docs/pacmap.md
In satijalab/seurat-wrappers: Community-Provided Methods and Extensions for the Seurat Object
Running PaCMAP on a Seurat Object
Compiled: Nov 20, 2024
This vignette demonstrates how to run PaCMAP, a dimensionality reduction
method that can be used for providing robust and trustworthy
visualization, on a Seurat object. If you use our work, please cite both
papers:
Understanding How Dimension Reduction Tools Work: An Empirical
Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data
Visualization
Yingfan Wang, Haiyang Huang, Cynthia Rudin & Yaron Shaposhnik
Journal of Machine Learning Research, 2021
doi: https://doi.org/10.48550/arXiv.2012.04456
Towards a comprehensive evaluation of dimension reduction methods for
transcriptomic data visualization
Haiyang Huang, Yingfan Wang, Cynthia Rudin and Edward P. Browne
Communications biology, 2022
Prerequisites to install:
In addition to R packages, PaCMAP relies on Python to deliver high
performance. To streamline the installation process and make environment
management easier, we strongly recommend you to use
anaconda(https://www.anaconda.com/download) or
miniconda(https://docs.anaconda.com/miniconda/miniconda-install/) for
managing Python environments. Below, we provide step-by-step
instructions on how to properly install PaCMAP after you have
installed one of these tools.
Create a conda environment with PaCMAP installed:
conda create -n "pacmap" python=3.12 # Install in the environment called "pacmap"
conda activate pacmap
conda install -y conda-forge::pacmap
To run PaCMAP, you need to connect your R console to the corresponding
conda environment. If your Conda/Miniconda installation is located in a
non-default directory, you might set up the conda variable as
/path/to/your/conda. This ensures the correct environment is used
during the installation.
reticulate::use_condaenv(condaenv = "pacmap", conda = "auto")
library(Seurat)
library(SeuratData)
library(SeuratWrappers)
PaCMAP on PBMC3k
To learn more about this dataset, type ?pbmc3k
InstallData("pbmc3k")
pbmc3k.final <- LoadData("pbmc3k",type="pbmc3k.final")
# Initial processing to select variable features
pbmc3k.final <- UpdateSeuratObject(pbmc3k.final)
pbmc3k.final <- FindVariableFeatures(pbmc3k.final)
# run PaCMAP on Seurat object.
pbmc3k.final <- RunPaCMAP(object = pbmc3k.final, features=VariableFeatures(pbmc3k.final))
## Applied PCA, the dimensionality becomes 100
## PaCMAP(n_neighbors=10, n_MN=5, n_FP=20, distance=euclidean, lr=1.0, n_iters=(100, 100, 450), apply_pca=True, opt_method='adam', verbose=True, intermediate=False, seed=11)
## Finding pairs
## Found nearest neighbor
## Calculated sigma
## Found scaled dist
## Pairs sampled successfully.
## ((26380, 2), (13190, 2), (52760, 2))
## Initial Loss: 32494.857421875
## Iteration: 10, Loss: 25802.580078
## Iteration: 20, Loss: 21603.363281
## Iteration: 30, Loss: 19970.650391
## Iteration: 40, Loss: 18992.988281
## Iteration: 50, Loss: 18181.544922
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## Iteration: 650, Loss: 6952.405762
## Elapsed time: 1.11s
# visualize markers
features.plot <- c('CD3D', 'MS4A1', 'CD8A', 'GZMK', 'GZMB', 'FCGR3A')
DimPlot(object=pbmc3k.final,reduction="pacmap")
pbmc3k.final <- NormalizeData(pbmc3k.final, verbose = FALSE)
FeaturePlot(pbmc3k.final, features.plot, ncol = 2, reduction="pacmap")
You can also specify
dims of your original dataset for running PaCMAP
# run PaCMAP on Seurat object.
pbmc3k.final <- RunPaCMAP(object = pbmc3k.final, dims=2:5)
## X is normalized
## PaCMAP(n_neighbors=10, n_MN=5, n_FP=20, distance=euclidean, lr=1.0, n_iters=(100, 100, 450), apply_pca=True, opt_method='adam', verbose=True, intermediate=False, seed=11)
