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 studies | PCA algorithms | Commands 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 method | prcomp/svd (R) PCA (Python, sklearn) |
Bhaduri et al. [94] | Downsampling | Unknown |
Loompy [93] | SKL | IncrementalPCA (Python, sklearn) |
Scanpy [93] | IRLBA | PCA (Python, sklearn) |
| Â | SKL | IncrementalPCA (Python, sklearn) |
|  | Halko’s method | TruncatedSVD (Python, sklearn) |
Cell Ranger [22] | IRLBA | irlb (Python, from scratch) |
Seurat2 [49] | IRLBA | irlba (R, irlba) |
Scran [50] | Golub-Kahan method | svd (R) |
| Â | IRLBA | irlba (R, irlba) |
SAFE [76] | IRLBA | irlba (R, irlba) |
MAGIC [52] | Golub-Kahan method | svds (MATLAB) |
|  | Halko’s method | randPCA (MATLAB, from scratch) |
|  | Halko’s method | PCA (Python, sklearn) |
Harmony [57] | IRLBA | irlba (R, irlba) |
Scater [82] | Golub-Kahan method | prcomp (R) |
| Â | IRLBA | irlba (R, irlba) |
GiniClust2 [59] | IRLBA | propack.svd (R, svd) |
SIMLR[75] | Halko’s method | fast.rsvd (R, from scratch) |
SEQC[89] | Golub-Kahan method | PCA (Python, sklearn) |
|  | Halko’s method | PCA (Python, sklearn) |
CellFishing.jl [61] | Li’s method | rsvd (Julia, from scratch) |