seutils

Utility functions for Seurat

Wrapper/helper functions written to smooth out Seurat pipelines... See this wonderful website from the Satija Lab to learn more about Seurat!

seutils.R (seurat utils)

Seurat utility functions, to make your life easier.

Generic Seurat utils

Features()

Quickly grab feature names from any Assay within a Seurat object - analogous to Seurat::Cells()

grepGene()

Quick function to look for genes in a Seurat object that match a certain pattern. Can also set a filter.pattern to exclude genes which contain the string. Built on grep and Seurat

ens2gene()

Convert a list of ensembl IDs to gene symbols. Compatible with outputs from biomaRt.

npcs()

Calculate how many principal components account for X% of the variance.

collapseMultimappers()

Quickly collapse multimapper genes in a Seurat assay (non-unique features, marked by a "." in the feature name). Note, the literal regex used is \\.

seuPreProcess()

Generic single-cell pipeline, based on this vignette from the Satija Lab

AddCellTypeIdents()

Add cell type labels given a clustering output

Spatial tools

addSpatialLocation()

Add spatial location for each bead/spot within a Seurat object. Written to work with the whitelists from 10x Genomics (for Visium) and Curio Biosciences (for Seeker/SlideSeq)

removeSpatialSinglets()

Strategy to remove singlets based on spatial position/nearest neighbors - filters out beads/spots with fewer than K neighbors with D distance units

rotateClockwise90N()

Rotate the spatial embedding of a sample clockwise, 90 degrees, N times (i.e. N=2 to rotate 180 degrees clockwise). Useful if you have Visium/SlideSeq samples that were sectioned in different orientations.

spatialRel2Abs()

Convert relative (row/col) positions to absolute (X/Y) positions

subsetLoupeJson()

Subset Visium Seurat object based on spots manually selected via loupe browser

Single-cell

TODO

seuplots.R (seurat plots)

Additional plots not included in Seurat

seuListPlot()

Wrapper function for neatly plotting multiple Visium objects and multiple features into a grid. Input is a list() of Seurat objects. Includes automatic color scaling across objects and lots of options for customization. Built on patchwork. Combine with coord_fixed() to maintain true spatial coordinates. See vignettes for details.

#TODO- update args #TODO- add compatibility with standard spatial handling by Seurat authors

seuListPlot <- function(
  seu.list, # list of Seurat objects
  features=NULL, # genes/metadata entries/etc to plot
  alt.titles=NULL, # alternative titles for genes/features being passed
  sample.titles=NULL, # Sample IDs (y-axis labels)
  assay='Spatial',
  reduction="space",
  slot="data",
  legend.position="bottom",
  pt.size=1,
  font.size=8,
  axis.title.angle.y=90, #y-axis title angle (parameter for ggplot::element_text)
  combine=TRUE,
  abs.heights=TRUE, # Use absolute heights to size each subplot
  nrow=NULL,
  ncol=NULL,
  colormap="viridis", # either a viridis option, a vector of colors, or a list of options corresponding to `features`
  colormap.direction=1,
  colormap.same.scale=F, #whether (T) or not (F) to set all features to the same colormap scale
  na.value=gray(0.85), # color for na.value (spot where gene is not detected)
  verbose=FALSE
)

Usage notes on seuListPlot():

• Looks for the spatial position in the reduction passed (Check SEU@reductions), which means this also works with PCA/UMAP/PHATE/etc embeddings!
• Only works with continuous variables for now (i.e. gene expression, nCounts, deconvolution estimates) - does NOT work with cluster IDs
• Keeps the same color scale for all samples in each feature; use colormap.same.scale=T to force the same color scale across all samples AND features (i.e, when working with scaled gene expression values)
• The na.value is the color used for cells/spots/beads where the feature value is either missing or NA. I recommend using the default light gray option (gray(0.85))

seuCoMap()

Plot co-expression of any two genes/features, where "co-expression" can be abstracted to an generic function (default is just prod, to see expression of Gene_A times expression of Gene_B). Built on top of seuListPlot(), so it also takes a list of Seurat objects.

seuCoMap <- function(
  seu.list,
  features=NULL, # pair of genes
  alt.titles=NULL, # alternative titles for genes/features being passed
  sample.titles=NULL, # Sample IDs (y-axis labels)
  assay='Spatial', # Either a single assay, or a pair of assays (useful for spliced vs. unspliced plotting)
  reduction="space",
  slot="data", # Either a single slot, or a pair of slots
  legend.position="bottom",
  pt.size=1,
  font.size=8,
  axis.title.angle.y=90, #y-axis title angle (parameter for ggplot::element_text)
  combine=TRUE,
  abs.heights=TRUE, # Use absolute heights to size each subplot
  nrow=NULL,
  ncol=NULL,
  colormap=NULL, # either a viridis option, a vector of colors, or a list of options corresponding to `features`
  colormap.direction=1,
  colormap.same.scale=F, #whether (T) or not (F) to set all features to the same colormap scale
  na.value=gray(0.69), # color for na.value (spot where gene is not detected)
  comap.fxn = prod,
  coex.name = NULL, # plot title for computed co-expression values
  verbose=FALSE
)

#TODO

• Volcano plots (ggvolcano_v2)
• Silhouette plot