Single Cell Proteomics data processing and analysis
29 October 2024
Package
scp 1.17.0
1 The scp package
The scp package is used to process and analyse mass spectrometry
(MS)-based single cell proteomics (SCP) data. The functions rely on a
specific data structure that wraps
QFeatures
objects (Gatto and Vanderaa (2023)) around
SingleCellExperiment
objects (Amezquita et al. (2020)). This data structure could be seen as
Matryoshka dolls were the SingleCellExperiment objects are small
dolls contained in the bigger QFeatures doll.
The SingleCellExperiment class provides a dedicated framework for
single-cell data. The SingleCellExperiment serves as an interface to
many cutting-edge methods for processing, visualizing and analysis
single-cell data. More information about the SingleCellExperiment
class and associated methods can be found in the
OSCA book.
The QFeatures class is a data framework dedicated to manipulate and
process MS-based quantitative data. It preserves the relationship
between the different levels of information: peptide to spectrum match
(PSM) data, peptide data and protein data. The QFeatures package
also provides an interface to many utility functions to streamline the
processing MS data. More information about MS data analysis tools can
be found in the
RforMassSpectrometry project.
(#fig:scp_framework)scp relies on SingleCellExperiment and QFeatures objects.
Before running the vignette we need to load the scp package.
library("scp")We also load ggplot2 and dplyr for convenient data
manipulation and plotting.
library("ggplot2")
library("dplyr")2 Before you start
This vignette will guide you through some common steps of mass
spectrometry-based single-cell proteomics (SCP) data analysis. SCP is
an emerging field and further research is required to develop a
principled analysis workflow. Therefore, we do not guarantee that the
steps presented here are the best steps for this type of data analysis.
This vignette performs the steps that were described in the SCoPE2
landmark paper (Specht et al. (2021)) and that were reproduced in another
work using the scp package (Vanderaa and Gatto (2021)). The replication on
the full SCoPE2 dataset using scp is available in
this vignette.
We hope to convince the reader that,
although the workflow is probably not optimal, scp has the full
potential to perform standardized and principled data analysis. All
functions presented here are comprehensively documented, highly modular,
can easily be extended with new algorithms. Suggestions, feature
requests or bug reports are warmly welcome. Feel free to open an issue
in the
GitHub repository.
This workflow can be applied to any MS-based SCP data. The minimal requirement to follow this workflow is that the data should contain the following information:
runCol/Raw.file: field in the feature data and the sample data that gives the names of MS acquisition runs or files.quantCols: field in the sample data that links to columns in the quantification data and that allows to link samples to MS channels (more details in another vignette).SampleType: field in the sample data that provides the type of sample that is acquired (carrier, reference, single-cell,…). Only needed for multiplexing experiments.Potential.contaminant: field in the feature data that marks contaminant peptides.Reverse: field in the feature data that marks reverse peptides.PIF: field in the feature data that provides spectral purity.PEPordart_PEP: field in the feature data that provides peptide posterior error probabilities.Modified.sequence: field in the feature data that provides the peptide identifiers.Leading.razor.protein: field in the feature data that provides the protein identifiers.- At least one field in the feature data that contains quantification
values. In this case, there are 16 quantification columns named as
Reporter.intensity.followed by an index (1to16).
Each required field will be described more in detail in the corresponding sections. Names can be adapted by the user to more meaningful ones or adapted to other output tables.
3 Read in SCP data
The first step is to read in the PSM quantification table generated
by, for example, MaxQuant (Tyanova, Temu, and Cox (2016)). We created a small
example data by subsetting the MaxQuant evidence.txt table provided
in the SCoPE2 landmark paper (Specht et al. (2021)). The mqScpData table
is a typical example of what you would get after reading in a CSV file
using read.csv or read.table. See ?mqScpData for more
information about the table content.
data("mqScpData")We also provide an example of a sample annotation table that provides
useful information about the samples that are present in the example
data. See ?sampleAnnotation for more information about the table
content.
data("sampleAnnotation")As a note, the example sample data contains 5 different types of samples
(SampleType) that can be found in a TMT-based SCP data set:
table(sampleAnnotation$SampleType)
#>
#> Blank Carrier Macrophage Monocyte Reference Unused
#> 19 3 20 5 3 14- The carrier channels (
Carrier) contain 200 cell equivalents and are meant to boost the peptide identification rate. - The normalization channels (
Reference) contain 5 cell equivalents and are used to partially correct for between-run variation. - The unused channels (
Unused) are channels that are left empty due to isotopic cross-contamination by the carrier channel. - The negative control channels (
Blank) contain samples that do not contain any cell but are processed as single-cell samples. - The single-cell sample channels contain the single-cell samples of
interest, that are macrophage (
Macrophage) or monocyte (Monocyte).
