runAPL: Compute and plot Association Plot

In ClemensKohl/APL: Association Plots

Compute and plot Association Plot

Description

Computes singular value decomposition and coordinates for the Association Plot.

runAPL.SingleCellExperiment: Computes singular value decomposition and coordinates for the Association Plot from SingleCellExperiment objects with reducedDim(obj, "CA") slot (optional).

runAPL.Seurat: Computes singular value decomposition and coordinates for the Association Plot from Seurat objects, optionally with a DimReduc Object in the "CA" slot.

Usage

runAPL(
  obj,
  group,
  caobj = NULL,
  dims = NULL,
  nrow = 10,
  top = 5000,
  score = TRUE,
  mark_rows = NULL,
  mark_cols = caobj@group,
  reps = 3,
  python = FALSE,
  row_labs = TRUE,
  col_labs = TRUE,
  type = "plotly",
  show_cols = FALSE,
  show_rows = TRUE,
  score_cutoff = 0,
  score_color = "rainbow",
  pd_method = "elbow_rule",
  pd_reps = 1,
  pd_use = TRUE,
  ...
)

## S4 method for signature 'matrix'
runAPL(
  obj,
  group,
  caobj = NULL,
  dims = NULL,
  nrow = 10,
  top = 5000,
  score = TRUE,
  mark_rows = NULL,
  mark_cols = NULL,
  reps = 3,
  python = FALSE,
  row_labs = TRUE,
  col_labs = TRUE,
  type = "plotly",
  show_cols = FALSE,
  show_rows = TRUE,
  score_cutoff = 0,
  score_color = "rainbow",
  pd_method = "elbow_rule",
  pd_reps = 1,
  pd_use = TRUE,
  ...
)

## S4 method for signature 'SingleCellExperiment'
runAPL(
  obj,
  group,
  caobj = NULL,
  dims = NULL,
  nrow = 10,
  top = 5000,
  score = TRUE,
  mark_rows = NULL,
  mark_cols = NULL,
  reps = 3,
  python = FALSE,
  row_labs = TRUE,
  col_labs = TRUE,
  type = "plotly",
  show_cols = FALSE,
  show_rows = TRUE,
  score_cutoff = 0,
  score_color = "rainbow",
  pd_method = "elbow_rule",
  pd_reps = 1,
  pd_use = TRUE,
  ...,
  assay = "counts"
)

## S4 method for signature 'Seurat'
runAPL(
  obj,
  group,
  caobj = NULL,
  dims = NULL,
  nrow = 10,
  top = 5000,
  score = TRUE,
  mark_rows = NULL,
  mark_cols = NULL,
  reps = 3,
  python = FALSE,
  row_labs = TRUE,
  col_labs = TRUE,
  type = "plotly",
  show_cols = FALSE,
  show_rows = TRUE,
  score_cutoff = 0,
  score_color = "rainbow",
  pd_method = "elbow_rule",
  pd_reps = 1,
  pd_use = TRUE,
  ...,
  assay = Seurat::DefaultAssay(obj),
  slot = "counts"
)

## S4 method for signature 'dgCMatrix'
runAPL(
  obj,
  group,
  caobj = NULL,
  dims = NULL,
  nrow = 10,
  top = 5000,
  score = TRUE,
  mark_rows = NULL,
  mark_cols = NULL,
  reps = 3,
  python = FALSE,
  row_labs = TRUE,
  col_labs = TRUE,
  type = "plotly",
  show_cols = FALSE,
  show_rows = TRUE,
  score_cutoff = 0,
  score_color = "rainbow",
  pd_method = "elbow_rule",
  pd_reps = 1,
  pd_use = TRUE,
  ...
)

