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Run Force-Directed Layout (Fruchterman-Reingold algorithm)

Source: R/RunFR.R
RunFR.Rd

Run Force-Directed Layout (Fruchterman-Reingold algorithm)

Usage

RunFR(object, ...)

# S3 method for class 'Seurat'
RunFR(
  object,
  reduction = NULL,
  dims = NULL,
  features = NULL,
  assay = NULL,
  layer = "data",
  graph = NULL,
  neighbor = NULL,
  k.param = 20,
  ndim = 2,
  niter = 500,
  reduction.name = "FR",
  reduction.key = "FR_",
  verbose = TRUE,
  seed.use = 11L,
  ...
)

# Default S3 method
RunFR(
  object,
  assay = NULL,
  ndim = 2,
  niter = 500,
  reduction.key = "FR_",
  verbose = TRUE,
  seed.use = 11L,
  ...
)

Arguments

object

An object. This can be a Seurat object, a Neighbor object, or a Graph object.

...

Additional arguments to be passed to igraph::layout_with_fr.

reduction

The reduction to be used. Default is NULL.

dims

The dimensions to be used. Default is NULL.

features

The features to be used. Default is NULL.

assay

The assay to be used. Default is NULL.

layer

The layer to be used. Default is "data".

graph

The name of the Graph object to be used. Default is NULL.

neighbor

The name of the Neighbor object to be used. Default is NULL.

k.param

The number of nearest neighbors to consider. Default is 20.

ndim

The number of dimensions for the force-directed layout. Default is 2.

niter

The number of iterations for the force-directed layout. Default is 500.

reduction.name

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

reduction.key

The prefix for the column names of the force-directed layout embeddings. Default is "FR_".

verbose

Whether to print the message. Default is TRUE.

