp data bp data tag odor CAS tag cons data merged data tag bp data Pelz 2006 AntEC50 join two coordinate vectors that represent the projection of consensus response of propyl acetate onto fitted curve gt x01 lt c DoORnorm RP Or22a 5 which RP Or22aSName propyl acetate DoORnorm RP Or22a 5 which RP Or22aSName propyl acetate gt y01 c 0 2 dec50Soutput which dec50S outputSName propyl acetate projected Y gt lines x x01 y y01 Draw a line with arrow that points the back projected value gt arrows xO x01 2 yO yO1 2 x1 1 02 yl y01 2 draw the tricks and labels that represent the 24 backprojected scale labels2 lt seq 0 2 1 length 7 The scale of backprojection is linear correlated to the scale of normalized measured data labels4 lt dec50Srescale 1 labels2 dec50Srescale 2 axis 4 at labels2 labels labels4 model expo n 26 MD 0 014307 Pelz 2006 AntE C50 0 0 02 04 06 08 1 0 merged data Figure 10 Back projection The consensus data was put on x data Pelz 2006 AntECS0 was put on y Yellow lines indicate the odorants that have not been measured i
4. Asym exp exp lrc F asymp x Asym RO Asym e e x Inverse asymp log x Asym RO Asym exp 1rc x 4 5 6 7 8 9 28 log x Asym RO Asym 10 f inv asymp x a el STEP 2 Select the best fitting model from ten optional models based on correlation coefficients Now we have a model function Yf X 11 STEP 3 Project the observation points onto the fitted line minimizing the distance between the observation point and the projected point min x X y f XP 12 then the optimum can be found by following equation FOGuu XY tu F X o 13 We take the derivative with respect to X The result shows as calculation details are shown in appendix 1 X 655 X f X Yoas O Dor 14 xc Mem with this equation we can compute the X coordinate so as Y coordinate on the functional line which s distance to the observation is minimum STEP 4 Compute the distance between two points on the functional line ds J14 Y ax 15 STEP 5 29 Transfer the distance values in values from 0 to 1 In DoOR package three basic functions are used for heterogeneous datasets integration Function modelfunction is used for estimating the parameters cal model for choosing the optimized model step 1 and step 2 Function projectPoints executes all five steps to produce a consensus set of response values Example for heterogeneous d
5. force binary DoOR test 44 6 Acknowledgment 7 References Meister M amp Bonhoeffer T Tuning and topography in an odor map on the rat olfactory bulb J Neurosci 2001 27 1351 1360 Sachse S Rappert A amp Galizia C G The spatial representation of chemical structures in the antennal lobe of honeybees steps towards the olfactory code 1999 77 3970 3982 Kim H Golub G H amp Park H Missing value estimation for DNA microarray gene ex pression data local least squares imputation Bioinformatics 2005 21 187 198 Hallem E A amp Carlson J R Coding of odors by a receptor repertoire Cell 2006 125 143 160 Kreher S A Mathew D Kim J amp Carlson J R Translation of sensory input into be havioral output via an olfactory system Neuron 2008 59 110 124 Couto A Alenius M amp Dickson B J Molecular anatomical and functional organiza tion of the Drosophila olfactory system Curr Biol 2005 15 1535 1547 de Bruyne M Foster K amp Carlson J R Odor coding in the Drosophila antenna Neu ron 2001 30 537 552 Root C M Semmelhack J L Wong A M Flores J amp Wang J W Propagation of olfactory information in Drosophila Proc Natl Acad Sci U S A 2007 104 11826 11831 http www maths bris ac uk maman computerstuff Rhelp Rpackages html R Development Core Team 2009 R A language and environment for statistical computing In Vienna Austria R Foundation
6. This is due to the complexity of odorant receptors and the close to infinite number of odors according to previous studies Meister et al 2001 Sachse et al 1999 odorants that have similar structure elicit similar response patterns By following this concept the odorant response of the target odor can be estimated as a linear combination Kim et al 2005 in cases where we could find similar odors There are several approaches to select similar odors sorting by chemical and physical properties coherent odors that have large absolute values of Pearson correlation coefficients and a third party odorant matrix In most cases odorant response pattern can not be classified simply by chemical and physical properties but more satisfactory mapped onto chemical space Schmuker and Schneider 2007 In this study we are mining the information of odorant similarity from own response data matrix In other words to say we select the similar odors by targeting the numeric feature space of odorant responses that have large absolute values of Pearson correlation coefficients Estimating the response of receptor Or22a to ethanol CAS 64 17 5 18 target receptor and odor receptor lt Or22a CAS 64 17 5 data response matrix responseMatrix response matrix assign response value NA to target odor receptor responseMatrix CAS receptor lt NA show the measured value a sub function that is used for finding k o
7. odors may not have been found in some odorant receptors Inhibition is defined as a value that is lower than the spontaneous firing rate In order to visualize inhibitory responses we subtract the spontaneous firing rate from the consensus response values and then renormalize the response spectrum max x CERES max x SFR reseSEg X SFR where x is the vector containing the response values of given receptor and SFR is a numeric value indicating the spontaneous firing rate of this receptor gt data response matrix gt resetRM apply response matrix 2 function x resetSFR x x 1 apply resetSFR on each column the first value of each column is the value of spontaneous firing rate gt ORdotplot resetRM c 150 240 cex labels 0 7 type BW dot Size 1 5 16 NO NINON A gt DIMARWOONAHAWWN SAANOOAIPORAWW w R e oO 1 I A I JOXoO1YOo 1 Q0 A 000 KO o LJ NO co Oo oi AW ONBMDARWOBNOOUIAW CM ect RR St FUSS ON Ta eN NNAONNA NONOWOQOQONN gt 0Q NON gt gt V0OANN AE AIN NDODO O e ee9 o e o O5 P MON e e ce ec e Oo ee 1 oon I MO 28238 eee e M TO V A t T 0 G6 6 0 O O0 C6 0 0 O O Ta C0 O 0 Ta C G 0 0 0 O D 0 CHOOT HTT C6 0 C O C O O O VO 0 0 C C c CNCO ELO V SNA OH OMA COIQICO SF QNIOCOCOCOLOCNICICOCOLOLOCOP AA ON DBMS P 1 NO TN QICOCOLOLOLOLOLO LOCO GNIS S EOD D a909ogGo ODLOAAADEN SEE erOe
