This function performs a standard single-cell analysis workflow.
Usage
standard_scop(
srt,
prefix = "Standard",
assay = NULL,
do_normalization = NULL,
normalization_method = "LogNormalize",
do_HVF_finding = TRUE,
HVF_method = "vst",
nHVF = 2000,
HVF = NULL,
do_scaling = TRUE,
vars_to_regress = NULL,
regression_model = "linear",
linear_reduction = "pca",
linear_reduction_dims = 50,
linear_reduction_dims_use = NULL,
linear_reduction_params = list(),
force_linear_reduction = FALSE,
nonlinear_reduction = "umap",
nonlinear_reduction_dims = c(2, 3),
nonlinear_reduction_params = list(),
force_nonlinear_reduction = TRUE,
neighbor_metric = "euclidean",
neighbor_k = 20L,
cluster_algorithm = "louvain",
cluster_resolution = 0.6,
verbose = TRUE,
seed = 11
)Arguments
- srt
A Seurat object.
- prefix
A prefix to add to the names of intermediate objects created by the function. Default is
"Standard".- assay
The name of the assay to use for the analysis. If NULL, the default assay of the Seurat object will be used.
- do_normalization
Whether to perform normalization. If NULL, normalization will be performed if the specified assay does not have scaled data.
- normalization_method
The method to use for normalization. Options are
"LogNormalize","SCT", or"TFIDF". Default is"LogNormalize".- do_HVF_finding
Whether to perform high variable feature finding. If TRUE, the function will force to find the highly variable features (HVF) using the specified HVF method.
- HVF_method
The method to use for finding highly variable features. Options are
"vst","mvp", or"disp". Default is"vst".- nHVF
The number of highly variable features to select. If NULL, all highly variable features will be used.
- HVF
A vector of feature names to use as highly variable features. If NULL, the function will use the highly variable features identified by the HVF method.
- do_scaling
Whether to perform scaling. If TRUE, the function will force to scale the data using the ScaleData function.
- vars_to_regress
A vector of feature names to use as regressors in the scaling step. If NULL, no regressors will be used.
- regression_model
The regression model to use for scaling. Options are
"linear","poisson", or"negativebinomial". Default is"linear".- linear_reduction
The linear dimensionality reduction method to use. Options are
"pca","svd","ica","nmf","mds", or"glmpca". Default is"pca".- linear_reduction_dims
The number of dimensions to keep after linear dimensionality reduction. Default is
50.- linear_reduction_dims_use
The dimensions to use for downstream analysis. If NULL, all dimensions will be used.
- linear_reduction_params
A list of parameters to pass to the linear dimensionality reduction method.
- force_linear_reduction
Whether to force linear dimensionality reduction even if the specified reduction is already present in the Seurat object.
- nonlinear_reduction
The nonlinear dimensionality reduction method to use. Options are
"umap","umap-naive","tsne","dm","phate","pacmap","trimap","largevis", or"fr". Default is"umap".- nonlinear_reduction_dims
The number of dimensions to keep after nonlinear dimensionality reduction. If a vector is provided, different numbers of dimensions can be specified for each method. Default is
c(2, 3).- nonlinear_reduction_params
A list of parameters to pass to the nonlinear dimensionality reduction method.
- force_nonlinear_reduction
Whether to force nonlinear dimensionality reduction even if the specified reduction is already present in the Seurat object. Default is
TRUE.- neighbor_metric
The distance metric to use for finding neighbors. Options are
"euclidean","cosine","manhattan", or"hamming". Default is"euclidean".- neighbor_k
The number of nearest neighbors to use for finding neighbors. Default is
20.- cluster_algorithm
The clustering algorithm to use. Options are
"louvain","slm", or"leiden". Default is"louvain".- cluster_resolution
The resolution parameter to use for clustering. Larger values result in fewer clusters. Default is
0.6.- verbose
Whether to print the message. Default is
TRUE.- seed
The random seed to use for reproducibility. Default is
11.
Examples
library(Matrix)
PrepareEnv()
#> ℹ [2025-09-20 14:07:48] Preparing scop Python Environment
#> ℹ [2025-09-20 14:07:48] Environment name: scop_env
#> ℹ [2025-09-20 14:07:48] Python version: 3.10-1
#> ℹ [2025-09-20 14:07:48] Number of packages to install: 21
#> ℹ [2025-09-20 14:07:48] Auto-detecting conda...
#> ℹ [2025-09-20 14:07:49] Using existing environment: /usr/share/miniconda/envs/scop_env
#> ℹ [2025-09-20 14:07:49] Checking and installing required packages...
