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 naturallog 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 logtransformation is applied. For counts per million
(CPM) set scale.factor = 1e6;
none No normalization

scale.factor 
Sets the scale factor for celllevel 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" Likelihoodratio 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(AUC0.5) * 2) ranked matrix of putative
differentially expressed genes.
"t" Identify differentially abundant features between two
groups of samples using the Student's ttest.
"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 scRNAseq 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 pvalues '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
meanvariance 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")