This package provides a bootstrap imputation method for dropout events in scRNAseq data published here.
Jul. 12, 2020
- Version 1.0.3.
- Include k neighbors as a parameter for NN network.
- Bug fixes.
- Independent of scRNAseq pipeline.
- Informative genes may be determined using the
computeHVGfunction (also the default) or any other package (e.g. Seurat, scran) and indicated withselect_genes.
- R (>= 3.4)
- Python (>= 3.0)
The SNN clustering step still uses the Louvain Algorithm but now borrows from the implementation in the Giotto pipeline.
Install the rescue package using devtools.
install.packages("devtools", repos="http://cran.rstudio.com/")
library(devtools)
devtools::install_github("seasamgo/rescue")
library(rescue)- pandas
- networkx
- community (from python-louvain)
The python modules will be installed automatically in a miniconda environment when installing rescue. However, it will ask you whether you want to install them and you can opt out and go for a manual installation if that is preferred.
Install with pip
pip install pandas
pip install networkx
pip install python-louvain
If you chose the manual installation and have multiple python versions installed, you may preemptively force the reticulate package to use the desired version by specifying the path to the python version you want to use. This can be done using the python_path parameter within the bootstrapImputation function or directly set at the beginning of your script. For example:
reticulate::use_python('~/anaconda2/bin/python', required = T)bootstrapImputation takes a log-normalized expression matrix and
returns a list containing the imputed and original matrices.
bootstrapImputation(
expression_matrix, # expression matrix
select_cells = NULL, # subset cells
select_genes = NULL, # informative genes
proportion_genes = 0.6, # proportion of genes to sample
log_transformed = TRUE, # whether expression matrix is log-transformed
log_base = exp(1), # log base of log-transformation
bootstrap_samples = 100, # number of samples
number_pcs = 8, # number of PC's to consider
k_neighbors = 30, # number of neighbors for NN network
snn_resolution = 0.9, # clustering resolution
impute_index = NULL, # specify counts to impute, defaults to zero values
use_mclapply = FALSE, # run in parallel
cores = 2, # number of parallel cores
return_individual_results = FALSE, # return sample means
python_path = NULL, # path to the python version to use, defaults to default path
verbose = FALSE # print progress to console
)Similar cells are determined with shared nearest neighbors clustering
upon the principal components of informative gene expression
(e.g. highly variable or differentially expressed genes). The names of
these informative genes may be indicated with select_genes, which
defaults to the most highly variable. For more, please view the help
files.
To illustrate, we’ll need the Splatter package to simulate some scRNAseq data.
install.packages("BiocManager", repos="http://cran.rstudio.com/")
BiocManager::install("splatter")
library(splatter)We’ll consider a hypothetical example of 500 cells and 10,000 genes containing five distinct cell types of near equal size, then introduce some dropout events.
params <- splatter::newSplatParams(
nGenes = 1e4,
batchCells = 500,
group.prob = rep(.2, 5),
de.prob = .05,
dropout.mid = rep(0, 5),
dropout.shape = rep(-.5, 5),
dropout.type = 'group',
seed = 940
)
splat <- splatter::splatSimulate(params = params, method = 'groups')
cell_types <- SummarizedExperiment::colData(splat)$Group
cell_types <- as.factor(gsub('Group', 'Cell type ', cell_types))For visualization purposes we’ll demonstrate using the Seurat pipeline, as with our published work but now v3. However, any pipeline which fits the needs of your downstream analysis will do.
install.packages("Seurat", repos="http://cran.rstudio.com/")
library(Seurat)First, we should remove genes that lost all counts to dropout.
counts_true <- SummarizedExperiment::assays(splat)$TrueCounts
counts_dropout <- SummarizedExperiment::assays(splat)$counts
comparable_genes <- rowSums(counts_dropout) != 0Next, we normalize and scale the data.
expression_true <- Seurat::CreateSeuratObject(counts = counts_true)
expression_true <- Seurat::NormalizeData(expression_true)
expression_true <- Seurat::ScaleData(expression_true, features = rownames(expression_true))
expression_dropout <- Seurat::CreateSeuratObject(counts = counts_dropout[comparable_genes, ])
expression_dropout <- Seurat::NormalizeData(expression_dropout)
expression_dropout <- Seurat::ScaleData(expression_dropout, features = rownames(expression_dropout))The last step is dimension reduction with PCA and then visualization with t-SNE.
expression_true <- Seurat::RunPCA(expression_true, features = rownames(expression_true), verbose = FALSE)
expression_true <- Seurat::SetIdent(expression_true, value = cell_types)
expression_true <- Seurat::RunTSNE(expression_true)
Seurat::DimPlot(expression_true, reduction = "tsne")
expression_dropout <- Seurat::RunPCA(expression_dropout, features = rownames(expression_dropout), verbose = FALSE)
expression_dropout <- Seurat::SetIdent(expression_dropout, value = cell_types)
expression_dropout <- Seurat::RunTSNE(expression_dropout)
Seurat::DimPlot(expression_dropout, reduction = "tsne")
It’s clear that dropout has distorted our evaluation of the data by cell type as compared to what we should see with the full set of counts. Now let’s impute zero counts to recover missing expression values and reevaluate.
impute <- rescue::bootstrapImputation(expression_matrix = expression_dropout@assays$RNA@data) # python_path can be set here
expression_imputed <- Seurat::CreateSeuratObject(counts = impute$final_imputation)
expression_imputed <- Seurat::ScaleData(expression_imputed)
expression_imputed <- Seurat::RunPCA(expression_imputed, features = rownames(expression_imputed), verbose = FALSE)
expression_imputed <- Seurat::SetIdent(expression_imputed, value = cell_types)
expression_imputed <- Seurat::RunTSNE(expression_imputed)
Seurat::DimPlot(expression_imputed, reduction = "tsne")
The recovery of missing expression values due to dropout events allows us to more accurately distinguish cell types with basic data visualization techniques in this simulated example.
Dries, R., et al. (2019). Giotto, a pipeline for integrative analysis and visualization of single-cell spatial transcriptomic data. BioRxiv. doi: https://doi.org/10.1101/701680.
Satija R., et al. (2019). Seurat: Tools for Single Cell Genomics. R package version 3.1.0. https://CRAN.R-project.org/package=Seurat.
Tracy, S., Dries, R., Yuan, G. C. (2019). RESCUE: imputing dropout events in single-cell RNA-sequencing data. BMC Bioinformatics, 20(388). doi: https://doi.org/10.1186/s12859-019-2977-0.
Zappia, L., Phipson, B., Oshlack, A. (2017). Splatter: simulation of single-cell RNA sequencing data. Genome Biology, 18(1):174. doi: https://doi.org/10.1186/s13059-017-1305-0.
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