Table 1 Comprehensive performance of each method in real-world scRNA-seq datasets with experimentally annotated doublets.

From: Chord: an ensemble machine learning algorithm to identify doublets in single-cell RNA sequencing data

 

PAUC800

PAUC900

PAUC950

PAUC975

AUC

PR

bcds

0.598456581

0.697609168

0.747471204

0.772440146

0.797428571

0.465471429

Chord

0.602189241

0.701382997

0.751248502

0.77621856

0.801214286

0.464642857

ChordP

0.614164051

0.713609623

0.763485434

0.788453732

0.8132

0.466514286

cxds

0.576279854

0.675031498

0.724828357

0.749788808

0.774785714

0.367342857

doubletCells

0.396983835

0.487445535

0.535271112

0.559821949

0.584685714

0.173985714

DoubletDetection

0.569370526

0.666356657

0.715610186

0.740426505

0.793114286

0.500857143

DoubletFinder

0.537830717

0.636466839

0.686221775

0.711172069

0.736171429

0.339428571

Scrublet

0.564203075

0.663581473

0.713449225

0.738419459

0.763414286

0.399771429

Solo

0.604236942

0.703224153

0.752987364

0.777930394

0.803142857

0.434714286

  1. The average performance of various methods in all datasets. The indexes are the pAUC800, pAUC900, pAUC950, pAUC975, AUC and AUPRC.
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