calcAgreement {rliger} | R Documentation |
Calculate agreement metric after integration
Description
This metric quantifies how much the factorization and alignment distorts the geometry of the original datasets. The greater the agreement, the less distortion of geometry there is. This is calculated by performing dimensionality reduction on the original and quantile aligned (or just factorized) datasets, and measuring similarity between the k nearest neighbors for each cell in original and aligned datasets. The Jaccard index is used to quantify similarity, and is the final metric averages across all cells.
Note that for most datasets, the greater the chosen nNeighbor
, the
greater the agreement in general. Although agreement can theoretically
approach 1, in practice it is usually no higher than 0.2-0.3.
Usage
calcAgreement(
object,
ndims = 40,
nNeighbors = 15,
useRaw = FALSE,
byDataset = FALSE,
seed = 1,
dr.method = NULL,
k = nNeighbors,
use.aligned = NULL,
rand.seed = seed,
by.dataset = byDataset
)
Arguments
object |
|
ndims |
Number of factors to produce in NMF. Default |
nNeighbors |
Number of nearest neighbors to use in calculating Jaccard
index. Default |
useRaw |
Whether to evaluate just factorized |
byDataset |
Whether to return agreement calculated for each dataset
instead of the average for all datasets. Default |
seed |
Random seed to allow reproducible results. Default |
dr.method |
[defunct] We no longer support other methods but just NMF. |
k , rand.seed , by.dataset |
[Deprecated] See Usage for replacement. |
use.aligned |
[defunct] Use |
Value
A numeric vector of agreement metric. A single value if
byDataset = FALSE
or each dataset a value otherwise.
Examples
if (requireNamespace("RcppPlanc", quietly = TRUE)) {
pbmc <- pbmc %>%
normalize %>%
selectGenes %>%
scaleNotCenter %>%
runINMF %>%
quantileNorm
calcAgreement(pbmc)
}