SmoothKNN: Smooth signature scores by kNN

In carmonalab/UCell: Rank-based signature enrichment analysis for single-cell data

Smooth signature scores by kNN

Description

This function performs smoothing of single-cell scores by weighted average of the k-nearest neighbors. It can be useful to 'impute' scores by neighboring cells and partially correct data sparsity. While this function has been designed to smooth UCell scores, it can be applied to any numerical metadata contained in SingleCellExperiment or Seurat objects

Usage

## S3 method for class 'Seurat'
SmoothKNN(
  obj = NULL,
  signature.names = NULL,
  reduction = "pca",
  k = 10,
  decay = 0.1,
  up.only = FALSE,
  BNPARAM = AnnoyParam(),
  BPPARAM = SerialParam(),
  suffix = "_kNN",
  assay = NULL,
  slot = "data",
  sce.expname = NULL,
  sce.assay = NULL
)

## S3 method for class 'SingleCellExperiment'
SmoothKNN(
  obj = NULL,
  signature.names = NULL,
  reduction = "PCA",
  k = 10,
  decay = 0.1,
  up.only = FALSE,
  BNPARAM = AnnoyParam(),
  BPPARAM = SerialParam(),
  suffix = "_kNN",
  assay = NULL,
  slot = "data",
  sce.expname = c("UCell", "main"),
  sce.assay = NULL
)

SmoothKNN(
  obj = NULL,
  signature.names = NULL,
  reduction = "pca",
  k = 10,
  decay = 0.1,
  up.only = FALSE,
  BNPARAM = AnnoyParam(),
  BPPARAM = SerialParam(),
  suffix = "_kNN",
  assay = NULL,
  slot = "data",
  sce.expname = c("UCell", "main"),
  sce.assay = NULL
)

Arguments

obj	Input object - either a SingleCellExperiment object or a Seurat object.
signature.names	The names of the signatures (or any numeric metadata column) for which to calculate kNN-smoothed scores
reduction	Which dimensionality reduction to use for kNN smoothing. It must be already present in the input object.
k	Number of neighbors for kNN smoothing
decay	Exponential decay for nearest neighbor weight: (1-decay)^n
up.only	If set to TRUE, smoothed scores will only be allowed to increase by smoothing
BNPARAM	A BiocNeighborParam object specifying the algorithm to use for kNN calculation.
BPPARAM	A BiocParallel::bpparam() object for parallel computing, e.g. MulticoreParam or SnowParam
suffix	Suffix to append to metadata columns for the new knn-smoothed scores
assay	For Seurat objects only - do smoothing on expression data from this assay. When NULL, only looks in metadata
slot	For Seurat objects only - do smoothing on expression data from this slot
sce.expname	For sce objects only - which experiment stores the signatures to be smoothed. Set to 'main' for smoothing gene expression stored in the main sce experiment.
sce.assay	For sce objects only - pull data from this assay

Value

An augmented obj with the smoothed signatures. If obj is a Seurat object, smoothed signatures are added to metadata; if obj is a SingleCellExperiment object, smoothed signatures are returned in a new altExp. See the examples below.

Examples

#### Using Seurat ####
library(Seurat)
gene.sets <- list(Tcell = c("CD2","CD3E","CD3D"),
                Myeloid = c("SPI1","FCER1G","CSF1R"))
data(sample.matrix)
obj <- Seurat::CreateSeuratObject(sample.matrix)                
# Calculate UCell scores
obj <- AddModuleScore_UCell(obj,features = gene.sets, name=NULL)
# Run PCA
obj <- FindVariableFeatures(obj) |> NormalizeData() |> ScaleData() |> RunPCA(npcs=5)
# Smooth signatures
obj <- SmoothKNN(obj, k=3, signature.names=names(gene.sets))
head(obj[[]])

#### Using SingleCellExperiment ####
library(SingleCellExperiment)
library(scater)
data(sample.matrix)
sce <- SingleCellExperiment(list(counts=sample.matrix))
gene.sets <- list( Tcell = c("CD2","CD3E","CD3D"),
                  Myeloid = c("SPI1","FCER1G","CSF1R"))
# Calculate UCell scores
sce <- ScoreSignatures_UCell(sce, features=gene.sets, name=NULL)
# Run PCA
sce <- logNormCounts(sce)
sce <- runPCA(sce, scale=TRUE, ncomponents=5)
# Smooth signatures
sce <- SmoothKNN(sce, k=3, signature.names=names(gene.sets))
# See results
altExp(sce, 'UCell')
assays(altExp(sce, 'UCell'))
# Plot on UMAP
sce <- runUMAP(sce, dimred="PCA")
plotUMAP(sce, colour_by = "Tcell_kNN", by_exprs_values = "UCell_kNN")

carmonalab/UCell documentation built on April 26, 2024, 11:51 p.m.