pick_dims: Compute statistics to help choose the number of dimensions

In ClemensKohl/APL: Association Plots

Compute statistics to help choose the number of dimensions

Description

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.

Usage

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

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

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

Arguments

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 to 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".

Details

• "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.

Value

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.

Examples

# 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 which explain 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")

ClemensKohl/APL documentation built on May 25, 2024, 3:15 p.m.