Chord

remove single cells Doublets by integrating tools! Chord uses the gbm/AdBoost algorithm to integrate different methods for stable and accurate doublets filtered results.

parameter:

method the boost method ("adaboost" or "gbm")

seu the input seurat object

sce the input sce object

seed an integer, random seed

k an integer,k-means param k

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

addmethods2 the table merged with other method's scores2

addmethods1 the table merged with other method's scores1

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")

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.

Install:

remotes::install_github('chris-mcginnis-ucsf/DoubletFinder') 

devtools::install_github('kostkalab/scds',ref="master")

install.packages("adabag")

install.packages("gbm")

devtools::install_github("13308204545/Chord") 

Quick start:

chord（seu="input seurat object",doubletrate="estimated doubletrate",overkill=T,outname="the name you want"）

Q:how to estimate doubletrate?

A:It depends on the number of cells in the sample. 10X can be referred：doubletrate = ~0.9% per 1,000 cells.

Q:how to remove doublets

A:The doublets' barcodes are in the file "outname_doublets.csv"

Choose overkill combination

You can choose any combination of methods for overkill by setting the overkilllist parameter (a vector of cells to be remove in overkill)

Boost more methods:

1.Using any method to evaluate the dataset "overkilled.robj", adding the results of socres to "simulated_data.scores.csv".

2.Using any method to evaluate the dataset "seu.robj", adding the results of socres to "simulated_data.scores.csv".

3.In the same dir, run the codes:

load("seu.robj")
load("sce.robj")
chord(seu = seu,sce=sce,doubletrat="estimated doubletrate 2",overkill=T,outname="the name you want 2",addmethods1 ="real_data.scores.csv",addmethods2 = "simulated_data.scores.csv" )

4.The doublets' barcodes are in the file "outname2_doublets.csv"

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