#> ℹ [2025-09-20 14:07:49] Installing conda packages
#> ℹ [2025-09-20 14:07:50] Checking 3 packages in environment: scop_env
#> ℹ [2025-09-20 14:07:51] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 14:07:53] Found 205 packages installed
#> ✔ [2025-09-20 14:07:53] leidenalg 0.10.2
#> ✔ [2025-09-20 14:07:53] tbb 2022.2.0
#> ✔ [2025-09-20 14:07:53] python-igraph 0.11.9
#> ℹ [2025-09-20 14:07:53] Installing pip packages
#> ℹ [2025-09-20 14:07:55] Checking 18 packages in environment: scop_env
#> ℹ [2025-09-20 14:07:55] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 14:07:57] Found 205 packages installed
#> ✔ [2025-09-20 14:07:57] matplotlib 3.10.3
#> ✔ [2025-09-20 14:07:57] numba 0.59.1
#> ✔ [2025-09-20 14:07:57] llvmlite 0.42.0
#> ✔ [2025-09-20 14:07:57] numpy 1.26.4
#> ✔ [2025-09-20 14:07:57] palantir 1.4.1
#> ✔ [2025-09-20 14:07:57] pandas 2.0.3
#> ✔ [2025-09-20 14:07:57] scanpy 1.11.3
#> ✔ [2025-09-20 14:07:57] scikit-learn 1.7.0
#> ✔ [2025-09-20 14:07:57] scipy 1.15.3
#> ✔ [2025-09-20 14:07:57] scvelo 0.3.3
#> ✔ [2025-09-20 14:07:57] wot 1.0.8.post2
#> ✔ [2025-09-20 14:07:57] trimap 1.1.4
#> ✔ [2025-09-20 14:07:57] pacmap 0.8.0
#> ✔ [2025-09-20 14:07:57] phate 1.0.11
#> ✔ [2025-09-20 14:07:57] bbknn 1.6.0
#> ✔ [2025-09-20 14:07:57] scanorama 1.7.4
#> ✔ [2025-09-20 14:07:57] scvi-tools 1.2.1
#> ✔ [2025-09-20 14:07:57] cellrank 2.0.7
#> ✔ [2025-09-20 14:08:00] Python Environment Ready
#> conda environment:
#> conda: /usr/share/miniconda/bin/conda
#> environment: /usr/share/miniconda/envs/scop_env
#> python config:
#> python: /usr/share/miniconda/envs/scop_env/bin/python3.10
#> libpython: /usr/share/miniconda/envs/scop_env/lib/libpython3.10.so
#> pythonhome: /usr/share/miniconda/envs/scop_env:/usr/share/miniconda/envs/scop_env
#> version: 3.10.1 | packaged by conda-forge | (main, Dec 22 2021, 01:39:36) [GCC 9.4.0]
#> numpy: /usr/share/miniconda/envs/scop_env/lib/python3.10/site-packages/numpy
#> numpy_version: 1.26.4
#>
#> NOTE: Python version was forced by use_python() function
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-09-20 14:08:00] Start standard scop workflow...
#> ℹ [2025-09-20 14:08:01] Checking a list of <Seurat> object...
#> ! [2025-09-20 14:08:01] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2025-09-20 14:08:01] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2025-09-20 14:08:03] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-09-20 14:08:04] Use the separate HVF from srt_list
#> ℹ [2025-09-20 14:08:04] Number of available HVF: 2000
#> ℹ [2025-09-20 14:08:04] Finished check
#> ℹ [2025-09-20 14:08:04] Perform `Seurat::ScaleData()`
#> Warning: Different features in new layer data than already exists for scale.data
#> ℹ [2025-09-20 14:08:05] 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 [2025-09-20 14:08:06] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-09-20 14:08:06] Reorder clusters...
#> ! [2025-09-20 14:08:06] Using `Seurat::AggregateExpression()` to calculate pseudo-bulk data for <Assay5>
#> ℹ [2025-09-20 14:08:06] Perform umap nonlinear dimension reduction
#> ℹ [2025-09-20 14:08:06] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:08:06] UMAP will return its model
#> ℹ [2025-09-20 14:08:10] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:08:10] UMAP will return its model
#> ✔ [2025-09-20 14:08:15] Run scop standard workflow done
CellDimPlot(
pancreas_sub,
group.by = "SubCellType"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
# Use a combination of different linear
# or non-linear dimension reduction methods
linear_reductions <- c(
"pca", "nmf", "mds", "glmpca"
)
pancreas_sub <- standard_scop(
pancreas_sub,
linear_reduction = linear_reductions,
nonlinear_reduction = "umap"
)
#> ℹ [2025-09-20 14:08:15] Start standard scop workflow...
#> ℹ [2025-09-20 14:08:15] Checking a list of <Seurat> object...
#> ℹ [2025-09-20 14:08:16] Data 1/1 of the `srt_list` has been log-normalized
#> ℹ [2025-09-20 14:08:16] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-09-20 14:08:16] Use the separate HVF from srt_list
#> ℹ [2025-09-20 14:08:16] Number of available HVF: 2000
#> ℹ [2025-09-20 14:08:16] Finished check
#> ℹ [2025-09-20 14:08:16] Perform `Seurat::ScaleData()`
#> ℹ [2025-09-20 14:08:17] 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 [2025-09-20 14:08:18] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-09-20 14:08:18] Reorder clusters...