Using readSCP, we combine both tables in a QFeatures object
formatted as described above.
scp <- readSCP(assayData = mqScpData,
colData = sampleAnnotation,
runCol = "Raw.file",
removeEmptyCols = TRUE)
#> Checking arguments.
#> Loading data as a 'SummarizedExperiment' object.
#> Splitting data in runs.
#> Formatting sample annotations (colData).
#> Formatting data as a 'QFeatures' object.
scp
#> An instance of class QFeatures containing 4 assays:
#> [1] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 395 rows and 11 columns
#> [2] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 487 rows and 11 columns
#> [3] 190321S_LCA10_X_FP97_blank_01: SingleCellExperiment with 109 rows and 11 columns
#> [4] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 370 rows and 16 columnsSee here that the 3 first assays contain 11 columns that correspond to the TMT-11 labels and the last assay contains 16 columns that correspond to the TMT-16 labels.
Important: More details about the usage of readSCP() and how to
read your own data set are provided in the Load data using readSCP
vignette.
Another way to get an overview of the scp object is to plot the
QFeatures object. This will create a graph where each node is an
assay and links between assays are denoted as edges.
plot(scp)
4 Clean missing data
All single-cell data contain many zeros. The zeros can be biological
zeros or technical zeros and differentiating between the two types is
not a trivial task. To avoid artefacts in downstream steps, we replace
the zeros by the missing value NA. The zeroIsNA function takes the
QFeatures object and the name(s) or index/indices of the assay(s) to
clean and automatically replaces any zero in the selected quantitative
data by NA.
scp <- zeroIsNA(scp, i = 1:4)5 Filter PSMs
A common steps in SCP is to filter out low-confidence PSMs. Each PSM
assay contains feature meta-information that are stored in the
rowData of the assays. The QFeatures package allows to quickly
filter the rows of an assay by using these information. The available
variables in the rowData are listed below for each assay.
rowDataNames(scp)
#> CharacterList of length 4
#> [["190222S_LCA9_X_FP94BM"]] uid Sequence Length ... residual participated
#> [["190321S_LCA10_X_FP97AG"]] uid Sequence Length ... residual participated
#> [["190321S_LCA10_X_FP97_blank_01"]] uid Sequence ... residual participated
#> [["190914S_LCB3_X_16plex_Set_21"]] uid Sequence ... residual participated5.1 Filter features based on feature annotations
Below are some examples of criteria that are used to identify low-confidence. The information is readily available since this was computed by MaxQuant:
- Remove PSMs that are matched to contaminants
- Remove PSMs that are matched to the decoy database
- Keep PSMs that exhibit a high PIF (parental ion fraction), indicative of the purity of a spectrum
We can perform this filtering using the filterFeatures function from
QFeatures. filterFeatures automatically accesses the feature
annotations and selects the rows that meet the provided condition(s). For
instance, Reverse != "+" keeps the rows for which the Reverse
variable in the rowData is not "+" (i.e. the PSM is not matched
to the decoy database).
scp <- filterFeatures(scp,
~ Reverse != "+" &
Potential.contaminant != "+" &
!is.na(PIF) & PIF > 0.8)
#> 'Reverse' found in 4 out of 4 assay(s)
#> 'Potential.contaminant' found in 4 out of 4 assay(s)
#> 'PIF' found in 4 out of 4 assay(s)5.2 Filter assays based on detected features
To avoid proceeding with failed runs, another interesting filter is to
remove assays with too few features. If a batch contains less than,
for example, 150 features we can then suspect something wrong happened
in that batch and it should be removed. Using dims, we can query the
dimensions (hence the number of features and the number of samples) of
all assays contained in the dataset.
dims(scp)
#> 190222S_LCA9_X_FP94BM 190321S_LCA10_X_FP97AG 190321S_LCA10_X_FP97_blank_01
#> [1,] 283 318 60
#> [2,] 11 11 11
#> 190914S_LCB3_X_16plex_Set_21
#> [1,] 200
#> [2,] 16Actually, a QFeatures object can be seen as a three-order array:
\(features \times samples \times assay\). Hence, QFeatures supports
three-order subsetting x[rows, columns, assays]. We first select the
assays that have sufficient PSMs (the number of rows is greater than
150), and then subset the scp object for the assays that meet the
criterion.
keepAssay <- dims(scp)[1, ] > 150
scp <- scp[, , keepAssay]
#> Warning: 'experiments' dropped; see 'drops()'
#> harmonizing input:
#> removing 11 sampleMap rows not in names(experiments)
#> removing 11 colData rownames not in sampleMap 'primary'
scp
#> An instance of class QFeatures containing 3 assays:
#> [1] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 283 rows and 11 columns
#> [2] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 318 rows and 11 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 200 rows and 16 columnsNotice the 190321S_LCA10_X_FP97_blank_01 sample was removed because
it did not contain sufficient features, as expected from a blank
run. This could also have been the case for failed runs.
