library("QFeatures") library("ggplot2") library("dplyr") library("magrittr")
We are going to use a subset of the CPTAC study 6 containing
conditions A and B [@Paulovich:2010]. The peptide-level data, as
processed by MaxQuant [@Cox:2008] is available in the
basename(f <- msdata::quant(pattern = "cptac", full.names = TRUE))
From the names of the columns, we see that the quantitative columns,
"Intensity." (note the space!) are at positions 56 to
names(read.delim(f)) (i <- grep("Intensity\\.", names(read.delim(f))))
We now read these data using the
readQFeatures function. The peptide
level expression data will be imported into R as an instance of class
cptac with an assay named
peptides. We also use
fnames argument to set the row-names of the
peptides assay to
the peptide sequences.
library("QFeatures") cptac <- readQFeatures(f, ecol = i, sep = "\t", name = "peptides", fnames = "Sequence")
Below we update the sample (column) annotations to encode the two groups, 6A and 6B, and the original sample numbers.
cptac$group <- rep(c("6A", "6B"), each = 3) cptac$sample <- rep(7:9, 2) colData(cptac)
filterFeatures(cptac, ~ Reverse == "")
filterFeatures(cptac, ~ Potential.contaminant == "")
library("magrittr") cptac <- cptac %>% filterFeatures(~ Reverse == "") %>% filterFeatures(~ Potential.contaminant == "")
The spreadsheet that was read above contained numerous variables that are returned by MaxQuant, but not necessarily necessary in the frame of a downstream statistical analysis.
The only ones that we will be needing below are the peptides sequences
and the protein identifiers. Below, we store these variables of
interest and filter them using the
rowvars <- c("Sequence", "Proteins", "Leading.razor.protein") cptac <- selectRowData(cptac, rowvars) rowDataNames(cptac)
Missing values can be very numerous in certain proteomics experiments
and need to be dealt with carefully. The first step is to assess their
presence across samples and features. But before being able to do so,
we need to replace 0 by
NA, given that MaxQuant encodes missing data
with a 0 using the
cptac <- zeroIsNA(cptac, i = seq_along(cptac)) nNA(cptac, i = seq_along(cptac))
The output of the
nNA function tells us that
In this dataset, we have such a high number of peptides without any
data because the 6 samples are a subset of a larger dataset, and these
peptides happened to be absent in groups A and B. Below, we use
filterNA to remove all the peptides that contain one or more missing
values by using
pNA = 0 (which also is the default value).
cptac <- filterNA(cptac, i = seq_along(cptac), pNA = 0) cptac
I we wanted to keep peptides that have up to 90% of missing values,
corresponsing in this case to those that have only one value (i.e 5/6
percent of missing values), we could have set
pNA to 0.9.
impute method can be used to perform missing value imputation
using a variety of imputation methods. The method takes an instance of
QFeatures (or a
SummarizedExperiment) as input, an a
character naming the desired method (see
?impute for the complete
list with details) and returns a new instance of class
SummarizedExperiment) with imputed data.
As described in more details in [@Lazar:2016], there are two types of mechanisms resulting in missing values in LC/MSMS experiments.
Missing values resulting from absence of detection of a feature, despite ions being present at detectable concentrations. For example in the case of ion suppression or as a result from the stochastic, data-dependent nature of the MS acquisition method. These missing value are expected to be randomly distributed in the data and are defined as missing at random (MAR) or missing completely at random (MCAR).
Biologically relevant missing values, resulting from the absence of the low abundance of ions (below the limit of detection of the instrument). These missing values are not expected to be randomly distributed in the data and are defined as missing not at random (MNAR).
