Skip to contents

PICtR workflow wrapper

Source: R/wrapper_function.R
sketch_wrapper.Rd

Characterizes cells into ratio_high and ratio_low cells for physically interacting cell analysis, samples a representative subset of cells using SketchData and runs the standard Seurat analysis workflow.

Usage

sketch_wrapper(
  channel = channel,
  meta_data = NULL,
  assay = "FACS",
  FSC.A = "FSC.A",
  FSC.H = "FSC.H",
  n_sketch_cells = 50000,
  n_components = "all",
  clst_algorithm = 1,
  resolution = c(0.5, 1, 2, 3, 4),
  min_clst_size = 100,
  meta.k = "auto",
  obj_name = "obj_sketched_non_projected",
  ratio = TRUE,
  thresholding_method = "otsu",
  hist_breaks = 2000,
  group_by = NULL,
  verbose = TRUE,
  BPcell_dir = NULL,
  overwrite = F,
  working_dir = getwd(),
  seed = 42
)

Arguments

channel

A data frame with the dimensionality cells x flow cytometry parameters.

meta_data

A data frame with meta_data for every cell (Default=NULL).

assay

A character string with the name of the assay to be created (Default="FACS").

FSC.A

The name (string) of the column containing the FSC.A scatter parameter (Default="FACS.A").

FSC.H

The name (string) of the column containing the FSC.H scatter parameter (Default="FACS.H").

n_sketch_cells

The number of cells to be subsampled by SketchData (Default=50000).

n_components

The number of components computed and used during analysis. "all" or given as an integer (Default="all").

clst_algorithm

Algorithm used for clustering. For Seurat's FindClusters: 1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm; 4 = Leiden algorithm). Leiden requires the leidenalg python. Alternatively, "hdbscan" for Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) on the UMAP embedding, see hdbscan), "flowMeans" for non-parametric clustering and segmented-regression-based change point detection, see flowMeans, or "flowSOM" for self-organizing maps, see run.flowsom, (Default: Louvain, 1)

resolution

A numerical vector with the desired resolutions for clustering (Default: c(0.5,1,2,3,4)). Only used for Louvain, SLM, and Leiden clustering.

min_clst_size

minimum cluster size for HDBSCAN. See details at hdbscan (Default: 100).

meta.k

meta k for FlowSOM clustering, see run.flowsom (Default: "auto").

obj_name

The name used for storing the Seurat object (character).

ratio

Boolean variable. If TRUE the FSC ratio (FSC.A/FSC.H) will be calculated.

thresholding_method

Method for thresholding. One of "otsu", "triangle", "kmeans", or any method from the autothresholdr package. For details see ?calculateThreshold() (Default = "otsu").

hist_breaks

Number of histogram breaks for methods that rely on histograms. Should be >100 (Default: 2000).

group_by

Optional grouping parameter to calculate the FSC ratio (FSC.A/FSC.H) thresholding method for.

verbose

Verbosity (Boolean).

BPcell_dir

Optional directory with the counts matrix for open_matrix_dir.

overwrite

Overwrite existing BPCells directory? (Default: FALSE)

working_dir

Directory path to be used as working directory (character string).

seed

Seed for reproducibility (Default: 42).

Value

Seurat object.

References

Hao et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nature Biotechnology (2023). doi: https://doi.org/10.1038/s41587-023-01767-y.

Van Gassen S et al. (2015) FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytom Part J Int Soc Anal Cytol 87: 636-645. https://onlinelibrary.wiley.com/doi/full/10.1002/cyto.a.22625.

Aghaeepour, N., Nikolic, R., Hoos, H.H. and Brinkman, R.R. (2011), Rapid cell population identification in flow cytometry data. Cytometry, 79A: 6-13. https://doi.org/10.1002/cyto.a.21007.