5.3 Filter features based on SCP metrics
Another type of filtering is specific to SCP. In the SCoPE2 analysis, the authors suggest a filters based on the sample to carrier ratio (SCR), that is the reporter ion intensity of a single-cell sample divided by the reporter ion intensity of the carrier channel (200 cells) from the same batch. It is expected that the carrier intensities are much higher than the single-cell intensities.
The SCR can be computed using the computeSCR function from
scp. The function must be told which channels are the samples that
must be divided and which channel contains the carrier. This
information is provided in the sample annotations and is accessed using
the colData, under the SampleType field.
table(colData(scp)[, "SampleType"])
#>
#> Blank Carrier Macrophage Monocyte Reference Unused
#> 3 3 20 5 3 4In this dataset, SampleType gives the type of sample that is present
in each TMT channel. The SCoPE2 protocole includes 5 types of samples:
- The carrier channels (
Carrier) contain 200 cell equivalents and are meant to boost the peptide identification rate. - The normalization channels (
Reference) contain 5 cell equivalents and are used to partially correct for between-run variation. - The unused channels (
Unused) are channels that are left empty due to isotopic cross-contamination by the carrier channel. - The negative control channels (
Blank) contain samples that do not contain any cell but are processed as single-cell samples. - The single-cell sample channels contain the single-cell samples of
interest, that are macrophage (
Macrophage) or monocyte (Monocyte).
The computeSCR function expects the following input:
- The
QFeaturesdataset - The assay name(s) or index/indices for which the SCR should be computed
colvar: the variable in the sample annotations (colData) that hold the information used to discriminate sample channels from carrier channels.carrierPattern: a string pattern (following regular expression syntax) that identifies the carrier channel in each batch.samplePattern: a string pattern (following regular expression syntax) that identifies the samples to divide.
Optionally, you can also provide the following arguments:
rowDataName: the name of the column in therowDatawhere to store the computed SCR for each feature.sampleFUN: when multiple samples are present in an assay, there are as many SCR as there are samples that need to be summarized to a single value per feature.sampleFUNtells which function to use for summarizing the sample values before computing the SCR; the default is themean.carrierFUN: some designs might include several carriers per run (not the case in this example). Similarly tosampleFUN,carrierFUNtells which function to use for summarizing the carrier values before computing the SCR; the default is the same function assampleFUN.
The function creates a new field in the rowData of the assays. We
compute the average SCR for each PSM and store it in the corresponding
rowData, under the MeanSCR column.
scp <- computeSCR(scp,
i = 1:3,
colvar = "SampleType",
carrierPattern = "Carrier",
samplePattern = "Macrophage|Monocyte",
sampleFUN = "mean",
rowDataName = "MeanSCR")Before applying the filter, we plot the distribution of the average
SCR. We collect the rowData from several assays in a single table
DataFrame using the rbindRowData function from QFeatures.
rbindRowData(scp, i = 1:3) |>
data.frame() |>
ggplot(aes(x = MeanSCR)) +
geom_histogram() +
geom_vline(xintercept = c(1/200, 0.1),
lty = c(2, 1)) +
scale_x_log10()
The expected ratio between single cells and the carrier is 1/200
(dashed line). We can see that the distribution mode is slightly
shifted towards higher ratios with a mode around 0.01. However, there
are a few PSMs that stand out of the distribution and have a much
higher signal than expected, indicating something wrong happened
during the quantification of those PSMs. We therefore filter out PSMs
with an average SCR higher than 0.1 (solide line). This is again
easily performed using the filterFeatures functions.
scp <- filterFeatures(scp,
~ !is.na(MeanSCR) &
MeanSCR < 0.1)
#> 'MeanSCR' found in 3 out of 3 assay(s)5.4 Filter features to control for FDR
Finally, we might also want to control for false discovery rate (FDR).
MaxQuant already computes posterior error probabilities (PEP), but
filtering on PEPs is too conservative (Käll et al. (2008)) so we provide the
pep2qvalue function to convert PEPs to q-values that are directly
related to FDR. We here compute the q-values from the PEP (dart_PEP)
across all 3 assays. dart_PEP contains the PEP values that have been
updated using the DART-ID algorithm (Chen, Franks, and Slavov (2019)). The function will
store the results in the rowData, we here asked to name the new
column qvalue_PSMs.
scp <- pep2qvalue(scp,
i = 1:3,
PEP = "dart_PEP",
rowDataName = "qvalue_PSMs")We also allow to compute q-values at peptide or protein level rather
than PSM. In this case, you need to supply the groupBy argument.
