Last updated: 2020-08-14

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Knit directory: bootcamp/

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html fbc726d John Blischak 2020-08-14 Build site.
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Rmd 4d2c078 John Blischak 2020-08-14 Initial commit of RNA-seq scripts.

Setup

Load packages.

library(limma)
library(edgeR)

Import counts.

rawData <- read.table("data/counts.txt",
                      header = TRUE,
                      stringsAsFactors = FALSE)
dim(rawData)
[1] 5292   18
genes <- subset(rawData, select = Geneid:Length)
counts <- rawData[, 7:18]
rownames(counts) <- genes$Geneid
colnames(counts) <- gsub("\\.*bam\\.*", "", colnames(counts))
group <- c(rep("mutant", 6), rep("wildtype", 6))
group <- factor(group, levels = c("wildtype", "mutant"))
x <- DGEList(counts = counts,
             group = group,
             genes = genes)
class(x)
[1] "DGEList"
attr(,"package")
[1] "edgeR"

Process features

Calculate log2 counts per million (log2cpm).

log2cpm <- cpm(x, log = TRUE)

Plot density of expression values for each sample.

plotDensities(log2cpm, group = group, main = "Raw")

Version Author Date
fbc726d John Blischak 2020-08-14

Only keep features which have at least 10 counts in at least 4 wildtype or 4 mutant samples.

keep <- filterByExpr(x, group = group)
sum(keep)
[1] 4904
x <- x[keep, ]

Re-calculate log2cpm and re-plot densities.

log2cpm <- cpm(x, log = TRUE)
plotDensities(log2cpm, group = group, main = "Filtered")

Version Author Date
fbc726d John Blischak 2020-08-14

Normalize the samples, re-calculate log2cpm, and re-plot densities.

x <- calcNormFactors(x)
log2cpm <- cpm(x, log = TRUE)
plotDensities(log2cpm, group = group, main = "Normalized")

Version Author Date
fbc726d John Blischak 2020-08-14

Process samples

Confirm that the mutant samples are null for SNF2.

barplot(log2cpm["YOR290C", ], main = "SNF2")

Version Author Date
fbc726d John Blischak 2020-08-14

Perform PCA.

plotMDS(log2cpm, gene.selection = "common")

Version Author Date
fbc726d John Blischak 2020-08-14

Remove outlier sample.

x <- x[, colnames(x) != "mutant.06"]
dim(x)
[1] 4904   11

Re-calculate log2cpm and re-perform PCA.

log2cpm <- cpm(x, log = TRUE)
plotMDS(log2cpm, gene.selection = "common")

Version Author Date
fbc726d John Blischak 2020-08-14

Model

\[ Y = \beta_{0} + \beta_{mutant} + \epsilon \]

design <- model.matrix(~x$samples$group)
v <- voom(x, design, plot = TRUE)

Version Author Date
fbc726d John Blischak 2020-08-14 
fit <- lmFit(v, design)
fit <- eBayes(fit)

Explore results

Count number of differentially expressed features.

summary(decideTests(fit))
       (Intercept) x$samples$groupmutant
Down             0                  1035
NotSig           0                  3010
Up            4904                   859

View top 10 differentially expressed genes.

topTable(fit, coef = 2)
         Geneid  Chr  Start    End Strand Length     logFC   AveExpr         t
YML123C YML123C XIII  24037  25800      -   1764 -4.695983  8.083253 -34.40824
YDR033W YDR033W   IV 508147 509109      +    963 -3.672469  8.210557 -33.83913
YGR234W YGR234W  VII 959904 961103      +   1200 -4.237337  7.607611 -34.01898
YER081W YER081W    V 322686 324095      +   1410  3.214874  8.395070  26.55375
YPL019C YPL019C  XVI 514511 517018      -   2508 -2.499383  7.830653 -25.76347
YOR153W YOR153W   XV 619840 624375      +   4536 -2.701209  9.508846 -25.23499
YDR077W YDR077W   IV 600793 601809      +   1017 -3.231475 10.656406 -24.52067
YIL121W YIL121W   IX 132244 133872      +   1629 -2.637367  6.416078 -23.33140
YBR067C YBR067C   II 372103 372735      -    633 -2.925393  8.205030 -22.85657
YOR273C YOR273C   XV 834452 836431      -   1980 -2.113479  7.866356 -22.02893
             P.Value    adj.P.Val        B
YML123C 8.302180e-17 1.780947e-13 28.33391
YDR033W 1.089486e-16 1.780947e-13 28.28710
YGR234W 9.993394e-17 1.780947e-13 28.06935
YER081W 5.572096e-15 6.831390e-12 24.64724
YPL019C 9.075423e-15 8.901175e-12 24.15217
YOR153W 1.267725e-14 1.036154e-11 23.86019
YDR077W 2.013250e-14 1.410426e-11 23.40554
YIL121W 4.476938e-14 2.439434e-11 22.44055
YBR067C 6.226596e-14 3.053522e-11 22.27667
YOR273C 1.124107e-13 5.011473e-11 21.69191

Create a barplot of the top DE feature:

barplot(log2cpm["YML123C", ], las = 2, cex.names = 0.75)

Version Author Date
fbc726d John Blischak 2020-08-14

Visualize p-value distribution.

hist(fit$p.value[, 2], main = "p-value distribution")

Version Author Date
fbc726d John Blischak 2020-08-14

Visualize residual variation versus magnitude of expression.

plotSA(fit)

Version Author Date
fbc726d John Blischak 2020-08-14

Create a volcano plot.

volcanoplot(fit, coef = 2, highlight = 5, names = fit$genes$Geneid)

Version Author Date
fbc726d John Blischak 2020-08-14 

sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux Server release 6.10 (Santiago)

Matrix products: default
BLAS:   /gpfs/group/dml129/default/BDR_bootcamp2020/sw/ood_rserver/R/opt/R/4.0.2/lib/R/lib/libRblas.so
LAPACK: /gpfs/group/dml129/default/BDR_bootcamp2020/sw/ood_rserver/R/opt/R/4.0.2/lib/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] edgeR_3.30.3    limma_3.44.3    workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5      knitr_1.29      whisker_0.4     magrittr_1.5   
 [5] lattice_0.20-41 R6_2.4.1        rlang_0.4.7     stringr_1.4.0  
 [9] tools_4.0.2     grid_4.0.2      xfun_0.16       git2r_0.27.1   
[13] htmltools_0.5.0 yaml_2.2.1      digest_0.6.25   rprojroot_1.3-2
[17] later_1.1.0.1   promises_1.1.1  fs_1.5.0        glue_1.4.1     
[21] evaluate_0.14   rmarkdown_2.3   stringi_1.4.6   compiler_4.0.2 
[25] backports_1.1.8 locfit_1.5-9.4  httpuv_1.5.4