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Table 1 Use cases of PCA implementations in scRNA-seq studies

From: Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

scRNA-seq studiesPCA algorithmsCommands or functions used in the studies
In most cases [13, 42, 43, 51, 52, 55, 56, 58, 60, 63, 65, 74, 77, 82, 85, 91, 93]Golub-Kahan methodprcomp/svd (R) PCA (Python, sklearn)
Bhaduri et al. [94]DownsamplingUnknown
Loompy [93]SKLIncrementalPCA (Python, sklearn)
Scanpy [93]IRLBAPCA (Python, sklearn)
 SKLIncrementalPCA (Python, sklearn)
 Halko’s methodTruncatedSVD (Python, sklearn)
Cell Ranger [22]IRLBAirlb (Python, from scratch)
Seurat2 [49]IRLBAirlba (R, irlba)
Scran [50]Golub-Kahan methodsvd (R)
 IRLBAirlba (R, irlba)
SAFE [76]IRLBAirlba (R, irlba)
MAGIC [52]Golub-Kahan methodsvds (MATLAB)
 Halko’s methodrandPCA (MATLAB, from scratch)
 Halko’s methodPCA (Python, sklearn)
Harmony [57]IRLBAirlba (R, irlba)
Scater [82]Golub-Kahan methodprcomp (R)
 IRLBAirlba (R, irlba)
GiniClust2 [59]IRLBApropack.svd (R, svd)
SIMLR[75]Halko’s methodfast.rsvd (R, from scratch)
SEQC[89]Golub-Kahan methodPCA (Python, sklearn)
 Halko’s methodPCA (Python, sklearn)
CellFishing.jl [61]Li’s methodrsvd (Julia, from scratch)