#> ! [2025-09-20 14:08:18] Using `Seurat::AggregateExpression()` to calculate pseudo-bulk data for <Assay5>
#> ℹ [2025-09-20 14:08:18] Perform umap nonlinear dimension reduction
#> ℹ [2025-09-20 14:08:18] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:08:18] UMAP will return its model
#> Warning: Key ‘StandardpcaUMAP2D_’ taken, using ‘standardpcaumap2d_’ instead
#> ℹ [2025-09-20 14:08:22] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:08:22] UMAP will return its model
#> Warning: Key ‘StandardpcaUMAP3D_’ taken, using ‘standardpcaumap3d_’ instead
#> ℹ [2025-09-20 14:08:27] Perform nmf linear dimension reduction
#> ℹ [2025-09-20 14:08:27] zdebruine/RcppML installed successfully
#>
#> iter | tol
#> ---------------
#> 1 | 6.94e-01
#> 2 | 9.76e-02
#> 3 | 3.18e-02
#> 4 | 1.49e-02
#> 5 | 8.15e-03
#> 6 | 5.06e-03
#> 7 | 3.40e-03
#> 8 | 2.43e-03
#> 9 | 1.80e-03
#> 10 | 1.37e-03
#> 11 | 1.07e-03
#> 12 | 8.59e-04
#> 13 | 7.01e-04
#> 14 | 5.85e-04
#> 15 | 5.07e-04
#> 16 | 4.47e-04
#> 17 | 4.03e-04
#> 18 | 3.67e-04
#> 19 | 3.32e-04
#> 20 | 2.98e-04
#> 21 | 2.72e-04
#> 22 | 2.48e-04
#> 23 | 2.28e-04
#> 24 | 2.11e-04
#> 25 | 1.99e-04
#> 26 | 1.91e-04
#> 27 | 1.85e-04
#> 28 | 1.76e-04
#> 29 | 1.72e-04
#> 30 | 1.68e-04
#> 31 | 1.62e-04
#> 32 | 1.54e-04
#> 33 | 1.45e-04
#> 34 | 1.35e-04
#> 35 | 1.24e-04
#> 36 | 1.15e-04
#> 37 | 1.06e-04
#> 38 | 9.81e-05
#> 39 | 9.10e-05
#> 40 | 8.53e-05
#> 41 | 8.05e-05
#> 42 | 7.62e-05
#> 43 | 7.25e-05
#> 44 | 6.94e-05
#> 45 | 6.73e-05
#> 46 | 6.61e-05
#> 47 | 6.49e-05
#> 48 | 6.36e-05
#> 49 | 6.22e-05
#> 50 | 6.04e-05
#> 51 | 5.84e-05
#> 52 | 5.62e-05
#> 53 | 5.39e-05
#> 54 | 5.05e-05
#> 55 | 4.69e-05
#> 56 | 4.39e-05
#> 57 | 4.11e-05
#> 58 | 3.85e-05
#> 59 | 3.60e-05
#> 60 | 3.38e-05
#> 61 | 3.18e-05
#> 62 | 3.01e-05
#> 63 | 2.88e-05
#> 64 | 2.77e-05
#> 65 | 2.69e-05
#> 66 | 2.59e-05
#> 67 | 2.50e-05
#> 68 | 2.40e-05
#> 69 | 2.30e-05
#> 70 | 2.20e-05
#> 71 | 2.12e-05
#> 72 | 2.05e-05
#> 73 | 2.00e-05
#> 74 | 1.96e-05
#> 75 | 1.94e-05
#> 76 | 1.90e-05
#> 77 | 1.87e-05
#> 78 | 1.82e-05
#> 79 | 1.72e-05
#> 80 | 1.62e-05
#> 81 | 1.54e-05
#> 82 | 1.45e-05
#> 83 | 1.37e-05
#> 84 | 1.31e-05
#> 85 | 1.25e-05
#> 86 | 1.20e-05
#> 87 | 1.16e-05
#> 88 | 1.11e-05
#> 89 | 1.07e-05
#> 90 | 1.02e-05
#> 91 | 9.91e-06
#> ℹ StandardBE_ 1
#> ℹ Positive: Ccnd1, Spp1, Mdk, Rps2, Ldha, Pebp1, Cd24a, Dlk1, Krt8, Mgst1
#> ℹ Clu, Gapdh, Eno1, Prdx1, Cldn10, Mif, Cldn7, Npm1, Dbi, Vim
#> ℹ Sox9, Rpl12, Aldh1b1, Rplp1, Wfdc2, Krt18, Tkt, Aldoa, Hspe1, Ptma
#> ℹ Negative: Tmem108, Poc1a, Epn3, Wipi1, Tmcc3, Nhsl1, Fgf12, Plekho1, Tecpr2, Zbtb4
#> ℹ Gm10941, Trf, Man1c1, Hmgcs1, Nipal1, Jam3, Pgap1, Alpl, Kcnip3, Tnr
#> ℹ Gm15915, Rbp2, Cbfa2t2, Sh2d4a, Bbc3, Megf6, Naaladl2, Fam46d, Hist2h2ac, Tox2
#> ℹ StandardBE_ 2
#> ℹ Positive: Spp1, Gsta3, Sparc, Vim, Atp1b1, Mt1, Dbi, Anxa2, Rps2, Id2
#> ℹ Rpl22l1, Rplp1, Mgst1, Clu, Sox9, Cldn6, Mdk, Pdzk1ip1, Bicc1, 1700011H14Rik
#> ℹ Rps12, S100a10, Cldn3, Rpl36a, Ppp1r1b, Adamts1, Serpinh1, Mt2, Ifitm2, Rpl39
#> ℹ Negative: Rpa3, Aacs, Tmem108, Poc1a, Epn3, Wipi1, B830012L14Rik, Tmcc3, Wsb1, Plekho1
#> ℹ Ppp2r2b, Tecpr2, Zbtb4, Haus8, Trf, Gm5420, Man1c1, Hmgcs1, Nipal1, Jam3
#> ℹ Tcerg1, Pgap1, Snrpa1, Alpl, Larp1b, Kcnip3, Tnr, Lsm12, Ptbp3, Gm15915
#> ℹ StandardBE_ 3
#> ℹ Positive: Cck, Mdk, Gadd45a, Neurog3, Selm, Sox4, Btbd17, Tmsb4x, Btg2, Cldn6
#> ℹ Cotl1, Ptma, Jun, Ppp1r14a, Rps2, Ifitm2, Neurod2, Igfbpl1, Gnas, Krt7
#> ℹ Nkx6-1, Aplp1, Ppp3ca, Lrpap1, Rplp1, Hn1, Rps12, Mfng, BC023829, Smarcd2