Arguments

obj	A numeric matrix, Seurat or SingleCellExperiment object. For sequencing a count matrix, gene expression values with genes in rows and samples/cells in columns. Should contain row and column names.
group	Numeric/Character. Vector of indices or column names of the columns to calculate centroid/x-axis direction.
caobj	A "cacomp" object as outputted from 'cacomp()'. If not supplied will be calculated. Default NULL.
dims	Integer. Number of dimensions to keep. Default NULL (keeps all dimensions).
nrow	Integer. The top nrow scored row labels will be added to the plot if score = TRUE. Default 10.
top	Integer. Number of most variable rows to retain. Default 5000 rows (set NULL to keep all).
score	Logical. Whether rows should be scored and ranked. Ignored when a vector is supplied to mark_rows. Default TRUE.
mark_rows	Character vector. Names of rows that should be highlighted in the plot. If not NULL, score is ignored. Default NULL.
mark_cols	Character vector. Names of cols that should be highlighted in the plot.
reps	Integer. Number of permutations during scoring. 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.
row_labs	Logical. Whether labels for rows indicated by rows_idx should be labeled with text. Default TRUE.
col_labs	Logical. Whether labels for columns indicated by cols_idx should be labeled with text. Default TRUE.
type	"ggplot"/"plotly". For a static plot a string "ggplot", for an interactive plot "plotly". Default "plotly".
show_cols	Logical. Whether column points should be plotted.
show_rows	Logical. Whether row points should be plotted.
score_cutoff	Numeric. Rows (genes) with a score >= score_cutoff will be colored according to their score if show_score = TRUE.
score_color	Either "rainbow" or "viridis".
pd_method	Which method to use for pick_dims (pick_dims).
pd_reps	Number of repetitions performed when using "elbow_rule" in 'pick_dims'. (pick_dims)
pd_use	Whether to use 'pick_dims' (pick_dims) to determine the number of dimensions. Ignored when 'dims' is set by the user.
...	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. The Seurat assay slot from which to extract the count matrix.

Details

The function is a wrapper that calls 'cacomp()', 'apl_coords()', 'apl_score()' and finally 'apl()' for ease of use. The chosen defaults are most useful for genomics experiments, but for more fine grained control the functions can be also run individually for the same results. If score = FALSE, nrow and reps are ignored. If mark_rows is not NULL score is treated as if FALSE.

Value

Association Plot (plotly object).

References

Association Plots: Visualizing associations in high-dimensional correspondence analysis biplots
Elzbieta Gralinska, Martin Vingron
bioRxiv 2020.10.23.352096; doi: https://doi.org/10.1101/2020.10.23.352096

Examples

set.seed(1234)

# Simulate counts
cnts <- mapply(function(x){rpois(n = 500, lambda = x)},
               x = sample(1:100, 50, replace = TRUE))
rownames(cnts) <- paste0("gene_", 1:nrow(cnts))
colnames(cnts) <- paste0("cell_", 1:ncol(cnts))

# (nonsensical) APL
runAPL(obj = cnts,
       group = 1:10,
       dims = 10,
       top = 500,
       score = TRUE,
       show_cols = TRUE,
       type = "ggplot")

########################
# SingleCellExperiment #
########################
library(SingleCellExperiment)
set.seed(1234)

# Simulate counts
cnts <- mapply(function(x){rpois(n = 500, lambda = x)},
               x = sample(1:100, 50, replace = TRUE))
rownames(cnts) <- paste0("gene_", 1:nrow(cnts))
colnames(cnts) <- paste0("cell_", 1:ncol(cnts))

sce <- SingleCellExperiment(assays=list(counts=cnts))

# (nonsensical) APL
runAPL(obj = sce,
       group = 1:10,
       dims = 10,
       top = 500,
       score = TRUE,
       show_cols = TRUE,
       type = "ggplot",
       assay = "counts")

###########
# Seurat  #
###########
library(Seurat)
set.seed(1234)

# Simulate counts
cnts <- mapply(function(x){rpois(n = 500, lambda = x)},
               x = sample(1:100, 50, replace = TRUE))
rownames(cnts) <- paste0("gene_", 1:nrow(cnts))
colnames(cnts) <- paste0("cell_", 1:ncol(cnts))

seu <- CreateSeuratObject(counts = cnts)

# (nonsensical) APL
runAPL(obj = seu,
       group = 1:10,
       dims = 10,
       top = 500,
       score = TRUE,
       show_cols = TRUE,
       type = "ggplot",
       assay = "RNA",
       slot = "counts")
set.seed(1234)

# Simulate counts
cnts <- mapply(function(x){rpois(n = 500, lambda = x)},
               x = sample(seq(0.01,0.1,by=0.01), 50, replace = TRUE))
rownames(cnts) <- paste0("gene_", 1:nrow(cnts))
colnames(cnts) <- paste0("cell_", 1:ncol(cnts))
cnts <- Matrix::Matrix(cnts)

# (nonsensical) APL
runAPL(obj = cnts,
       group = 1:10,
       dims = 10,
       top = 500,
       score = TRUE,
       show_cols = TRUE,
       type = "ggplot")

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