DA_Seurat {benchdamic} | R Documentation |
DA_Seurat
Description
Fast run for Seurat differential abundance detection method.
Usage
DA_Seurat(
object,
pseudo_count = FALSE,
test.use = "wilcox",
contrast,
norm = c("TMM", "TMMwsp", "RLE", "upperquartile", "posupperquartile", "none",
"ratio", "poscounts", "iterate", "TSS", "CSSmedian", "CSSdefault"),
verbose = TRUE
)
Arguments
object |
phyloseq object.
|
pseudo_count |
add 1 to all counts if TRUE (default
pseudo_count = FALSE ).
|
test.use |
Denotes which test to use. Available options are:
"wilcox" : Identifies differentially expressed genes between two
groups of cells using a Wilcoxon Rank Sum test (default)
"bimod" : Likelihood-ratio test for single cell gene expression,
(McDavid et al., