--- title: "QFeatures in a nutshell" author: - name: Laurent Gatto - name: Christophe Vanderaa output: BiocStyle::html_document: self_contained: yes toc: true toc_float: true toc_depth: 2 code_folding: show bibliography: scp.bib date: "`r BiocStyle::doc_date()`" package: "`r BiocStyle::pkg_ver('scp')`" vignette: > %\VignetteIndexEntry{QFeatures in a nutshell} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", crop = NULL ## cf https://stat.ethz.ch/pipermail/bioc-devel/2020-April/016656.html ) ``` This vignette briefly recaps the main concepts of `QFeatures` on which `scp` relies. More in depth information is to be found in the `QFeatures` [vignettes](https://rformassspectrometry.github.io/QFeatures/articles/QFeatures.html). # The `QFeatures` class The `QFeatures` class is based on the `MultiAssayExperiment` class that holds a collection of `SummarizedExperiment` (or other classes that inherits from it) objects termed **assays**. The assays in a `QFeatures` object have a hierarchical relation: proteins are composed of peptides, themselves produced by spectra, as depicted in figure below. ![](https://rformassspectrometry.github.io/QFeatures/articles/QFeatures_files/figure-html/featuresplot-1.png) Those links are stored as part as the `QFeatures` object and connect the assays together. We load an example dataset from the `scp` package that is formatted as an `QFeatures` object and plot those connection. ```{r hierarchy, message = FALSE, out.width = "600px"} library(scp) data("scp1") plot(scp1) ``` # Accessing the data The `QFeatures` class contains all the available and metadata. We here show how to retrieve those different pieces of information. ## Quantitative data The quantitative data, stored as matrix-like objects, can be accessed using the `assay` function. For example, we here extract the quantitative data for the first MS batch (and show a subset of it): ```{r assay_data} assay(scp1, "190321S_LCA10_X_FP97AG")[1:5, ] ``` Note that you can retrieve the list of available assays in a `QFeatures` object using the `names()` function. ```{r names} names(scp1) ``` ## Feature metadata For each individual assay, there is feature metadata available. We extract the list of metadata tables by using `rowData()` on the `QFeatures` object. ```{r rowData} rowData(scp1) rowData(scp1)[["proteins"]] ``` You can also retrieve the names of each `rowData` column for all assays with `rowDataNames`. ```{r rowDataNames} rowDataNames(scp1) ``` You can also get the `rowData` from different assays in a single table using the `rbindRowData` function. It will keep the common `rowData` variables to all selected assays (provided through `i`). ```{r rbindRowData} rbindRowData(scp1, i = 1:5) ``` ## Sample metadata The sample metadata is retrieved using `colData` on the `QFeatures` object. ```{r colData} colData(scp1) ``` Note that you can easily access a `colData` column using the `$` operator. See here how we extract the sample types from the `colData`. ```{r colData_dollar} scp1$SampleType ``` # Subsetting the data There are three dimensions we want to subset for: - Assays - Samples - Features Therefore, `QFeatures` support a three-index subsetting. This is performed through the simple bracket method `[feature, sample, assay]`. ## Subset assays Suppose that we want to focus only on the first MS batch (`190321S_LCA10_X_FP97AG`) for separate processing of the data. Subsetting the `QFeatures` object for that assay is simply: ```{r subset_assay} scp1[, , "190321S_LCA10_X_FP97AG"] ``` An alternative that results in exactly the same output is using the `subsetByAssay` method. ```{r, subsetByAssay} subsetByAssay(scp1, "190321S_LCA10_X_FP97AG") ``` ## Subset samples Subsetting samples is often performed after sample QC where we want to keep only quality samples and sample of interest. In our example, the different samples are either technical controls or single-cells (macrophages and monocytes). Suppose we are only interested in macrophages, we can subset the data as follows: ```{r, subset_samples} scp1[, scp1$SampleType == "Macrophage", ] ``` An alternative that results in exactly the same output is using the `subsetByColData` method. ```{r, subsetByColData} subsetByColData(scp1, scp1$SampleType == "Macrophage") ``` ## Subset features Subsetting for features does more than simply subsetting for the features of interest, it will also take the features that are linked to that feature. Here is an example, suppose we are interested in the `Q02878` protein. ```{r subset_features} scp1["Q02878", , ] ``` You can see it indeed retrieved that protein from the `proteins` assay, but it also retrieved 11 associated peptides in the `peptides` assay and 19 associated PSMs in 2 different MS runs. An alternative that results in exactly the same output is using the `subsetByColData` method. ```{r, subsetByFeature} subsetByFeature(scp1, "Q02878") ``` You can also subset features based on the `rowData`. This is performed by `filterFeatures`. For example, we want to remove features that are associated to reverse sequence hits. ```{r, filterFeatures} filterFeatures(scp1, ~ Reverse != "+") ``` Note however that if an assay is missing the variable that is used to filter the data (in this case the `proteins` assay), then all features for that assay are removed. You can also subset the data based on the feature missingness using `filterNA`. In this example, we filter out proteins with more than 70 \% missing data. ```{r, filterNA} filterNA(scp1, i = "proteins", pNA = 0.7) ``` # Common processing steps We here provide a list of common processing steps that are encountered