MAR and MCAR values can be reasonably well tackled by many imputation methods. MNAR data, however, requires some knowledge about the underlying mechanism that generates the missing data, to be able to attempt data imputation. MNAR features should ideally be imputed with a left-censor (for example using a deterministic or probabilistic minimum value) method. Conversely, it is recommended to use hot deck methods (for example nearest neighbour, maximum likelihood, etc) when data are missing at random.
data(se_na2) x <- assay(impute(se_na2, "zero")) x[x != 0] <- 1 suppressPackageStartupMessages(library("gplots")) heatmap.2(x, col = c("lightgray", "black"), scale = "none", dendrogram = "none", trace = "none", keysize = 0.5, key = FALSE, RowSideColors = ifelse(rowData(se_na2)$randna, "orange", "brown"), ColSideColors = rep(c("steelblue", "darkolivegreen"), each = 8))
It is anticipated that the identification of both classes of missing
values will depend on various factors, such as feature intensities and
experimental design. Below, we use perform mixed imputation, applying
nearest neighbour imputation on the
features that are assumed to contain randomly distributed missing
values (if any) (yellow on figure \@ref(fig:miximp)) and a
deterministic minimum value imputation on the
r sum(!rowData(se_na2)$randna) proteins that display a non-random pattern
of missing values (brown on figure \@ref(fig:miximp)).
When analysing continuous data using parametric methods (such as t-test or linear models), it is often necessary to log-transform the data. The figure below (left) show that how our data is mainly composed of small values with a long tail of larger ones, which is a typical pattern of quantitative omics data.
Below, we use the
logTransform function to log2-transform our
data. This time, instead of overwriting the peptides assay, we are
going to create a new one to contain the log2-transformed data.
cptac <- addAssay(cptac, logTransform(cptac[]), name = "peptides_log") cptac
par(mfrow = c(1, 2)) limma::plotDensities(assay(cptac[])) limma::plotDensities(assay(cptac[]))
QFeatures objects can be normalised with the
function. The type of normalisation is defined by the
argument; below, we use quantiles normalisation, store the normalised
data into a new experiment, and visualise the resulting data.
cptac <- addAssay(cptac, normalize(cptac[["peptides_log"]], method = "quantiles"), name = "peptides_norm") cptac
par(mfrow = c(1, 2)) limma::plotDensities(assay(cptac[["peptides_log"]])) limma::plotDensities(assay(cptac[["peptides_norm"]]))
At this stage, it is possible to directly use the peptide-level intensities to perform a statistical analysis [@Goeminne:2016], or aggregate the peptide-level data into protein intensities, and perform the differential expression analysis at the protein level.
To aggregate feature data, we can use the
that takes the following inputs:
QFeaturesinstance that contains the peptide quantitation data -
"cptac"in our example;
i: the name or index of the assay that contains the (normalised) peptide quantitation data -
"peptides_norm"in our case;
fcol: the feature variable (in the assay above) to be used to define what peptides to aggregate -
"Proteins"here, given that we want to aggregate all peptides that belong to one protein (group);
name: the name of the new aggregates assay -
"proteins"in this case;
fun, the function that will compute this aggregation - we will be using the default value, namely
cptac <- aggregateFeatures(cptac, i = "peptides_norm", fcol = "Proteins", name = "proteins") cptac
We obtain a final 1125 quantified proteins in the new
assay. Below, we display the quantitation data for the first 6
proteins and their respective variables. The latter shown that number
of peptides that were using during the aggregation step (
We can get a quick overview of this
.n variable by computing the
table below, that shows us that we have 405 proteins that are based on
a single peptides, 230 that are based on two, 119 that are based on
three, ... and a single protein that is the results of aggregating 44
P02787ups|TRFE_HUMAN_UPS and visualise its expression
pattern in the 2 groups at the protein and peptide level.
library("ggplot2") library("dplyr") longFormat(cptac["P02787ups|TRFE_HUMAN_UPS", ]) %>% as.data.frame() %>% mutate(group = ifelse(grepl("A", colname), "A", "B")) %>% mutate(sample = sub("Intensity\\.", "", colname)) %>% ggplot(aes(x = sample, y = value, colour = rowname, shape = group)) + geom_point() + facet_grid(~ assay)
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