8. on the website After all components have been installed you need to change the PATH environment variable to locate the command prompts The environment variable can be found by right clicking on the My Computer then clicking on the advanced tab Find the path and then add C Perl bin C cygwin C mingwin bin NOTE PLEASE do not delete other path variables Start R source the function and read the data by typing source default val R source projectPoints R gt Or22a read table Or22a txt 43 gt Orl3a read table Orl3a txt Specify the function names and data names respectively gt g cs lt c Or22a Orl3a gt funs lt c default val projectPoints build a package template gt package skeleton list c Ors funs name DoOR test Creating directories Creating DESCRIPTION Creating Read and delete me Saving functions and data Making help files Done Further steps are described in DoOR test Read and delete me Then you will find a directory containing man data R and two files Read and delete me and DESCRIPTION Edit the DESCRIPTION files and all Rd files in the man directory simply by filling the missing text and answering the instruction questions After you finished editing you can create a package by the following C Remd check DoOR test C Remd build
9. positive and negative response respectively If the data is consensus a color ramp will be used for coding response intensity Two examples show PlotChemicals for Or47b The spontaneous firing rate SFR were subtracted from response values for both cases 11 Response Profile of Or47b Hallem 2006 EN gt data Or47b E2 hexenal gamma butyrolactone 2 3 butanedione gt Or47bHallem G methyl 5 hepten 2 one methyl salicylate Or47b rc 1 B 5 hex l hexaricate etazpinene SF alpha humulene beta myrcene substract spontaneous firing geranyl cetate p ethyl ME value from odorant response one cymene values gamma d cal ctone ethyl decanoate delta decalactone nonanoic acid methyl benzoate gt Or47bHallem 5 lt eugenol i Or47bHallem 5 4 ethyiguaiacol trans hyll Or47bHallem 1 5 etras saver wesieerenere heptanoic acid gt PlotChemicals geranio Or47bHallem order Or47bHallem prec ammonium hydroxide dimethyl sulfide 5 apn pinene alpha terpineol linalool oxide decreasing TRUE PAS W ctanoic acid I thy b t tag Name x range c 40 gihyl benzoate 20 1 N e N o AB o Figure 1 response profile of Or47b measured by Hallem in the empty neuron preparation Red color codes for positive values and blue for negative values gt d a t a O 1 4 7 b Response Profile of Or47b consensus value ia butyrolactone RP O
10. 198 0 49344978 NA 0 49344978 0 841724338 1 096304885 0 632018454 A list of two results is given in data tan22a One is tan22a Double Observations the other is tan22a Single Observation Both results have a same formation ID indicates the original position of data x and y x and y indicate the coordinate of observation X and Y indicate the coordinate of projected point on the functional line distance 30 indicates the distances between xmin f xmin and all points on the functional line NDR indicates the normalized distances across all the distance values Data tan22a Double Observations means that those odors were taken by both studies whereas data tan22a Single Observation means that those odors were not tested by both studies but either of them Since two data sets have a same odor arrangement We can address the odorant names according to their ID gt doubObserv ID lt tan22a Double Observations ID doubObserv data data frame Name Or22a doubObserv ID 2 CAS Or22a doubObs rv ID 4 model response tan22a Double Observations 7 Hallem 2006 EN9r22 doubObserv ID 5 Pelz 2006 AntEC50 Or22a doubObserv ID 13 gt ordered doubObserv data doubObserv data order doubObserv data 3 decreasing TRUE gt rownames doubObserv data lt seq 1 dim doubObserv data EI gt ordered doubObserv data Name CAS model response Hallem 2006 EN Pelz 2006 AntEC50 14 ethyl hexa
11. 2007 Schmuker 2007 TR contains data that were recorded in wild type neurons ab6A by de Bruyne and published as Schmuker 2007 13 gt data Orl3a gt comparDiagram x Orl3a y Orl3a by x Kreher 2008 EN by y Schmuker 2007 TR data X Kreher 2008 EN data Y Schmuker 2007 TR 3391 86 4 7 5898 0 1 eee 1001275 ENSE mn 6728 26 3 _ L 628 6377 a ENT O 71 36 3 O 123 92 2 7 oO 124 38 9 7 o 105 54 4 O 0 SFR oO 431 03 8 7 141 78 6 7 108 94 1 105 873 1 93 15 2 O 106 44 5 DO 119 36 8 J O 100 52 7 oO 98 86 2 T T T 1 189 126 63 0 36 126 189 Figure 3 Comparison of the two studies The values on the left side were ordered decreasingly the values on the right side were ordered by matching the CAS number to the left bo Visualize the response profile of odor receptors to given odors VV VN data odor data response matrix cas c 71 36 3 123 92 2 79 09 4 consResp findRespNorm cas response matrix head consResp ORs ab2B ab2B ab2B ab3B ab3B ab3B Nu C ND P Odor 71 36 3 123 92 2 79 09 4 71 36 3 123 92 2 79 09 4 Response 0 009879338 0 037249586 NA 0 070703791 0 048678680 NA responseMatrix show first six rows 14 gt PlotReceptors consResp odor data odor tag Name Chemical Responses Crossing Odorant Receptors
