Question: edgeR RNA-seq analysis (glm, plots, and heatmaps)
1
gravatar for emblake
2.3 years ago by
emblake50
United States
emblake50 wrote:

I have analyzed the following data and have a few questions:

  DonorID       Group
1   Donor1          T0
2   Donor1         IL2
3   Donor1      IL2.ZA
4   Donor1    IL2.OKT3
5   Donor1 IL2.OKT3.ZA
6   Donor2          T0
7   Donor2         IL2
8   Donor2      IL2.ZA
9   Donor2    IL2.OKT3
10  Donor2 IL2.OKT3.ZA
11  Donor3          T0
12  Donor3         IL2
13  Donor3      IL2.ZA
14  Donor3    IL2.OKT3
15  Donor3 IL2.OKT3.ZA
16  Donor4          T0
17  Donor4         IL2
18  Donor4      IL2.ZA
19  Donor4    IL2.OKT3
20  Donor4 IL2.OKT3.ZA
21  Donor5          T0
22  Donor5         IL2
23  Donor5      IL2.ZA
24  Donor5    IL2.OKT3
25  Donor5 IL2.OKT3.ZA
26  Donor6          T0
27  Donor6         IL2
28  Donor6      IL2.ZA
29  Donor6    IL2.OKT3
30  Donor6 IL2.OKT3.ZA
>read.counts <- read.table("gene_counts_sorted.txt", header=TRUE)
>row.names(read.counts) <- read.counts$Geneid
>read.counts <- read.counts[ , -c(1:6)]
>colnames(read.counts) <- substr(colnames(read.counts),start=1,stop=4)
>head(read.counts)
                  SM01  SM02  SM03  SM04  SM05  SM06   SM07  SM08   SM09  SM10  SM11
ENSG00000210049      3     0     0     0     0     3      2     0      1     0     1
ENSG00000211459  22691 12565 13539 12681 11088 19204  13729  9507  15937  8022 13655
ENSG00000210077      1     6     5     4     4     2      6     3      3     1     3
ENSG00000210082 110091 88215 94014 75575 62912 99244 105988 75145 128197 73856 87062
ENSG00000276345      0     7     1     2     0    45     47    40     68    35     2
ENSG00000209082     29    91    66    79    44    29     83    48     91    56    29
                 SM12   SM13  SM14  SM15   SM16  SM17  SM18  SM19  SM20   SM21  SM22
ENSG00000210049     0      0     0     0      2     1     1     0     0      1     2
ENSG00000211459 11671  16176 11632 10562  20922 10334  9286 10838  7985  23120  6394
ENSG00000210077     4      3     3     2      3     6     4     3     0      4     2
ENSG00000210082 82396 139216 99996 87296 130829 61860 55904 89196 60536 146520 42290
ENSG00000276345     2      1     0     0     41    23    26    41    29      1     0
ENSG00000209082    67     73    39    40     47    36    45    60    44     45    50
                 SM23  SM24   SM25   SM26   SM27  SM28  SM29   SM30
ENSG00000210049     1     0      0      4      0     0     2      2
ENSG00000211459  9203 10932  13105  34180  12067 10515 10435  14079
ENSG00000210077     3     1      7      4      7     5     3      3
ENSG00000210082 57311 85217 103404 188883 106017 94485 93028 111348
ENSG00000276345     1     0      3      1      2     0     3      0
ENSG00000209082    53    73    118     49    116    77    77    100

>sample.type <- factor(c ("T0", "IL2", "IL2ZA", "IL2OKT3", "IL2OKT3ZA","T0", "IL2", "IL2ZA", "IL2OKT3", "IL2OKT3ZA","T0", "IL2", "IL2ZA", "IL2OKT3", "IL2OKT3ZA","T0", "IL2", "IL2ZA", "IL2OKT3", "IL2OKT3ZA","T0", "IL2", "IL2ZA", "IL2OKT3", "IL2OKT3ZA", "T0", "IL2", "IL2ZA", "IL2OKT3", "IL2OKT3ZA"))
>sample.type <- relevel(sample.type, "T0")
>donor <- factor(rep(c("Donor1","Donor2","Donor3","Donor4","Donor5","Donor6"),c(5,5,5,5,5,5)))
>y <- DGEList(counts = read.counts , group = sample.type)
>keep <- rowSums( cpm(y) > 1) >= 6
>y <- y[keep ,]
>y$samples$lib.size <- colSums(y$counts)
>y <- calcNormFactors(y , method = "TMM")
>plotMDS(y, method="bcv", col=as.numeric(y$samples$group))
>legend("center", legend=levels(y$samples$group))

