Enrico Ferrero | 27 Jul 15:40 2013
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Re: Quickest way to convert IDs in a data frame?

Hi everybody,

Marc, thanks for clarifying things. The behaviour of the select()
function is absolutely sensible. Maybe it should be made explicit
somewhere in the documentation that, when working with data frames,
the user is expected to use the merge() function in conjunction with
it. I also agree with Hervé that having options to tweak and customize
the output would be an extremely positive thing and a step in the
right direction. In addition to a "select" argument, one can also
think of a "remove.na.rows" that evaluates to either TRUE or FALSE.
But then again, using merge() after select() already deals with these
issues quite well.

What I think should be investigated more closely at the moment is the
unexpected behaviour select() exhibits when one SYMBOL or ALIAS (and
potentially other types of ID, I don't know) maps to more than one
ENTREZID. As exemplified by James' code below, this causes the output
to be truncated, and I highly doubt this is intentional:

> select(org.Hs.eg.db, rep("ADORA2A", 4), "ENTREZID", "ALIAS")
    ALIAS ENTREZID
1 ADORA2A      135
2 ADORA2A      135
3 ADORA2A      135
4 ADORA2A      135

> select(org.Hs.eg.db, rep("AGT", 4), "ENTREZID", "ALIAS")
  ALIAS ENTREZID
1   AGT      183
2   AGT      189

Warning message:
In .generateExtraRows(tab, keys, jointype) :
  'select' and duplicate query keys resulted in 1:many mapping between
keys and return rows

It would be great to have your views on this.
Best,

On 26 July 2013 21:46, Hervé Pagès <hpages@...> wrote:
> Hi Marc,
>
> On 07/26/2013 12:57 PM, Marc Carlson wrote:
> ...
>
>> Hello everyone,
>>
>> Sorry that I saw this thread so late.  Basically, select() does *try* to
>> keep your initial keys and map them each to an equivalent number of
>> unique values.  We did actually anticipate that people would *want* to
>> cbind() their results.
>>
>> But as you discovered there are many circumstances where the data make
>> this kind of behavior impossible.
>>
>> So passing in NAs as keys for example can't ever find anything
>> meaningful.  Those will simply have to be removed before we can
>> proceed.  And, it is also impossible to maintain a 1:1 mapping if you
>> retrieve fields that have many to one relationships with your initial
>> keys (also seen here).
>>
>> For convenience, when this kind of 1:1 output is already impossible (as
>> it is for most of your examples), select will also try to simplify the
>> output by removing rows that are identical all the way across etc..
>>
>> My aim was that select should try to do the most reasonable thing
>> possible based on the data we have in each case.  The rationale is that
>> in the case where there are 1:many mappings, you should not be planning
>> to bind those directly onto any other data.frames anyways (as this
>> circumstance would require you to call merge() instead). So in that
>> case, non-destructive simplification seems beneficial.
>
>
> Other tools in our infrastructure use an extra argument to pick-up 1
> thing in case of multiple mapping e.g. findOverlaps() has the 'select'
> argument with possible values "all", "first", "last", and "arbitrary".
> Also nearest() and family have this argument and it accepts similar
> values.
>
> Couldn't select() use a similar approach? The default should be "all"
> so the current behavior is preserved but if it's something else then
> the returned data.frame should align with the input.
>
> Thanks,
>
> H.
>
>
>>
>> I hope this clarifies things,
>>
>>
>>    Marc
>>
>>
>>
>>>
>>>> As I
>>>> mentioned in my first post, the for loop function works, but it's
>>>> highly inefficient.
>>>>
>>>> Any help is greatly appreciated, thank you.
>>>>
>>>> Best,
>>>>
>>>>
>>>>
>>>> On 25 July 2013 23:18, Hervé Pagès <hpages@...> wrote:
>>>>>
>>>>> Hi James,
>>>>>
>>>>> You're right.
>>>>>
>>>>> It's actually both: NAs *and* duplicated keys that are mapped to
>>>>> more than 1 row are removed from the input. I don't think this
>>>>> is documented.
