cellpypes (version 0.3.0)

class_to_deseq2: Create DESeq2 object for a given cell type

Description

Create a DESeq2 data set (`dds' in the DESeq2 vignette) for the specified class (cell type).

Usage

class_to_deseq2(obj, meta_df, class, design = ~condition)

Value

A DESeq2 object (e.g. dds)

Arguments

obj

A cellpypes object, see section cellpypes Objects below.

meta_df

Data frame where each column helps to identify a pseudobulk. Typical columns of meta_df are for example patient, treatment and cell type -- anything that uniquely identifies a replicate / batch / 10x run. Each row in meta_df corresponds to a single cell in your raw count matrix.

class

The name of cellpypes class for which you want to test for differential expression.

design

A formula based on columns in meta_df. To test differential expression between two groups in meta_df$condition, use formula ~ condition. More complex formulas (e.g. with interactions) are possible, for example ~ genotype + treatment + genotype:treatment.

cellpypes Objects

A cellpypes object is a list with four slots:

raw

(sparse) matrix with genes in rows, cells in columns

totalUMI

the colSums of obj$raw

embed

two-dimensional embedding of the cells, provided as data.frame or tibble with two columns and one row per cell.

neighbors

index matrix with one row per cell and k columns, where k is the number of nearest neighbors (we recommend 15<k<100, e.g. k=50). Here are two ways to get the neighbors index matrix:

• Use find_knn(featureMatrix)$idx, where featureMatrix could be principal components, latent variables or normalized genes (features in rows, cells in columns).

• use as(seurat@graphs[["RNA_nn"]], "dgCMatrix")> .1 to extract the kNN graph computed on RNA. The > .1 ensures this also works with RNA_snn, wknn/wsnn or any other available graph – check with names(seurat@graphs).

Examples

data("simulated_umis") 
# Meta data
ncells <- ncol(simulated_umis$raw)
dummy_variable <- function(x) factor(sample(x, ncells, replace=TRUE))
meta_data <- data.frame(patient=dummy_variable(paste0("patient", 1:6)),
                        treatment=dummy_variable(c("control", "treated")))

obj <- rule(simulated_umis, "T", "CD3E",">", 1e-4)
 # > 5 s in CRAN check
dds <- class_to_deseq2(obj, meta_data, "T", ~ treatment)