dreval is an R package aimed at evaluation and comparison of reduced dimension
representations of high-dimensional data. Given one or more reduced dimension
representations, and a "reference" representation (which can be the original,
high-dimensional representation or a baseline low-dimensional one),
will calculate a collection of metrics quantifying how well each of the
evaluated representations recapitulates the structure of the observations in the
dreval, you need the
devtools) R package, which can
be installed from CRAN. The following commands installs first
The input to
dreval is a
object, containing one or more assays and one or more reduced dimension
representation. By default, the
logcounts assay will be used as the reference
representation, against which each of the provided reduced dimension
representations will be evaluated. However, any other assay or reduced dimension
representation can be used as the reference data, by setting the arguments to
dreval() function accordingly.
The package contains a small example single-cell RNA-seq data set with
measurements for approximately 1,800 highly variable genes across 2,700 PBMCs.
The object contains eight reduced dimension representations: 25-dimensional PCA,
2-dimensional PCA, and 2-dimensional t-SNE and 2-dimensional UMAP
representations generated with different values of the perplexity/number of
nearest neighbors. We use the
dreval() function to evaluate how well each of
these retain the structure of the cells based on the
data(pbmc3ksub) dre <- dreval(sce = pbmc3ksub, refType = "assay", refAssay = "logcounts", nSamples = 1000, kTM = 50)
For detailed information about the arguments to
dreval() we refer to the
help page of the function:
The output of
dreval() is a list with two elements, named
scores element is a data.frame with all the calculated evaluation
scores for each of the reduced dimension representations, while the
element is a list of diagnostic plots.
plotRankSummary() function can be used to aggregate the information across
all evaluation metrics. Each reduced dimension representation will be assigned a
rank for each metric, and the sum of these ranks across all metrics, as well as
the contribution from each metric, is visualized by the function. Metrics aimed
at evaluating the preservation of global structure are colored blue, while those
aimed at evaluating the preservation of local structure are colored red.
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