Suppose we want to compute the q-values at protein level, we can fetch
the protein information stored under Leading.razor.protein in the
rowData. This time, we store the q-values in a new field called
qvalue_proteins.
scp <- pep2qvalue(scp,
i = 1:3,
PEP = "dart_PEP",
groupBy = "Leading.razor.protein",
rowDataName = "qvalue_proteins")We can now filter the PSM to control, let’s say, the protein FDR at
1%. This can be performed using filterFeatures because the q-values
were stored in the rowData.
scp <- filterFeatures(scp,
~ qvalue_proteins < 0.01)
#> 'qvalue_proteins' found in 3 out of 3 assay(s)6 Process the PSM data
6.1 Relative reporter ion intensity
In order to partialy correct for between-run variation, SCoPE2
suggests computing relative reporter ion intensities. This means that
intensities measured for single-cells are divided by the reference
channel containing 5-cell equivalents. We use the divideByReference
function that divides channels of interest by the reference
channel. Similarly to computeSCR, we can point to the samples and
the reference columns in each assay using the annotation contained in
the colData.
We here divide all columns (using the regular expression wildcard .)
by the reference channel (Reference).
scp <- divideByReference(scp,
i = 1:3,
colvar = "SampleType",
samplePattern = ".",
refPattern = "Reference")7 Aggregate PSM data to peptide data
Now that the PSM assays are processed, we can aggregate them to
peptides. This is performed using the aggregateFeaturesOverAssays
function. For each assay, the function aggregates several PSMs into a
unique peptide. This is best illustrated by the figure below.
(#fig:features_aggregation)Conceptual illustration of feature aggregation.
Remember there currently are three assays containing the PSM data.
scp
#> An instance of class QFeatures containing 3 assays:
#> [1] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 239 rows and 11 columns
#> [2] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 277 rows and 11 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 186 rows and 16 columnsThe PSMs are aggregated over the fcol feature variable, here the
modified peptide sequence. We also need to supply an aggregating
function that will tell how to combine the quantitative data of the
PSMs to aggregate. We here aggregate the PSM data using the median
value per sample thanks to the matrixStats:colMedians()
function. Other functions can be used and we refer to
?aggregateFeatures for more information about available aggregation
functions. The aggregateFeaturesOverAssays() function will create a
new assay for each aggregated assay. We name the aggregated assays
using the original names and appending peptides_ at the start.
scp <- aggregateFeaturesOverAssays(scp,
i = 1:3,
fcol = "Modified.sequence",
name = paste0("peptides_", names(scp)),
fun = matrixStats::colMedians, na.rm = TRUE)Notice that 3 new assays were created in the scp object. Those new
assays contain the aggregated features while the three first assays
are unchanged. This allows to keep track of the data processing.
scp
#> An instance of class QFeatures containing 6 assays:
#> [1] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 239 rows and 11 columns
#> [2] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 277 rows and 11 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 186 rows and 16 columns
#> [4] peptides_190222S_LCA9_X_FP94BM: SingleCellExperiment with 216 rows and 11 columns
#> [5] peptides_190321S_LCA10_X_FP97AG: SingleCellExperiment with 256 rows and 11 columns
#> [6] peptides_190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 168 rows and 16 columnsUnder the hood, the QFeatures architecture preserves the
relationship between the aggregated assays. See ?AssayLinks for more
information on relationships between assays. This is illustrated on
the QFeatures plot:
plot(scp)
8 Join the SCoPE2 sets in one assay
Up to now, we kept the data belonging to each MS run in separate
assays. We now combine all batches into a single assay. This is done
using the joinAssays function from the QFeatures package. Note
that we now use the aggregated assays, so assay 4 to 6.
scp <- joinAssays(scp,
i = 4:6,
name = "peptides")In this case, one new assay is created in the scp object that
combines the data from assay 4 to 6. The samples are always distinct
so the number of column in the new assay (here \(48\)) will always
equals the sum of the columns in the assays to join (here \(16 + 16 + 16\)). The feature in the joined assay might contain less features than
the sum of the rows of the assays to join since common features
between assays are joined in a single row.
plot(scp)
9 Filter single-cells
Another common step in single-cell data analysis pipelines is to remove low-quality cells. After subsetting for the samples of interest, we will use 2 metrics: the median relative intensities per cell and the median coefficient of variation (CV) per cell.
