Chord

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remove single cells Doublets by integrating tools!

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# 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:
```R
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:
```R
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
```R
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".

![image](https://github.com/13308204545/Chord/blob/main/pictures/readme1.png)

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

![image](https://github.com/13308204545/Chord/blob/main/pictures/readme2.png)

3.In the same dir, run the codes:
```R
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"

## References

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Al'Aref, S.J., Singh, G., Choi, J.W., Xu, Z., Maliakal, G., van Rosendael, A.R., Lee, B.C., Fatima, Z., Andreini, D., Bax, J.J., et al. (2020). A Boosted Ensemble Algorithm for Determination of Plaque Stability in High-Risk Patients on Coronary CTA. JACC Cardiovasc Imaging 13, 2162-2173.

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Chord

Chord 최신버젼 다운로드

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