pick_dims: Compute statistics to help choose the number of dimensions
In elagralinska/APLpackage: Association Plots

Description Usage Arguments Details Value Examples

Allow the user to choose from 4 different methods ("avg_inertia", "maj_inertia", "scree_plot" and "elbow_rule") to estimate the number of dimensions that best represent the data.

pick_dims(
  obj,
  mat = NULL,
  method = "scree_plot",
  reps = 3,
  python = TRUE,
  return_plot = FALSE,
  ...
)

## S4 method for signature 'cacomp'
pick_dims(
  obj,
  mat = NULL,
  method = "scree_plot",
  reps = 3,
  python = TRUE,
  return_plot = FALSE,
  ...
)

## S4 method for signature 'Seurat'
pick_dims(
  obj,
  mat = NULL,
  method = "scree_plot",
  reps = 3,
  python = TRUE,
  return_plot = FALSE,
  ...,
  assay = Seurat::DefaultAssay(obj),
  slot = "counts"
)

## S4 method for signature 'SingleCellExperiment'
pick_dims(
  obj,
  mat = NULL,
  method = "scree_plot",
  reps = 3,
  python = TRUE,
  return_plot = FALSE,
  ...,
  assay = "counts"
)

`obj`	A "cacomp" object as outputted from 'cacomp()', a "Seurat" object with a "CA" DimReduc object stored, or a "SingleCellExperiment" object with a "CA" dim. reduction stored.
`mat`	A numeric matrix. For sequencing a count matrix, gene expression values with genes in rows and samples/cells in columns. Should contain row and column names.
`method`	String. Either "scree_plot", "avg_inertia", "maj_inertia" or "elbow_rule" (see Details section). Default "scree_plot".
`reps`	Integer. Number of permutations to perform when choosing "elbow_rule". Default 3.
`python`	A logical value indicating whether to use singular-value decomposition from the python package torch. This implementation dramatically speeds up computation compared to 'svd()' in R.
`return_plot`	TRUE/FALSE. Whether a plot should be returned when choosing "elbow_rule". Default FALSE.
`...`	Arguments forwarded to methods.
`assay`	Character. The assay from which extract the count matrix for SVD, e.g. "RNA" for Seurat objects or "counts"/"logcounts" for SingleCellExperiments.
`slot`	Character. Data slot of the Seurat assay. E.g. "data" or "counts". Default "counts".

"avg_inertia" calculates the number of dimensions in which the inertia is above the average inertia. "maj_inertia" calculates the number of dimensions in which cumulatively explain up to 80% of the total inertia. "scree_plot" plots a scree plot. "elbow_rule" Formalization of the commonly used elbow rule. Permutes the rows for each column and reruns 'cacomp()' for a total of 'reps' times. The number of relevant dimensions is obtained from the point where the line for the explained inertia of the permuted data intersects with the actual data.

For 'avg_inertia', 'maj_inertia' and 'elbow_rule' (when 'return_plot=FALSE') returns an integer, indicating the suggested number of dimensions to use. 'scree_plot' returns a ggplot object. 'elbow_rule' (for 'return_plot=TRUE') returns a list with two elements: "dims" contains the number of dimensions and "plot" a ggplot.

# Simulate counts
cnts <- mapply(function(x){rpois(n = 500, lambda = x)}, x = sample(1:20, 50, replace = TRUE))
rownames(cnts) <- paste0("gene_", 1:nrow(cnts))
colnames(cnts) <- paste0("cell_", 1:ncol(cnts))

# Run correspondence analysis.
ca <- cacomp(obj = cnts)

# pick dimensions with the elbow rule. Returns list.

set.seed(2358)
pd <- pick_dims(obj = ca,
                mat = cnts,
                method = "elbow_rule",
                return_plot = TRUE,
                reps = 10)
pd$plot
ca_sub <- subset_dims(ca, dims = pd$dims)

# pick dimensions with cumulatively >80% of total inertia. Returns vector.
pd <- pick_dims(obj = ca,
                method = "maj_inertia")
ca_sub <- subset_dims(ca, dims = pd)

################################
# pick_dims for Seurat objects #
################################
library(Seurat)
set.seed(1234)

# Simulate counts
cnts <- mapply(function(x){rpois(n = 500, lambda = x)}, x = sample(1:20, 50, replace = TRUE))
rownames(cnts) <- paste0("gene_", 1:nrow(cnts))
colnames(cnts) <- paste0("cell_", 1:ncol(cnts))

# Create Seurat object
seu <- CreateSeuratObject(counts = cnts)

# run CA and save in dim. reduction slot.
seu <- cacomp(seu, return_input = TRUE, assay = "RNA", slot = "counts")

# pick dimensions
pd <- pick_dims(obj = seu,
                method = "maj_inertia",
                assay = "RNA",
                slot = "counts")

##############################################
# pick_dims for SingleCellExperiment objects #
##############################################
library(SingleCellExperiment)
set.seed(1234)

# Simulate counts
cnts <- mapply(function(x){rpois(n = 500, lambda = x)},
               x = sample(1:20, 50, replace = TRUE))
rownames(cnts) <- paste0("gene_", 1:nrow(cnts))
colnames(cnts) <- paste0("cell_", 1:ncol(cnts))

# Create SingleCellExperiment object
sce <- SingleCellExperiment(assays=list(counts=cnts))

# run CA and save in dim. reduction slot.
sce <- cacomp(sce, return_input = TRUE, assay = "counts")

# pick dimensions
pd <- pick_dims(obj = sce,
                method = "maj_inertia",
                assay = "counts")