9.1 Filter samples of interest
We can subset the cells of interest, that is the negative control
samples, the macrophages and the monocytes. This
can easily be done by taking advantage of the colData and the
subsetting operators. Recall that QFeatures objects support
three-order subsetting, x[rows, columns, assays], where columns are
the samples of interest.
scp
#> An instance of class QFeatures containing 7 assays:
#> [1] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 239 rows and 11 columns
#> [2] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 277 rows and 11 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 186 rows and 16 columns
#> [4] peptides_190222S_LCA9_X_FP94BM: SingleCellExperiment with 216 rows and 11 columns
#> [5] peptides_190321S_LCA10_X_FP97AG: SingleCellExperiment with 256 rows and 11 columns
#> [6] peptides_190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 168 rows and 16 columns
#> [7] peptides: SingleCellExperiment with 384 rows and 38 columns
scp <- scp[, scp$SampleType %in% c("Blank", "Macrophage", "Monocyte"), ]The subsetting removes unwanted samples from all assays. The filtered data set contains the same number of assays with the same number of features, but the number of columns (hence sampled) decreased.
scp
#> An instance of class QFeatures containing 7 assays:
#> [1] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 239 rows and 8 columns
#> [2] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 277 rows and 8 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 186 rows and 12 columns
#> [4] peptides_190222S_LCA9_X_FP94BM: SingleCellExperiment with 216 rows and 8 columns
#> [5] peptides_190321S_LCA10_X_FP97AG: SingleCellExperiment with 256 rows and 8 columns
#> [6] peptides_190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 168 rows and 12 columns
#> [7] peptides: SingleCellExperiment with 384 rows and 28 columns9.2 Filter based on the median relative intensity
We compute the median relative reporter ion intensity for each cell
separately and apply a filter based on this statistic. This procedure
recalls that of library size filtering commonly performed in scRNA-Seq
data analysis, where the library size is the sum of the counts in each
single cell. We compute and store the median intensity in the
colData.
medians <- colMedians(assay(scp[["peptides"]]), na.rm = TRUE)
scp$MedianRI <- mediansLooking at the distribution of the median per cell can highlight low-quality cells.
colData(scp) |>
data.frame() |>
ggplot() +
aes(x = MedianRI,
y = SampleType,
fill = SampleType) +
geom_boxplot() +
scale_x_log10()
The negative control samples should not contain any peptide information and are therefore used to assess the amount of background signal. The graph above confirms that the signal measured in single-cells (macrophages and monocytes) is above the background signal, hence no filtering is needed. Would it not be the case, the same procedure as in the previous section can be used for selecting the cells that have an associated median RI lower that a defined threshold.
9.3 Filter based on the median CV
The median CV measures the consistency of quantification for a group
of peptides that belong to a protein. We remove cells that exhibit
high median CV over the different proteins. We compute the median CV
per cell using the computeMedianCV function from the scp
package. The function takes the peptides assay and computes the CV
for each protein in each cell. To perform this, we must supply the
name of the rowData field that contains the protein information
through the groupBy argument. We also only want to compute CVs if we
have at least 5 peptides per protein. Finally, we also perform a
normalization and divide the columns by the median. The computed
median CVs are automatically stored in the colData under the name
that is supplied, here MedianCV.
scp <- medianCVperCell(scp,
i = 1:3,
groupBy = "Leading.razor.protein",
nobs = 5,
norm = "div.median",
na.rm = TRUE,
colDataName = "MedianCV")The computed CVs are stored in the colData of the peptides assay
and holds the median CV per cell computed using at least 5
observations (peptides). The main interest of computing the median CV
per cell is to filter cells with reliable quantification. The negative
control samples are not expected to have reliable quantifications and
hence can be used to estimate an empirical null distribution of the
CV. This distribution helps defining a threshold that filters out
single-cells that contain noisy quantification.
getWithColData(scp, "peptides") |>
colData() |>
data.frame() |>
ggplot(aes(x = MedianCV,
fill = SampleType)) +
geom_boxplot() +
geom_vline(xintercept = 0.65)
We can see that the protein quantification for single-cells are much more consistent than for negative control samples. Based on the distribution of the negative controls, we decide to keep the cells that have a median CV lower than 0.65. Note this example is inaccurate because the null distribution is based on only 3 negative controls, but more sets could lead to a better estimation of the CV null distribution.
scp <- scp[, !is.na(scp$MedianCV) & scp$MedianCV < 0.65, ]We can now remove the negative controls since all QC metrics are now computed.
scp <- scp[, scp$SampleType != "Blank", ]10 Process the peptide data
In this vignette, the peptide data are further processed before aggregation to proteins. The steps are: normalization, filter peptides based on missing data and log-transformation.
10.1 Normalization
The columns (samples) of the peptide data are first normalized by
dividing the relative intensities by the median relative
intensities. Then, the rows (peptides) are normalized by dividing the
relative intensities by the mean relative intensities. The normalized
data is stored in a separate assay. This normalization procedure is
suggested in the SCoPE2 analysis and is applied using the sweep
method. Beside the dataset and the assay to normalize, the method
expects a MARGIN, that is either row-wise (1) or column-wise (2)
transformation, the FUN function to apply and STATS, a vector of
values to apply. More conventional normalization procedure can be
found in ?QFeatures::normalize.