>>>>>
>>>>> I wonder if select() behavior couldn't be a little bit simpler by
>>>>> either preserving or removing all duplicated keys, and not just some
>>>>> of them (on a somewhat arbitrary criteria).
>>>>>
>>>>> Thanks,
>>>>> H.
>>>>>
>>>>>
>>>>>
>>>>> On 07/25/2013 02:57 PM, James W. MacDonald wrote:
>>>>>>
>>>>>>
>>>>>> Hi Enrico and Herve,
>>>>>>
>>>>>> This has to do with duplicate entries, but only when the duplicate
>>>>>> entry
>>>>>> maps to many ENTREZID:
>>>>>>
>>>>>>   > select(org.Hs.eg.db, rep("ADORA2A", 4), "ENTREZID", "ALIAS")
>>>>>>       ALIAS ENTREZID
>>>>>> 1 ADORA2A      135
>>>>>> 2 ADORA2A      135
>>>>>> 3 ADORA2A      135
>>>>>> 4 ADORA2A      135
>>>>>>
>>>>>>   > select(org.Hs.eg.db, rep("AGT", 4), "ENTREZID", "ALIAS")
>>>>>>     ALIAS ENTREZID
>>>>>> 1   AGT      183
>>>>>> 2   AGT      189
>>>>>> Warning message:
>>>>>> In .generateExtraRows(tab, keys, jointype) :
>>>>>>     'select' and duplicate query keys resulted in 1:many mapping
>>>>>> between
>>>>>> keys and return rows
>>>>>>
>>>>>>   > select(org.Hs.eg.db, "AGT", "ENTREZID", "ALIAS")
>>>>>>     ALIAS ENTREZID
>>>>>> 1   AGT      183
>>>>>> 2   AGT      189
>>>>>> Warning message:
>>>>>> In .generateExtraRows(tab, keys, jointype) :
>>>>>>     'select' resulted in 1:many mapping between keys and return rows
>>>>>>
>>>>>>
>>>>>> So in the instances where a gene symbol maps to more than one
>>>>>> ENTREZID,
>>>>>> the output gets truncated, whereas if it is a one-to-one mapping, it
>>>>>> does not.
>>>>>>
>>>>>> Best,
>>>>>>
>>>>>> Jim
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> On 7/25/2013 5:06 PM, Enrico Ferrero wrote:
>>>>>>>
>>>>>>>
>>>>>>> Hi,
>>>>>>>
>>>>>>> Hervé, that's exactly what I'm trying to say.
>>>>>>>
>>>>>>> Attached to this email is a tab delimited file with two columns of
>>>>>>> GeneSymbols (or Aliases), and here is some simple code to reproduce
>>>>>>> the unexpected behaviour:
>>>>>>>
>>>>>>> library(org.Hs.eg.db)
>>>>>>> mydf<- read.table("testdata.txt", sep="\t", header=TRUE, as.is=TRUE)
>>>>>>> mytest<- select(org.Hs.eg.db, key=mydf$GeneSymbol1, keytype="ALIAS",
>>>>>>> cols=c("SYMBOL","ENTREZID","ENSEMBL"))
>>>>>>> # check that mytest has less rows than mydf
>>>>>>> nrow(mydf)
>>>>>>> nrow(mytest)
>>>>>>> # pick a random row: they don't match
>>>>>>> mydf[250,]
>>>>>>> mytest[250,]
>>>>>>>
>>>>>>> Ideally, mytest should have the same number and position of rows of
>>>>>>> mydf so that I can then cbind them.
>>>>>>> If mytest has more rows because of multiple mappings that's also
>>>>>>> fine:
>>>>>>> I can always use merge(mydf, mytest), right?
>>>>>>>
>>>>>>> Thanks a lot to both for your help, it's very appreciated.
>>>>>>> Best,
>>>>>>>
>>>>>>>
>>>>>>> On 25 July 2013 21:32, Hervé Pagès<hpages@...>  wrote:
>>>>>>>>
>>>>>>>>
>>>>>>>> Hi Enrico,
>>>>>>>>
>>>>>>>>
>>>>>>>> On 07/25/2013 01:20 PM, James W. MacDonald wrote:
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Hi Enrico,
>>>>>>>>>
>>>>>>>>> Please don't take things off-list (e.g., use reply-all).
