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Run generalized principal components analysis (GLMPCA)

Source: R/RunGLMPCA.R
RunGLMPCA.Rd

Run generalized principal components analysis (GLMPCA)

Usage

RunGLMPCA(object, ...)

# S3 method for class 'Seurat'
RunGLMPCA(
  object,
  assay = NULL,
  layer = "counts",
  features = NULL,
  L = 5,
  fam = c("poi", "nb", "nb2", "binom", "mult", "bern"),
  rev.gmlpca = FALSE,
  ndims.print = 1:5,
  nfeatures.print = 30,
  reduction.name = "glmpca",
  reduction.key = "GLMPC_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

# S3 method for class 'Assay'
RunGLMPCA(
  object,
  assay = NULL,
  layer = "counts",
  features = NULL,
  L = 5,
  fam = c("poi", "nb", "nb2", "binom", "mult", "bern"),
  rev.gmlpca = FALSE,
  ndims.print = 1:5,
  nfeatures.print = 30,
  reduction.key = "GLMPC_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

# S3 method for class 'Assay5'
RunGLMPCA(
  object,
  assay = NULL,
  layer = "counts",
  features = NULL,
  L = 5,
  fam = c("poi", "nb", "nb2", "binom", "mult", "bern"),
  rev.gmlpca = FALSE,
  ndims.print = 1:5,
  nfeatures.print = 30,
  reduction.key = "GLMPC_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

# Default S3 method
RunGLMPCA(
  object,
  assay = NULL,
  layer = "counts",
  features = NULL,
  L = 5,
  fam = c("poi", "nb", "nb2", "binom", "mult", "bern"),
  rev.gmlpca = FALSE,
  ndims.print = 1:5,
  nfeatures.print = 30,
  reduction.key = "GLMPC_",
  verbose = TRUE,
  seed.use = 11,
  ...
)

Arguments

object

An object. Can be a Seurat object, an assay object, or a matrix-like object.

...

Additional arguments to be passed to the glmpca::glmpca function.

assay

Which assay to use. If NULL, the default assay of the Seurat object will be used.

layer

Which layer to use. Default is data.

features

A character vector of features to use. Default is NULL.

L

The number of components to be computed. Default is 5.

fam

The family of the generalized linear model to be used. Currently supported values are "poi", "nb", "nb2", "binom", "mult", and "bern". Default is "poi".

rev.gmlpca

Whether to perform reverse GLMPCA (i.e., transpose the input matrix) before running the analysis. Default is FALSE.

ndims.print

The dimensions (number of components) to print in the output. Default is 1:5.

nfeatures.print

The number of features to print in the output. Default is 30.

reduction.name

The name of the reduction to be stored in the Seurat object. Default is "glmpca".

reduction.key

The prefix for the column names of the basis vectors. Default is "GLMPC_".

verbose

Whether to print the message. Default is TRUE.