#> ℹ Negative: Elovl6, Tmem108, Poc1a, Epn3, Nop56, Wipi1, B830012L14Rik, Rrp15, Rfc1, Fgf12
#> ℹ Slc20a1, Ppp2r2b, Lama1, Tecpr2, Zbtb4, Eif1ax, Fam162a, P4ha3, Gm10941, Tenm4
#> ℹ Pde4b, Gm5420, Man1c1, Hmgcs1, Pgap1, Mgst2, Larp1b, Kcnip3, Tnr, Lsm12
#> ℹ StandardBE_ 4
#> ℹ Positive: Spp1, Cyr61, Krt18, Tpm1, Krt8, Myl12a, Vim, Jun, Anxa5, Tnfrsf12a
#> ℹ Csrp1, Sparc, Cldn7, Nudt19, Anxa2, Clu, Myl9, Atp1b1, Cldn3, Tagln2
#> ℹ S100a10, 1700011H14Rik, Cd24a, Rps2, Dbi, Id2, Lurap1l, Rplp1, Myl12b, Klf6
#> ℹ Negative: Rpa3, Elovl6, Aacs, Tmem108, Poc1a, Tmcc3, Rfc1, Plekho1, Slc20a1, Ppp2r2b
#> ℹ Lama1, Tecpr2, Gm10941, Tenm4, Pde4b, Man1c1, Nipal1, Jam3, Pgap1, Alpl
#> ℹ Mgst2, Kcnip3, Tnr, Ptbp3, Gm15915, Cntln, Ocln, Fras1, Rbp2, Cbfa2t2
#> ℹ StandardBE_ 5
#> ℹ Positive: 2810417H13Rik, Rrm2, Hmgb2, Dut, Pcna, Lig1, H2afz, Tipin, Tuba1b, Tk1
#> ℹ Mcm5, Dek, Tyms, Gmnn, Ran, Tubb5, Rfc2, Srsf2, Ranbp1, Orc6
#> ℹ Mcm3, Uhrf1, Gins2, Dnajc9, Mcm6, Siva1, Rfc3, Mcm7, Rpa2, Ptma
#> ℹ Negative: 1110002L01Rik, Aacs, Wipi1, B830012L14Rik, Tmcc3, Trib1, Fgf12, Plekho1, Ppp2r2b, Lama1
#> ℹ Tenm4, Trf, Gm5420, Man1c1, Jam3, Mgst2, Kcnip3, Tnr, Gm15915, Cbfa2t2
#> ℹ Sh2d4a, Bbc3, Fkbp9, Ano6, Prkcb, Megf6, Fam46d, Slc52a3, Ankrd2, Tox2
#> ℹ [2025-09-20 14:08:37] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-09-20 14:08:37] Reorder clusters...
#> ! [2025-09-20 14:08:37] Using `Seurat::AggregateExpression()` to calculate pseudo-bulk data for <Assay5>
#> ℹ [2025-09-20 14:08:37] Perform umap nonlinear dimension reduction
#> ℹ [2025-09-20 14:08:37] Non-linear dimensionality reduction (umap) using (Standardnmf) dims (1-50) as input
#> ℹ [2025-09-20 14:08:37] UMAP will return its model
#> ℹ [2025-09-20 14:08:42] Non-linear dimensionality reduction (umap) using (Standardnmf) dims (1-50) as input
#> ℹ [2025-09-20 14:08:42] UMAP will return its model
#> ℹ [2025-09-20 14:08:46] Perform mds linear dimension reduction
#> ℹ [2025-09-20 14:08:48] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-09-20 14:08:48] Reorder clusters...
#> ! [2025-09-20 14:08:48] Using `Seurat::AggregateExpression()` to calculate pseudo-bulk data for <Assay5>
#> ℹ [2025-09-20 14:08:48] Perform umap nonlinear dimension reduction
#> ℹ [2025-09-20 14:08:48] Non-linear dimensionality reduction (umap) using (Standardmds) dims (1-50) as input
#> ℹ [2025-09-20 14:08:48] UMAP will return its model
#> ℹ [2025-09-20 14:08:52] Non-linear dimensionality reduction (umap) using (Standardmds) dims (1-50) as input
#> ℹ [2025-09-20 14:08:52] UMAP will return its model
#> ℹ [2025-09-20 14:08:57] Perform glmpca linear dimension reduction
#> ℹ [2025-09-20 14:08:57] Installing: glmpca...
#>
#> → Will install 1 package.
#> → The package (0 B) is cached.
#> + glmpca 0.2.0
#>
#> ℹ No downloads are needed, 1 pkg is cached
#> ✔ Got glmpca 0.2.0 (x86_64-pc-linux-gnu-ubuntu-24.04) (230.79 kB)
#> ✔ Installed glmpca 0.2.0 (1s)
#> ✔ 1 pkg + 1 dep: kept 1, added 1, dld 1 (230.79 kB) [1.9s]
#> ℹ [2025-09-20 14:08:59] glmpca installed successfully
#> ℹ StandardGLMPC_ 1
#> ℹ Positive: Cartpt, Pkib, Srrm3, Tnr, Pax6os1, Pnma2, Skap1, Kcnb2, Dusp26, Gpr119
#> ℹ Galr1, Tmem8c, Actc1, Cntfr, Aard, Fut1, Cd3g, Crhr2, Ppp2r2c, Snap25
#> ℹ Ceacam10, Ucn3, Sept3, Isl1, Bhlhe41, Celf6, Bace2, Mbl2, Sez6l, Elavl3
#> ℹ Negative: Rxrg, Tnfrsf19, AA986860, Peg12, Apcs, Ugt2b36, Rab32, Cdh3, Tmem213, Rassf9
#> ℹ Ckap2l, Krt23, Spp2, Slc43a1, Slc39a8, Gm684, Sult1a1, Calr4, P4ha3, Fbxo27
#> ℹ Vnn1, Otud1, Akr1b7, Slc22a2, Serpinb6b, Pmp22, Gas1, Ctgf, Lrr1, Traip