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 7/25/2013 2:17 PM, Enrico Ferrero wrote:
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> Hi James,
>>>>>>>>>>
>>>>>>>>>> Thanks very much for your help.
>>>>>>>>>> There is an issue that needs to be solved before thinking about
>>>>>>>>>> what's
>>>>>>>>>> the best approach in my opinion.
>>>>>>>>>>
>>>>>>>>>> I don't understand why, but the object created with the call to
>>>>>>>>>> select
>>>>>>>>>> (test in my example, first.two in yours) has a different number of
>>>>>>>>>> rows from the original object (df in my example). Specifically
>>>>>>>>>> it has
>>>>>>>>>> *less* rows.
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> I'm surprised it has less rows. It can definitely have more, when
>>>>>>>> some
>>>>>>>> of the keys passed to select() are mapped to more than 1 row, but my
>>>>>>>> understanding was that select() would propagate unmapped keys to the
>>>>>>>> output by placing them in rows stuffed with NAs. So maybe I
>>>>>>>> misunderstood how select() works, or its behavior was changed, or
>>>>>>>> there is a bug somewhere. Could you please send the code that allows
>>>>>>>> us to reproduce this? Thanks.
>>>>>>>>
>>>>>>>> H.
>>>>>>>>
>>>>>>>>
>>>>>>>>> If all symbols were converted to all possible Entrez IDs,
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> I would expect it to have more rows, not less. To me, it looks
>>>>>>>>>> like
>>>>>>>>>> not all rows are looked up and returned.
>>>>>>>>>>
>>>>>>>>>> Do you see what I mean?
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Sure. You could be using outdated gene symbols. Or perhaps you are
>>>>>>>>> using
>>>>>>>>> a mixture of symbols and aliases. Which is even cooler than just
>>>>>>>>> all
>>>>>>>>> symbols:
>>>>>>>>>
>>>>>>>>>    >  symb<- c(Rkeys(org.Hs.egSYMBOL)[1:10],
>>>>>>>>> Rkeys(org.Hs.egALIAS2EG)[31:45])
>>>>>>>>>    >  symb
>>>>>>>>>     [1] "A1BG"     "A2M"      "A2MP1"    "NAT1" "NAT2"     "AACP"
>>>>>>>>>     [7] "SERPINA3" "AADAC"    "AAMP"     "AANAT" "AAMP"     "AANAT"
>>>>>>>>> [13] "DSPS"     "SNAT"     "AARS"     "CMT2N" "AAV"      "AAVS1"
>>>>>>>>> [19] "ABAT"     "GABA-AT"  "GABAT"    "NPD009" "ABC-1"    "ABC1"
>>>>>>>>> [25] "ABCA1"
>>>>>>>>>    >  select(org.Hs.eg.db, symb, "ENTREZID","SYMBOL")
>>>>>>>>>         SYMBOL ENTREZID
>>>>>>>>> 1      A1BG        1
>>>>>>>>> 2       A2M        2
>>>>>>>>> 3     A2MP1        3
>>>>>>>>> 4      NAT1        9
>>>>>>>>> 5      NAT2       10
>>>>>>>>> 6      AACP       11
>>>>>>>>> 7  SERPINA3       12
>>>>>>>>> 8     AADAC       13
>>>>>>>>> 9      AAMP       14
>>>>>>>>> 10    AANAT       15
>>>>>>>>> 11     AAMP       14
>>>>>>>>> 12    AANAT       15
>>>>>>>>> 13     DSPS<NA>
>>>>>>>>> 14     SNAT<NA>
>>>>>>>>> 15     AARS       16
>>>>>>>>> 16    CMT2N<NA>
>>>>>>>>> 17      AAV<NA>
>>>>>>>>> 18    AAVS1       17
>>>>>>>>> 19     ABAT       18
>>>>>>>>> 20  GABA-AT<NA>
>>>>>>>>> 21    GABAT<NA>
>>>>>>>>> 22   NPD009<NA>
>>>>>>>>> 23    ABC-1<NA>
>>>>>>>>> 24     ABC1<NA>