Campello, R.J.G.B., Moulavi, D., Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_14

Examples

obj <- sketch_wrapper(channel = demo_lcmv,
                      meta_data = demo_lcmv,
                      n_sketch_cells = 5000,
                      clst_algorithm = 1,
                      ratio = TRUE,
                      thresholding_method = "otsu", 
                      working_dir = tempdir())
#> Calculation of Ratio
#> Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
#> Warning: Matrix compression performs poorly with non-integers.
#> • Consider calling convert_matrix_type if a compressed integer matrix is intended.
#> This message is displayed once every 8 hours.
#> Your newly generated object will be saved under: /var/folders/zq/k957_k5s79v97_ptgc729fm1yy94mx/T//RtmpD9jdFT/obj_sketched_non_projected.rds
#> Sketching started...
#> Finding variable features for layer counts
#> Warning: NaNs produced
#> Calcuating Leverage Score
#> Attempting to cast layer counts to dgCMatrix
#> Attempting to cast layer data to dgCMatrix
#> Sketching is done
#> Finding variable features for layer counts
#> Warning: Chernobyl! trL>n 15
#> Warning: Chernobyl! trL>n 15
#> Warning: NaNs produced
#> Centering and scaling data matrix
#> Warning: Requested number is larger than the number of available items (15). Setting to 15.
#> Warning: Requested number is larger than the number of available items (15). Setting to 15.
#> Warning: Requested number is larger than the number of available items (15). Setting to 15.
#> Warning: Requested number is larger than the number of available items (15). Setting to 15.
#> Warning: Requested number is larger than the number of available items (15). Setting to 15.
#> PC_ 1 
#> Positive:  CD4, Ly6G, CD11b, CD90-1, CD8, CD11c, SSC.H, MHCII 
#> Negative:  FSC.A, ratio, SSC.A, FSC.H, CD45-2, CD3, CD19 
#> PC_ 2 
#> Positive:  CD4, CD45-2, CD3, CD90-1, CD8, MHCII, CD19, ratio 
#> Negative:  CD11b, SSC.H, Ly6G, SSC.A, FSC.H, CD11c, FSC.A 
#> PC_ 3 
#> Positive:  CD4, CD90-1, CD3, SSC.H, CD11b, SSC.A, FSC.H, CD8 
#> Negative:  MHCII, CD19, ratio, CD45-2, FSC.A, CD11c, Ly6G 
#> PC_ 4 
#> Positive:  CD19, CD90-1, CD4, Ly6G, ratio, FSC.A, SSC.A, MHCII 
#> Negative:  CD11c, CD8, CD45-2, CD11b, CD3, SSC.H, FSC.H 
#> PC_ 5 
#> Positive:  CD11c, MHCII, CD4, CD90-1, CD45-2, SSC.H, CD11b, SSC.A 
#> Negative:  CD8, Ly6G, CD19, CD3, FSC.H, FSC.A, ratio 
#> Computing nearest neighbor graph
#> Computing SNN
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#> 
#> Number of nodes: 5000
#> Number of edges: 164643
#> 
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.9193
#> Number of communities: 14
#> Elapsed time: 0 seconds
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#> 
#> Number of nodes: 5000
#> Number of edges: 164643
#> 
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.8813
#> Number of communities: 19
#> Elapsed time: 0 seconds
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#> 
#> Number of nodes: 5000
#> Number of edges: 164643
#> 
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.8238
#> Number of communities: 24
#> Elapsed time: 0 seconds
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#> 
#> Number of nodes: 5000
#> Number of edges: 164643
#> 
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.7740
#> Number of communities: 32
#> Elapsed time: 0 seconds
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#> 
#> Number of nodes: 5000
#> Number of edges: 164643
#> 
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.7367
#> Number of communities: 38
#> Elapsed time: 0 seconds
#> Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
#> To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
#> This message will be shown once per session
#> UMAP will return its model
#> 15:36:19 UMAP embedding parameters a = 0.9922 b = 1.112
#> 15:36:19 Read 5000 rows and found 15 numeric columns
#> 15:36:19 Using Annoy for neighbor search, n_neighbors = 30
#> 15:36:19 Building Annoy index with metric = cosine, n_trees = 50
#> 0%   10   20   30   40   50   60   70   80   90   100%
#> [----|----|----|----|----|----|----|----|----|----|
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> |
#> 15:36:20 Writing NN index file to temp file /var/folders/zq/k957_k5s79v97_ptgc729fm1yy94mx/T//RtmpD9jdFT/file6da85cd8e101
#> 15:36:20 Searching Annoy index using 1 thread, search_k = 3000
#> 15:36:21 Annoy recall = 100%
#> 15:36:21 Commencing smooth kNN distance calibration using 1 thread
#>  with target n_neighbors = 30
#> 15:36:21 Initializing from normalized Laplacian + noise (using RSpectra)
#> 15:36:21 Commencing optimization for 500 epochs, with 202814 positive edges
#> 15:36:21 Using rng type: pcg
#> 15:36:26 Optimization finished
#> The object will be updated and saved