Predict Method for Collection of Gene Set Signatures

Source: R/plot-methods.R, R/predict-methods.R

These method signatures apply gene set signatures optionally augmented with (semi-)quantitative information to the prediction of sample and cell identities in SummarizedExperiment objects.

barplotPredictionCount(se, highlight = character(0), labels = TRUE)

barplotPredictionProportion(se, highlight = character(0),
  labels = TRUE)

reducedDimPrediction(se, highlight = character(0), redDimType = "PCA",
  x = 1, y = 2, labels = TRUE)

# S3 method for GeneSetCollection
predict(object, se, assay.type = "counts",
  method = c("ProportionPositive", "PP"), ...)

# S3 method for Sets
predict(object, se, assay.type = "counts",
  method = c("ProportionPositive", "PP"), ...)

Arguments

se

An object of class inheriting from SummarizedExperiment.
highlight

Character vector indicating names of signatures to highlight.
labels

Logical value indicating whether to display labels.
redDimType

Name of the reduced dimension result type to display.
x

Name of the covariate to display on the x-axis.
y

Name of the covariate to display on the y-axis.
object

A set of signatures of class inheriting from Sets or GeneSetCollection.
assay.type

A string specifying which assay values to use, e.g., "counts" or "logcounts".
method

Prediction method. See section "Prediction methods".
...

Additional arguments affecting the predictions produced.

Value

The object se, updated as follows:

  • in the metadata slot, a "hancock" item is added (or updated) with information tracing the prediction process (e.g., method, signatures).

  • in the "colData" slot, a DataFrame is nested in a new (or updated) "hancock" column. This DataFrame contains predicted labels in the first column and additional information in further columns for each column in se.

Functions

  • barplotPredictionCount: Returns a ggplot bar plot displaying the count of samples predicted for each gene signature.

  • barplotPredictionProportion: Returns a ggplot bar plot displaying the proportion of samples predicted for each gene signature.

  • reducedDimPrediction: Returns a ggplot scatter plot displaying the first reduced dimension result in reducedDims(se).

Prediction methods

ProportionPositive, PP

Requires prior cluster membership information. Computes the proportion of samples positive for each signature in each cluster. Assigns to each cluster the signature detected in the highest proportion of samples.

See also

predictByProportionPositive.

Examples

# Example data ----
library(SummarizedExperiment)
nsamples <- 100
u <- matrix(rpois(20000, 2), ncol=nsamples)
rownames(u) <- paste0("Gene", sprintf("%03d", seq_len(nrow(u))))
colnames(u) <- paste0("Cell", sprintf("%03d", seq_len(ncol(u))))
se <- SummarizedExperiment(assays=list(counts=u))

bs <- Sets(
    relations=DataFrame(
        element = c("Gene001", "Gene002", "Gene003", "Gene004"),
        set     = c(rep("Cell type 1", 2), rep("Cell type 2", 2))
    )
)

# Example usage ----

se1 <- se
colData(se1)[, "cluster"] <- factor(sample(head(LETTERS, 3), ncol(se1), replace=TRUE))
se1 <- predict(bs, se1, method="ProportionPositive", cluster.col="cluster")
# Visualise the count of samples predicted for each signature in each cluster
barplotPredictionCount(se1, highlight=c("Cell type 1"))

barplotPredictionProportion(se1, highlight=c("Cell type 2"))

library(SingleCellExperiment)
sce1 <- as(se1, "SingleCellExperiment")
reducedDim(sce1, "PCA") <- prcomp(t(assay(sce1)))$x
reducedDimPrediction(sce1, highlight="Cell type 1", redDimType="PCA", x=1, y=2)