>>>>>>>>> 25    ABCA1       19
>>>>>>>>>    >  select(org.Hs.eg.db, symb, "ENTREZID","ALIAS")
>>>>>>>>>          ALIAS ENTREZID
>>>>>>>>> 1      A1BG        1
>>>>>>>>> 2       A2M        2
>>>>>>>>> 3     A2MP1        3
>>>>>>>>> 4      NAT1        9
>>>>>>>>> 5      NAT1     1982
>>>>>>>>> 6      NAT1     6530
>>>>>>>>> 7      NAT1    10991
>>>>>>>>> 8      NAT2       10
>>>>>>>>> 9      NAT2    81539
>>>>>>>>> 10     AACP       11
>>>>>>>>> 11 SERPINA3       12
>>>>>>>>> 12    AADAC       13
>>>>>>>>> 13     AAMP       14
>>>>>>>>> 14    AANAT       15
>>>>>>>>> 15     DSPS       15
>>>>>>>>> 16     SNAT       15
>>>>>>>>> 17     AARS       16
>>>>>>>>> 18    CMT2N       16
>>>>>>>>> 19      AAV       17
>>>>>>>>> 20    AAVS1       17
>>>>>>>>> 21     ABAT       18
>>>>>>>>> 22  GABA-AT       18
>>>>>>>>> 23    GABAT       18
>>>>>>>>> 24   NPD009       18
>>>>>>>>> 25    ABC-1       19
>>>>>>>>> 26     ABC1       19
>>>>>>>>> 27     ABC1    63897
>>>>>>>>> 28    ABCA1       19
>>>>>>>>> Warning message:
>>>>>>>>> In .generateExtraRows(tab, keys, jointype) :
>>>>>>>>>      'select' and duplicate query keys resulted in 1:many mapping
>>>>>>>>> between
>>>>>>>>> keys and return rows
>>>>>>>>>    >  mget(c("1982","6530","10991"), org.Hs.egGENENAME)
>>>>>>>>> $`1982`
>>>>>>>>> [1] "eukaryotic translation initiation factor 4 gamma, 2"
>>>>>>>>>
>>>>>>>>> $`6530`
>>>>>>>>> [1] "solute carrier family 6 (neurotransmitter transporter,
>>>>>>>>> noradrenalin), member 2"
>>>>>>>>>
>>>>>>>>> $`10991`
>>>>>>>>> [1] "solute carrier family 38, member 3"
>>>>>>>>>
>>>>>>>>> Best,
>>>>>>>>>
>>>>>>>>> Jim
>>>>>>>>>
>>>>>>>>>> On 25 July 2013 18:17, James W. MacDonald<jmacdon@...>   wrote:
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> Hi Enrico,
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On 7/25/2013 12:56 PM, Enrico Ferrero wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> Dear James,
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks very much for your prompt reply.
>>>>>>>>>>>> I knew the problem was the for loop and the select function is
>>>>>>>>>>>> indeed
>>>>>>>>>>>> a lot faster than that and works perfectly with toy data.
>>>>>>>>>>>>
>>>>>>>>>>>> However, this is what happens when I try to use it with real
>>>>>>>>>>>> data:
>>>>>>>>>>>>
>>>>>>>>>>>>> test<- select(org.Hs.eg.db, keys=df$GeneSymbol,
>>>>>>>>>>>>> keytype="ALIAS",
>>>>>>>>>>>>> cols=c("SYMBOL","ENTREZID","ENSEMBL"))
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> Warning message:
>>>>>>>>>>>> In .generateExtraRows(tab, keys, jointype) :
>>>>>>>>>>>>       'select' and duplicate query keys resulted in 1:many
>>>>>>>>>>>> mapping
>>>>>>>>>>>> between
>>>>>>>>>>>> keys and return rows
>>>>>>>>>>>>
>>>>>>>>>>>> which is probably the warning you mentioned.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> That's not the warning I mentioned, but it does point out the
>>>>>>>>>>> same
>>>>>>>>>>> issue,
>>>>>>>>>>> which is that there is a one to many mapping between symbol and
>>>>>>>>>>> entrez gene
>>>>>>>>>>> ID.
