qvalue object

The qvalue package is a popular package to estimate q-values and local false discovery rates. To get started, we can load the hedenfalk dataset included in the qvalue package:

library(qvalue)
data(hedenfalk)

qobj <- qvalue(hedenfalk$p)
names(qobj)

## [1] "call"       "pi0"        "qvalues"    "pvalues"    "lfdr"      
## [6] "pi0.lambda" "lambda"     "pi0.smooth"

qobj is a qvalue object, generated from the p-values contained in the hedenfalk dataset. If we wanted to use a package such as dplyr or ggplot, we would need to convert the results into a data frame. The biobroom package makes this conversion easy by using the tidy, augment and glance functions:

• tidy returns one row for each choice of the tuning parameter lambda.
• augment returns one row for each provided p-value, including the computed q-value and local false discovery rate.
• glance returns a single row containing the estimated pi0.

Applying these functions to qobj:

library(biobroom)
# use tidy/augment/glance to restructure the qvalue object
head(tidy(qobj))

## # A tibble: 6 × 3
##   lambda smoothed   pi0
##    <dbl> <lgl>    <dbl>
## 1   0.05 FALSE    0.852
## 2   0.1  FALSE    0.807
## 3   0.15 FALSE    0.782
## 4   0.2  FALSE    0.756
## 5   0.25 FALSE    0.731
## 6   0.3  FALSE    0.714

head(augment(qobj))

## # A tibble: 6 × 3
##   p.value q.value  lfdr
##     <dbl>   <dbl> <dbl>
## 1  0.0121  0.0882 0.168
## 2  0.0750  0.209  0.413
## 3  0.995   0.668  1    
## 4  0.0418  0.162  0.309
## 5  0.846   0.632  1    
## 6  0.252   0.372  0.693

head(glance(qobj))

## # A tibble: 1 × 2
##     pi0 lambda
##   <dbl>  <dbl>
## 1 0.670   0.95

The original data, or in this example the gene names, can be inputted into augment using the data argument:

# create sample names
df <- data.frame(gene = 1:length(hedenfalk$p))
head(augment(qobj, data = df))

## # A tibble: 6 × 4
##    gene p.value q.value  lfdr
##   <int>   <dbl>   <dbl> <dbl>
## 1     1  0.0121  0.0882 0.168
## 2     2  0.0750  0.209  0.413
## 3     3  0.995   0.668  1    
## 4     4  0.0418  0.162  0.309
## 5     5  0.846   0.632  1    
## 6     6  0.252   0.372  0.693

The tidied data can be used to easily create plots:

library(ggplot2)
# use augmented data to compare p-values to q-values
ggplot(augment(qobj), aes(p.value, q.value)) + geom_point() +
  ggtitle("Simulated P-values versus Computed Q-values") + theme_bw()

Additionally, we can extract out important information such as significant genes under a false discovery rate threshold:

library(dplyr)

# Find significant genes under 0.05 threshold
sig.genes <- augment(qobj) %>% filter(q.value < 0.05)
head(sig.genes)

## # A tibble: 6 × 3
##    p.value q.value   lfdr
##      <dbl>   <dbl>  <dbl>
## 1 0.000713  0.0232 0.0366
## 2 0.00175   0.0365 0.0597
## 3 0.00265   0.0429 0.0752
## 4 0.00116   0.0301 0.0476
## 5 0.00298   0.0448 0.0803
## 6 0.00235   0.0397 0.0704

DESeq2 objects

To demonstrate tidying on DESeq2 objects we have used the published airway RNA-Seq experiment, available as a package from Bioconductor:

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("airway")

Import the airway dataset:

library(DESeq2)
library(airway)

data(airway)
airway_se <- airway

airway_se is a SummarizedExperiment object, which is a type of object used by the DESeq2 package. Next, we create a DESeqDataSet object and show the output of tidying this object:

airway_dds <- DESeqDataSet(airway_se, design = ~cell + dex)

head(tidy(airway_dds))

## # A tibble: 6 × 3
##   gene            sample     count
##   <chr>           <chr>      <int>
## 1 ENSG00000000003 SRR1039508   679
## 2 ENSG00000000005 SRR1039508     0
## 3 ENSG00000000419 SRR1039508   467
## 4 ENSG00000000457 SRR1039508   260
## 5 ENSG00000000460 SRR1039508    60
## 6 ENSG00000000938 SRR1039508     0