seed.use

The random seed to be used. Default is 11.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-11-13 12:22:42] Start standard scop workflow...
#> ℹ [2025-11-13 12:22:42] Checking a list of <Seurat> object...
#> ! [2025-11-13 12:22:42] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2025-11-13 12:22:43] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 12:22:45] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-11-13 12:22:45] Use the separate HVF from srt_list
#> ℹ [2025-11-13 12:22:46] Number of available HVF: 2000
#> ℹ [2025-11-13 12:22:46] Finished check
#> ℹ [2025-11-13 12:22:46] Perform `Seurat::ScaleData()`
#> ℹ [2025-11-13 12:22:46] Perform pca linear dimension reduction
#> StandardPC_ 1 
#> Positive:  Aplp1, Cpe, Gnas, Fam183b, Map1b, Hmgn3, Pcsk1n, Chga, Tuba1a, Bex2 
#> 	   Syt13, Isl1, 1700086L19Rik, Pax6, Chgb, Scgn, Rbp4, Scg3, Gch1, Camk2n1 
#> 	   Cryba2, Pcsk2, Pyy, Tspan7, Mafb, Hist3h2ba, Dbpht2, Abcc8, Rap1b, Slc38a5 
#> Negative:  Spp1, Anxa2, Sparc, Dbi, 1700011H14Rik, Wfdc2, Gsta3, Adamts1, Clu, Mgst1 
#> 	   Bicc1, Ldha, Vim, Cldn3, Cyr61, Rps2, Mt1, Ptn, Phgdh, Nudt19 
#> 	   Smtnl2, Smco4, Habp2, Mt2, Col18a1, Rpl12, Galk1, Cldn10, Acot1, Ccnd1 
#> StandardPC_ 2 
#> Positive:  Rbp4, Tagln2, Tuba1b, Fkbp2, Pyy, Pcsk2, Iapp, Tmem27, Meis2, Tubb4b 
#> 	   Pcsk1n, Dbpht2, Rap1b, Dynll1, Tubb2a, Sdf2l1, Scgn, 1700086L19Rik, Scg2, Abcc8 
#> 	   Atp1b1, Hspa5, Fam183b, Papss2, Slc38a5, Scg3, Mageh1, Tspan7, Ppp1r1a, Ociad2 
#> Negative:  Neurog3, Btbd17, Gadd45a, Ppp1r14a, Neurod2, Sox4, Smarcd2, Mdk, Pax4, Btg2 
#> 	   Sult2b1, Hes6, Grasp, Igfbpl1, Gpx2, Cbfa2t3, Foxa3, Shf, Mfng, Tmsb4x 
#> 	   Amotl2, Gdpd1, Cdc14b, Epb42, Rcor2, Cotl1, Upk3bl, Rbfox3, Cldn6, Cer1 
#> StandardPC_ 3 
#> Positive:  Nusap1, Top2a, Birc5, Aurkb, Cdca8, Pbk, Mki67, Tpx2, Plk1, Ccnb1 
#> 	   2810417H13Rik, Incenp, Cenpf, Ccna2, Prc1, Racgap1, Cdk1, Aurka, Cdca3, Hmmr 
#> 	   Spc24, Kif23, Sgol1, Cenpe, Cdc20, Hist1h1b, Cdca2, Mxd3, Kif22, Ska1 
#> Negative:  Anxa5, Pdzk1ip1, Acot1, Tpm1, Anxa2, Dcdc2a, Capg, Sparc, Ttr, Pamr1 
#> 	   Clu, Cxcl12, Ndrg2, Hnf1aos1, Gas6, Gsta3, Krt18, Ces1d, Atp1b1, Muc1 
#> 	   Hhex, Acadm, Spp1, Enpp2, Bcl2l14, Sat1, Smtnl2, 1700011H14Rik, Tgm2, Fam159a 
#> StandardPC_ 4 
#> Positive:  Glud1, Tm4sf4, Akr1c19, Cldn4, Runx1t1, Fev, Pou3f4, Gm43861, Pgrmc1, Arx 
#> 	   Cd200, Lrpprc, Hmgn3, Ppp1r14c, Pam, Etv1, Tsc22d1, Slc25a5, Akap17b, Pgf 
#> 	   Fam43a, Emb, Jun, Krt8, Dnajc12, Mid1ip1, Ids, Rgs17, Uchl1, Alcam 
#> Negative:  Ins2, Ins1, Ppp1r1a, Nnat, Calr, Sytl4, Sdf2l1, Iapp, Pdia6, Mapt 
#> 	   G6pc2, C2cd4b, Npy, Gng12, P2ry1, Ero1lb, Adra2a, Papss2, Arhgap36, Fam151a 
#> 	   Dlk1, Creld2, Gip, Tmem215, Gm27033, Cntfr, Prss53, C2cd4a, Lyve1, Ociad2 
#> StandardPC_ 5 
#> Positive:  Pdx1, Nkx6-1, Npepl1, Cldn4, Cryba2, Fev, Jun, Chgb, Gng12, Adra2a 
#> 	   Mnx1, Sytl4, Pdk3, Gm27033, Nnat, Chga, Ins2, 1110012L19Rik, Enho, Krt7 
#> 	   Mlxipl, Tmsb10, Flrt1, Pax4, Tubb3, Prrg2, Gars, Frzb, BC023829, Gm2694 
#> Negative:  Irx2, Irx1, Gcg, Ctxn2, Tmem27, Ctsz, Tmsb15l, Nap1l5, Pou6f2, Gria2 
#> 	   Ghrl, Peg10, Smarca1, Arx, Lrpap1, Rgs4, Ttr, Gast, Tmsb15b2, Serpina1b 
#> 	   Slc16a10, Wnk3, Ly6e, Auts2, Sct, Arg1, Dusp10, Sphkap, Dock11, Edn3 
#> ℹ [2025-11-13 12:22:47] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-11-13 12:22:47] Reorder clusters...
#> ℹ [2025-11-13 12:22:48] Perform umap nonlinear dimension reduction
#> ℹ [2025-11-13 12:22:48] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 12:22:48] UMAP will return its model
#> ℹ [2025-11-13 12:22:53] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-11-13 12:22:53] UMAP will return its model
#> ✔ [2025-11-13 12:22:57] Run scop standard workflow done
pancreas_sub <- RunFR(
  object = pancreas_sub,
  features = SeuratObject::VariableFeatures(pancreas_sub)
)
#> ℹ [2025-11-13 12:22:57] Running force-directed layout
#> ℹ [2025-11-13 12:22:58] Computing nearest neighbor graph and SNN
#> ℹ [2025-11-13 12:23:03] Force-directed layout computed
CellDimPlot(
  pancreas_sub,
  group.by = "CellType",
  reduction = "fr"
)