>>>>>>>>>>>
>>>>>>>>>>> So now you have to decide if you want to be naive (or stupid,
>>>>>>>>>>> depending on
>>>>>>>>>>> your perspective) or not. You could just cover your eyes and
>>>>>>>>>>> do this:
>>>>>>>>>>>
>>>>>>>>>>> first.two<- first.two[!duplicated(first.two$SYMBOL),]
>>>>>>>>>>>
>>>>>>>>>>> which will choose for you the first symbol -> gene ID mapping and
>>>>>>>>>>> nuke the
>>>>>>>>>>> rest. That's nice and quick, but you are making huge assumptions.
>>>>>>>>>>>
>>>>>>>>>>> Or you could decide to be a bit more sophisticated and do
>>>>>>>>>>> something like
>>>>>>>>>>>
>>>>>>>>>>> thelst<- tapply(1:nrow(first.two), first.two$SYMBOL, function(x)
>>>>>>>>>>> first.two[x,])
>>>>>>>>>>>
>>>>>>>>>>> At this point you can take a look at e.g., thelst[1:10] to see
>>>>>>>>>>> what
>>>>>>>>>>> we just
>>>>>>>>>>> did
>>>>>>>>>>>
>>>>>>>>>>> thelst<- do.call("rbind", lapply(thelst, function(x) c(x[1,1],
>>>>>>>>>>> paste(x[,2],
>>>>>>>>>>> collapse = "|")))
>>>>>>>>>>>
>>>>>>>>>>> and here you can look at head(thelst).
>>>>>>>>>>>
>>>>>>>>>>> Then you can check to ensure that the first column of thelst is
>>>>>>>>>>> identical to
>>>>>>>>>>> the first column of df, and proceed as before.
>>>>>>>>>>>
>>>>>>>>>>> But there is still the problem of the multiple mappings. As an
>>>>>>>>>>> example:
>>>>>>>>>>>
>>>>>>>>>>>> thelst[1:5]
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> $HBD
>>>>>>>>>>>         SYMBOL  ENTREZID
>>>>>>>>>>> 2535    HBD      3045
>>>>>>>>>>> 2536    HBD 100187828
>>>>>>>>>>>
>>>>>>>>>>> $KIR3DL3
>>>>>>>>>>>           SYMBOL  ENTREZID
>>>>>>>>>>> 17513 KIR3DL3    115653
>>>>>>>>>>> 17514 KIR3DL3 100133046
>>>>>>>>>>>
>>>>>>>>>>>> mget(as.character(thelst[[1]][,2]), org.Hs.egGENENAME)
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> $`3045`
>>>>>>>>>>> [1] "hemoglobin, delta"
>>>>>>>>>>>
>>>>>>>>>>> $`100187828`
>>>>>>>>>>> [1] "hypophosphatemic bone disease"
>>>>>>>>>>>
>>>>>>>>>>>> mget(as.character(thelst[[2]][,2]), org.Hs.egGENENAME)
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> $`115653`
>>>>>>>>>>> [1] "killer cell immunoglobulin-like receptor, three domains,
>>>>>>>>>>> long
>>>>>>>>>>> cytoplasmic tail, 3"
>>>>>>>>>>>
>>>>>>>>>>> $`100133046`
>>>>>>>>>>> [1] "killer cell immunoglobulin-like receptor three domains long
>>>>>>>>>>> cytoplasmic
>>>>>>>>>>> tail 3"
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> So HBD is the gene symbol for two different genes! If this gene
>>>>>>>>>>> symbol is in
>>>>>>>>>>> your data, you will now have attributed your data to two genes
>>>>>>>>>>> that
>>>>>>>>>>> apparently are not remotely similar. if KIR3DL3 is in your data,
>>>>>>>>>>> then it
>>>>>>>>>>> worked out OK for that gene.
