scone_easybake: Wrapper for Running Essential SCONE Modules

In YosefLab/scone: Single Cell Overview of Normalized Expression data

Wrapper for Running Essential SCONE Modules

Description

Wrapper for Running Essential SCONE Modules

Usage

scone_easybake(
  expr,
  qc,
  bio = NULL,
  batch = NULL,
  negcon = NULL,
  verbose = c("0", "1", "2"),
  out_dir = getwd(),
  seed = 112233,
  filt_cells = TRUE,
  filt_genes = TRUE,
  always_keep_genes = NULL,
  fnr_maxiter = 1000,
  norm_impute = c("yes", "no", "force"),
  norm_scaling = c("none", "sum", "deseq", "tmm", "uq", "fq", "detect"),
  norm_rezero = FALSE,
  norm_k_max = NULL,
  norm_qc_expl = 0.5,
  norm_adjust_bio = c("yes", "no", "force"),
  norm_adjust_batch = c("yes", "no", "force"),
  eval_dim = NULL,
  eval_expr_expl = 0.1,
  eval_poscon = NULL,
  eval_negcon = negcon,
  eval_max_kclust = 10,
  eval_stratified_pam = TRUE,
  report_num = 13,
  out_rda = FALSE,
  ...
)

Arguments

expr	matrix. The expression data matrix (genes in rows, cells in columns).
qc	data frame. The quality control (QC) matrix (cells in rows, metrics in columns) to be used for filtering, normalization, and evaluation.
bio	factor. The biological condition to be modeled in the Adjustment Step as variation to be preserved. If adjust_bio="no", it will not be used for normalization, but only for evaluation.
batch	factor. The known batch variable to be included in the adjustment model as variation to be removed. If adjust_batch="no", it will not be used for normalization, but only for evaluation.
negcon	character. The genes to be used as negative controls for filtering, normalization, and evaluation. These genes should be expressed uniformily across the biological phenomenon of interest. Default NULL.
verbose	character. Verbosity level: higher level is more verbose. Default "0".
out_dir	character. Output directory. Default getwd().
seed	numeric. Random seed. Default 112233.
filt_cells	logical. Should cells be filtered? Set to FALSE if low quality cells have already been excluded. If cells are not filtered, then initial gene filtering (the one that is done prior to cell filtering) is disabled as it becomes redundant with the gene filtering that is done after cell filtering. Default TRUE.
filt_genes	logical. Should genes be filtered post-sample filtering? Default TRUE.
always_keep_genes	logical. A character vector of gene names that should never be excluded (e.g., marker genes). Default NULL.
fnr_maxiter	numeric. Maximum number of iterations in EM estimation of expression posteriors. If 0, then FNR estimation is skipped entirely, and as a consequence no imputation will be performed, disregarding the value of the "norm_impute" argument. Default 1000.
norm_impute	character. Should imputation be included in the comparison? If 'force', only imputed normalizations will be run. Default "yes."
norm_scaling	character. Scaling options to be included in the Scaling Step. Default c("none", "sum", "deseq", "tmm", "uq", "fq", "detect"). See details.
norm_rezero	logical. Restore prior zeroes and negative values to zero following normalization. Default FALSE.
norm_k_max	numeric. Max number (norm_k_max) of factors of unwanted variation modeled in the Adjustment Step. Default NULL.
norm_qc_expl	numeric. In automatic selection of norm_k_max, what fraction of variation must be explained by the first norm_k_max PCs of qc? Default 0.5. Ignored if norm_k_max is not NULL.
norm_adjust_bio	character. If 'no' it will not be included in the model; if 'yes', both models with and without 'bio' will be run; if 'force', only models with 'bio' will be run. Default "yes."
norm_adjust_batch	character. If 'no' it will not be modeled in the Adjustment Step; if 'yes', both models with and without 'batch' will be run; if 'force', only models with 'batch' will be run. Default "yes."
eval_dim	numeric. The number of principal components to use for evaluation. Default NULL.
eval_expr_expl	numeric. In automatic selection of eval_dim, what fraction of variation must be explained by the first eval_dim PCs of expr? Default 0.1. Ignored if eval_dim is not NULL.
eval_poscon	character. The genes to be used as positive controls for evaluation. These genes should be expected to change according to the biological phenomenon of interest.
eval_negcon	character. Alternative negative control gene list for evaluation only.
eval_max_kclust	numeric. The max number of clusters (> 1) to be used for pam tightness evaluation. If NULL, tightness will be returned NA.
eval_stratified_pam	logical. If TRUE then maximum ASW for PAM_SIL is separately computed for each biological-cross-batch condition (accepting NAs), and a weighted average is returned as PAM_SIL. Default TRUE.
report_num	numeric. Number of top methods to report. Default 13.
out_rda	logical. If TRUE, sconeResults.Rda file with the object that the scone function returns is saved in the out_dir (may be very large for large datasets, but useful for post-processing) Default FALSE.
...	extra params passed to the metric_sample_filter and scone when they're called by easybake

Details

"ADD DESCRIPTION"

Value

Directory structure "ADD DESCRIPTION"

Examples

set.seed(101)
mat <- matrix(rpois(1000, lambda = 5), ncol=10)
colnames(mat) <- paste("X", 1:ncol(mat), sep="")
obj <- SconeExperiment(mat)
res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
           evaluate=TRUE, k_ruv=0, k_qc=0, eval_kclust=2, 
           bpparam = BiocParallel::SerialParam())
qc = as.matrix(cbind(colSums(mat),colSums(mat > 0)))
rownames(qc) = colnames(mat)
colnames(qc) = c("NREADS","RALIGN")
## Not run: 
scone_easybake(mat, qc = as.data.frame(qc), verbose = "2", 
   norm_adjust_bio= "no",
   norm_adjust_batch= "no", norm_k_max = 0,
   fnr_maxiter = 0, filt_cells=FALSE, filt_genes=FALSE,
   eval_stratified_pam = FALSE,
   out_dir="~/scone_out")

## End(Not run)

YosefLab/scone documentation built on March 12, 2024, 10:48 p.m.