Package 'demuxSNP'

Title: scRNAseq demultiplexing using cell hashing and SNPs
Description: This package assists in demultiplexing scRNAseq data using both cell hashing and SNPs data. The SNP profile of each group os learned using high confidence assignments from the cell hashing data. Cells which cannot be assigned with high confidence from the cell hashing data are assigned to their most similar group based on their SNPs. We also provide some helper function to optimise SNP selection, create training data and merge SNP data into the SingleCellExperiment framework.
Authors: Michael Lynch [aut, cre] , Aedin Culhane [aut]
Maintainer: Michael Lynch <[email protected]>
License: GPL-3
Version: 1.5.0
Built: 2024-11-19 04:00:41 UTC
Source: https://github.com/bioc/demuxSNP

Help Index

Add SNPs to SingleCellExperiment object

Description

Add SNPs to SingleCellExperiment object

Usage

add_snps(sce, mat, thresh = 0.8)

Arguments

sce

object of class SingleCellExperiment
mat

object of class matrix, output from VarTrix in 'consensus' mode (default)
thresh

threshold presence of SNP, defaults to 0.8

Value

Updated SingleCellExperiment object with snps in altExp slot

Examples

data(multiplexed_scrnaseq_sce, vartrix_consensus_snps)
multiplexed_scrnaseq_sce <- add_snps(sce = multiplexed_scrnaseq_sce, 
mat = vartrix_consensus_snps, 
thresh = 0.8)

Return a character vector of top n most frequent genes from a SingleCellExperiment object.

Description

Returns a character vector of the top n most frequently expressed genes from the counts of the SingleCellExperiment object. Expression is based on having a count > 0 in a given cell.

Usage

common_genes(sce, n = 100)

Arguments

sce

a SingleCellExperiment object
n

number of genes to be returned. Defaults to n=100.

Value

character vector of n most frequently expressed genes.

Examples

data(multiplexed_scrnaseq_sce)
multiplexed_scrnaseq_sce <- common_genes(multiplexed_scrnaseq_sce)

Sample vcf file

Description

VCF file containing SNPs from a subset of the 1k Genomes common variants HG38 genome build.

Usage

data(commonvariants_1kgenomes_subset)

Format

An object of class CollapsedVCF with 2609 rows and 0 columns.

Value

commonvariants_1kgenomes_subset

An object of class CollapsedVcf

Source

https://cellsnp-lite.readthedocs.io/en/latest/snp_list.html

Run demuxmix to determine high-confidence calls

Description

Run demuxmix to determine high-confidence calls

Usage

high_conf_calls(sce, assay = "HTO", pacpt = 0.95)

Arguments

sce

Object of class SingleCellExperiment with HTO (or similar) altExp assay
assay

Name of altExp for cell hashing counts to be retrieved from
pacpt

acceptance probability for demuxmix model

Value

Updated SingleCellExperiment object with logical vector indicating training data, data to be classified (all cells) and assigned labels for all cells.

Examples

data(multiplexed_scrnaseq_sce)
multiplexed_scrnaseq_sce <- high_conf_calls(multiplexed_scrnaseq_sce)

SingleCellExperiment object containing multiplexed RNA and HTO data from six biological smamples

Description

Example SingleCellExperiment object containing demultiplexed scRNAseq data from six donors, used throughout and built upon in demuxSNP workflow.

Usage

data(multiplexed_scrnaseq_sce)

Format

An object of class SingleCellExperiment with 259 rows and 2000 columns.

Value

multiplexed_scrnaseq_sce

An object of class SingleCellExperiment

Reassign cells using knn

Description

k-nearest neighbour classification of cells. Training data is intended to be labels of cells confidently called using cell hashing based methods and their corresponding SNPs. Prediction data can be remaining cells but can also include the training data. Doublets are simulated by randomly combining 'd' SNP profiles from each grouping combination.

Usage

reassign(
  sce,
  k = 10,
  d = 10,
  train_cells = sce$train,
  predict_cells = sce$predict
)

Arguments

sce

object of class SingleCellExperiment
k

number of neighbours used in knn, defaults to 10
d

number of doublets per group combination to simulate, defaults to 10
train_cells

logical vector specifying which cells to use to train classifier
predict_cells

logical vector specifying which cells to classify

Value

A SingleCellExperiment with updated group assignments called 'knn'

Examples

data(multiplexed_scrnaseq_sce, vartrix_consensus_snps)
multiplexed_scrnaseq_sce <- high_conf_calls(multiplexed_scrnaseq_sce)
multiplexed_scrnaseq_sce <- add_snps(sce = multiplexed_scrnaseq_sce, 
mat = vartrix_consensus_snps, 
thresh = 0.8)
multiplexed_scrnaseq_sce <- reassign(sce = multiplexed_scrnaseq_sce, k = 10)

Reassign cells using balanced knn with jaccard distance

Description

k-nearest neighbour classification of cells. Training data is intended to be labels of cells confidently called using cell hashing based methods and their corresponding SNPs. Prediction data can be remaining cells but can also include the training data. Doublets are simulated by randomly combining 'd' SNP profiles from each grouping combination.