MDS plot

>design <- model.matrix(~group+donor, data=y$samples)
        >colnames(design) <- sub("^Group", "", colnames(design))
        >design
             (Intercept) groupIL2 groupIL2OKT3 groupIL2OKT3ZA groupIL2ZA donorDonor2 donorDonor3
        SM01           1        0            0              0          0           0           0
        SM02           1        1            0              0          0           0           0
        SM03           1        0            0              0          1           0           0
        SM04           1        0            1              0          0           0           0
        SM05           1        0            0              1          0           0           0
        SM06           1        0            0              0          0           1           0
        SM07           1        1            0              0          0           1           0
        SM08           1        0            0              0          1           1           0
        SM09           1        0            1              0          0           1           0
        SM10           1        0            0              1          0           1           0
        SM11           1        0            0              0          0           0           1
        SM12           1        1            0              0          0           0           1
        SM13           1        0            0              0          1           0           1
        SM14           1        0            1              0          0           0           1
        SM15           1        0            0              1          0           0           1
        SM16           1        0            0              0          0           0           0
        SM17           1        1            0              0          0           0           0
        SM18           1        0            0              0          1           0           0
        SM19           1        0            1              0          0           0           0
        SM20           1        0            0              1          0           0           0
        SM21           1        0            0              0          0           0           0
        SM22           1        1            0              0          0           0           0
        SM23           1        0            0              0          1           0           0
        SM24           1        0            1              0          0           0           0
        SM25           1        0            0              1          0           0           0
        SM26           1        0            0              0          0           0           0
        SM27           1        1            0              0          0           0           0
        SM28           1        0            0              0          1           0           0
        SM29           1        0            1              0          0           0           0
        SM30           1        0            0              1          0           0           0
             donorDonor4 donorDonor5 donorDonor6
        SM01           0           0           0
        SM02           0           0           0
        SM03           0           0           0
        SM04           0           0           0
        SM05           0           0           0
        SM06           0           0           0
        SM07           0           0           0
        SM08           0           0           0
        SM09           0           0           0
        SM10           0           0           0
        SM11           0           0           0
        SM12           0           0           0
        SM13           0           0           0
        SM14           0           0           0
        SM15           0           0           0
        SM16           1           0           0
        SM17           1           0           0
        SM18           1           0           0
        SM19           1           0           0
        SM20           1           0           0
        SM21           0           1           0
        SM22           0           1           0
        SM23           0           1           0
        SM24           0           1           0
        SM25           0           1           0
        SM26           0           0           1
        SM27           0           0           1
        SM28           0           0           1
        SM29           0           0           1
        SM30           0           0           1
        attr(,"assign")
         [1] 0 1 1 1 1 2 2 2 2 2
        attr(,"contrasts")
        attr(,"contrasts")$group
        [1] "contr.treatment"

    attr(,"contrasts")$donor
    [1] "contr.treatment"