12. 55 0 09270386 0 09771639 0 09985090 0 11044907 0 14725503 0 15258732 Gr21a Or35a Or7a 0 20561416 0 22096924 0 22664469 Sort the data candi A according to vector w and b candi A na omit responseMatrix subsetodor selectReceptor the two most similar odors were selected nearestodor nearest target w candicate candi A k 2 gt nearestodor 67 56 1 71 36 3 0 9584361 0 7480300 selectedOdor names nearestodor A candi A selectedOdor A Or49b Or67a Or23a Or88a Or2a Or98a 67 56 1 0 02253503 0 02420906 0 01315592 0 04173466 0 05212804 0 04419391 71 36 3 0 03642539 0 17241852 0 19052745 0 05920648 0 12709516 0 08030602 Or85f Or47a Or59b Or43a Or82a Or67c 67 56 1 0 05433386 0 06942072 0 06127587 0 08184591 0 02205249 0 05499538 71 36 3 0 10937467 0 14079881 0 12511124 0 08638887 0 11748797 0 10443982 values of target odor across 20 Or65a Orl0a Or43b Orl19a Or85a Or47b 67 56 1 0 06790893 0 05113509 0 08518086 0 08343347 0 08906153 0 10439306 71 36 3 0 05121011 0 03203408 0 31520691 0 26421047 0 05089908 0 08538568 Or85b Or9a Or33b Gr21a Or35a Or7a 67 56 1 0 0688171 0 1605819 0 14964253 0 2056142 0 2381653 0 2029986 71 36 3 0 1766872 0 3957389 0 05647619 0 2754819 0 7847021 0 6133272 b responseMatrix selectedOdor receptor b 1 0 1179159 0 2589689 Up to here the linear combination of two linear systems la w and b 4 a Ww b A has been formed They share the same coefficients in two the linear sys
13. B 1 butanol B sopentyl acetate B propanoic acid B 1 butanol B isopentyl acetate B propanoic acid B 1 butanol B B B B B a a isopentyl acetate prop noic acid 1 butanol sopentyl acetate propanoic acid 1 butanol isopentyl acetate ac a prop noic acid b 1 butanol ac1b isopentyl acetate acib propanoic acid ac2a 1 butanol ac2a isopentyl acetate acaa prop noic acid ac2b 1 butanol ac2b isopentyl acetate ac2b propanoic acid acsa 1 butanol acda isopentyl acetate ac3a propanoic acid ac 1 butanol ac sopentyl acetate c4 propanoic acid a 1 butanol a isopentyl acetate a propanoic acid a 1 butanol a isopentyl acetate a propanoic acid a a a a a a P 1 butanol isopentyl acetate propanoic acid aa 1 butanol isopentyl acetate propanoic acid ri I 0 n Oo E og o 0 5 0 0 0 5 15 Figure 4 The color bar from red to blue indicates that the response intensity from most excitatory to most inhibitory White area between red and blue indicates that the odor response is weak Overview of response matrix with dotplot The consensus values in the response matrix are normalized within the range from 0 to 1 Because the response profiles of the odorant receptors have been globally normalized maximum response values of each receptor do not necessarily reach 1 indicating that the best
14. DoOR Database of Odorant Receptors User s Guide Preliminary Version Authors Shouwen Ma Daniel Miinch Martin Strauch Anja Nissler C Giovanni Galizia Zoology and Neurobiology University of Constance Germany August 2009 A free open source software Contents Document conventions aise cess heeccnstetnedanaeass cuueooacae cs qnensasaeasis voaeesenseslaytaies 3 1 Ti TrOGUCHON 250 sobs 5 venkese abiaseatteenephagusdnines sacaespteakendaeusieanss piasteestavenieees 4 2 l0 fi NN 5 S Webs ll mEmm 5 UMP Seg on MM Pr 5 3 Quick SEAR Wied eden eer spese des ass eeaavice sina staeseve sede V E 6 3 1 Access odorant receptor data ceeeeeeeeeeeee een 6 3 2 Access supported data iere eene yr onerare rear rennen ouk 8 33 Data NAGAI ZA arises eoe IIO Se In EIS CRI ROS vhs eo ek ener iaae 9 3 4 Odorant responses estimation cceeeeeeeee eere nnne 15 35 Back Projection Learner Sopa REEYA ae RERO SVEN EARS oS VE EA 19 4 opino M e VP db 22 4 1 Integration approach eeeeeeeeee eene enne 22 4 23 Mapping receptors into database eere enne 28 5 IExignsQ o aum E e rr E EUER EnS Rore CRM RUE 32 5 1 Import the data and update the supported data 32 5 2 Update response matrix Ment Fencccvccsccnccnsceccccsvcconcces 35 5 3 Build packages eee eral he ee eee re
15. Name CAS newdata 2009 nmr 136 l propanol 71 23 8 0 6 241 NA 111 1 1 0 4 242 NA 222 2 2 0 5 243 lt NA gt 333 3 3 0 7 We take the response data that was measured on the antennal lobe Root et al 2007 The responses indicate the values of fluorescence change gt Root 2007 ER lt data frame Name c Isoamyl acetate 1 hexen 3 ol 4 heptanol 3 octanone Benzyl acetate CAS c 123 92 2 4798 44 1 589 55 9 106 68 3 140 11 4 14 Orl10a 2c 70 0 0 NA NA ab5B c 81 0 0 NA NA Or22a c 130 0 12 NA NA Or43b c 127 97 90 NA NA Orl3a c 2 48 0 NA NA DPlm c 21 NA NA 164 12 Or42b c 104 NA NA 111 32 Or59b c 106 NA NA 90 108 gt write table Root 2007 ER Root 2007 ER txt gt loadRD gt importNewData file name Root 2007 ER fFile format txt dataFormat data format weightGlobNorm weight globNorm responseRange response range receptors ORs Data Version 1 0 Date Nov 04 2009 Function Version 1 0 Date Nov 09 2009 1 DPlm has been added into weight globNorm 1 589 55 9 is a new odor Data frames odor and data format will be updated 2 106 68 3 is a new odor Data frames odor and data format will be updated Only CAS column of data has been updated New receptor or ORN has been added in ORs please input the expression manually 39 1 DPl
16. ataset integration Two datasets for Or22a are shown Pelz 2006 AntEC50 and Hallem 2006 EN The range in Pelz 2006 AntEC50 ranges from 2 04 to 6 62 negative logarithmic concentration that is necessary to elicit the half maximal response while responses in Hallem 2006 EN range from 2 to 260 these are response frequencies in spikes s Different dimensionalities along the axes influence this result e g deviation along the spike axis would weigh more because the value ranges are larger Therefore each dataset was linearly scaled to a common range 0 1 using DoORnorm before mapping gt data Or22a gt range na omit Or22a Pelz 2006 AntEC50 1 2 04 6 62 gt range na omit Or22a Hallem 2006 EN 1 2 260 gt tan22a lt projectPoints x DoORnorm Or22a Pelz 2006 AntEC50 y DoORnorm Or22a Hallem 2006 EN tan22a Double Observations ID x y X Y distance NDR 1 86 0 02401747 0 4379845 0 05714813 0 4231506 0 4489362 0 2588112 2 137 0 07860262 0 4728682 0 07940243 0 4724867 0 5030594 0 2900131 3 139 0 00000000 0 2519380 0 02393086 0 2758688 0 2879997 0 1660315 4 0 0 0 143 0 09388646 0 4108527 0 05873606 0 4266848 0 4528108 0 2610449 Single Observation ID x y X Y distance NDR 15 79 0 38646288 NA 0 38646288 0 824054838 0 987736604 0 569428970 16 90 0 21397380 NA 0 21397380 0 711384795 0 778003000 0 448517799 17 92 0 07860262 NA 0 07860262 0 470727744 0 501127081 0 288899162 18