#> ℹ StandardGLMPC_ 2
#> ℹ Positive: Crem, Emb, Fam89a, Chid1, Hist1h2bc, Ndn, Pdia4, Serpinb1a, Ocln, Slc16a10
#> ℹ Slc25a5, Nupr1, Nfkbia, Gata6, Serpini1, Mid1ip1, Junb, Zfp36, Atic, Gpx7
#> ℹ Sqle, Aacs, Hadh, Aldoa, Gde1, Fbxo32, Glud1, Akr1c19, Gch1, Pgrmc1
#> ℹ Negative: Esyt3, Edn1, Kcnj13, Cpne5, 1700128E19Rik, Slc43a1, Sult1a1, Ankrd1, Plscr2, Myo5a
#> ℹ Tmem119, AU021092, Pmp22, Awat2, Kcnk10, Gm20649, Fam212b, Chrdl1, Fgfbp1, 1500035N22Rik
#> ℹ Tmem255b, Ccdc13, Nkain4, Platr22, Krt20, Sgol2a, Ramp3, Nhlrc1, Gm17056, Nhlh1
#> ℹ StandardGLMPC_ 3
#> ℹ Positive: Gchfr, Apold1, Mbl2, Slc16a6, Pde1a, Ptprt, Adgrf5, Gm2115, Slitrk6, Fam159b
#> ℹ Lrrc24, Nap1l5, Fbp1, Plet1, Pdzrn4, Cypt3, Cpxm1, Vgf, Kctd8, Il1r1
#> ℹ Fbln5, 4930539E08Rik, Gm11131, Cav2, Platr26, Sult1d1, Amph, Stmn3, Gm826, 4933440M02Rik
#> ℹ Negative: Adra2c, Nhlh1, Gm4319, Epb42, Lypd1, Adgrb1, Gm8773, Plbd1, Tmem114, Sema3g
#> ℹ Aqp7, Bhlhe22, Slc4a1, Parvb, Tgm7, Pthlh, Kif26a, Galr2, 1520401A03Rik, Fgf18
#> ℹ Olfml2b, Ankrd2, Wnt3, Mmp23, Tspear, Sapcd1, Fam212b, Notum, Pdcd1, 1700128E19Rik
#> ℹ StandardGLMPC_ 4
#> ℹ Positive: Srcin1, Gm28875, Tfpi, Smpx, Snhg11, Tmem255b, B230312C02Rik, Unc5b, 2200002D01Rik, Fut1
#> ℹ Kndc1, Lgals2, Tmem8c, Nkpd1, Mmp23, Tmtc1, 1110002O04Rik, Cntfr, Pmp22, Cpb2
#> ℹ Rtp4, Cldn2, Sparcl1, Cbln4, Ncf2, Fgb, Col1a1, Lix1l, Mag, 1700048O20Rik
#> ℹ Negative: Etv4, Icosl, Gm4319, Eci3, Nhlh1, Nrp2, Ppfibp2, Irf8, Sftpc, Egr4
#> ℹ Rasl11a, Ccl20, Esyt3, Ngf, Tssk6, Rps6kl1, Cxcl16, Sgol2a, Brip1, Flnc
#> ℹ Hist1h1a, Rdh5, Slfn9, Gm20649, Cbln1, 2410021H03Rik, Plaur, Rerg, Lockd, Scn9a
#> ℹ StandardGLMPC_ 5
#> ℹ Positive: Adgrf5, Gm13344, Ncf2, Scnn1g, A130057D12Rik, Lrrc9, Colec12, Fn3krp, R3hdml, Fam132b
#> ℹ Dlgap3, Gm16140, Kcnj13, Zfp518b, Mcm10, Gm17056, Nkx6-3, Gm11131, Hbb-bt, Zcchc12
#> ℹ Pdzrn4, Gchfr, Lsp1, Ckm, Troap, Msx1, Elavl3, Col3a1, Coro1a, Wee2
#> ℹ Negative: Tnni1, Vwa2, Tmem8c, Pou3f1, Prl, Plppr1, Crygn, Nepn, Gm20649, Fam124a
#> ℹ Tfpi, 2210407C18Rik, Hspa1a, Palm3, Gm6410, Skap1, Nlgn1, Acoxl, Tmem215, C530044C16Rik
#> ℹ Fkbp10, Arhgap36, Otc, Sftpc, Krtap16-1, Cartpt, Lgals9, Smoc2, Tmem132b, Wnt4
#> ℹ [2025-09-20 14:09:59] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-09-20 14:09:59] Reorder clusters...
#> ! [2025-09-20 14:09:59] Using `Seurat::AggregateExpression()` to calculate pseudo-bulk data for <Assay5>
#> ℹ [2025-09-20 14:10:00] Perform umap nonlinear dimension reduction
#> ℹ [2025-09-20 14:10:00] Non-linear dimensionality reduction (umap) using (Standardglmpca) dims (1-50) as input
#> ℹ [2025-09-20 14:10:00] UMAP will return its model
#> ℹ [2025-09-20 14:10:04] Non-linear dimensionality reduction (umap) using (Standardglmpca) dims (1-50) as input
#> ℹ [2025-09-20 14:10:04] UMAP will return its model
#> ✔ [2025-09-20 14:10:09] Run scop standard workflow done
plist1 <- lapply(
linear_reductions, function(lr) {
CellDimPlot(
pancreas_sub,
group.by = "SubCellType",
reduction = paste0(
"Standard", lr, "UMAP2D"
),
xlab = "", ylab = "", title = lr,
legend.position = "none",
theme_use = "theme_blank"
)
}
)
patchwork::wrap_plots(plotlist = plist1)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
nonlinear_reductions <- c(
"umap", "tsne", "dm", "phate",
"pacmap", "trimap", "largevis", "fr"
)
pancreas_sub <- standard_scop(
pancreas_sub,
linear_reduction = "pca",
nonlinear_reduction = nonlinear_reductions
)
#> ℹ [2025-09-20 14:10:09] Start standard scop workflow...
#> ℹ [2025-09-20 14:10:09] Checking a list of <Seurat> object...