## Divide columns by median
scp <- sweep(scp,
i = "peptides",
MARGIN = 2,
FUN = "/",
STATS = colMedians(assay(scp[["peptides"]]), na.rm = TRUE),
name = "peptides_norm_col")
## Divide rows by mean
scp <- sweep(scp,
i = "peptides_norm_col",
MARGIN = 1,
FUN = "/",
STATS = rowMeans(assay(scp[["peptides_norm_col"]]), na.rm = TRUE),
name = "peptides_norm")Notice each call to sweep created a new assay. Let’s have a look to
the current stat of the QFeatures plot:
plot(scp)
10.2 Remove peptides with high missing rate
Peptides that contain many missing values are not
informative. Therefore, another common procedure is to remove higly
missing data. In this example, we remove peptides with more than 99 %
missing data. This is done using the filterNA function from
QFeatures.
scp <- filterNA(scp,
i = "peptides_norm",
pNA = 0.99)10.3 Log-transformation
In this vignette, we perform log2-transformation using the
logTransform method from QFeatures. Other log-transformation can
be applied by changing the base argument.
scp <- logTransform(scp,
base = 2,
i = "peptides_norm",
name = "peptides_log")Similarly to sweep, logTransform creates a new assay in scp.
11 Aggregate peptide data to protein data
Similarly to aggregating PSM data to peptide data, we can aggregate
peptide data to protein data using the aggregateFeatures function.
scp <- aggregateFeatures(scp,
i = "peptides_log",
name = "proteins",
fcol = "Leading.razor.protein",
fun = matrixStats::colMedians, na.rm = TRUE)
#> Your quantitative and row data contain missing values. Please read the
#> relevant section(s) in the aggregateFeatures manual page regarding the
#> effects of missing values on data aggregation.The only difference between aggregateFeatures and
aggregateFeaturesOverAssays is that the second function can
aggregate several assay at once whereas the former only takes one
assay to aggregate. Hence, only a single assay, proteins, was
created in the scp object.
scp
#> An instance of class QFeatures containing 11 assays:
#> [1] 190222S_LCA9_X_FP94BM: SingleCellExperiment with 239 rows and 6 columns
#> [2] 190321S_LCA10_X_FP97AG: SingleCellExperiment with 277 rows and 6 columns
#> [3] 190914S_LCB3_X_16plex_Set_21: SingleCellExperiment with 186 rows and 6 columns
#> ...
#> [9] peptides_norm: SingleCellExperiment with 373 rows and 18 columns
#> [10] peptides_log: SingleCellExperiment with 373 rows and 18 columns
#> [11] proteins: SingleCellExperiment with 94 rows and 18 columnsAfter the second aggregation, the proteins assay in this example
contains quantitative information for 89 proteins in 15 single-cells.
12 Process the protein data
The protein data is further processed in three steps: normalization,
imputation (using the KNN algorithm) and batch correction (using the
ComBat algorithm).
12.1 Normalization
Normalization is performed similarly to peptide normalization. We use the same functions, but since the data were log-transformed at the peptide level, we subtract by the statistic (median or mean) instead of dividing.
## Center columns with median
scp <- sweep(scp, i = "proteins",
MARGIN = 2,
FUN = "-",
STATS = colMedians(assay(scp[["proteins"]]),
na.rm = TRUE),
name = "proteins_norm_col")
## Center rows with mean
scp <- sweep(scp, i = "proteins_norm_col",
MARGIN = 1,
FUN = "-",
STATS = rowMeans(assay(scp[["proteins_norm_col"]]),
na.rm = TRUE),
name = "proteins_norm")12.2 Imputation
The protein data contains a lot of missing values.
scp[["proteins_norm"]] |>
assay() |>
is.na() |>
mean()
#> [1] 0.3291962The average missingness in the proteins assay is around 25
%. Including more samples and hence more batches can increase the
missingness up to 70 % as seen for the complete SCoPE2 dataset
(Specht et al. (2021)). Whether imputation is beneficial or deleterious for
the data will not be discussed in this vignette. But taking those
missing value into account is essential to avoid artefacts in
downstream analyses. The data imputation is performed using the K
nearest neighbors algorithm, with k = 3. This is available from the
impute() mehtod. More details about the arguments can be found in
?impute::impute.knn.
scp <- impute(scp,
i = "proteins_norm",
name = "proteins_imptd",
method = "knn",
k = 3, rowmax = 1, colmax= 1,
maxp = Inf, rng.seed = 1234)
#> Imputing along margin 1 (features/rows).Note that after imputation, no value are missing.
scp[["proteins_imptd"]] |>
assay() |>
is.na() |>
mean()
#> [1] 012.3 Batch correction
A very important step for processing SCP data is to correct for batch effects. Batch effects are caused by technical variation occurring during different MS runs. Since only a small number of single-cells can be acquired at once, batch effects are unavoidable.
The ComBat() function from the sva package can be used to perform
batch correction as it is performed in the SCoPE2 analysis. We do not
claim that ComBat is the best algorithm for batch correcting SCP
data and other batch correcting methods could be used using the same
procedure.