Only the gene counts are outputted since there has been no analysis performed. We perform an analysis on the data and then tidy the resulting object:

# differential expression analysis
deseq <- DESeq(airway_dds)
results <- results(deseq)
# tidy results
tidy_results <- tidy(results)
head(tidy_results)

## # A tibble: 6 × 7
##   gene            baseMean estimate stderror statistic   p.value p.adjusted
##   <chr>              <dbl>    <dbl>    <dbl>     <dbl>     <dbl>      <dbl>
## 1 ENSG00000000003  709.      0.381     0.101     3.79   0.000152    0.00128
## 2 ENSG00000000005    0      NA        NA        NA     NA          NA      
## 3 ENSG00000000419  520.     -0.207     0.112    -1.84   0.0653      0.196  
## 4 ENSG00000000457  237.     -0.0379    0.143    -0.264  0.792       0.911  
## 5 ENSG00000000460   57.9     0.0882    0.287     0.307  0.759       0.895  
## 6 ENSG00000000938    0.318   1.38      3.50      0.394  0.694      NA

As an example to show how easy it is to manipulate the resulting object, tidy_results, we can use ggplot2 to create a volcano plot of the p-values:

ggplot(tidy_results, aes(x=estimate, y=log(p.value),
                         color=log(baseMean))) + geom_point(alpha=0.5) +
  ggtitle("Volcano Plot For Airway Data via DESeq2") + theme_bw()

##edgeR objects

Here we use the hammer dataset included in biobroom package. edgeR can be used to perform differential expression analysis as follows:

library(edgeR)
data(hammer)

hammer.counts <- Biobase::exprs(hammer)[, 1:4]
hammer.treatment <- Biobase::phenoData(hammer)$protocol[1:4]

y <- DGEList(counts=hammer.counts,group=hammer.treatment)
y <- calcNormFactors(y)
y <- estimateCommonDisp(y)
y <- estimateTagwiseDisp(y)
et <- exactTest(y)

The results of the analysis are stored in et, which is an DGEExact object. We can tidy this object using biobroom:

head(tidy(et))

## # A tibble: 6 × 4
##   gene               estimate  logCPM    p.value
##   <chr>                 <dbl>   <dbl>      <dbl>
## 1 ENSRNOG00000000001   2.65    1.49   0.00000131
## 2 ENSRNOG00000000007  -0.409  -0.226  1         
## 3 ENSRNOG00000000008   2.22   -0.407  0.129     
## 4 ENSRNOG00000000009   0      -1.31   1         
## 5 ENSRNOG00000000010   0.0331  1.79   1         
## 6 ENSRNOG00000000012  -3.39    0.0794 0.00375

glance shows a summary of the experiment: the number of genes found significant (at a specified alpha), and which contrasts were compared to get the results.

glance(et, alpha = 0.05)

##   significant     comparison
## 1        6341 control/L5 SNL

Additionally, we can can easily manipulate the resulting object and create a volcano plot of the p-values using ggplot2:

ggplot(tidy(et), aes(x=estimate, y=log(p.value), color=logCPM)) +
  geom_point(alpha=0.5) + ggtitle("Volcano Plot for Hammer Data via EdgeR") +
  theme_bw()

##limma objects

To demonstrate how biobroom works with limma objects, we generate some simulated data to test the tidier for limma objects.

# create random data and design
dat <- matrix(rnorm(1000), ncol=4)
dat[, 1:2] <- dat[, 1:2] + .5  # add an effect
rownames(dat) <- paste0("g", 1:nrow(dat))
des <- data.frame(treatment = c("a", "a", "b", "b"),
                  confounding = rnorm(4))

We then use lmFit and eBayes (functions included in limma) to fit a linear model and use tidy to convert the resulting object into tidy format:

lfit <- lmFit(dat, model.matrix(~ treatment + confounding, des))
eb <- eBayes(lfit)

head(tidy(lfit))

## # A tibble: 6 × 3
##   gene  term       estimate
##   <chr> <fct>         <dbl>
## 1 g1    treatmentb  -1.24  
## 2 g2    treatmentb   0.623 
## 3 g3    treatmentb   0.494 
## 4 g4    treatmentb  -0.0737
## 5 g5    treatmentb  -0.179 
## 6 g6    treatmentb  -1.88

head(tidy(eb))