in single-cell proteomics data processing and that are already available in the `QFeatures` package. All functions below require the user to select one or more assays from the `QFeatures` object. This is passed through the `i` argument. Note that some datasets may contain hundreds of assays and providing the assay selection manually can become cumbersome. We therefore suggest the user to use regular expression (aka regex) to chose from the `names()` of the `QFeautres` object. A detailed cheatsheet about regex in R can be found [here](https://rstudio-pubs-static.s3.amazonaws.com/74603_76cd14d5983f47408fdf0b323550b846.html). ## Missing data assignment It often occurs that in MS experiements, 0 values are not true zeros but rather signal that is too weak to be detected. Therefore, it is advised to consider 0 values as missing data (`NA`). You can use `zeroIsNa` to automatically convert 0 values to `NA` in assays of interest. For instance, we here replace missing data in the `peptides` assay. ```{r, zeroIsNA} table(assay(scp1, "peptides") == 0) scp1 <-zeroIsNA(scp1, "peptides") table(assay(scp1, "peptides") == 0) ``` ## Feature aggregation Shotgun proteomics analyses, bulk as well as single-cell, acquire and quantify peptides. However, biological inference is often performed at protein level. Protein quantitations can be estimated through feature aggregation. This is performed by `aggregateFeatures`, a function that takes an assay from the `Qfeatures` object and that aggregates its features with respect to a grouping variable in the `rowData` (`fcol`) and an aggregation function. ```{r aggregateFeatures} aggregateFeatures(scp1, i = "190321S_LCA10_X_FP97AG", fcol = "protein", name = "190321S_LCA10_X_FP97AG_aggr", fun = MsCoreUtils::robustSummary) ``` You can see that the aggregated function is added as a new assay to the `QFeatures` object. Note also that, under the hood, `aggregateFeatures` keeps track of the relationship between the features of the newly aggregated assay and its parent. ## Normalization An ubiquituous step that is performed in biological data analysis is normalization that is meant to remove undesired variability and to make different samples comparable. The `normalize` function offers an interface to a wide variety of normalization methods. See `?MsCoreUtils::normalize_matrix` for more details about the available normalization methods. Below, we normalize the samples so that they are mean centered. ```{r normalize} normalize(scp1, "proteins", method = "center.mean", name = "proteins_mcenter") ``` Other custom normalization can be applied using the `sweep` method, where normalization factors have to be supplied manually. As an example, we here normalize the samples using a scaled size factor. ```{r sweep} sf <- colSums(assay(scp1, "proteins"), na.rm = TRUE) / 1E4 sweep(scp1, i = "proteins", MARGIN = 2, ## 1 = by feature; 2 = by sample STATS = sf, FUN = "/", name = "proteins_sf") ``` ## Log transformation The `QFeatures` package also provide the `logTransform` function to facilitate the transformation of the quantitative data. We here show its usage by transforming the protein data using a base 2 logarithm with a pseudo-count of one. ```{r logTransform} logTransform(scp1, i = "proteins", base = 2, pc = 1, name = "proteins_log") ``` ## Imputation Finally, `QFeatures` offers an interface to a wide variety of imputation methods to replace missing data by estimated values. The list of available methods is given by `?MsCoreUtils::impute_matrix`. We demonstrate the use of this function by replacing missing data using KNN imputation. ```{r impute} anyNA(assay(scp1, "proteins")) scp1 <- impute(scp1, i = "proteins", method ="knn", k = 3) anyNA(assay(scp1, "proteins")) ``` # Data visualization Visualization of the feature and sample metadata is rather straightforward since those are stored as tables (see section *Accessing the data*). From those tables, any visualization tool can be applied. Note however that using `ggplot2` require `data.frame`s or `tibble`s but `rowData` and `colData` are stored as `DFrames` objects. You can easily convert one data format to another. For example, we plot the parental ion fraction (measure of spectral purity) for each of the three MS batches. ```{r vis1, message = FALSE, fig.width = 6.5} rd <- rbindRowData(scp1, i = 1:3) library("ggplot2") ggplot(data.frame(rd)) + aes(y = PIF, x = assay) + geom_boxplot() ``` Combining the metadata and the quantitative data is more challenging since the risk of data mismatch is increased. The `QFeatures` package therefore provides th `longFormat` function to transform a `QFeatures` object in a long `DFrame` table. For instance, we plot the quantitative data distribution for the first assay according to the acquisition channel index and colour with respect to the sample type. Both pieces of information are taken from the `colData`, so we provide them as `colvars`. ```{r longFormat} lf <- longFormat(scp1[, , 1], colvars = c("SampleType", "Channel")) ggplot(data.frame(lf)) + aes(x = Channel, y = value, colour = SampleType) + geom_boxplot() ``` A more in-depth tutorial about data visualization from a `QFeatures` object is provided in the `QFeautres` visualization [vignette](https://rformassspectrometry.github.io/QFeatures/articles/Visualization.html). # Session information {-} ```{r setup2, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "", crop = NULL ) ``` ```{r sessioninfo, echo=FALSE} sessionInfo() ``` # License This vignette is distributed under a [CC BY-SA license](https://creativecommons.org/licenses/by-sa/2.0/) license. # Reference