>y <- estimateDisp(y, design, robust=TRUE)
>fit <- glmQLFit(y, design, robust=TRUE) 
>my.contrasts <- makeContrasts(IL2OKT3vsIL2=groupIL2OKT3-groupIL2, IL2ZAvsIL2=groupIL2ZA-groupIL2, IL2OKT3ZAvsIL2OKT3=groupIL2OKT3ZA-groupIL2OKT3, IL2OKT3ZAvsIL2ZA=groupIL2OKT3ZA-groupIL2ZA, levels=design)
>res.IL2OKT3vsIL2 <- glmQLFTest(fit, contrast=my.contrasts[,"IL2OKT3vsIL2"])
>topTags(res.IL2OKT3vsIL2)
Coefficient:  -1*groupIL2 1*groupIL2OKT3 
                    logFC     logCPM         F       PValue          FDR
ENSG00000179388 9.4249435 1.00999138 132.28175 1.871660e-10 3.024040e-06
ENSG00000122877 5.7056899 1.69505046  86.08397 6.873421e-10 4.723965e-06
ENSG00000164400 5.2392202 0.01694888  73.55545 8.771365e-10 4.723965e-06
ENSG00000100600 1.2819733 4.82944185  69.21670 3.535977e-09 1.428270e-05
ENSG00000123358 3.8735521 2.08762833  67.99143 8.273365e-09 2.673455e-05
ENSG00000102096 0.7811110 9.04755438  49.25912 1.121108e-07 2.773154e-04
ENSG00000171777 1.2141784 3.72067108  46.33382 1.201466e-07 2.773154e-04
ENSG00000184489 0.8327849 4.06781417  41.09405 3.398334e-07 6.863361e-04
ENSG00000211899 1.4237289 3.31708910  37.53086 1.131164e-06 2.030692e-03
ENSG00000119508 4.8508194 0.48975178  36.55454 1.600683e-06 2.586224e-03
>IL2OKT3vsIL2_results <- topTags(res.IL2OKT3vsIL2, n=Inf)
>write.csv(IL2OKT3vsIL2_results, file="IL2OKT3vsIL2_results.csv")
>IL2OKT3vsIL2_results <- read.csv("~/path/to/IL2OKT3vsIL2_results.csv")
>colnames(IL2OKT3vsIL2_results)
[1] "X"      "logFC"  "logCPM" "F"      "PValue" "FDR"   
>names(IL2OKT3vsIL2_results) <- c("GeneID", "logFC", "logCPM", "F", "PValue", "FDR")
>tr.IL2OKT3vsIL2 <- glmTreat(fit, contrast=my.contrasts[,"IL2OKT3vsIL2"], lfc=log2(1.5))
>library(biomaRt)
>ensemblLatest <- useMart("ensembl")
>ensembl_86 <- useMart(biomart="ENSEMBL_MART_ENSEMBL", dataset = "hsapiens_gene_ensembl")
>flt <- listFilters(ensembl_86)
>annotation <- getBM(attributes = c("ensembl_gene_id", "hgnc_symbol", "description"), filters = "ensembl_gene_id", values = IL2OKT3vsIL2_results[,1], mart = ensembl_86)
>IL2OKT3vsIL2_ann_results <- merge(IL2OKT3vsIL2_results, annotation, by.x=1, by.y=1, sort=FALSE)
>logCPM <- cpm(y,prior.count = 2,log = TRUE) 
>o <- order(tr.IL2OKT3vsIL2$table$PValue)
>logCPM <- logCPM[o[1:30],]
>logCPM <- t(scale(t(logCPM)))
>library(gplots)
>col.pan <- colorpanel(100,"blue","white","red")
>heatmap.2(logCPM,col=col.pan,Rowv = TRUE,scale = "none",trace = "none",dendrogram = "both",cexRow = 1, cexCol = 1.4,margin = c(10,9), lhei = c(2,10),lwid = c(2,6))

Heatmap

Before I go too far in the analysis comparing the remaining treatment groups, please see the following questions:

  1. In the MDS plot, how do I set the legend colors to match the sample colors? I'm not sure how to plot each point so that donor and treatment are properly identified.
  2. Is the design matrix properly setup to compare treatment groups while removing the baseline control ("T0" samples) and accounting for donor effect? design <- model.matrix(~group+donor, data=y$samples) and my.contrasts <- makeContrasts(IL2OKT3vsIL2=groupIL2OKT3-groupIL2, IL2ZAvsIL2=groupIL2ZA-groupIL2, IL2OKT3ZAvsIL2OKT3=groupIL2OKT3ZA-groupIL2OKT3, IL2OKT3ZAvsIL2ZA=groupIL2OKT3ZA-groupIL2ZA, levels=design). I'm not interested in DE between donors, only treatments.
  3. In the heatmap, how do I rename the rows and supply a legend to identify the donor?

Thanks in advance!!

mds-plot edger rna-seq heatmap • 1.5k views
ADD COMMENTlink modified 2.3 years ago by Devon Ryan88k • written 2.3 years ago by emblake50
0
gravatar for Devon Ryan
2.3 years ago by
Devon Ryan88k
Freiburg, Germany
Devon Ryan88k wrote:
  1. Since you're not using the default method="logFC", it seems you'll have to manually reproduce what plotMDS() is doing to get an appropriate MDS object. The code for plotMDS() is here. Just skip the plotting part and return the a1 object, which you can then plot manually however you like.
  2. The donor effect is accounted for. The baseline is meaningless in your contrast (one could argue that IL2 is the baseline in your example contrast). As an aside, normally if you're going to do contrasts like this you'll remove the intercept (at least in some other packages, this would have the benefit of ensuring symmetric fold-change shrinkage...though I don't know if that's the case for edgeR).
  3. Just add row names.
ADD COMMENTlink written 2.3 years ago by Devon Ryan88k
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 2.3.0
Traffic: 1231 users visited in the last hour