SCOPfunctions

An R package of functions for single cell -omics analysis. Fits into Seurat workflow.

Install

Install using devtools:

devtools::install_github("CBMR-Single-Cell-Omics-Platform/SCOPfunctions")

install manually

git clone https://www.github.com/CBMR-Single-Cell-Omics-Platform/SCOPfunctions.git

then from R:

install.packages("./SCOPfunctions", type="source", repos=NULL)

Usage

library("SCOPfunctions")
library("Seurat")

Download the example data used in the Seurat hashing vignette at https://satijalab.org/seurat/archive/v3.1/hashing_vignette.html

Follow the initial steps of the hashing vignette up till and including the HTO normalization

Preprocess

Generate an area plot showing how proportions of singlets, doublets and negatives vary with the positive quantile

p_quantile = SCOPfunctions::prep_HTO_q_area_plot(
  seurat_obj=pbmc.hashtag,
  vec_range_quantile=seq(0.8,0.99,0.01),
  n_cores_max=Inf
)

infer intra-hash doublets

# First demultiplex hashtags 
q = 0.98
pbmc.hashtag = Seurat::HTOdemux(pbmc.hashtag,assay = "HTO", positive.quantile = q)

# use inter-hash doublets to infer intra-hash doublets
pbmc.hashtag = SCOPfunctions::prep_intrahash_doub(
  seurat_obj=pbmc.hashtag,
  assay = "RNA",
  npcs=20,
  randomSeed = 12345
)

Do QC on RNA assay

pbmc.hashtag  = prep_qc_rna(
  seurat_obj=pbmc.hashtag,
  assay = "RNA"
  )

Differential expression


# first find clusters (after normalizing the RNA, finding variable features and scaling the data - not shown)
pbmc.hashtag <- FindNeighbors(pbmc.hashtag, reduction = "pca", dims = 1:20)
pbmc.hashtag <- FindClusters(pbmc.hashtag, resolution = 10, verbose = FALSE)

# find DE genes for cluster 0
df_DE = SCOPfunctions::DE_MAST_RE_seurat(
  object=pbmc.hashtag,
  random_effect.vars="hash.ID",
  test.use = "MAST",
  ident.1 = "0",
  group.by = "seurat_clusters"
)

find the activity values for a geneset


# as an example, just use the top DE genes for cluster 0
vec_geneWeights <- seq(from = 1, to = 0.1, by = -0.1)
vec_geneWeights <- vec_geneWeights/sum(vec_geneWeights)

names(vec_geneWeights) =  head(rownames(df_DE), 10)

pbmc.hashtag$my_geneset_embeddings <- geneset_embed(
  mat_datExpr = as.matrix(GetAssayData(pbmc.hashtag, slot="scale.data", assay="SCT")),
  vec_geneWeights=vec_geneWeights,
  min_feats_present = 5)

find the activity values for a list of genesets


pbmc.hashtag <- geneset_embed_list_seurat(
  seurat_obj = pbmc.hashtag,
  list_vec_geneWeights=list_vec_geneWeights,
  slot="scale.data",
  assay="SCT",
  min_feats_present = 5,
  n_cores_max = Inf)

Plot results

plot the distribution of cell clusters in different samples

plot_barIdentGroup(seurat_obj=pbmc.hashtag,
                    var_ident="sample_ID",
                    var_group="cluster",
                    vec_group_colors=NULL,
                    f_color=colorRampPalette(brewer.pal(n=11, name="RdYlBu")),
                    do_plot = F)

plot a cluster * feature grid of gene expression violin plots

# Here we just use the top variable genes, but normally we would use cluster marker genes
plot_vlnGrid(seurat_obj,
              slot="data",
              var_group="cluster",
              vec_features=head(VariableFeatures(seurat_obj),n=15),
              vec_group_colors=NULL,
              f_color = colorRampPalette(brewer.pal(n=11, name="RdYlBu")))

make a network plot of a set of co-expressed features

SCOPfunctions::plot_network(
  mat_datExpr=as.matrix(GetAssayData(seurat_obj, slot="data")),
  vec_geneImportance=vec_geneImportance,
  vec_genes_highlight=c(),
  n_max_genes=50,
  igraph_algorithm = "drl",
  fontface_labels="bold.italic",
  color_edge = "grey70",
  edge_thickness = 1)

Contribute

Issues and pull requests are welcome! All contributions should be in line with the usethis code of conduct. This package uses the methods and R tools set out in R packages. All Pull Requests should follow the tidyverse style guide.