17. ated value the selected receptors and the selected odors with absolute values of Pearson correlation coefficients to the target odor Or22a There is a wrapper function available for estimating all NA entries in a response data example data response matrix est_data lt DoOREst da response matrix nodor 2 22 EV b guess analysis n NRMSE oO E analysis 3 5 Back Projection The consensus response matrix containing the normalized data 0 1 allows us for theoretical analysis of olfactory coding From an experimentalist s point of view the odorant response values are more useful if they are given in spikes sec or fluorescence change Therefore we back project the merged dataset onto the original datasets We take Or22a as example Currently this is the receptor with the most responses available and it has also been measured with many different techniques such as single sensillum recordings and calcium imaging 23 gt data response matrix gt data Or22a gt Cons Or22a lt response matrix Or223a Combine the vector cons Or22a to the data format gt data data format gt RP Or22a data frame data format merged data cons Or22a match data format CAS rownames response matrix R9 combine two data accordingly to the odors Project the data RP Or22a back to the normalized mean response data which was measured with calcium imaging
18. cerrereryse cere OO0060 000000600060060600000000060000 0000600000000060 9 Figure 5 show the odorant responses across receptors with a dot plot A blank represents no available data the size of dot represents the intensity of odorant response filled circle and open circle response positive and negative values respectively Show the functional antennal lobe response to a single odor gt cas c 110 43 0 2 heptanone gt data OGN load OGN data that maps receptors to glumeruli gt data AL256 load antennal lobe piture gt data response matrix gt h2ep findRespNorm cas responseMatrix response matrix ALimage h2ep main 2 heptanone CAS 110 43 0 OGN OGN AL256 AL256 17 2 heptanone CAS 110 43 0 mum DA3 D DL NA DA4m DL4 yag DM3 L5 BG DA2 DA4i DL1 VAtd DM5 vae s pco DC3 0 VAdIm v VATI b m WE A CATCH ae VAS VM3 VC3 DM2t yMr d 0 a Figure 6 The functional antennal lobe response to a single odor Colors go from blue most inhibitory response over white around baseline to red most excitatory response Light grey glomeruli shine through from deeper slices background BG Unmapped UM glomeruli are shown in dark grey in these cases the receptor glomerulus mapping is currently unknown 3 4 Odorant responses estimation Although there are a lot of studies on odorant responses the Drosophila olfactome is still quite incomplete
19. ctrophysiological recordings performed on basiconic sensilla without knowing the expressed Ors Assuming that Orl3a is expressed in ab6A out of the 61 datasets in DoOR the data from Kreher and Nissler both should match best to the ab6A As Orl3a is already included into the database we first have to split the dataset into data coming from single sensilla recordings Bruyne 2001 WT and Schmuker 2007 TR and data that comes from studies recording identified receptor neurons Nissler 2007 EC50 Nissler 2007 nmr and Kreher 2008 EN gt data Orl3a gt names Orl3a 1 Class Name 33 eID CAS 5 Schmuker 2007 TR Bruyne 2001 WT 7 Nissler 2007 EC50 Nissler 2007 nmr 9 Kreher 2008 EN gt ab6A lt Orl3al c 1 6 gt Orl3aNMR lt Orl3al c 1 4 7 8 then merge those as a consensus data 33 gt RP ab6A lt modelRP ab6A plot TRUE S model response gt RP Or13aNMR lt modelRP Or13aNMR plot TRUE Smodel response gt data response matrix Assign the response data a new name gt new response matrix lt response matrix match odor names between RP ab6A and new response matrix gt matchOdorgab6A lt match RP ab6A CAS rownames new response matrix rename the Ori13a to ab6A gt colnames new response matrix which names new response matrix Orl3a lt ab6A replace response data of Orl3a by RP ab6A gt new res
20. dors with the highest Pearson Correlation coefficient nearest lt function target candicate k N lt nrow candicate if missing k k lt N if N lt k message The number of available odors is smaller than the default 3 so that only available odors will be selected k lt N absolute values of Pearson correlation coefficients between target and candicates absCorr lt abs apply candicate 1 function x cor test target x Sestimate sorted_absCorr lt sort absCorr decreasing TRUE return sorted _absCorr 1 k responseMatrix lt as data frame responseMatrix localize the target receptor and odor in sorted response matrix whereTargetReceptor lt match receptor colnames responseMatrix whereTargetodor lt match CAS rownames responseMatrix non NA vectors b candicateOdors and w candicateReceptors as candicates candicateReceptors which is na responseMatrix whereTargetodor Name candicateReceptors colnames responseMatrix candicateReceptors candicateOdors lt which is na responseMatrix whereTargetReceptor Name candicateOdors rownames responseMatrix candicateOdors candi A na omit responseMatrix candicateOdors candicateReceptors 19 match the odorant location of data candi A to responseMatrix ma ma vector available recepto w lt c as matr
21. ed that if the permutation is equal TRUE the update process may take several minutes gt updateDatabase receptor Or92a permutation TRUE 1 The optimized sequence with the lowest mean MD 0 0121 ig 41 1 Bruyne 2001 RR Bruyne 2001 WT Dobritsa 2003 EN Galizia 2009 nmr There were 50 or more warnings use warnings to see the first 50 gt warnings Warning messages 1 In optimize ff2 interval X tol 1e 04 NA Inf replaced by maximum positive value 2 In optimize ff2 interval X tol 1e 04 NA Inf replaced by maximum positive value 3 In optimize ff2 interval X tol 1e 04 The result also shows that there were 50 or more warnings These are due to that not all sequence combination can be merged 5 3 Build packages For Linux Users might want to build their own package if some data or functions have been introduced into DoOR There is a manual for writing R extension available at http cran r project org doc manuals Please refer to this for a detailed explanation In a Linux environment user should create a main directory for the package mkdir DoOR function cd DoOR function Create two directories called R and man under main directory If users have data an extra directory called data should be created as well cd DoOR function DoOR function mkdir R
