The goal of protti is to provide flexible functions and workflows for proteomics quality control and data analysis, within a single, user-friendly package. It can be used for label-free DDA, DIA and SRM data generated with search tools and software such as Spectronaut, MaxQuant, Proteome Discoverer and Skyline. Both limited proteolysis mass spectrometry (LiP-MS) and regular bottom-up proteomics experiments can be analysed.
protti is developed and maintained by members of the lab of Paola Picotti at ETH Zurich. Our lab is focused on protein structural changes that occur in response to perturbations such as metabolite, drug and protein binding-events, as well as protein aggregation and enzyme activation (Piazza 2018, Piazza 2020, Cappelletti, Hauser & Piazza 2021). We have devoloped mass spectrometry-based structural and chemical proteomic methods aimed at monitoring protein conformational changes in the complex cellular milieu (Feng 2014).
There is a wide range of functions protti provides to the user. The main areas of application are:
protti is implemented as an R package.
You can install the release version from
CRAN using the
install.packages("protti", dependencies = TRUE)
Note: If you do not have
devtools installed make sure to do so by
removing the comment sign (#).
# install.packages("devtools") devtools::install_github("jpquast/protti", dependencies = TRUE)
dependencies = TRUE argument in both
devtools::install_github() also installs suggested packages that are
required for some functions to work. If this argument is not included
functions that use a package that is not installed by default will throw
an error and prompt the user to install the missing package. If you
happen to run into problems during the installation of protti we
recommend removing this argument and installing packages manually if
they are needed for a certain function.
Since protti is designed to be a flexible tool for the analysis of your data, there are many ways in which it can be used. In this section we will give a general overview for a very simple pipeline that takes a result from the search tool of your choice and in a few steps returns a list of significantly changing proteins or peptides. To ensure that you have your data in the right format please check out the input preparation vignette.
A complete list of functions and their documentation is available
here. Within R you can
access the same documentation by calling
? followed by the function
name without parenthesis.
In general functions with the prefix
qc_* are used for quality control
of your data. Functions starting with
fetch_* allow you to retrieve
data from a database directly into your R session. When a function
filter_* it is meant to be used to filter your data prior
For more in detail workflow suggestions and demonstrations of various functions, you can have a look at the package vignettes. These include:
In this example we are going to analyse synthetic data of which we know
the ground truth. The same principles would apply to any real data.
Before you start analysing your data you should load all required
packages. protti is designed to work well with the
tidyverse package family and we will use
them for this example. Therefore, you should also load them before you
get started. Note: If you do not have the
tidyverse installed you can
do so by removing the comment sign (#) in front of the
install.packages() function. This will install them directly from
# Load protti library(protti) # Install the tidyverse if necessary # install.packages("tidyverse") # Load tidyverse packages. Can also be done by calling library(tidyverse) library(dplyr) library(magrittr)
Usually the search tool of your choice generates a report for you that
has either a
.csv format. You can easily load reports into R
by using the
read_protti() function. This function is a wrapper around
fread() function from the
data.table package and the
clean_names() function from the
janitor package. This will allow you
to not only load your data into R very fast, but also to clean up the
column names into lower snake case. This will make it easier to remember
them and to use them in your data analysis.
# Load data data <- read_protti("filename.csv")
Since we will use synthetic data for this example we are going to call
create_synthetic_data() function from protti. Of course you do
not need to do this step in your analysis pipeline.
The data this function creates is similar to data obtained from a LiP-MS experiment. Please note that any of the steps in this workflow can also be applied to protein abundance data that contains protein IDs and protein intensities.
set.seed(42) # Makes example reproducible # Create synthetic data data <- create_synthetic_data(n_proteins = 100, frac_change = 0.05, n_replicates = 4, n_conditions = 2, method = "effect_random", additional_metadata = FALSE) # The method "effect_random" as opposed to "dose-response" just randomly samples # the extend of the change of significantly changing peptides for each condition. # They do not follow any trend and can go in any direction.
Before you start analysing your data it is recommended that you filter out any observations not necessary for your analysis. These include for example:
On your own data you can easily achieve this with
function. Our synthetic data does not require any filtering at this
Due to the fact that variances increase with increasing raw intensities,
statistical tests would have a bias towards lower-intensity peptides or
proteins. Therefore you should log2 transform your data to correct for
this mean-variance relationship. We do not need to do this for the
synthetic data as it is already log2 transformed. For your own data just
mutate() together with
In addition to filtering and log2 transformation it is also advised to
normalise your data to equal out small differences in overall sample
intensities that result from unequal sample concentrations. protti
normalise() function for this purpose. For this example
we will use median normalisation (
method = "median"). This function
generates an additional column called
contains the normalised intensities.
Note: If your search tool already normalised your data you should not normalise it another time.
normalised_data <- data %>% normalise(sample = sample, intensity_log2 = peptide_intensity_missing, method = "median")
The next step is to deal with missing data points. You could choose to
impute missing data in a later step, but this is only recommended if
only a small proportion of your data is missing. In order to calculate
statistical significance of differentially abundant peptides or proteins
we would like to have at least a minimum number of observations per
condition. The protti function
assign_missingness() checks for
each treatment-to-reference condition if the defined minimum number of
observations is satisfied and assigns a missingness type to each
comparison as follows.
If a certain condition has all replicates while the other one has less
than 20% (adjusted downward) of total possible replicates, the case is
considered to be “missing not at random” (
MNAR). In order to be
labeled “missing at random” (
MAR) 70% (adjusted downward) of total
replicates need to be present in both conditions. If you performed an
experiment with 4 replicates that means that both conditions need to
contain at least 2 observations. Comparisons that have too few
observations are labeled
NA. These will not be imputed if imputation
is performed later on using the
impute() function. You can read the
exact details in the documentation of this function and also adjust the
thresholds if you want to be more or less conservative with how many
data points to retain.
data_missing <- normalised_data %>% assign_missingness(sample = sample, condition = condition, grouping = peptide, intensity = normalised_intensity_log2, ref_condition = "condition_1", retain_columns = c(protein, change_peptide)) # Next to the columns it generates, assign_missingness only contains the columns # you provide as input in its output. If you want to retain additional columns you # can provide them in the retain_columns argument.
Note: Instead of “peptide” in the
grouping argument you can provide
protein IDs in case you are working with protein abundance data.
However, then intensities should be protein intensities and not peptide
For the calculation of abundance changes and the associated
significances protti provides the function
calculate_diff_abundance(). You can choose between different
statistical methods. For this example we will chose a moderated t-test.
The type of missingness assigned to a comparison does not have any
influence on the statistical test. However, by default (can be changed)
comparisons with missingness
NA are filtered out prior to p-value
adjustment. This means that in addition to imputation, the user can use
missingness cutoffs also in order to define which comparisons are too
incomplete to be trustworthy even if significant.
result <- data_missing %>% calculate_diff_abundance(sample = sample, condition = condition, grouping = peptide, intensity_log2 = normalised_intensity_log2, missingness = missingness, comparison = comparison, filter_NA_missingness = TRUE, method = "moderated_t-test", retain_columns = c(protein, change_peptide))
Next we can use a Volcano plot to visualize significantly changing
peptides with the function
volcano_plot(). You can choose to create an
interactive plot with the
interactive argument. Please note that this
is not recommended for large datasets.
result %>% volcano_plot(grouping = peptide, log2FC = diff, significance = pval, method = "target", target_column = change_peptide, target = TRUE, legend_label = "Ground Truth", significance_cutoff = c(0.05, "adj_pval"))
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