Run UMAP on the aligned cell factors (result from
alignFactors
), or unaligned cell factors (raw result from
runIntegration
)) to generate a 2D embedding for visualization
(or general dimensionality reduction). Has option to run on subset of
factors. It is generally recommended to use this method for dimensionality
reduction with extremely large datasets. The underlying UMAP calculation
imports uwot umap
.
Usage
runUMAP(
object,
useRaw = NULL,
useDims = NULL,
nDims = 2,
distance = c("cosine", "euclidean", "manhattan", "hamming"),
nNeighbors = 20,
minDist = 0.1,
dimredName = "UMAP",
asDefault = NULL,
seed = 42,
verbose = getOption("ligerVerbose", TRUE),
k = nDims,
use.raw = useRaw,
dims.use = useDims,
n_neighbors = nNeighbors,
min_dist = minDist,
rand.seed = seed
)
Arguments
- object
liger object with factorization results.
- useRaw
Whether to use un-aligned cell factor loadings (\(H\) matrices). Default
NULL
search for aligned factor loadings first and un-aligned loadings then.- useDims
Index of factors to use for computing the embedding. Default
NULL
uses all factors.- nDims
Number of dimensions to reduce to. Default
2
.- distance
Character. Metric used to measure distance in the input space. Default
"cosine"
, alternative options include:"euclidean"
,"manhattan"
and"hamming"
.- nNeighbors
Number of neighboring points used in local approximations of manifold structure. Default
20
.- minDist
Numeric. Controls how tightly the embedding is allowed compress points together. Default
0.1
.- dimredName
Name of the variable in
cellMeta
slot to store the result matrix. Default"UMAP"
.- asDefault
Logical, whether to set the resulting dimRed as default for visualization. Default
NULL
will set it when no default is set.- seed
Random seed for reproducibility. Default
42
.- verbose
Logical. Whether to show information of the progress. Default
getOption("ligerVerbose")
orTRUE
if users have not set.- k, use.raw, dims.use, n_neighbors, min_dist, rand.seed
Deprecated. See Usage section for replacement.
Value
The object
where a "UMAP"
variable is updated in the
cellMeta
slot with the whole 2D embedding matrix.
Details
For nNeighbors
, larger values will result in more global
structure being preserved at the loss of detailed local structure. In general
this parameter should often be in the range 5 to 50, with a choice of 10 to
15 being a sensible default.
For minDist
, larger values ensure embedded points are more evenly
distributed, while smaller values allow the algorithm to optimize more
accurately with regard to local structure. Sensible values are in the range
0.001 to 0.5, with 0.1 being a reasonable default.