hi Yoong,

On Wed, Mar 12, 2014 at 5:04 PM, Yoong [guest] <guest@bioconductor.org>wrote:

>
> Hi Mike,
>
> I have a quick question on contrasts. I am little bit confused with the
> result that I am getting from the contrasts I am making. The reason is I
> have 2 timepoints (6h and 12h), 3 different genotypes (PI, Sal, and R), and
> two different temperatures (20C and 30C). I am interested in seeing what
> genes are upregulated and downregulated for all the genotypes from
> timepoint 6h to 12h at 30C versus 20C.


​Can you be more specific? Do you mean, genes with a log fold change of 30C
versus 20C which is different in time 12h than in time 6h?

I can't resist to comment, I *highly* recommend using a CSV or TSV file for
the phenotypic data (colData). I have seen multiple times: a bioinformatic
analysis with curious results, only to find a stray comma or space when
entering phenotypic data as character vectors. This can happen when
scrolling up and down through the R script and accidentally typing a key.
I'm not implying that you have a mistake here, but that it's generally
safer practice to read in CSVs.

​Mike​



> I am not sure if I am achieving that from my R analysis. Can you please
> take a look at my R command lines and see if the results I am getting from
> my analysis are genes that are differentially expressed at 30C versus 20C
> from 6h to 12h over all the genotypes?
>
> Many thanks,
> Yoong
>
>
>
>
>  -- output of sessionInfo():
>
> > DEAnatomy = read.table("C:/Users/yfy/Desktop/NewMicroscopyDE.txt",
> row.names =1 , header = T, sep = "\t")
>
> > colData=data.frame(row.names = colnames(DEAnatomy),
> +
> temp=c("20C","20C","20C","20C","20C","20C","30C","30C","30C","30C","30C","30C","20C","20C","20C","20C","20C","20C","30C","30C","30C","30C","30C","30C","20C","20C","20C","20C","20C","20C","30C","30C","30C","30C","30C","30C"),
> +
> genotype=c("PI","PI","PI","PI","PI","PI","PI","PI","PI","PI","PI","PI","SAL","SAL","SAL","SAL","SAL","SAL","SAL","SAL","SAL","SAL","SAL","SAL","R","R","R","R","R","R","R","R","R","R","R","R"),
> +
> time=c("6","6","6","12","12","12","6","6","6","12","12","12","6","6","6","12","12","12","6","6","6","12","12","12","6","6","6","12","12","12","6","6","6","12","12","12"))
>
> > library("DESeq2", lib.loc="C:/Users/yfy/Documents/R/win-library/3.0")
>
> > dds = DESeqDataSetFromMatrix(countData = DEAnatomy, colData = colData,
> design = ~genotype+time+temp)
>
> > colData(dds)$time = relevel(colData(dds)$ time, "6")
>
> > dds2 = DESeq(dds, betaPrior = FALSE)
>
> > resultsNames(dds2)
> [1] "Intercept"           "genotype_RNAi_vs_PI"
> [3] "genotype_SAL_vs_PI"  "time_12_vs_6"
> [5] "temp_30C_vs_20C"
>
> > resctrst2<-results(dds2, contrast=c("time","12","6"))
>
> > res<-resctrst2[order(resctrst2$padj),]
>
> > sig_results<-res[which(res$padj<.05),]
>
> > mcols(resctrst2)
> DataFrame with 6 rows and 2 columns
>           type                     description
>    <character>                     <character>
> 1 intermediate     the base mean over all rows
> 2      results  log2 fold change: time 12 vs 6
> 3      results    standard error: time 12 vs 6
> 4      results    Wald statistic: time 12 vs 6
> 5      results Wald test p-value: time 12 vs 6
> 6      results            BH adjusted p-values
>
> --
> Sent via the guest posting facility at bioconductor.org.
>

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