22. for Statistical Computing Schmuker M Schneider G 2007 Processing and classification of chemical data inspired by insect olfaction Proc Natl Acad Sci U S A 104 20285 20289 45
23. it View Insert Format Tools Data Window Help Ac EHSA SRAY Be SJS o o a C L bi Ez Name CID CAS SFR NA SFR 2 other water 962 7732 18 5 3 amine ammoniurr 14923 1335 21 56 4 amine putrescine 1045 110 60 1 5 amine cadaverine 273 462 94 2 5 amine ammonia 222 7664 41 7 7 amine ethanolam 700 141 43 5 8 amine heptylamir 8127 111 68 2 9 amine isoamylarr 7894 107 85 7 10 amine dimethylar 674 124 40 3 11 O ring gamma bu 7302 96 48 0 12 O ring gamma he 12756 695 06 7 13 O ring gamma oc 7704 104 50 7 Figure 13 After you have entered the response values reimport your response data in DoOR Orx read csv data format csv In case you want to combine your data into one of receptor response data newdata data frame CAS c 111 1 1 222 2 2 71 23 8 333 3 3 newdata 2009 nmr c 0 4 0 5 0 6 0 7 newdata CAS newdata 2009 nmr 1 111 1 1 0 4 2 222 2 2 0 5 3 71 23 8 0 6 4 333 3 3 0 7 The new data contains 4 odors Up to now only odor 71 23 8 is included in DoOR To combine newdata into the Or22a dataset the DoOR function combData is used gt data Or22a gt new Or22a combData datal Or22a data2 newdata by data2 newdata 2009 n mr Show the data new Or22a only with row 136 and new added rows from 241 to 243 by columns Name CAS and newdata 2009 nmr 38 gt new Or22a c 136 251 254 c Name CAS newdata 2009 nmr
24. ix selectReceptor names w lt w lt sort w selectRecepto vector trix y rS pr represents th represents th selectReceptor r names w respons responseMatrix CAS lt names responseMatrix CAS respons tchodor lt match rownames candi A rownames responseMatrix tchReceptor match colnames candi A colnames responseMa matchReceptor matchReceptor values of target receptor across available odors b responseMatrix rownames candi A receptor names b lt rownames candi A b sort b subsetodor lt names b 2 b 95 48 7 100 52 7 SFR 64 19 7 138 86 3 124 38 9 111 87 5 0 01134385 0 03116479 0 03391304 0 05070711 0 05625201 0 06253786 0 06800288 513 85 9 6728 26 3 106 24 1 98 86 2 119 36 8 79 09 4 105 87 3 0 07515067 0 08536189 0 08736033 0 08911544 0 09521574 0 10370175 0 10486154 67 56 1 67 64 1 97 53 0 141 78 6 111 27 3 431 03 8 71 36 3 0 11791586 0 13079865 0 14174257 0 16653614 0 21616181 0 22948048 0 25896885 110 43 0 3391 86 4 123 92 2 628 63 7 105 54 4 109 60 4 0 26849417 0 30067607 0 52526482 0 55014927 0 58844967 0 68763607 gt w Or49b Or67a Or23a Or88a Or2a Or98a Or85f 0 01337175 0 02607130 0 03288981 0 03299875 0 03727293 0 04071159 0 04269889 Or47a Or59b Or43a Or82a Or67c Or65a Orl10a 0 04651597 0 04852848 0 05742629 0 06121915 0 06323438 0 06464458 0 07466297 Or43b Orl9a Or85a Or47b Or85b Or9a Or33b 0 081942
25. le the two datasets Pelz 2006 nmr and Hallem 2006 EN were acquired by measuring the same odorant receptor Or22a in Drosophila melanogaster Because several odors were shared by the two studies their merger is possible STEP 1 Fitting two datasets onto each other using least squares regression The available linear and nonlinear regression is performed by minimizing the sum of squared distances between the line and the observations The distance is not measured orthogonally but vertically which means the regression is not symmetric We try to fit the data with five models linear exponential and three non linear models and their inverse models The parameters will be estimated by the generic R fitting functions 1m and nls for the linear model and the nonlinear models respectively The parameters of the inverse functions are estimated by interchanging the variables Linear f dl X atb x 1 Inverse linear TEE ST f inv linear x b B eX Q Exponential fas X 7a tbe 3 Inverse exponential 27 Jide x _ Sigmoid Asym 1 exp xmid x scal Asym pw p J xmid x 13h eu Inverse Sigmoid xmid scal log Asym x 1 Jon sigmoid x xmid scal i log e G x 1 asympOff Asym 1 exp exp lrc x c0 Tawo x Asym l dE HOO Q Inverse asympOff cO log 1 x Asym exp l1rc c log re T ian X c0 DP a e asymp Asym RO
26. libraries with the library command see 2 2 help on DoOR specific topics will be available gt DoOOR function gt DoOR data 3 Quick Start 3 1 Access odorant receptor data Load all datasets including the precomputed response matrix and data gt loadRD Show information on a specific odorant receptor for example Or22a in detail by typing gt Or22a or gt help Or22a The command or help is used to load the documentation of DoOR function and data We also integrated receptor information You will see the description for this receptor the format of the data the biological information about the receptor including sequence location expression housed sensillum and neuron co expression information targeted glomerulus and further comments and references As an example take Or47b Or47b Description Response profile of Or47b Usage Or47b DoOR data R Documentation Or47b Description Response profile of Or47b Usage data Or47b Format A data frame with 251 observations on the following 8 variables Class a factor with levels acid alcohol aldehyde amine arom ester ketone N ring O ring other sulfid terpene Name a factor with odorant names CID a character vector with compound ID CAS a factor with odorant CAS numbers Hallem 2006 EN a numeric vector electrophysiological recording in empty neuron from Hallem et al 2006 Hallem and Carlson 2006 Hallem 2004 EN a nume