## # A tibble: 6 × 6
##   gene  term       estimate statistic p.value   lod
##   <chr> <fct>         <dbl>     <dbl>   <dbl> <dbl>
## 1 g1    treatmentb  -1.24     -1.11    0.269  -5.11
## 2 g2    treatmentb   0.623     0.556   0.579  -5.52
## 3 g3    treatmentb   0.494     0.441   0.660  -5.57
## 4 g4    treatmentb  -0.0737   -0.0658  0.948  -5.65
## 5 g5    treatmentb  -0.179    -0.160   0.873  -5.64
## 6 g6    treatmentb  -1.88     -1.68    0.0950 -4.42

Analysis can easily be performed from the tidied data. The package ggplot2 can be used to make a volcano plot of the p-values:

ggplot(tidy(eb), aes(x=estimate, y=log(p.value), color=statistic)) + 
  geom_point() + ggtitle("Nested Volcano Plots for Simulated Data Processed with limma") +
  theme_bw()

##ExpressionSet objects

tidy can also be run directly on ExpressionSet objects, as described in another popular Bioconductor package Biobase. The hammer dataset we used above (which is included in the biobroom package) is an ExpressionSet object, so we’ll use that to demonstrate.

library(Biobase)

head(tidy(hammer))

## # A tibble: 6 × 3
##   gene               sample    value
##   <chr>              <chr>     <int>
## 1 ENSRNOG00000000001 SRX020102     2
## 2 ENSRNOG00000000007 SRX020102     4
## 3 ENSRNOG00000000008 SRX020102     0
## 4 ENSRNOG00000000009 SRX020102     0
## 5 ENSRNOG00000000010 SRX020102    19
## 6 ENSRNOG00000000012 SRX020102     7

We can add the phenotype information by using the argument addPheno:

head(tidy(hammer, addPheno = TRUE))

## # A tibble: 6 × 8
##   gene               sample  sample.id num.tech.reps protocol strain Time  value
##   <chr>              <chr>   <fct>             <dbl> <fct>    <fct>  <fct> <int>
## 1 ENSRNOG00000000001 SRX020… SRX020102             1 control  Sprag… 2 mo…     2
## 2 ENSRNOG00000000007 SRX020… SRX020102             1 control  Sprag… 2 mo…     4
## 3 ENSRNOG00000000008 SRX020… SRX020102             1 control  Sprag… 2 mo…     0
## 4 ENSRNOG00000000009 SRX020… SRX020102             1 control  Sprag… 2 mo…     0
## 5 ENSRNOG00000000010 SRX020… SRX020102             1 control  Sprag… 2 mo…    19
## 6 ENSRNOG00000000012 SRX020… SRX020102             1 control  Sprag… 2 mo…     7

Now we can easily visualize the distribution of counts for each protocol by using ggplot2:

ggplot(tidy(hammer, addPheno=TRUE), aes(x=protocol, y=log(value))) +
  geom_boxplot() + ggtitle("Boxplot Showing Effect of Protocol on Expression")

##MSnSet Objects

tidy can also be run directly on MSnSet objects from MSnbase, which as specialised containers for quantitative proteomics data.

library(MSnbase)
data(msnset)

head(tidy(msnset))

## # A tibble: 6 × 3
##   protein sample.id   value
##   <chr>   <chr>       <dbl>
## 1 X1      iTRAQ4.114  1348.
## 2 X10     iTRAQ4.114   740.
## 3 X11     iTRAQ4.114 27638.
## 4 X12     iTRAQ4.114 31893.
## 5 X13     iTRAQ4.114 26144.
## 6 X14     iTRAQ4.114  6448.

head(tidy(msnset, addPheno = TRUE))

## # A tibble: 6 × 5
##   protein sample        mz reporters  value
##   <chr>   <chr>      <dbl> <fct>      <dbl>
## 1 X1      iTRAQ4.114  114. iTRAQ4     1348.
## 2 X10     iTRAQ4.114  114. iTRAQ4      740.
## 3 X11     iTRAQ4.114  114. iTRAQ4    27638.
## 4 X12     iTRAQ4.114  114. iTRAQ4    31893.
## 5 X13     iTRAQ4.114  114. iTRAQ4    26144.
## 6 X14     iTRAQ4.114  114. iTRAQ4     6448.