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Plot feature loadings for dimensional reduction data

PlotFeatureLoadings.Rd

This function can be used to visualize the relative contribution of features (e.g. genes) to dimensionality reduction vectors. The function provides three modes to draw a bar plot, a dot plot or a heatmap.

Usage

PlotFeatureLoadings(
  object,
  dims = 1,
  reduction = "pca",
  nfeatures = 30,
  mode = c("dotplot", "barplot", "heatmap"),
  type = c("positive", "negative", "centered"),
  fill = "grey",
  color = "black",
  bar_width = 0.9,
  pt_size = 3,
  pt_stroke = 0.5,
  linetype = "dashed",
  color_by_loadings = FALSE,
  gradient_colors = RColorBrewer::brewer.pal(n = 9, name = "Reds"),
  ncols = NULL
)

Arguments

object

An object of class Seurat

dims

An integer vector of dimensions to plot feature loadings for

reduction

A character specifying the dimensionality reduction to use

nfeatures

Number of features to show

mode

Plot mode. One of "barplot", "dotplot" or "heatmap"

type

Mode used to select features:

  • "positive" : select features with highest loadings

  • "negative" : select features with lowest loadings

  • "centered" : split selection of features to include top and bottom loadings

fill

Fill color for barplot and dotplot

color

Border color for barplot and dotplot

bar_width

With of barplot provided as a proportion

pt_size

Size of points in dotplot

pt_stroke

Width of border for points in dotplot

linetype

Select a line type to be used for dotplot, e.g. "solid", "longdash" or "blank"

color_by_loadings

Should the fill color of barplot or points reflect the feature loadings?

gradient_colors

Colors to use for gradient if color_by_loadings=TRUE

ncols

Number of columns used for final patchwork

Value

An object of class patchwork

Select type

For centered dimensionality reduction vectors, such as principal components, it is best to select features with the highest and lowest loadings. For other types of dimensionality reduction results, it is likely better to only select the features with the highest loadings, in which case type="positive" is the appropriate choice.

Select mode

barplots or dotplots can be used to get detailed information about the loadings for individual factors whereas heatmap is useful to summarize the loadings for multiple factors. The heatmap option will override the type options and only select the top features. With the heatmap mode, the values data will also be scaled within each dimensionality reduction vectors to range between 0 and 1.

Examples

{
library(semla)

se_mbrain <- readRDS(system.file("extdata/mousebrain", "se_mbrain", package = "semla"))

# Run PCA
se_mbrain <- se_mbrain |>
                ScaleData() |>
                RunPCA()

# Plot feature loadings for PC_1 as a dotplot
PlotFeatureLoadings(se_mbrain, reduction = "pca", dims = 1,
                    mode = "dotplot", type = "centered")

# Plot feature loadings for PC_1 and PC_2 as barplots
PlotFeatureLoadings(se_mbrain, reduction = "pca", dims = 1:2,
                    mode = "barplot", type = "centered")