#> ℹ [2025-09-20 14:10:10] Data 1/1 of the `srt_list` has been log-normalized
#> ℹ [2025-09-20 14:10:10] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2025-09-20 14:10:10] Use the separate HVF from srt_list
#> ℹ [2025-09-20 14:10:10] Number of available HVF: 2000
#> ℹ [2025-09-20 14:10:11] Finished check
#> ℹ [2025-09-20 14:10:11] Perform `Seurat::ScaleData()`
#> ℹ [2025-09-20 14:10:11] 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 [2025-09-20 14:10:12] Perform `Seurat::FindClusters()` with louvain and `cluster_resolution` = 0.6
#> ℹ [2025-09-20 14:10:12] Reorder clusters...
#> ! [2025-09-20 14:10:12] Using `Seurat::AggregateExpression()` to calculate pseudo-bulk data for <Assay5>
#> ℹ [2025-09-20 14:10:12] Perform umap nonlinear dimension reduction
#> ℹ [2025-09-20 14:10:12] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:10:12] UMAP will return its model
#> ℹ [2025-09-20 14:10:17] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:10:17] UMAP will return its model
#> ℹ [2025-09-20 14:10:22] Perform tsne nonlinear dimension reduction
#> ℹ [2025-09-20 14:10:22] Non-linear dimensionality reduction (tsne) using (Standardpca) dims (1-50) as input
#> Read the 1000 x 50 data matrix successfully!
#> OpenMP is working. 1 threads.
#> Using no_dims = 2, perplexity = 30.000000, and theta = 0.500000
#> Computing input similarities...
#> Building tree...
#> Done in 0.15 seconds (sparsity = 0.123070)!
#> Learning embedding...
#> Iteration 50: error is 67.143519 (50 iterations in 0.23 seconds)
#> Iteration 100: error is 58.277912 (50 iterations in 0.11 seconds)
#> Iteration 150: error is 57.729309 (50 iterations in 0.11 seconds)
#> Iteration 200: error is 57.507201 (50 iterations in 0.12 seconds)
#> Iteration 250: error is 57.429325 (50 iterations in 0.12 seconds)
#> Iteration 300: error is 1.071661 (50 iterations in 0.11 seconds)
#> Iteration 350: error is 0.933223 (50 iterations in 0.11 seconds)
#> Iteration 400: error is 0.897008 (50 iterations in 0.11 seconds)
#> Iteration 450: error is 0.880754 (50 iterations in 0.11 seconds)
#> Iteration 500: error is 0.872784 (50 iterations in 0.11 seconds)
#> Iteration 550: error is 0.865676 (50 iterations in 0.11 seconds)
#> Iteration 600: error is 0.859900 (50 iterations in 0.11 seconds)
#> Iteration 650: error is 0.852929 (50 iterations in 0.11 seconds)
#> Iteration 700: error is 0.845604 (50 iterations in 0.11 seconds)
#> Iteration 750: error is 0.841040 (50 iterations in 0.11 seconds)
#> Iteration 800: error is 0.839081 (50 iterations in 0.11 seconds)
#> Iteration 850: error is 0.836511 (50 iterations in 0.11 seconds)
#> Iteration 900: error is 0.834460 (50 iterations in 0.11 seconds)
#> Iteration 950: error is 0.831349 (50 iterations in 0.11 seconds)
#> Iteration 1000: error is 0.830510 (50 iterations in 0.11 seconds)
#> Fitting performed in 2.34 seconds.
#> ℹ [2025-09-20 14:10:24] Non-linear dimensionality reduction (tsne) using (Standardpca) dims (1-50) as input
#> Read the 1000 x 50 data matrix successfully!
#> OpenMP is working. 1 threads.
#> Using no_dims = 3, perplexity = 30.000000, and theta = 0.500000
#> Computing input similarities...
#> Building tree...
#> Done in 0.14 seconds (sparsity = 0.123070)!
#> Learning embedding...
#> Iteration 50: error is 64.031848 (50 iterations in 0.51 seconds)
#> Iteration 100: error is 58.186915 (50 iterations in 0.26 seconds)
#> Iteration 150: error is 57.747960 (50 iterations in 0.23 seconds)
#> Iteration 200: error is 57.553685 (50 iterations in 0.23 seconds)
#> Iteration 250: error is 57.429018 (50 iterations in 0.23 seconds)
#> Iteration 300: error is 1.030081 (50 iterations in 0.24 seconds)
#> Iteration 350: error is 0.868321 (50 iterations in 0.25 seconds)
#> Iteration 400: error is 0.816735 (50 iterations in 0.25 seconds)
#> Iteration 450: error is 0.794752 (50 iterations in 0.25 seconds)
#> Iteration 500: error is 0.781963 (50 iterations in 0.25 seconds)
#> Iteration 550: error is 0.774223 (50 iterations in 0.25 seconds)
#> Iteration 600: error is 0.769137 (50 iterations in 0.26 seconds)
#> Iteration 650: error is 0.765521 (50 iterations in 0.26 seconds)
#> Iteration 700: error is 0.761562 (50 iterations in 0.27 seconds)
#> Iteration 750: error is 0.758342 (50 iterations in 0.28 seconds)
#> Iteration 800: error is 0.755965 (50 iterations in 0.28 seconds)
#> Iteration 850: error is 0.753431 (50 iterations in 0.28 seconds)
#> Iteration 900: error is 0.751412 (50 iterations in 0.28 seconds)
#> Iteration 950: error is 0.749502 (50 iterations in 0.28 seconds)
#> Iteration 1000: error is 0.747763 (50 iterations in 0.28 seconds)
#> Fitting performed in 5.44 seconds.
#> ℹ [2025-09-20 14:10:30] Perform dm nonlinear dimension reduction
#> ℹ [2025-09-20 14:10:30] Non-linear dimensionality reduction (dm) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:10:30] Installing: destiny...
