Description Usage Arguments Details Value Feature selection Using reduced dimensions Using alternative Experiments Author(s) References See Also Examples
View source: R/runDiffusionMap.R
Produce a diffusion map for the cells, based on the data in a SingleCellExperiment object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | calculateDiffusionMap(x, ...)
## S4 method for signature 'ANY'
calculateDiffusionMap(
x,
ncomponents = 2,
ntop = 500,
subset_row = NULL,
scale = FALSE,
transposed = FALSE,
...
)
## S4 method for signature 'SummarizedExperiment'
calculateDiffusionMap(x, ..., exprs_values = "logcounts")
## S4 method for signature 'SingleCellExperiment'
calculateDiffusionMap(
x,
...,
exprs_values = "logcounts",
dimred = NULL,
n_dimred = NULL
)
runDiffusionMap(x, ..., altexp = NULL, name = "DiffusionMap")
|
x |
For For |
... |
For the For |
ncomponents |
Numeric scalar indicating the number of diffusion components to obtain. |
ntop |
Numeric scalar specifying the number of features with the highest variances to use for dimensionality reduction. |
subset_row |
Vector specifying the subset of features to use for dimensionality reduction. This can be a character vector of row names, an integer vector of row indices or a logical vector. |
scale |
Logical scalar, should the expression values be standardized? |
transposed |
Logical scalar, is |
exprs_values |
Integer scalar or string indicating which assay of |
dimred |
String or integer scalar specifying the existing dimensionality reduction results to use. |
n_dimred |
Integer scalar or vector specifying the dimensions to use if |
altexp |
String or integer scalar specifying an alternative experiment containing the input data. |
name |
String specifying the name to be used to store the result in the |
The function DiffusionMap
is used internally to compute the diffusion map.
The behaviour of DiffusionMap
seems to be non-deterministic, in a manner that is not responsive to any set.seed
call.
The reason for this is unknown.
For calculateDiffusionMap
, a matrix is returned containing the diffusion map coordinates for each cell (row) and dimension (column).
For runDiffusionMap
, a modified x
is returned that contains the diffusion map coordinates in reducedDim(x, name)
.
This section is relevant if x
is a numeric matrix of (log-)expression values with features in rows and cells in columns;
or if x
is a SingleCellExperiment and dimred=NULL
.
In the latter, the expression values are obtained from the assay specified by exprs_values
.
The subset_row
argument specifies the features to use for dimensionality reduction.
The aim is to allow users to specify highly variable features to improve the signal/noise ratio,
or to specify genes in a pathway of interest to focus on particular aspects of heterogeneity.
If subset_row=NULL
, the ntop
features with the largest variances are used instead.
We literally compute the variances from the expression values without considering any mean-variance trend,
so often a more considered choice of genes is possible, e.g., with scran functions.
Note that the value of ntop
is ignored if subset_row
is specified.
If scale=TRUE
, the expression values for each feature are standardized so that their variance is unity.
This will also remove features with standard deviations below 1e-8.
If x
is a SingleCellExperiment, the method can be applied on existing dimensionality reduction results in x
by setting the dimred
argument.
This is typically used to run slower non-linear algorithms (t-SNE, UMAP) on the results of fast linear decompositions (PCA).
We might also use this with existing reduced dimensions computed from a priori knowledge (e.g., gene set scores), where further dimensionality reduction could be applied to compress the data.
The matrix of existing reduced dimensions is taken from reducedDim(x, dimred)
.
By default, all dimensions are used to compute the second set of reduced dimensions.
If n_dimred
is also specified, only the first n_dimred
columns are used.
Alternatively, n_dimred
can be an integer vector specifying the column indices of the dimensions to use.
When dimred
is specified, no additional feature selection or standardization is performed.
This means that any settings of ntop
, subset_row
and scale
are ignored.
If x
is a numeric matrix, setting transposed=TRUE
will treat the rows as cells and the columns as the variables/diemnsions.
This allows users to manually pass in dimensionality reduction results without needing to wrap them in a SingleCellExperiment.
As such, no feature selection or standardization is performed, i.e., ntop
, subset_row
and scale
are ignored.
This section is relevant if x
is a SingleCellExperiment and altexp
is not NULL
.
In such cases, the method is run on data from an alternative SummarizedExperiment nested within x
.
This is useful for performing dimensionality reduction on other features stored in altExp(x, altexp)
, e.g., antibody tags.
Setting altexp
with exprs_values
will use the specified assay from the alternative SummarizedExperiment.
If the alternative is a SingleCellExperiment, setting dimred
will use the specified dimensionality reduction results from the alternative.
This option will also interact as expected with n_dimred
.
Note that the output is still stored in the reducedDims
of the output SingleCellExperiment.
It is advisable to use a different name
to distinguish this output from the results generated from the main experiment's assay values.
Aaron Lun, based on code by Davis McCarthy
Haghverdi L, Buettner F, Theis FJ (2015). Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31(18), 2989-2998.
DiffusionMap
, to perform the underlying calculations.
plotDiffusionMap
, to quickly visualize the results.
1 2 3 4 5 6 | example_sce <- mockSCE()
example_sce <- logNormCounts(example_sce)
example_sce <- runDiffusionMap(example_sce)
reducedDimNames(example_sce)
head(reducedDim(example_sce))
|
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