# Plot feature loadings for PC_1 and color bars by loading
PlotFeatureLoadings(se_mbrain, reduction = "pca", dims = 1:2,
                    mode = "barplot", type = "centered",
                    color_by_loadings = TRUE,
                    gradient_colors = RColorBrewer::brewer.pal(n = 11, name = "RdBu") |> rev())
}
#> Centering and scaling data matrix
#> PC_ 1 
#> Positive:  Nrgn, Olfm1, Cck, Nptxr, Rtn1, Snca, Nov, Tmsb4x, Lamp5, Egr1 
#> 	   Crym, Cpne6, Coro1a, Arc, Hpca, Sst, Nr4a1, Npy, Chgb, Neurod6 
#> 	   Snap25, Myh7, Uchl1, Eef1a2, Cort, Grp, Stmn2, Rprm, Spink8, Mfge8 
#> Negative:  Mbp, Plp1, Apod, Mobp, Ptgds, Mog, Mal, Mag, Cnp, Aldh1a1 
#> 	   Opalin, Tcf7l2, Ddc, Lhx1os, Slc6a3, Ret, Th, Slc18a2, Sncg, Drd2 
#> 	   Chrna6, Slc10a4, Spp1, En1, Dlk1, Calb2, Hbb-bs, Pvalb, Col1a2, Tnnt1 
#> PC_ 2 
#> Positive:  Myoc, Gfap, Col1a2, Fmod, Slc13a4, Hba-a1, Hbb-bt, Slc6a20a, Hba-a2, Hbb-bs 
#> 	   Acta2, Tagln, Mgp, Ogn, Vtn, H2-Aa, Lyz2, Cd74, Myl9, Dcn 
#> 	   Myh11, Ptgds, Cytl1, H2-Eb1, Hmgcs2, Clu, Mfge8, Emp1, Npy, Cnn1 
#> Negative:  Th, Uchl1, Slc18a2, En1, Slc10a4, Slc6a3, Chrna6, Stmn2, Ret, Snap25 
#> 	   Drd2, Dlk1, Ddc, Scg2, Sncg, Eef1a2, Rtn1, Chga, Pcp4, Calb2 
#> 	   Mobp, Mbp, Chgb, Fabp5, Plp1, Pvalb, Mog, Mal, Mag, Snca 
#> PC_ 3 
#> Positive:  Trbc2, Arc, Egr1, Myl4, Nr4a1, Mbp, Mobp, Pvalb, Plp1, Opalin 
#> 	   Mog, Mal, Cnp, Mag, Snap25, Lamp5, Ighm, Tcf7l2, Cplx3, Tgm3 
#> 	   Ighg2c, Pcp4, Hpca, Ly6d, Eef1a2, Tnnt1, Chga, Neurod6, Prph, Ctgf 
#> Negative:  Nnat, Slc18a2, Dlk1, Slc6a3, Slc10a4, En1, Th, Chrna6, Dcn, Sncg 
#> 	   Cpne7, Drd2, Ddc, Ret, Hpcal1, Ecel1, Cpne6, Col1a2, Trh, Calb2 
#> 	   Cd24a, Fmod, Mgp, Snca, Lypd1, Slc13a4, Fibcd1, Crym, Spink8, Slc6a20a 
#> PC_ 4 
#> Positive:  Nr4a1, Arc, Lamp5, Egr1, Myl4, Trbc2, Chrna6, Tagln, En1, Snap25 
#> 	   Th, Slc18a2, Acta2, Col1a2, Myh11, Fmod, Hba-a2, Slc10a4, Slc6a3, Slc13a4 
#> 	   Ret, Tgm3, Hbb-bt, Slc6a20a, Ighm, Hbb-bs, Hba-a1, Vtn, Mgp, Drd2 
#> Negative:  Spink8, Fibcd1, Tmsb4x, Nnat, Lefty1, Crym, Cpne7, Nos1, Dcn, Cpne6 
#> 	   Grp, Homer3, Htr3a, Tac1, Fabp5, C1ql2, Trh, Opalin, Mog, Plp1 
#> 	   Mag, Calb2, Ecel1, Gfap, Cnp, Tcf7l2, Lypd1, Vgll3, Hpcal1, Mal 
#> PC_ 5 
#> Positive:  Pcp4, Tcf7l2, Snap25, Uchl1, Eef1a2, Chga, Stmn2, Lhx1os, Calb2, Scg2 
#> 	   Fabp5, Bok, Prkcd, Pvalb, Cartpt, Chgb, Tnnt1, Gpx3, Slc20a1, Rtn1 
#> 	   C1ql2, Spp1, Hpcal1, Ptgds, Pitx2, Lypd1, Aldh1a1, Ecel1, Vtn, Nme7 
#> Negative:  En1, Chrna6, Th, Slc18a2, Slc10a4, Ddc, Lefty1, Arc, Slc6a3, Neurod6 
#> 	   Grp, Nov, Lamp5, Fibcd1, Nr4a1, Drd2, Spink8, Vip, Myl4, Ret 
#> 	   Egr1, Dlk1, Nrgn, Gfap, Trbc2, Tgm3, Myh7, Tac2, Sncg, Npy