#>
#> → Will install 15 packages.
#> → All 15 packages (0 B) are cached.
#> + DEoptimR 1.1-4
#> + RcppEigen 0.3.4.0.2
#> + TTR 0.24.4
#> + VIM 6.2.6
#> + destiny 3.22.0 [bld][cmp]
#> + ggplot.multistats 1.0.1
#> + ggthemes 5.1.0
#> + knn.covertree 1.0
#> + laeken 0.5.3
#> + pcaMethods 2.0.0 [bld][cmp]
#> + ranger 0.17.0
#> + robustbase 0.99-6
#> + smoother 1.3
#> + vcd 1.4-13
#> + xts 0.14.1
#> ✔ All system requirements are already installed.
#>
#> ℹ No downloads are needed, 15 pkgs are cached
#> ✔ Got DEoptimR 1.1-4 (x86_64-pc-linux-gnu-ubuntu-24.04) (74.64 kB)
#> ✔ Got ranger 0.17.0 (x86_64-pc-linux-gnu-ubuntu-24.04) (473.78 kB)
#> ✔ Got destiny 3.22.0 (source) (891.59 kB)
#> ✔ Got TTR 0.24.4 (x86_64-pc-linux-gnu-ubuntu-24.04) (524.49 kB)
#> ✔ Got ggthemes 5.1.0 (x86_64-pc-linux-gnu-ubuntu-24.04) (453.69 kB)
#> ✔ Got knn.covertree 1.0 (x86_64-pc-linux-gnu-ubuntu-24.04) (91.51 kB)
#> ✔ Got pcaMethods 2.0.0 (source) (1.04 MB)
#> ✔ Got smoother 1.3 (x86_64-pc-linux-gnu-ubuntu-24.04) (22.69 kB)
#> ✔ Got xts 0.14.1 (x86_64-pc-linux-gnu-ubuntu-24.04) (1.22 MB)
#> ✔ Got ggplot.multistats 1.0.1 (x86_64-pc-linux-gnu-ubuntu-24.04) (31.64 kB)
#> ✔ Got vcd 1.4-13 (x86_64-pc-linux-gnu-ubuntu-24.04) (1.30 MB)
#> ✔ Got RcppEigen 0.3.4.0.2 (x86_64-pc-linux-gnu-ubuntu-24.04) (1.85 MB)
#> ✔ Got robustbase 0.99-6 (x86_64-pc-linux-gnu-ubuntu-24.04) (3.13 MB)
#> ✔ Got laeken 0.5.3 (x86_64-pc-linux-gnu-ubuntu-24.04) (3.16 MB)
#> ℹ Installing system requirements
#> ℹ Executing `sudo sh -c apt-get -y update`
#> Get:1 file:/etc/apt/apt-mirrors.txt Mirrorlist [144 B]
#> Hit:2 http://azure.archive.ubuntu.com/ubuntu noble InRelease
#> Hit:3 http://azure.archive.ubuntu.com/ubuntu noble-updates InRelease
#> Hit:4 http://azure.archive.ubuntu.com/ubuntu noble-backports InRelease
#> Hit:5 http://azure.archive.ubuntu.com/ubuntu noble-security InRelease
#> Hit:6 https://packages.microsoft.com/repos/azure-cli noble InRelease
#> Hit:7 https://packages.microsoft.com/ubuntu/24.04/prod noble InRelease
#> Reading package lists...
#> ℹ Executing `sudo sh -c apt-get -y install libcurl4-openssl-dev libssl-dev make cmake libicu-dev`
#> Reading package lists...
#> Building dependency tree...
#> Reading state information...
#> libcurl4-openssl-dev is already the newest version (8.5.0-2ubuntu10.6).
#> libssl-dev is already the newest version (3.0.13-0ubuntu3.5).
#> make is already the newest version (4.3-4.1build2).
#> cmake is already the newest version (3.28.3-1build7).
#> libicu-dev is already the newest version (74.2-1ubuntu3.1).
#> 0 upgraded, 0 newly installed, 0 to remove and 41 not upgraded.
#> ℹ Building pcaMethods 2.0.0
#> ✔ Installed DEoptimR 1.1-4 (69ms)
#> ✔ Installed ggplot.multistats 1.0.1 (100ms)
#> ✔ Installed ggthemes 5.1.0 (145ms)
#> ✔ Installed knn.covertree 1.0 (129ms)
#> ✔ Installed laeken 0.5.3 (81ms)
#> ✔ Installed ranger 0.17.0 (78ms)
#> ✔ Installed RcppEigen 0.3.4.0.2 (101ms)
#> ✔ Installed robustbase 0.99-6 (92ms)
#> ✔ Installed smoother 1.3 (70ms)
#> ✔ Installed TTR 0.24.4 (120ms)
#> ✔ Installed vcd 1.4-13 (96ms)
#> ✔ Installed VIM 6.2.6 (112ms)
#> ✔ Installed xts 0.14.1 (65ms)
#> ✔ Built pcaMethods 2.0.0 (8.4s)
#> ✔ Installed pcaMethods 2.0.0 (1s)
#> ℹ Building destiny 3.22.0
#> ✔ Built destiny 3.22.0 (27.2s)
#> ✔ Installed destiny 3.22.0 (54ms)
#> ✔ 1 pkg + 111 deps: kept 95, added 15, dld 14 (14.27 MB) [43s]
#> ℹ [2025-09-20 14:11:13] destiny installed successfully
#> Warning: In destiny::DiffusionMap(data = as_matrix(object), n_eigs = ndcs,
#> sigma = sigma, k = k, distance = dist.method, verbose = verbose,
#> ...) :
#> extra argument ‘graph’ will be disregarded
#> finding knns......done. Time: 0.61s
#> Calculating transition probabilities......done. Time: 0.01s
#>
#> 'as(<dsCMatrix>, "dgTMatrix")' is deprecated.