We first extract the assay to process.
sce <- getWithColData(scp, "proteins_imptd")
#> Warning: 'experiments' dropped; see 'drops()'Next, we need to provide a design matrix and the batch annotation to
Combat. The design matrix allows to protect variables of interest,
in our case SampleType.
batch <- sce$runCol
model <- model.matrix(~ SampleType, data = colData(sce))We then load and call ComBat and overwrite the data matrix. Recall
the data matrix can be accessed using the assay function.
library(sva)
assay(sce) <- ComBat(dat = assay(sce),
batch = batch,
mod = model)Finally, we add the batch corrected assay to the QFeatures object
and create the feature links.
scp <- addAssay(scp,
y = sce,
name = "proteins_batchC")
scp <- addAssayLinkOneToOne(scp,
from = "proteins_imptd",
to = "proteins_batchC")For the last time, we plot the overview of the fully processed data set:
plot(scp)
13 Dimension reduction
Because each assay contains SingelCellExperiment objects, we can
easily apply methods developed in the scRNA-Seq field. A useful
package for dimension reduction on single-cell data is the scater.
library(scater)
#> Loading required package: SingleCellExperiment
#> Loading required package: scuttleThis package provides streamline functions to computes various dimension reduction such as PCA, UMAP, t-SNE, NMF, MDS, ….
13.1 PCA
PCA can be computed using the runPCA method. It returns a
SingleCellExperiment object for which the dimension reduction
results are stored in the reducedDim slot.
scp[["proteins_batchC"]] <- runPCA(scp[["proteins_batchC"]],
ncomponents = 5,
ntop = Inf,
scale = TRUE,
exprs_values = 1,
name = "PCA")The computed PCA can be displayed using the plotReducedDim function. The
dimred arguments should give the name of the dimension reduction results to
plot, here we called it PCA. The samples are colored by type of sample.
plotReducedDim(scp[["proteins_batchC"]],
dimred = "PCA",
colour_by = "SampleType",
point_alpha = 1)
This is a minimalistic example with only a few plotted cells, but the original SCoPE2 dataset contained more than thousand cells.
13.2 UMAP
Similarly to PCA, we can compute a UMAP using the runUMAP
method. Note however that the UMAP implementation requires a
initialization, usually provided by PCA. The previous PCA results are
used automatically when supplying dimred = "PCA" (PCA is the name
of the dimension reduction result that we supplied in the previous
section).
scp[["proteins_batchC"]] <- runUMAP(scp[["proteins_batchC"]],
ncomponents = 2,
ntop = Inf,
scale = TRUE,
exprs_values = 1,
n_neighbors = 3,
dimred = "PCA",
name = "UMAP")The computed UMAP can be displayed using the plotReducedDim
function. The dimred arguments gives the name of the dimension
reduction results to plot, here we called it UMAP. The samples are
colored by type of sample.
plotReducedDim(scp[["proteins_batchC"]],
dimred = "UMAP",
colour_by = "SampleType",
point_alpha = 1)
The UMAP plot is a very interesting plot for large datasets. A UMAP on this small example dataset is not useful but is shown for illustration.
14 Monitoring data processing
QFeatures keeps the links between the different assays along the
processing of the data. This greatly facilitates the visualization of
the quantitative data for a features at the different processing
levels. For instance, suppose we are interested
in the protein Plastin-2 (protein ID is P13796). A useful QC is to
monitor the data processing at the PSM, peptide and protein
level. This can easily be done thanks to the QFeatures
framework. Using the subsetByFeature, we can extract the protein of
interest and its related features in the other assays. The data is
formatted to a long format table that can easily be plugged in the
ggplot2 visualization tool.
## Get the features related to Plastin-2 (P13796)
subsetByFeature(scp, "P13796") |>
## Format the `QFeatures` to a long format table
longFormat(colvars = c("runCol", "SampleType", "quantCols")) |>
data.frame() |>
## This is used to preserve ordering of the samples and assays in ggplot2
mutate(assay = factor(assay, levels = names(scp)),
Channel = sub("Reporter.intensity.", "TMT-", quantCols),
Channel = factor(Channel, levels = unique(Channel))) |>
## Start plotting
ggplot(aes(x = Channel, y = value, group = rowname, col = SampleType)) +
geom_point() +
## Plot every assay in a separate facet
facet_wrap(facets = vars(assay), scales = "free_y", ncol = 3) +
## Annotate plot
xlab("Channels") +
ylab("Intensity (arbitrary units)") +
## Improve plot aspect
theme(axis.text.x = element_text(angle = 90),
strip.text = element_text(hjust = 0),
legend.position = "bottom")
This graph helps to keep track of the data processing. We can see how the different PSMs are progressively aggregated to peptides and then to proteins as well as how the normalization, imputation or batch correction impact the distribution of the quantifications.
