Analysis of variance for single-cell proteomics

Analysis of variance investigates the contribution of each effects in capturing the variance in the data. Analysis of variance is part of the scplainer workflow.

Usage

scpVarianceAnalysis(object, name)

scpVarianceAggregate(varianceList, fcol)

scpVariancePlot(
  varianceList,
  effect = "Residuals",
  by = "percentExplainedVar",
  top = Inf,
  decreasing = TRUE,
  combined = TRUE,
  fcol = NULL,
  colourSeed = 1234
)

Arguments

object: An object that inherits from the SummarizedExperiment class. It must contain an estimated ScpModel in its metadata.
name: A character(1) providing the name to use to retrieve the model results. When retrieving a model and name is missing, the name of the first model found in object is used.
varianceList: A list of tables returned by scpVarianceAnalysis().
fcol: A character(1) indicating the column to use for grouping features. Typically, this would be protein or gene names for grouping proteins.
effect: A character(1) used to filter theb results. It indicates which effect should be considered when sorting the results.
by: A character(1) used to filter the results. It indicates which variable should be considered when sorting the results. Can be one of: "SS", "df", or "percentExplainedVar".
top: A numeric(1) used to filter the results. It indicates how many features should be plotted. When top = Inf (default), all feature are considered.
decreasing: A logical(1) indicating whether the effects should be ordered decreasingly (TRUE, default) or increasingly (FALSE) depending on the value provided by by.
combined: A logical(1) indicating whether the results should be combined across all features. When TRUE, the barplot shows the explained variance for the complete dataset.
colourSeed: A integer(1) providing a seed that is used when randomly sampling colours for the effects. Change the number to generate another colour scheme.

Running the variance analysis

scpVarianceAnalysis() computes the amount of data (measured as the sums of squares) that is captured by each model variable, but also that is not modelled and hence captured in the residuals. The proportion of variance explained by each effect is the sums of squares for that effect divided by the sum of all sums of squares for each effect and residuals. This is computed for each feature separately. The function returns a list of DataFrames with one table for each effect.

scpVarianceAggregate() combines the analysis of variance results for groups of features. This is useful, for example, to return protein-level results when data is modelled at the peptide level. The function takes the list of tables generated by scpVarianceAnalysis() and returns a new list of DataFrames with aggregated results.

Exploring variance analysis results

scpAnnotateResults() adds annotations to the component analysis results. The annotations are added to all elements of the list returned by scpComponentAnalysis(). See the associated man page for more information.

scpVariancePlot() takes the list of tables generated by scpVarianceAnalysis() and returns a ggplot2 bar plot. The bar plot shows the proportion of explained variance by each effect and the residual variance. By default, the function will combine the results over all features, showing the effect's contributions on the complete data set. When combine = FALSE, the results are shown for individual features, with additional arguments to control how many and which features are shown. Bars can also be grouped by fcol. This is particularly useful when exploring peptide level results, but grouping peptides that belong to the same protein (note that you should not use scpVarianceAggregate() in that case).

References

scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data Christophe Vanderaa, Laurent Gatto bioRxiv 2023.12.14.571792; doi: https://doi.org/10.1101/2023.12.14.571792.

Author

Christophe Vanderaa, Laurent Gatto

Examples

data("leduc_minimal")

####---- Run analysis of variance ----####

(var <- scpVarianceAnalysis(leduc_minimal))
#> DataFrameList of length 5
#> names(5): Residuals Channel Set MedianIntensity SampleType

####---- Annotate results ----####

## Add peptide annotations available from the rowData
var <- scpAnnotateResults(
    var, rowData(leduc_minimal), by = "feature", by2 = "Sequence"
)

####---- Plot results ----####

## Plot the analysis of variance through the whole data
scpVariancePlot(var)


## Plot the analysis of variance for the top 20 peptides with highest
## percentage of variance explained by the cell type
scpVariancePlot(
    var, effect = "SampleType", top = 20, combined = FALSE
)


## Same but grouped by protein
scpVariancePlot(
    var, effect = "SampleType", top = 20, combined = FALSE, fcol = "gene"
)


####---- Aggregate results ----####

## Aggregate to protein-level results
varProtein <- scpVarianceAggregate(var, fcol = "gene")
scpVariancePlot(
    varProtein, effect = "SampleType", top = 20, combined = FALSE
)