>>>>>>>>>>>
>>>>>>>>>>> Best,
>>>>>>>>>>>
>>>>>>>>>>> Jim
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>> The real problem is that the number of rows is now different for
>>>>>>>>>>>> the 2
>>>>>>>>>>>> objects:
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> nrow(df); nrow(test)
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> [1] 573
>>>>>>>>>>>> [1] 201
>>>>>>>>>>>>
>>>>>>>>>>>> So I obviously can't put the new data into the original df. My
>>>>>>>>>>>> impression is that when the 1 to many mapping arises, the select
>>>>>>>>>>>> functions exits, with that warning message. As a result, my test
>>>>>>>>>>>> object is incomplete.
>>>>>>>>>>>>
>>>>>>>>>>>> On top of that, and I can't really explain this, the row
>>>>>>>>>>>> positions are
>>>>>>>>>>>> messed up, e.g.
>>>>>>>>>>>>
>>>>>>>>>>>>> all.equal(df[100,],test[100,])
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> returns FALSE.
>>>>>>>>>>>>
>>>>>>>>>>>> How can I work around this?
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks a  lot!
>>>>>>>>>>>>
>>>>>>>>>>>> Best,
>>>>>>>>>>>>
>>>>>>>>>>>> On 25 July 2013 16:58, James W. MacDonald<jmacdon@...> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> Hi Enrico,
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On 7/25/2013 11:35 AM, Enrico Ferrero wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hello,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I often have data frames where I need to perform ID
>>>>>>>>>>>>>> conversions on
>>>>>>>>>>>>>> one
>>>>>>>>>>>>>> or
>>>>>>>>>>>>>> more of the columns while preserving the order of the rows,
>>>>>>>>>>>>>> e.g.:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> GeneSymbol    Value1    Value2
>>>>>>>>>>>>>> GS1    2.5    0.1
>>>>>>>>>>>>>> GS2    3    0.2
>>>>>>>>>>>>>> ..
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> And I want to obtain:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> GeneSymbol    EntrezGeneID    Value1 Value2
>>>>>>>>>>>>>> GS1    EG1    2.5    0.1
>>>>>>>>>>>>>> GS2    EG2    3    0.2
>>>>>>>>>>>>>> ..
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> What I've done so far was to create a function that uses
>>>>>>>>>>>>>> org.Hs.eg.db to
>>>>>>>>>>>>>> loop over the rows of the column and does the conversion:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> library(org.Hs.eg.db)
>>>>>>>>>>>>>> alias2EG<- function(x) {
>>>>>>>>>>>>>> for (i in 1:length(x)) {
>>>>>>>>>>>>>> if (!is.na(x[i])) {
>>>>>>>>>>>>>> repl<- org.Hs.egALIAS2EG[[x[i]]][1]
>>>>>>>>>>>>>> if (!is.null(repl)) {
>>>>>>>>>>>>>> x[i]<- repl
>>>>>>>>>>>>>> }
>>>>>>>>>>>>>> else {
>>>>>>>>>>>>>> x[i]<- NA
>>>>>>>>>>>>>> }
>>>>>>>>>>>>>> }
>>>>>>>>>>>>>> }
>>>>>>>>>>>>>> return(x)
>>>>>>>>>>>>>> }
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> I should first note that gene symbols are not unique, so you
>>>>>>>>>>>>> are
>>>>>>>>>>>>> taking a
>>>>>>>>>>>>> chance on your mappings. Is there no other annotation for your
>>>>>>>>>>>>> data?
>>>>>>>>>>>>>
>>>>>>>>>>>>> In addition, you should note that it is almost always better to
>>>>>>>>>>>>> think of
>>>>>>>>>>>>> objects as vectors and matrices in R, rather than as things
>>>>>>>>>>>>> that
>>>>>>>>>>>>> need to
>>>>>>>>>>>>> be
>>>>>>>>>>>>> looped over (e.g., R isn't Perl or C).