We have dedicated a separate
vignette
that describes more in details how to visualize and explore data in a
QFeatures object.
Session information
R Under development (unstable) (2024-10-21 r87258)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.1 LTS
Matrix products: default
BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_GB LC_COLLATE=C
[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
time zone: America/New_York
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] scater_1.35.0 scuttle_1.17.0
[3] SingleCellExperiment_1.29.0 sva_3.55.0
[5] BiocParallel_1.41.0 genefilter_1.89.0
[7] mgcv_1.9-1 nlme_3.1-166
[9] dplyr_1.1.4 scpdata_1.13.1
[11] ExperimentHub_2.15.0 AnnotationHub_3.15.0
[13] BiocFileCache_2.15.0 dbplyr_2.5.0
[15] ggplot2_3.5.1 scp_1.17.0
[17] QFeatures_1.17.0 MultiAssayExperiment_1.33.0
[19] SummarizedExperiment_1.37.0 Biobase_2.67.0
[21] GenomicRanges_1.59.0 GenomeInfoDb_1.43.0
[23] IRanges_2.41.0 S4Vectors_0.45.0
[25] BiocGenerics_0.53.0 MatrixGenerics_1.19.0
[27] matrixStats_1.4.1 BiocStyle_2.35.0
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 jsonlite_1.8.9 magrittr_2.0.3
[4] ggbeeswarm_0.7.2 farver_2.1.2 rmarkdown_2.28
[7] zlibbioc_1.53.0 vctrs_0.6.5 memoise_2.0.1
[10] htmltools_0.5.8.1 S4Arrays_1.7.0 BiocBaseUtils_1.9.0
[13] curl_5.2.3 BiocNeighbors_2.1.0 SparseArray_1.7.0
[16] sass_0.4.9 bslib_0.8.0 plyr_1.8.9
[19] impute_1.81.0 cachem_1.1.0 igraph_2.1.1
[22] mime_0.12 lifecycle_1.0.4 pkgconfig_2.0.3
[25] rsvd_1.0.5 Matrix_1.7-1 R6_2.5.1
[28] fastmap_1.2.0 GenomeInfoDbData_1.2.13 clue_0.3-65
[31] digest_0.6.37 fdrtool_1.2.18 colorspace_2.1-1
[34] AnnotationDbi_1.69.0 irlba_2.3.5.1 lpsymphony_1.35.0
[37] RSQLite_2.3.7 beachmat_2.23.0 filelock_1.0.3
[40] labeling_0.4.3 fansi_1.0.6 httr_1.4.7
[43] abind_1.4-8 compiler_4.5.0 bit64_4.5.2
[46] withr_3.0.2 viridis_0.6.5 DBI_1.2.3
[49] highr_0.11 MASS_7.3-61 rappdirs_0.3.3
[52] DelayedArray_0.33.0 tools_4.5.0 vipor_0.4.7
[55] beeswarm_0.4.0 glue_1.8.0 grid_4.5.0
[58] cluster_2.1.6 reshape2_1.4.4 generics_0.1.3
[61] gtable_0.3.6 tidyr_1.3.1 ScaledMatrix_1.15.0
[64] BiocSingular_1.23.0 metapod_1.15.0 utf8_1.2.4
[67] XVector_0.47.0 ggrepel_0.9.6 BiocVersion_3.21.1
[70] pillar_1.9.0 stringr_1.5.1 limma_3.63.0
[73] splines_4.5.0 lattice_0.22-6 FNN_1.1.4.1
[76] survival_3.7-0 bit_4.5.0 annotate_1.85.0
[79] tidyselect_1.2.1 locfit_1.5-9.10 Biostrings_2.75.0
[82] knitr_1.48 gridExtra_2.3 bookdown_0.41
[85] ProtGenerics_1.39.0 edgeR_4.5.0 IHW_1.35.0
[88] xfun_0.48 statmod_1.5.0 stringi_1.8.4
[91] UCSC.utils_1.3.0 lazyeval_0.2.2 yaml_2.3.10
[94] evaluate_1.0.1 codetools_0.2-20 nipals_0.8
[97] MsCoreUtils_1.19.0 tibble_3.2.1 BiocManager_1.30.25
[100] cli_3.6.3 uwot_0.2.2 xtable_1.8-4
[103] munsell_0.5.1 jquerylib_0.1.4 Rcpp_1.0.13
[106] png_0.1-8 XML_3.99-0.17 parallel_4.5.0
[109] blob_1.2.4 AnnotationFilter_1.31.0 viridisLite_0.4.2
[112] slam_0.1-54 scales_1.3.0 purrr_1.0.2
[115] crayon_1.5.3 rlang_1.1.4 cowplot_1.1.3
[118] KEGGREST_1.47.0 License
This vignette is distributed under a CC BY-SA license license.