seed.use

Random seed for reproducibility. Default is 11.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-01-27 08:16:01] Start standard scop workflow...
#> ℹ [2026-01-27 08:16:02] Checking a list of <Seurat>...
#> ! [2026-01-27 08:16:02] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 08:16:02] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:16:04] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:16:05] Use the separate HVF from srt_list
#> ℹ [2026-01-27 08:16:05] Number of available HVF: 2000
#> ℹ [2026-01-27 08:16:05] Finished check
#> ℹ [2026-01-27 08:16:05] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-27 08:16:06] Perform pca linear dimension reduction
#> ℹ [2026-01-27 08:16:07] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-27 08:16:07] Reorder clusters...
#> ℹ [2026-01-27 08:16:07] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-27 08:16:07] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-27 08:16:11] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-01-27 08:16:16] Run scop standard workflow completed
pancreas_sub <- RunGLMPCA(pancreas_sub)
#> ℹ GLMPC_ 1 
#> ℹ Positive:  Barx2, Cartpt, Ptger3, Gm3448, Cypt3, Kng2, Gad1, 3930402G23Rik, Mesp1, Il1r2 
#> ℹ 	   Dusp26, Cdkn2b, Aard, Platr26, Prl, Ucn3, Slc38a5, Pcdh8, Lrrc6, 4930426D05Rik 
#> ℹ 	   Ky, Ctxn2, Pax6os1, Msx1, A930017K11Rik, 1700001C02Rik, Ifit1bl1, RP23-385E22.2, Gm933, Gm6086 
#> ℹ Negative:  Col6a1, Sparcl1, Col23a1, Hoxb4, Col1a1, Galnt16, Ctgf, Zfp385b, Col1a2, Islr 
#> ℹ 	   Isg15, Pmp22, P2ry2, Platr22, Plscr2, Gm6878, Gm26633, Kcnj8, Smpx, A730098A19Rik 
#> ℹ 	   Col3a1, Gm15640, Aif1, Ctsk, Cdkn2c, Peg12, Olfml2a, Krt20, Tnni3, Tnfrsf19 
#> ℹ GLMPC_ 2 
#> ℹ Positive:  Ncf2, Lmx1a, Cmklr1, Tgm7, Pthlh, Nhlh1, 1520401A03Rik, Gm15567, Lipg, Epb42 
#> ℹ 	   Adgrb1, Sema3g, Gm16140, Slc52a3, Neurog3, 1700128E19Rik, Crlf1, Notum, Acot11, Siglece 
#> ℹ 	   Eya2, Neurod2, Laptm5, Prom2, Gm8773, Fgf18, Wnt3, Bhlhe22, Megf11, Rasgrp3 
#> ℹ Negative:  Sst, Dkk2, Klhl14, Aif1, RP23-428N8.3, 4930539E08Rik, Ctsk, Fgb, Tnni3, Col1a2 
#> ℹ 	   Ctgf, Tac1, Tstd1, Col25a1, Col6a1, Col23a1, Lgr5, Crygn, Sparcl1, Nov 
#> ℹ 	   Ppy, 4930426D05Rik, Pyy, Otc, Hoxb4, Zfp385b, Sp140, Cbln4, Ceacam10, Gm26633 
#> ℹ GLMPC_ 3 
#> ℹ Positive:  Gtf2ird2, Fam198b, Tac1, 4933440M02Rik, Tstd1, 1700015F17Rik, Sst, Cbln1, Pkd2l1, Lmx1a 
#> ℹ 	   Slc4a10, Ctsk, RP23-58K20.3, 4430402I18Rik, Gm10382, Igfbp3, Klhl14, Gm15895, Fcgr3, 4930539E08Rik 
#> ℹ 	   Lst1, Cdc25c, Kif2c, Lgr5, Cenpf, Ankrd1, Icosl, Slfn2, Srgn, D7Ertd443e 
#> ℹ Negative:  Kcnj8, Cbln4, Col3a1, Sparcl1, Gm15640, Islr, Col5a1, Col1a1, Ghrl, Tex36 
#> ℹ 	   Npy, Guca2a, Col1a2, Galnt16, Col23a1, Sapcd1, Foxd3, Col6a1, Tmem119, L1td1 
#> ℹ 	   Gm38112, Ptpro, Olfml3, Tmtc1, Irs4, Tagln, Lrrtm3, Cxcl16, Lsp1, Nid1 
#> ℹ GLMPC_ 4 
#> ℹ Positive:  Npy, Aif1, Gm38112, Cldn18, Dlgap1, Rac2, Ins2, Gm11789, Ins1, Syndig1l 
#> ℹ 	   Sp5, Hist1h1a, Gm15640, Col25a1, P2ry14, Slfn2, Tmem215, Sst, Gip, Cbln4 
#> ℹ 	   1700024G13Rik, Iqgap3, P2ry1, Pif1, Gm933, Adam32, Ifitm1, RP23-58K20.3, Pf4, Hist1h1b 
#> ℹ Negative:  Sp140, Gast, Tstd1, Tnfaip8l3, D7Ertd443e, Fam46d, Lmod3, Rerg, 1500035N22Rik, Guca2a 
#> ℹ 	   Arhgap22, Pou6f2, Gm29440, RP23-385E22.2, Snai2, Irs4, Plbd1, Calb1, Ctsk, Nrn1 
#> ℹ 	   Gcg, Anxa1, Nxph1, Smpx, Ngf, Bhlhe23, Gm13375, Oasl2, 1110002O04Rik, Bmp2 
#> ℹ GLMPC_ 5 
#> ℹ Positive:  Cxcl10, Rac2, Srgn, Kcne2, Fcgr3, Sst, Ccl20, Anxa1, Aif1, Krt17 
#> ℹ 	   Tyrobp, Elovl4, Ltb, Plaur, Tex36, P2ry14, Itgb7, Lst1, Tmem100, Cyp11a1 
#> ℹ 	   Tnni3, Gm933, Cpa3, Gm17455, Cxcl16, Bmp2, Cd37, Arhgap22, Lrrtm3, Gpr6 
#> ℹ Negative:  Gad2, Sparcl1, 4933440M02Rik, Gcg, Galnt16, Oasl2, Islr, Col6a1, Hoxb4, Col1a2 
#> ℹ 	   Gsg1l, Nhs, Gm6878, Calb1, Col1a1, Lgr5, Ryr3, Ins1, Rspo1, Cdkn2c 
#> ℹ 	   4930539E08Rik, Tmem119, Pid1, Pmp22, Lmx1a, Ska3, Pkd2l1, Aspm, Tmem255b, Fam71b 
CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "glmpca"
)