27. m is a new receptor or ORN A new response data is builded Not only response data Orl0a ab5B etc but also the supported data ORs response range and weight globNorm were updated The message showed that there was a new integrated response data for glumerulus DP1m gt response range study min max n_odors 1 Hallem 2006 EN 24 00000000 294 000000 111 26 Galizia 2009 nmr 0 01244843 1 242804 105 27 Turner 2009 SC 40 00000000 96 000000 47 28 Root 2007 ER 0 00000000 164 000000 5 5 2 Update response matrix There are eight new datasets for receptors also sensillum and glomerulus Or 0a ab5B Or22a Or43b Or13a DP1m Or42b and Or59b from Root 2007 ER After importing data we can update the response matrix by merging the responses measured form different studies We take Or42b as example gt names Or42b 1 Class Name CLD CAS Bruyne 2001 WT 6 Bruyne 2001 RR Dobritsa 2003 EN Kreher 2008 EN Root 2007 ER After the new dataset Root 2007 ER has been introduced into the DoOR database we would like to merge Root 2007 ER with other study as a consensus data First we need to check how many overlapping datapoints they share because the data can not be merged if the amount of overlap is less than five gt apply Or42b c 5 8 2 function x as data frame na omit cbind x Or42b Root 2007 ER SBruyne 2001 WT 1 x V2 0 rows or O length r
28. n data Pelz 2006 AntEC50 and will back projected data The backprojected scale is shown on the right gt dec50 Srescale Intercept Slope 2 04 4 58 Soutput 2 04 2956 3 872 4 788 5 704 662 1 124 Class Name CID CAS Pelz 2006 AntEC50 consensus value projected Y bp data amine ammonia 222 7664 41 7 NA 0 081211965 0 1356004596 1 470990 25 The result is a list containing rescale and output The rescale parameters were estimated by computing the regression between unnormalized and normalized data of back projected data Pelz 2006 AntEC50 Note that the data Pelz 2006 AntEC50 have been normalized to the range 0 1 before plotting against the consensus data so that the back projected data will be rescaled by using following function bp data ntercept Slope In the output section the first four columns contain the odorant information Ammonia was not measured in Pelz 2006 AntEC50 indicated by NA The consensus responses is 0 081211965 the back projected normalized mean response is 1 4170990 26 4 Operation 4 1 Integration approach Heterogeneous dataset integration is a process by which two datasets of odor responses for the same receptor are brought together to a consensus data that pools the two datasets into one and thus collects the information of both The precondition of heterogeneous dataset integration is that two datasets have a sufficient number of common odor responses For examp
29. nnen 36 6 Acknowledgment eee eee eene eene noo e ete o o aine o e ccesescssvenssone4 References 4 1 eicere s opo rpeeete sadaddecbecedeccn sd eoque Giles Mi ceecs cdesssends 40 Document conventions Fonts Example Index Times New Roman Introduction Text Italic Or22a Species receptor names Courier New 2DOOR function R commands Courier sudo Linux gt R command line R comment not executed Linux command line 1 Introduction DoOR is the Database of Odor Responses that integrates Drosophila odor response data from different sources The DoOR algorithm for integrating odor responses is described in PUBLICATION DoOR is available as two libraries for R DoOR data contains the actual database and DoOR function comprises R functions for visualization and creating the database using the DoOR algorithm Up to date packages can be obtained from http neuro uni konstanz de DoOR This guide provides an introduction to the main features of DoOR a quick start navigation to show how to use R functions and the principle of data reconstruction Finally the guide will show you how to introduce your own data into DoOR For more detail and an overview of the DoOR Project see link to publication and http neuro uni konstanz de DoOR If you are only interested in the data you can access odor response profiles directly at that website without any need to implement DoOR within R If howeve
30. noate 123 66 0 0 9315796 228 6 62 T3 methyl hexanoate 106 70 7 0 8462074 260 6 00 11 ethyl butyrate 105 54 4 0 6339355 197 4 35 9 isopentyl acetate 123 92 2 0 6017955 236 4 01 7 pentyl acetate 628 63 7 0 5850395 162 4 13 6 butyl acetate 123 86 4 0 5190946 216 3 34 10 E2 hexenyl acetate 2497 18 9 0 4860582 198 3 22 12 ethyl propionate 105 37 3 0 4677935 192 3422 8 hexyl acetate 142 92 7 0 4112706 176 2 74 5 l octen 3 o1l 3391 86 4 0 2930847 TAS 2 42 2 1 butanol 71 36 3 0 2900131 124 2 40 4 3 methyl butanol 123 51 3 0 2610449 108 2 47 1 2 heptanone 110 43 0 0 2588112 TIS p 15 3 l hexanol 111 27 3 0 1660315 67 2 04 Visualize the data of model response and Hallem 2006 EN and Pelz 2006 AntEC50 gt op par mfrow c 1 3 gt op par las 2 cex lab 0 01 cex axis 0 7 31 gt barplot rev ordered doubObserv data 3 horiz T las 1 col lightgreen main model response gt barplot rev ordered doubObserv data 4 horiz T las 1 col lightblue main Hallem 2006 EN gt barplot rev ordered doubObserv data 5 horiz T las 1 col yellow main Pelz 2006 AntEC50 E O C Ss Ei em C O OC C C ll C E Ss O y mi L1 E L E C C L Ea L C E C o eo eo A o e eo N a o Od N 4A o model responses and the measured data from Hallem 2006 EN and Pelz 2006 AntEC50 Nevertheless not only overlapped odors but als
31. o some odors which were tested by either of studies can be generated as consensus data Similar process as above we can address the odorant names according to their ID gt singObserv lt tan22aS Single Observation ID gt singObserv data lt data frame Name Or22a singObserv 2 CAS Or22a singObserv 4 model response tan22aSSingle Observation 7 Hallem 2006 EN Or22a singObserv 5 Pelz 2006 nmr Or22a singObserv 13 singObserv data 10 15 10 ethyl 3 hydroxyhexanoate 2305 25 1 0 297995200 NA 2 43 11 beta butyrolactone 3068 88 0 0 215621065 NA 2 16 32 12 gamma valerolactone 108 29 2 282803313 NA 2 38 0 13 SFR SFR 0 004665498 4 NA 14 ammonium hydroxide 1336 21 6 0 034991236 17 NA 15 putrescine 110 60 1 0 032658486 16 NA 4 2 Mapping receptors into database Odorant responses of specific receptors can be measured by using transgenic techniques Kreher and his colleagues expressed Or s in the empty neuron preparation and tested them with a series of odors using electrophysiological recordings Kreher et al 2008 In addition Nissler expressed the calcium sensitive fluorescent protein G CaMP under control of the Or13a promoter in the corresponding neurons The odorant receptor 13a was first suggested to house in intermediate sensilla Couto et al 2005 whereas Nissler proposed that Or13a houses in basiconic sensilla ab6A Neuron ab6A was measured by de Bruyne de Bruyne et al 2001 using ele
