chord: Chord

In 13308204545/Chord: Chord

Chord

Description

remove doublet with scds,bcds,DoubletFinder.

Usage

chord(
  seu = NA,
  sce = NA,
  doubletrate = NA,
  mfinal = 40,
  k = 20,
  method = "gbm",
  overkill = T,
  overkillrate = 1,
  outname = "out",
  seed = 1,
  addmethods1 = NA,
  addmethods2 = NA,
  overkilllist = NA,
  adddoublt = NA,
  cxds.ntop = NA,
  cxds.binThresh = NA,
  bcds.ntop = NA,
  bcds.srat = NA,
  dbf.PCs = 1:10,
  dbf.pN = 0.25,
  dbf.pK = NA
)

Arguments

seu	the input sce object
mfinal	an integer, the number of iterations for which boosting is run or the number of trees to use. Defaults to mfinal=40 iterations.(only works when method="adaboost")
k	an integer,k-means param k
method	the boost method ("adaboost" or "gbm")
overkill	if True,use overkill
overkillrate	an integer,remove the top ?% doublet-liked cells of any methods' results.(0-1)
outname	The prefix of the output file
seed	an integer, random seed
addmethods1	the table merged with other method's scores1
addmethods2	the table merged with other method's scores2
overkilllist	a vector of cells to be remove in overkill
adddoublt	doubletrate of cells to be simulate
cxds.ntop	integer, indimessageing number of top variance genes to consider. Default: 500
cxds.binThresh	integer, minimum counts to consider a gene "present" in a cell. Default: 0
bcds.ntop	integer, indicating number of top variance genes to consider. Default: 500
bcds.srat	numeric, indicating ratio between orginal number of "cells" and simulated doublets; Default: 1
dbf.PCs	Number of statistically-significant principal components (e.g., as estimated from PC elbow plot); Default: 1:10
dbf.pN	The number of generated artificial doublets, expressed as a proportion of the merged real-artificial data. Default is set to 0.25, based on observation that DoubletFinder performance is largely pN-invariant (see McGinnis, Murrow and Gartner 2019, Cell Systems).
dbf.pK	The PC neighborhood size used to compute pANN, expressed as a proportion of the merged real-artificial data. No default is set, as pK should be adjusted for each scRNA-seq dataset. Optimal pK values can be determined using mean-variance-normalized bimodality coefficient.

Examples

chord(seu=NA,doubletrate=NA,k=20,overkill=T,overkillrate=1,outname="out",seed=1)

13308204545/Chord documentation built on June 14, 2022, 7:26 p.m.