>>>>>>>>>>>>>
>>>>>>>>>>>>> first.two<- select(org.Hs.eg.db, as.character(df$GeneSymbol),
>>>>>>>>>>>>> "ENTREZID",
>>>>>>>>>>>>> "SYMBOL")
>>>>>>>>>>>>>
>>>>>>>>>>>>> Note that there used to be a warning or an error (don't
>>>>>>>>>>>>> remember
>>>>>>>>>>>>> which)
>>>>>>>>>>>>> when
>>>>>>>>>>>>> you did something like this, stating that gene symbols are not
>>>>>>>>>>>>> unique,
>>>>>>>>>>>>> and
>>>>>>>>>>>>> that you shouldn't do this sort of thing. Apparently this
>>>>>>>>>>>>> warning has
>>>>>>>>>>>>> been
>>>>>>>>>>>>> removed, but the issue remains valid.
>>>>>>>>>>>>>
>>>>>>>>>>>>> ## check yourself
>>>>>>>>>>>>>
>>>>>>>>>>>>> all.equal(df$GeneSymbol, first.two$SYMBOL)
>>>>>>>>>>>>>
>>>>>>>>>>>>> ## if true, proceed
>>>>>>>>>>>>>
>>>>>>>>>>>>> df<- data.frame(first.two, df[,-1])
>>>>>>>>>>>>>
>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>
>>>>>>>>>>>>> Jim
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>> and then call the function like this:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> df$EntrezGeneID<- alias2GS(df$GeneSymbol)
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> This works well, but gets very slow when I need to do multiple
>>>>>>>>>>>>>> conversions
>>>>>>>>>>>>>> on large datasets.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Is there any way I can achieve the same result but in a
>>>>>>>>>>>>>> quicker, more
>>>>>>>>>>>>>> efficient way?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thank you.
>>>>>>>>>>>>>>
>>>>>>>>>>>>> --
>>>>>>>>>>>>> James W. MacDonald, M.S.
>>>>>>>>>>>>> Biostatistician
>>>>>>>>>>>>> University of Washington
>>>>>>>>>>>>> Environmental and Occupational Health Sciences
>>>>>>>>>>>>> 4225 Roosevelt Way NE, # 100
>>>>>>>>>>>>> Seattle WA 98105-6099
>>>>>>>>>>>>>
>>>>>>>>>>> --
>>>>>>>>>>> James W. MacDonald, M.S.
>>>>>>>>>>> Biostatistician
>>>>>>>>>>> University of Washington
>>>>>>>>>>> Environmental and Occupational Health Sciences
>>>>>>>>>>> 4225 Roosevelt Way NE, # 100
>>>>>>>>>>> Seattle WA 98105-6099
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>> --
>>>>>>>> Hervé Pagès
>>>>>>>>
>>>>>>>> Program in Computational Biology
>>>>>>>> Division of Public Health Sciences
>>>>>>>> Fred Hutchinson Cancer Research Center
>>>>>>>> 1100 Fairview Ave. N, M1-B514
>>>>>>>> P.O. Box 19024
>>>>>>>> Seattle, WA 98109-1024
>>>>>>>>
>>>>>>>> E-mail: hpages@...
>>>>>>>> Phone:  (206) 667-5791
>>>>>>>> Fax:    (206) 667-1319
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>> --
>>>>> Hervé Pagès
>>>>>
>>>>> Program in Computational Biology
>>>>> Division of Public Health Sciences
>>>>> Fred Hutchinson Cancer Research Center
>>>>> 1100 Fairview Ave. N, M1-B514
>>>>> P.O. Box 19024
>>>>> Seattle, WA 98109-1024
>>>>>
>>>>> E-mail: hpages@...
>>>>> Phone:  (206) 667-5791
>>>>> Fax:    (206) 667-1319
>>>>
>>>>
>>>>
>>>>
>>>
>>
>> _______________________________________________
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>
>
> --
> Hervé Pagès
>
> Program in Computational Biology
> Division of Public Health Sciences
> Fred Hutchinson Cancer Research Center
> 1100 Fairview Ave. N, M1-B514
> P.O. Box 19024
> Seattle, WA 98109-1024
>
> E-mail: hpages@...
> Phone:  (206) 667-5791
> Fax:    (206) 667-1319
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor@...
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor

--

-- 
Enrico Ferrero
PhD Student
Steve Russell Lab - Department of Genetics
FlyChip - Cambridge Systems Biology Centre
University of Cambridge

e.ferrero@...
http://flypress.gen.cam.ac.uk/

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