32. om different studies 3 2 Access odorant information The odorant information are acquired by sending a query to PubChem The result is displayed in a WWW browser gt showOdor CID 222 0dor data odor gt showOdor CAS 2 64 19 7 odor data odor Some function arguments such as ORs and odor are data frames which are needed for many functions and preloaded Supported data The background data list can be found in the documentation of the DoOR data package by typing DoOR data The background data consists of antennal lobe see ORimage OGN The map matching receptor to sensillum to receptor neuron and to glomerulus 5 a 10 For further details of supported data type gt odor or gt reference 3 3 Data visualization Show the response profile for a given odorant receptor The data frame should contain five columns odor class odor name compound ID CAS number and data column gt Or47bHallem lt Or47b c 1 5 gt head Or47bHallem Class Name CID CAS Hallem 2006 EN 1 NA SFR NA SFR 47 2 other water 962 TF 18 5 NA 3 amine ammonium hydroxide 14923 1336 21 6 39 4 amine putrescine 1045 11060 1 22 5 amine cadaverine 273 462 94 2 31 6 amine ammonia 222 7664 41 7 NA The response values can be both recording values and consensus values If the data is not consensus data in range of 1 1 only two colors blue and red are used for indicating
33. omeruli DA1 VA1lIm and VL2a are innervated by fruitless ex pressing neuron likely process sex pheromones during male courtship Manoli et al 2005 Stockinger et al 2005 Blocking synaptic transmission in these ORNs pro foundly reduced male courtship Stockinger et al 2005 References e Hallem and Carlson 2006 Hallem E A amp Carlson J R 2006 Coding of odors by a re ceptor repertoire Cell 125 143 160 e Hallem et al 2004 Hallem E A Ho M G and Carlson J R 2004 The Molecular Basis of Odor Coding in the Drosophila Antenna Cell 117 965 979 e Pelz 2005 Pelz D 2005 Functional characterization of Drosophila melanogaster Olfac tory Receptor Neurons 2005 Dissertationen online http www diss fu berlin de 2005 335 index html Freie Universitaet Berlin Examples data Or47b Show the data Or22a Class Name CID CAS Hallem 2006 EN Hallem 2004 EN Hallem 2004 WT Pelz 2006 EC50 al SFR lt NA gt SFR 4 7 T NA 2 other water 962 7732 18 5 NA NA NA NA 3 amine ammonium hydroxide 14923 1336 21 6 17 NA NA NA if 4 amine putrescine 1045 110 60 1 16 NA NA NAE 5 amine cadaverine 273 462 94 2 17 NA NA NA 6 amine ammonia 222 7664 41 7 NA NA NA NA gi amine ethanolamine 700 141 43 5 NA NA NA NA 8 amine heptylamine 8127 111 68 2 NA NA NA NA 4 NOTE The first four columns contain odorant class name compound ID CID and CAS number The following columns are response data fr
34. ow names 40 SBruyne 2001 RR x V2 172 147 104 SDobritsa 2003 EN li x V2 0 rows or 0 length row names SKreher 2008 EN X V2 172 6 104 Only two datasets Bruyne 2001 RR and Kreher 2008 EN share one overlapping odor with Root 2007 ER which to say Root 2007 ER can not be merged into database To show how to merge a new measured dataset into the database we take Or92a as an example names Or92a 1 Class Name ELD CAS 5 Bruyne 2001 WT Bruyne 2001 RR Dobritsa 2003 EN Galizia 2009 nmr Dataset Galizia 2009 nmr was measured in our lab using calcium imaging It shares 5 23 and 5 overlapping odors with Bruyne 2001 WT Bruyne 2001 RR and Dobritsa 2003 EN respectively Beside the first four information columns there are four data columns containing datasets for Or92a The entry Or92a in the response matrix can be updated using updateDatabase If the argument permutation is FALSE the data will be merged in routine sequence if TRUE the sequence is chosen from testing all possible permutations The mean correlations between all possible merged datasets resulting from all possible merging sequences and each original recording will be computed the sequence with the highest correlation is the one that will be used for the actual merging gt require gregmisc package gregmisc is required for permutation gt loadRD Not
35. ponse matrix matchOdor ab6A ab6A RP ab6A merged data gt matchOdor newOrl3a match newOrl3a CAS rownames new response matrix The receptors can be sorted into three groups expressed in adult larvae and both We need the data ORs the second column of ORs contains numeric values 0 1 2 and NA indicating expression in adult 0 in larvae 1 both 2 or not recorded NA respectively gt data ORs gt which in adult which is na match ORs 21 c 0 49 gt selected ORs c as character ORs which in adult 1 ede We only want to compare the response pattern between Orl3a and the receptors that express in the antennal sensilla so that we are excluding the receptors that are expressed 34 on the maxillary palp gt which in palp c Or42a Or71a Or33c Or85e Or46a Or Eoc Or85d pb2A since the colname Orl13a was replaced by ab6A antenna ORs selected ORs c match c which in palp ear 1 Seay seFected ORs gt res Nissler lt numeric gt for i in antenna ORs x Nissler lt RP Orl3aNMR merged data y lt new response matrix matchOdor newOrl3a 1i xy lt na omit cbind x Nissler y if is na which is na new response matrix matchOdor newOrl3a i 1 dim xy 1 0 if no data available for selected receptor or no overlapped values with the selected receptor then return NA and run next loop