## Finding pairs
## Found nearest neighbor
## Calculated sigma
## Found scaled dist
## Pairs sampled successfully.
## ((26380, 2), (13190, 2), (52760, 2))
## Initial Loss: 32494.857421875
## Iteration: 10, Loss: 25271.613281
## Iteration: 20, Loss: 21621.359375
## Iteration: 30, Loss: 19508.974609
## Iteration: 40, Loss: 18132.957031
## Iteration: 50, Loss: 17056.433594
## Iteration: 60, Loss: 16103.467773
## Iteration: 70, Loss: 15062.871094
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## Elapsed time: 1.08s
# visualize markers
features.plot <- c('CD3D', 'MS4A1', 'CD8A', 'GZMK', 'GZMB', 'FCGR3A')
DimPlot(object=pbmc3k.final,reduction="pacmap")
satijalab/seurat-wrappers documentation built on Nov. 23, 2024, 5:09 p.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Running PaCMAP on a Seurat Object
Compiled: Nov 20, 2024
This vignette demonstrates how to run PaCMAP, a dimensionality reduction method that can be used for providing robust and trustworthy visualization, on a Seurat object. If you use our work, please cite both papers:
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization
Yingfan Wang, Haiyang Huang, Cynthia Rudin & Yaron Shaposhnik
Journal of Machine Learning Research, 2021
doi: https://doi.org/10.48550/arXiv.2012.04456
Towards a comprehensive evaluation of dimension reduction methods for transcriptomic data visualization
Haiyang Huang, Yingfan Wang, Cynthia Rudin and Edward P. Browne
Communications biology, 2022
Prerequisites to install:
In addition to R packages, PaCMAP relies on Python to deliver high performance. To streamline the installation process and make environment management easier, we strongly recommend you to use anaconda(https://www.anaconda.com/download) or miniconda(https://docs.anaconda.com/miniconda/miniconda-install/) for managing Python environments. Below, we provide step-by-step instructions on how to properly install PaCMAP after you have installed one of these tools.
Create a conda environment with PaCMAP installed:
conda create -n "pacmap" python=3.12 # Install in the environment called "pacmap"
conda activate pacmap
conda install -y conda-forge::pacmap
To run PaCMAP, you need to connect your R console to the corresponding
conda environment. If your Conda/Miniconda installation is located in a
non-default directory, you might set up the conda variable as
/path/to/your/conda. This ensures the correct environment is used
during the installation.
reticulate::use_condaenv(condaenv = "pacmap", conda = "auto")
library(Seurat)
library(SeuratData)
library(SeuratWrappers)
PaCMAP on PBMC3k
To learn more about this dataset, type ?pbmc3k
InstallData("pbmc3k")
pbmc3k.final <- LoadData("pbmc3k",type="pbmc3k.final")
# Initial processing to select variable features
pbmc3k.final <- UpdateSeuratObject(pbmc3k.final)
pbmc3k.final <- FindVariableFeatures(pbmc3k.final)
# run PaCMAP on Seurat object.
pbmc3k.final <- RunPaCMAP(object = pbmc3k.final, features=VariableFeatures(pbmc3k.final))
## Applied PCA, the dimensionality becomes 100
## PaCMAP(n_neighbors=10, n_MN=5, n_FP=20, distance=euclidean, lr=1.0, n_iters=(100, 100, 450), apply_pca=True, opt_method='adam', verbose=True, intermediate=False, seed=11)
## Finding pairs
## Found nearest neighbor
## Calculated sigma
## Found scaled dist
## Pairs sampled successfully.
## ((26380, 2), (13190, 2), (52760, 2))
## Initial Loss: 32494.857421875
## Iteration: 10, Loss: 25802.580078
## Iteration: 20, Loss: 21603.363281
## Iteration: 30, Loss: 19970.650391
## Iteration: 40, Loss: 18992.988281
## Iteration: 50, Loss: 18181.544922
## Iteration: 60, Loss: 17354.800781
## Iteration: 70, Loss: 16440.773438
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## Iteration: 650, Loss: 6952.405762
## Elapsed time: 1.11s
# visualize markers
features.plot <- c('CD3D', 'MS4A1', 'CD8A', 'GZMK', 'GZMB', 'FCGR3A')
DimPlot(object=pbmc3k.final,reduction="pacmap")
pbmc3k.final <- NormalizeData(pbmc3k.final, verbose = FALSE)
FeaturePlot(pbmc3k.final, features.plot, ncol = 2, reduction="pacmap")
You can also specify
dims of your original dataset for running PaCMAP
# run PaCMAP on Seurat object.
pbmc3k.final <- RunPaCMAP(object = pbmc3k.final, dims=2:5)
## X is normalized
## PaCMAP(n_neighbors=10, n_MN=5, n_FP=20, distance=euclidean, lr=1.0, n_iters=(100, 100, 450), apply_pca=True, opt_method='adam', verbose=True, intermediate=False, seed=11)
## Finding pairs
## Found nearest neighbor
## Calculated sigma
## Found scaled dist
## Pairs sampled successfully.
## ((26380, 2), (13190, 2), (52760, 2))
## Initial Loss: 32494.857421875
## Iteration: 10, Loss: 25271.613281
## Iteration: 20, Loss: 21621.359375
## Iteration: 30, Loss: 19508.974609
## Iteration: 40, Loss: 18132.957031
## Iteration: 50, Loss: 17056.433594
## Iteration: 60, Loss: 16103.467773
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## Elapsed time: 1.08s
# visualize markers
features.plot <- c('CD3D', 'MS4A1', 'CD8A', 'GZMK', 'GZMB', 'FCGR3A')
DimPlot(object=pbmc3k.final,reduction="pacmap")
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