DA_Seurat: DA_Seurat

In mcalgaro93/benchdamic: Benchmark of differential abundance methods on microbiome data

DA_Seurat

Description

Fast run for Seurat differential abundance detection method.

Usage

DA_Seurat(
  object,
  assay_name = "counts",
  pseudo_count = FALSE,
  norm = "LogNormalize",
  scale.factor = 10000,
  test = "wilcox",
  contrast = NULL,
  verbose = TRUE
)

Arguments

object	a phyloseq or TreeSummarizedExperiment object.
assay_name	the name of the assay to extract from the TreeSummarizedExperiment object (default assayName = "counts"). Not used if the input object is a phyloseq.
pseudo_count	add 1 to all counts if TRUE (default pseudo_count = FALSE).
norm	Method for normalization. LogNormalize Feature counts for each sample are divided by the total counts of that sample and multiplied by the scale.factor. This is then natural-log transformed using log1p; CLR Applies a centered log ratio transformation; RC Relative counts. Feature counts for each sample are divided by the total counts of that sample and multiplied by the scale.factor. No log-transformation is applied. For counts per million (CPM) set scale.factor = 1e6; none No normalization
scale.factor	Sets the scale factor for cell-level normalization
test	Denotes which test to use. Available options are: "wilcox" Identifies differentially abundant features between two groups of samples using a Wilcoxon Rank Sum test (default). "bimod" Likelihood-ratio test for the feature abundances, (McDavid et al., Bioinformatics, 2013). "roc" Identifies 'markers' of feature abundance using ROC analysis. For each feature, evaluates (using AUC) a classifier built on that feature alone, to classify between two groups of cells. An AUC value of 1 means that abundance values for this feature alone can perfectly classify the two groupings (i.e. Each of the samples in group.1 exhibit a higher level than each of the samples in group.2). An AUC value of 0 also means there is perfect classification, but in the other direction. A value of 0.5 implies that the feature has no predictive power to classify the two groups. Returns a 'predictive power' (abs(AUC-0.5) * 2) ranked matrix of putative differentially expressed genes. "t" Identify differentially abundant features between two groups of samples using the Student's t-test. "negbinom" Identifies differentially abundant features between two groups of samples using a negative binomial generalized linear model. "poisson" Identifies differentially abundant features between two groups of samples using a poisson generalized linear model. "LR" Uses a logistic regression framework to determine differentially abundant features. Constructs a logistic regression model predicting group membership based on each feature individually and compares this to a null model with a likelihood ratio test. "MAST" Identifies differentially expressed genes between two groups of cells using a hurdle model tailored to scRNA-seq data. Utilizes the MAST package to run the DE testing. "DESeq2" Identifies differentially abundant features between two groups of samples based on a model using DESeq2 which uses a negative binomial distribution (Love et al, Genome Biology, 2014).
contrast	character vector with exactly three elements: the name of a factor in the design formula, the name of the numerator level for the fold change, and the name of the denominator level for the fold change.
verbose	an optional logical value. If TRUE, information about the steps of the algorithm is printed. Default verbose = TRUE.

Value

A list object containing the matrix of p-values 'pValMat', the matrix of summary statistics for each tag 'statInfo', and a suggested 'name' of the final object considering the parameters passed to the function.

See Also

CreateSeuratObject to create the Seurat object, AddMetaData to add metadata information, NormalizeData to compute the normalization for the counts, FindVariableFeatures to estimate the mean-variance trend, ScaleData to scale and center features in the dataset, and FindMarkers to perform differential abundance analysis.

Examples

set.seed(1)
# Create a very simple phyloseq object
counts <- matrix(rnbinom(n = 60, size = 3, prob = 0.5), nrow = 10, ncol = 6)
metadata <- data.frame("Sample" = c("S1", "S2", "S3", "S4", "S5", "S6"),
                       "group" = as.factor(c("A", "A", "A", "B", "B", "B")))
ps <- phyloseq::phyloseq(phyloseq::otu_table(counts, taxa_are_rows = TRUE),
                         phyloseq::sample_data(metadata))
                         
# Differential abundance
DA_Seurat(object = ps, contrast = c("group","B","A"))

# Perform a simple Wilcoxon test using Seurat on raw data
DA_Seurat(object = ps, contrast = c("group","B","A"), norm = "none", 
    test = "wilcox")

mcalgaro93/benchdamic documentation built on Nov. 28, 2024, 2:16 p.m.