36. r you want to add your own datasets or you want to use this package for other species or you want to perform other more advanced features you will need to install the package on your computer That is also the case if you want to work on a previous version of the package and or the data 2 Preliminaries 2 1 Installation DoOR is constructed under the R environment You will need to install R an free Statistics package that is available at http CRAN R project org The R archive provides both binary versions and source code Within R you will need to install and load two libraries DoOR data and DoOR function Windows For installing the packages type gt utils menuInstallLocal and select the DoOR function and DoOR data zip files Alternatively use the menu Packages install packages from local zip files This also works for Mac OS 10 5 Linux To install packages under Linux execute the following lines in a command line You might need administrator rights for the installation then you could for example add the command sudo in front of each line S R CM S R CM O INSTALL DoOR data_1 0 tar gz INSTALL DoOR function_1 0 tar gz O 2 2 Load the package Before using DoOR you need to load the packages first gt library DoOR function gt library DoOR data 2 3 Get Help General help on using R gt help start After loading the DoOR
37. r47b modelRP Or47b ec RP Or47b 4 RP Or47bSmodel response R R P TA mehexyt hexanoaie F vue gt sfr lt RP Or47b RP Or47b CAS NET SFR 5 E gt RP Or47b 5 RP Or47b 5 C trans cal Ben beta citronellol amp fr acet henon zx feptahoi acid geraniol ethyl octanoate 3 butanedior ammonium hydro gt PlotChemicals mety sahd RP Or47b order RP Or47b 5 E decreasing TRUE zal tag Name 020 4 46 po 0 05 000 005 0 10 x range c 0 3 0 1 AA T 1 12 Figure 1 Consensus response profile of Or47b Tuning Breadth Tuning Breadth is used for visualizing the response spectrum of a receptor Generalists have a distribution with broad tails while specialist receptors have a distinct peak that decreases sharply towards the tails Show the response spectrum of a receptor Tuning Curve Hallem 2006 EN Or22a gt data response matrix load response data gt x lt 06 4 response matrix Or22a gt x na omit x omit the 02 NA entries gt tuningBreadth x las 2 main tuning breadth of Or22a col lightblue ylim c 0 1 border NA Figure 2 Tuning curve shows the response profile of Or22a Compare two studies for an odorant receptor The responses of receptor Or 3a have been recorded in two studies Data Kreher 2008 EN contains data that were measured in an empty neuron preparation by Kreher s study published in
38. ric vector electrophysiological recording in empty neuron from Hallem et al 2004 Hallem et al 2004 Hallem 2004 WT a numeric vector electrophysiological recording in wild type from Hallem et al 2004 Hallem et al 2004 Pelz 2005 0r47bnmr a numeric vector normalized mean values of calcium imaging recording in wild type from Pelz et al 2005 Pelz 2005 Details Or47b Sequence location 2R 7 207 215 7 208 817 Tweedie et al 2009 Expression in adult Couto et al 2005 Vosshall et al 2000 Sensilla trichoid sensilla antenna Couto et al 2005 Neuron at4 Couto et al 2005 atX A Hallem et al 2004 atXA assigned by Hallem s paper Hallem et al 2004 indicates a formalized tentative nomenclature for trichoid neuron Coexpression no recorded Glomerulus VA lv Couto et al 2005 VA1lm Hallem et al 2004 Berdnik et al 2006 VA llm is innervated by fruitless expressing neuron Manoli et al 2005 Stockinger et al 2005 Comment l Or47b responded to virgin female extracts action potential 100 lt n lt 150 impulses s and male cuticular extracts action potential 100 lt n 150 impulses s but not mated female male genital material virgin female genital material mated fe male genital material and 11 cis vaccenyl acetate action potential n 50 impulses s van der Goes van Naters and Carlson 2007 2 Three gl
39. tems In order to estimate the a firstly we solve the coefficients x of following linear equation A x b where aii 015 Ay Xi bi A 221 9222 Gag o2 p by g 9 5 ant an a mn Xn b X is the coefficients of the linear system which is also applied in the linear system la w 6699 As a result a as a responser can be estimated by multiplying the predictor w with coefficients x Q W X In fact we could not always find the precise coefficients from a complex linear system We could however find a coefficient vector that brings A x as close as b 4 x Blf The pseudoinverse of a matrix is used to solve a vector x x A b The a can be estimated a w A b alpha t w PseudoInverse A as matrix b alpha 1 1 0 1173506 estimate the response value using DoOR function LLSIestPC LLSIestPC CAS CAS receptor receptor responseMatrix 21 responseMatrix nodor 2 Sestimation BERI el 0 1173506 selected receptors 1 Gr21a OrlO0a Orl9a Or23a Or2a Or33b Or35a Or43a Or43b 10ff OWA7a Or47b Or49b Or59b Or65a Or67a Or67c Or7a Or82a 39 Qg 5a Or85b Or85f Or88a Or98a Or9a Sselected odors 67 56 1 71 36 3 0 9584361 0 7480300 show the measured consensus value response matrix CAS receptor 1 0 1179159 Here the result shows as a list giving the estim
40. tifier CID in PubChem and CAS number The following columns contain the response values in the respective studies Study names are assigned as follows for example Pelz 2006 nmr is coded by Pelz the first author 2006 published year and nmr the measurement technique or data character nmr stands for normalized mean responses using the calcium imaging technique Missing values i e responses not measured in a particular study are coded with NA Class Name CID CAS Study 1 SFR lt NA gt SFR 2 other water 962 7732 18 5 3 amine ammonium hydroxide 14923 1336 21 6 4 amine putrescine 1045 110 60 1 5 amine cadaverine 273 462 94 2 6 amine ammonia 222 7664 41 7 7 amine ethanolamine 700 141 43 5 8 amine heptylamine 8127 11168 2 240 other 11 cis Vaccenyl Acetate NA 6186 98 7 Reading new odorant response data Assuming that an experiment has been performed with one or more odorants each odor has a response value or no recording NA The first step for integration into DoOR is usually to create an input file in txt or in csv format The input file should contain one oder per row including columns for the chemical name CAS number and the response value The CAS number is needed to unambigously identify the odor because there are multiple names for a chemical You can export a data template from the DoOR package gt write csv data format data format csv 34 I ieyosuse excel muster data File Ed
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