Usage

reassign_balanced(
  sce,
  k = 20,
  d_prop = 0.5,
  train_cells = sce$train,
  predict_cells = sce$predict,
  nmin = 50,
  n = NULL
)

Arguments

sce

object of class SingleCellExperiment
k

number of neighbours used in knn, defaults to 10
d_prop

determines number of doublets simulatted d, as a proportions of n (specified or calculated)
train_cells

logical vector specifying which cells to use to train classifier
predict_cells

logical vector specifying which cells to classify
nmin

min n per class (where available)
n

number of cells per group (otherwise will be calculated from data)

Value

A SingleCellExperiment with updated group assignments called 'knn_balanced'

Examples

data(multiplexed_scrnaseq_sce, vartrix_consensus_snps)
multiplexed_scrnaseq_sce <- high_conf_calls(multiplexed_scrnaseq_sce)
multiplexed_scrnaseq_sce <- add_snps(sce = multiplexed_scrnaseq_sce, 
mat = vartrix_consensus_snps, 
thresh = 0.8)
multiplexed_scrnaseq_sce <- reassign_balanced(sce = multiplexed_scrnaseq_sce, k = 10, d=0.5)

Reassign cells based on SNPs

Description

Reassign cells based on SNPs

Usage

reassign_centroid(
  sce,
  train_cells = sce$train,
  predict_cells = sce$predict,
  labels = sce$labels,
  min_cells = 30,
  key = "Hashtag"
)

Arguments

sce

SingleCellExperiment object
train_cells

logical, cells to be used for training
predict_cells

logical, cells to be used for prediction
labels

provisional cell labels
min_cells

minimum coverage (number of cells with read at SNP location) for SNP to be used for classification.
key

unique key in naming of singlet groups used with grep to remove doublet/negative/uncertain labels

Value

character vector containing reassignments

Examples

data(multiplexed_scrnaseq_sce, vartrix_consensus_snps)
multiplexed_scrnaseq_sce <- high_conf_calls(multiplexed_scrnaseq_sce)
multiplexed_scrnaseq_sce <- add_snps(sce = multiplexed_scrnaseq_sce, 
mat = vartrix_consensus_snps, 
thresh = 0.8)
multiplexed_scrnaseq_sce<-reassign_centroid(multiplexed_scrnaseq_sce)

Reassign cells using knn with jaccard distance

Description

k-nearest neighbour classification of cells. Training data is intended to be labels of cells confidently called using cell hashing based methods and their corresponding SNPs. Prediction data can be remaining cells but can also include the training data. Doublets are simulated by randomly combining 'd' SNP profiles from each grouping combination.

Usage

reassign_jaccard(
  sce,
  k = 10,
  d = 10,
  train_cells = sce$train,
  predict_cells = sce$predict
)

Arguments

sce

object of class SingleCellExperiment
k

number of neighbours used in knn, defaults to 10
d

number of doublets per group combination to simulate, defaults to 10
train_cells

logical vector specifying which cells to use to train classifier
predict_cells

logical vector specifying which cells to classify

Value

A SingleCellExperiment with updated group assignments called 'knn_jaccard'

Examples

data(multiplexed_scrnaseq_sce, vartrix_consensus_snps)
multiplexed_scrnaseq_sce <- high_conf_calls(multiplexed_scrnaseq_sce)
multiplexed_scrnaseq_sce <- add_snps(sce = multiplexed_scrnaseq_sce, 
mat = vartrix_consensus_snps, 
thresh = 0.8)
multiplexed_scrnaseq_sce <- reassign(sce = multiplexed_scrnaseq_sce, k = 10)

Subset common variants vcf file to only SNPs seen in 'top_genes'

Description

Subset common variants vcf file to only SNPs seen in 'top_genes'

Usage

subset_vcf(vcf, top_genes, ensdb)

Arguments

vcf

object of class CollapsedVCF
top_genes

output from 'common_genes' function, alternatively character vector containing custom gene names.
ensdb

object of class EnsDb corresponding to organism, genome of data

Value

object of class CollapsedVCF containing subset of SNPs from supplied vcf seen in commonly expressed genes

Examples

data(multiplexed_scrnaseq_sce, commonvariants_1kgenomes_subset)
top_genes <- common_genes(multiplexed_scrnaseq_sce)
ensdb <- EnsDb.Hsapiens.v86::EnsDb.Hsapiens.v86
small_vcf <- subset_vcf(commonvariants_1kgenomes_subset, top_genes, ensdb)

Sample VarTrix output

Description

A sample output from VarTrix corresponding to the sce SingleCellExperiment objec for a subset of SNPs located in well observed genes.

Usage

data(vartrix_consensus_snps)

Format

An object of class matrix (inherits from array) with 2542 rows and 2000 columns.

Value

vartrix_consensus_snps

An object of class matrix