#> Use 'as(as(., "generalMatrix"), "TsparseMatrix")' instead.
#> See help("Deprecated") and help("Matrix-deprecated").
#> performing eigen decomposition......done. Time: 0.01s
#> ℹ [2025-09-20 14:11:18] Non-linear dimensionality reduction (dm) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:11:18] destiny installed successfully
#> Warning: In destiny::DiffusionMap(data = as_matrix(object), n_eigs = ndcs,
#> sigma = sigma, k = k, distance = dist.method, verbose = verbose,
#> ...) :
#> extra argument ‘graph’ will be disregarded
#> finding knns......done. Time: 0.59s
#> Calculating transition probabilities......done. Time: 0.00s
#>
#> performing eigen decomposition......done. Time: 0.01s
#> ℹ [2025-09-20 14:11:22] Perform phate nonlinear dimension reduction
#> ℹ [2025-09-20 14:11:22] Non-linear dimensionality reduction (phate) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:11:24] Checking 1 packages in environment: scop_env
#> ℹ [2025-09-20 14:11:24] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 14:11:27] Found 205 packages installed
#> ✔ [2025-09-20 14:11:27] phate version: 1.0.11
#> ℹ [2025-09-20 14:11:29] Non-linear dimensionality reduction (phate) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:11:30] Checking 1 packages in environment: scop_env
#> ℹ [2025-09-20 14:11:31] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 14:11:33] Found 205 packages installed
#> ✔ [2025-09-20 14:11:33] phate version: 1.0.11
#> ℹ [2025-09-20 14:11:34] Perform pacmap nonlinear dimension reduction
#> ℹ [2025-09-20 14:11:34] Non-linear dimensionality reduction (pacmap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:11:36] Checking 1 packages in environment: scop_env
#> ℹ [2025-09-20 14:11:36] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 14:11:38] Found 205 packages installed
#> ✔ [2025-09-20 14:11:38] pacmap version: 0.8.0
#> ℹ [2025-09-20 14:12:01] Non-linear dimensionality reduction (pacmap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:12:02] Checking 1 packages in environment: scop_env
#> ℹ [2025-09-20 14:12:03] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 14:12:05] Found 205 packages installed
#> ✔ [2025-09-20 14:12:05] pacmap version: 0.8.0
#> ℹ [2025-09-20 14:12:05] Perform trimap nonlinear dimension reduction
#> ℹ [2025-09-20 14:12:05] Non-linear dimensionality reduction (trimap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:12:07] Checking 1 packages in environment: scop_env
#> ℹ [2025-09-20 14:12:07] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 14:12:09] Found 205 packages installed
#> ✔ [2025-09-20 14:12:10] trimap version: 1.1.4
#> ℹ [2025-09-20 14:12:25] Non-linear dimensionality reduction (trimap) using (Standardpca) dims (1-50) as input
#> ℹ [2025-09-20 14:12:26] Checking 1 packages in environment: scop_env
#> ℹ [2025-09-20 14:12:27] Retrieving package list for environment: scop_env
#> ℹ [2025-09-20 14:12:29] Found 205 packages installed
#> ✔ [2025-09-20 14:12:29] trimap version: 1.1.4
#> ℹ [2025-09-20 14:12:30] Perform largevis nonlinear dimension reduction
#> ℹ [2025-09-20 14:12:30] Non-linear dimensionality reduction (largevis) using (Standardpca) dims (1-50) as input
#> 14:12:30 Read 1000 rows and found 50 numeric columns
#> 14:12:30 Normalizing by max-abs
#> 14:12:30 Using FNN for neighbor search, n_neighbors = 150
#> 14:12:31 Commencing calibration for perplexity = 50 using 2 threads
#> 14:12:34 Initializing from random Gaussian with sd = 1e-4
#> 14:12:34 Commencing optimization for 202861 epochs, with 197600 positive edges
#> 14:12:34 Using rng type: pcg
#> 14:14:49 Optimization finished
#> ℹ [2025-09-20 14:14:49] Non-linear dimensionality reduction (largevis) using (Standardpca) dims (1-50) as input
#> 14:14:49 Read 1000 rows and found 50 numeric columns
#> 14:14:49 Normalizing by max-abs
#> 14:14:49 Using FNN for neighbor search, n_neighbors = 150
#> 14:14:51 Commencing calibration for perplexity = 50 using 2 threads
#> 14:14:54 Initializing from random Gaussian with sd = 1e-4
#> 14:14:54 Commencing optimization for 202861 epochs, with 197600 positive edges
#> 14:14:54 Using rng type: pcg
#> 14:17:18 Optimization finished
#> ℹ [2025-09-20 14:17:18] Perform fr nonlinear dimension reduction
#> ℹ [2025-09-20 14:17:18] Non-linear dimensionality reduction (fr) using (Standardpca_SNN) as input
#> ℹ [2025-09-20 14:17:18] Running force-directed layout
#> ℹ [2025-09-20 14:17:20] Force-directed layout computed
#> ℹ [2025-09-20 14:17:20] Non-linear dimensionality reduction (fr) using (Standardpca_SNN) as input
#> ℹ [2025-09-20 14:17:20] Running force-directed layout
#> ℹ [2025-09-20 14:17:21] Force-directed layout computed
#> ✔ [2025-09-20 14:17:21] Run scop standard workflow done
plist2 <- lapply(
nonlinear_reductions, function(nr) {
CellDimPlot(
pancreas_sub,
group.by = "SubCellType",
reduction = paste0(
"Standardpca", toupper(nr), "2D"
),
xlab = "", ylab = "", title = nr,
legend.position = "none",
theme_use = "theme_blank"
)
}
)
patchwork::wrap_plots(plotlist = plist2)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
