[BioC] Re-send directly to BioC on miRNA annoation

[Valerie Obenchain]

Hi William,

countOverlaps() was deprecated in R-2.14 and has been replaced by 
summarizeOverlaps(). You'll want to update R and use 
summarizeOverlaps(). When posting a question please remember to provide 
the output of sessionInfo().

Valerie


On 05/07/2012 02:52 PM, Wu, Huiyun wrote:
> Dear Steve,
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> thank you for your feedback. Below is what it returned.
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>> st<- sum(counts==0)
>> st
> [1] 12296
>
>
>
> Let me give more details of my situation. Say, I have read in 6 BAM files (s1 to s6). and then got a count table (filename = countTable) using countOverlaps function with resultant file head as below. Question here, is the rowname (first col) miRNA ID?
>
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>>     colnames(countTable)<- samples
>>    head(countTable)
>            s1 s2 s3 s4 s5 s6
>
> 1          1  1  3  0  2  2
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> 10         0  0  1  0  1  0
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> 100        0  1  0  1  3  3
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> 1000       0  7  1  1  3  3
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> 10000      0  6  2  8 16 18
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> 100008586  0  0  0  0  0  0
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>>     dim(countTable)
> [1] 22932     6
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>
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> I then filtered out those miRNAs whose expressions were 0 across all samples by the following code (it might be what you asked for).
>
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>>    newCountTable<- countTable[rowSums(countTable) != 0, ]
>>     dim(newCountTable)
> [1] 22105     6
>
>>     head(newCountTable)
>            s1 s2 s3 s4 s5 s6
>
> 1          1  1  3  0  2  2
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> 10         0  0  1  0  1  0
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> 100        0  1  0  1  3  3
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> 1000       0  7  1  1  3  3
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> 10000      0  6  2  8 16 18
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> 100009676  0  0  0  1  3  4
>
>
>
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>
> After annotating  using the following library, I got another count table.
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>
>
> entrezGenes<- rownames(newCountTable)
>
>     mapped_genes<- mappedkeys(org.Hs.egSYMBOL)
>
>     overlapgenes<- intersect(entrezGenes, mapped_genes)
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>     overlapsymbols<- as.list(org.Hs.egSYMBOL[overlapgenes])
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>     getGeneSymbol<- function(x) {
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>     if (x %in% overlapgenes) {
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>               return(overlapsymbols[[which(x == names(overlapsymbols))]])
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>        }
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>        else {
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>       return(NA)
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>         }
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>      }
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>      geneSymbols<- sapply(entrezGenes, getGeneSymbol)
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>      countTable<- cbind(geneSymbols, numberOfexons[names(numberOfexons) %in% entrezGenes], geneLength[names(geneLength) %in%  entrezGenes],
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>                    newCountTable)
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>
>
>> dim(countTable)
> [1] 22105     9
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>
>
> I further removed redundant IDs and NAs to generate an updated count table (dataset named 'd') for DE analysis
>
>
>
>> dim(d)
> [1] 22100     7
>
>
>
>> d[20:30,]
>            X s1 s2 s3 s4 s5  s6
>
> 20    7-Mar  1  2  2 11 12   5
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> 21    7-Sep  2  6  4  6 19   9
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> 22    8-Mar  1  2  0  1  6   4
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> 23    8-Sep  0  4  4  4  3  10
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> 24    9-Mar  1  3  5  1  6   9
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> 25    9-Sep  2  5  8 10 35  20
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> 26     A1BG  1  1  3  0  2   2
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> 27 A1BG-AS1  0  1  1  0  0   4
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> 28     A1CF  0  2  1  3  9   1
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> 29    A2LD1  1  1  2  0  1   6
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> 30      A2M  5 53 48 70 58 114
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> It looks like there are 25 technical controls, but majority were annotated.
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> thanks,
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>
>
> ---
>
> William Wu
>
> Vanderbilt University/Department of Biostatistics.
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> -----Original Message-----
> From: Steve Lianoglou [mailto:mailinglist.honeypot at gmail.com]
> Sent: Monday, May 07, 2012 1:59 PM
> To: Wu, Huiyun
> Cc: bioconductor at r-project.org<mailto:bioconductor at r-project.org>
> Subject: Re: [BioC] miRNA annotation
>
>
>
> Hi,
>
>
>
> On Mon, May 7, 2012 at 2:15 PM, Wu, Huiyun<william.wu at vanderbilt.edu<mailto:william.wu at vanderbilt.edu>>  wrote:
>
>> Hello there,
>> I am new in seq data analysis, and have difficulty in miRNA annotation. To clarify my question, I have the following codes.
>>    bamfile<- "GSE33858_1.bowtie_alignment.bam"
>>    aln<- readBamGappedAlignments(bamfile)
>>    txdb<-makeTranscriptDbFromUCSC(genome="hg19",tablename="knownGene")
>>    exonRangesList<- exonsBy(txdb, by = "gene")
>>    exonRanges<- unlist(exonRangesList)
>>    strand(exonRanges)<- "*"
>>    geneNames<- sub("\\..*$<file:///\\..*$>", "", names(exonRanges))
>>    exonRangesListNoStrand<- split(exonRanges, geneNames)
>>    numberOfexons<- sapply(width(exonRangesListNoStrand), length)
>>    geneLength<- sum(width(reduce(exonRangesListNoStrand)))
>>    # Counting reads for the BAM file
>>    counts<- countOverlaps(exonRangesListNoStrand, aln)
>>    names(counts)<- names(exonRangesListNoStrand)
>>    >  length(counts)
>>        [1] 22932
>> So my question is from aligned file to annotated file. Specifically, why I got 22932 features after mapping to hg19? I learned there are only less than 2000 miRNAs matched genes. I also know there are several RNA databases including miRBase for annotation. Did I do something logically wrong? It would be greatly appreciated if someone could show me how to implement this annotation in R.
>
>
> What does `sum(counts == 0)` give you?
>
>
>
> -steve
>
>
>
> --
>
> Steve Lianoglou
>
> Graduate Student: Computational Systems Biology
>
>   | Memorial Sloan-Kettering Cancer Center
>
>   | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact
>
>
>
>
> 	[[alternative HTML version deleted]]
>
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