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BioCellGen-public
MAGE_2020_Marker-Gene-Benchmarking
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70ebe49c
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70ebe49c
authored
Jan 27, 2021
by
Jeffrey Pullin
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notes/benchmarks.md
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# Benchmarks
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Overlap of methods
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Recovery of true markers (ROC, TPR/FPR, etc.)
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Recovery of known markers (from database + expert human annotation)
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@@ -4,19 +4,84 @@ This file contains information about the different methods we will benchmark.
### scanpy
This new default is t-test not t-test over_estim_var
By default compares one vs rest
Tests:
*
t-test: Default
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t-test_overestim_var (overestimates variance of each group)
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Logistic regression
Uses p-value correction
### Seurat
Testing one vs rest
Uses log fold change threshold
Tests:
*
Wilcoxon Rank Sum test (default)
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Likelihood ratio test (McDavid et. al. 2013)
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ROC - Evalulates a classifier built on each gene alone
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t-test
*
Negative Binomial GLM (not with shrinkage etc)
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Poisson GLM
*
Logistic regression (also prediction)
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MAST
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DESeq2
### scran
### Others
Pairwise testing b/w cluster and all other clusters
Tests
*
t-test
*
Binomial test
*
Wilcox rank sum test
Implements TREAT
### rankcorr
Multiclass marker selection
Non-parametric ranking transformation
"one-vs-all" method
### Venice
Odd algorithm based on the predictive accuravy of genes
One vs rest
In R package Signac
### Semitones
Can be used for other purposes
Uses enrichement scoring
### Other
Elastic net (scikit-learn) benchmarked in rankcorr
Random markers - sanity check on simulations
SCDE - found to be too slow in rankcorr
scGeneFit - not individual clusters, clustering as a whole
#### scTIM
COMET - only select 4 markers - designed for further FACS sorting
diffxpy?
scVI - not sure how it is used
Natranos et al. (2018)
Other single-cell DE methods
Come up with method categorisation
scTIM - doesn't find per cluster marker genes
singleCellHaystack - find regions where DEGs exists not for existing clusters
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@@ -6,3 +6,5 @@ Biomarkers - Seruat tutorials
Signature genes - bryan2018
Cell type specific vs disease markers
Marker genes as feature selection HVGs etc (SCMarker, Wang 2019)
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