K2preproc: Function to create K2 object for pre-processing

In montilab/K2Taxonomer: Creating annotated submodules of high-throughput data through recursive partitioning

Function to create K2 object for pre-processing

Description

This function will generate an object of class, K2. This will run pre-processing functions for running K2 Taxonomer procedure.

Usage

K2preproc(
  object,
  cohorts = NULL,
  eMatDS = NULL,
  colData = NULL,
  vehicle = NULL,
  variables = NULL,
  seuAssay = "RNA",
  seuAssayDS = "RNA",
  sceAssay = "logcounts",
  sceAssayDS = NULL,
  block = NULL,
  logCounts = TRUE,
  use = c("Z", "MEAN"),
  nFeats = "sqrt",
  featMetric = c("F", "mad", "sd", "Sn"),
  DGEmethod = c("limma", "mast"),
  DGEexpThreshold = 0.25,
  recalcDataMatrix = TRUE,
  nBoots = 500,
  useCors = 1,
  clustFunc = "cKmeansDownsampleSqrt",
  clustList = NULL,
  linkage = c("mcquitty", "ward.D", "ward.D2", "single", "complete", "average",
    "centroid"),
  info = NULL,
  genesets = NULL,
  qthresh = 0.05,
  cthresh = 0,
  ntotal = 20000,
  ScoreGeneSetMethod = c("GSVA", "AUCELL"),
  oneoff = TRUE,
  stabThresh = 0,
  geneURL = NULL,
  genesetURL = NULL
)

Arguments

object	An object of class matrix, dgCMatrix, Seurat, SingleCellExperiment, or ExpressionSet. For matrix and dgCMatrix, include genes and observations (single-cell/bulk profiles) as rows and columns, respectively.
cohorts	Character. The column in meta data of 'object' that has cohort IDs. Default NULL if no cohorts in data.
eMatDS	Numeric matrix. A matrix with the same number of observations as 'object' containing normalized expression data to be used in analyses downstream of partitioning results.
colData	data.frame. Only used if 'object' is a matrix or dgCMatrix. A data frame with named rows and columns containing observation data for each column in 'object'.
vehicle	The value in the cohort variable that contains the ID of observation to use as control. Default NULL if no vehicle to be used.
variables	Character. Columns in meta data of 'object' to control for in differential analyses.
seuAssay	Character. Name of assay in Seurat object containing expression data for running partitioning algorithm. If cohorts based on clustering, this should be the assay used.
seuAssayDS	Character. Name of assay in Seurat object containing expression data normalized expression data to be used in analyses downstream of partitioning algorithm.
sceAssay	Character. Name of assay in SingleCellExperimen object containing expression data for running partitioning algorithm. If cohorts based on clustering, this should be the assay used.
sceAssayDS	Character. Name of assay in SingleCellExperiment object containing expression data normalized expression data to be used in analyses downstream of partitioning algorithm.
block	Character. Block parameter in limma for modeling higherarchical data structure, such as multiple observations per individual.
logCounts	Logical. Whether or not expression values are log-scale counts or log normalized counts from RNA-seq. Default is TRUE.
use	Character. Options are 'Z' to generate test statistics or 'MEAN' to use means from differential analysis for clustering.
nFeats	'sqrt' or a numeric value <= number of features to subset for each partition.
featMetric	Character. Metric to use to assign gene-level variance/signal score. F: F-statistic from evaluating differences in means across cohort mad: Median absolute deviation sd: Standard deviation Sn: Robust scale estimator
DGEmethod	Character. Method for running differential gene expression analyses. Use one of either 'limma' (default) or 'mast'.
DGEexpThreshold	Numeric. A value between 0 and 1 indicating for filtering lowly expressed genes for partition-specific differential gene expression. Proportion of observations with counts > 0 in at least one subgroup at a specific partition.
recalcDataMatrix	Logical. Recalculate dataMatrix for each partion? Default is TRUE.
nBoots	nBoots A value of the number of bootstraps to run at each partition. Default is 500.
useCors	Numeric. Number of cores to use for parallelizable processes.
clustFunc	Character. Wrapper function to be used in recursive partitioning. cKmeansDownsampleSqrt: Perform constrained K-means clustering after subsampling each cohort by the square root of the number of observations cKmeansDownsampleSmallest: Perform constrained K-means clustering after subsampling each cohort by the size of the smallest cohort hclustWrapper: Perform hierarchical clustering
clustList	Optional named list of parameters to use with clustFunc. cKmeansDownsampleSqrt: maxIter: The maximum number of iterations to use with lcvqe() cKmeansDownsampleSmallest: maxIter: The maximum number of iterations to use with lcvqe() hclustWrapper: aggMethod: One of the hierarchichal methods specified by hclust() function distMetric: One of the distance metrics specified by dist() function
linkage	Character. Linkage criteria for splitting cosine matrix ('method' in hclust). 'average' by default.
info	Character. A vector of column names in meta data of 'object' that contain information to be used in cohort annotation of dashboard visualization
genesets	Named list. Feature sets to be includes in enrichment-based analyses.
qthresh	Numeric. A value between 0 and 1 indicating the FDR cuttoff to define feature sets.
cthresh	Numeric. A positive value for the coefficient cuttoff to define feature sets.
ntotal	Numeric. A positive value to use as the background feature count. 20000 by default.
ScoreGeneSetMethod	Character. Method for gene set scoring. Use one of either 'GSVA' (default) or 'AUCELL'.
oneoff	Logical. Allow 1 observation partition groups? Default is TRUE.
stabThresh	Numeric. A value between 0 and 1 indicatingThreshold for ending clustering.
geneURL	Named list. URLs linking genes to external resources.
genesetURL	Named list. URLs linking gene set to external resources.

Value

An object of class, 'K2'.

References

\insertRef

reed_2020K2Taxonomer \insertReflimmaK2Taxonomer \insertRefRousseeuw1993K2Taxonomer

montilab/K2Taxonomer documentation built on April 5, 2025, 3:58 a.m.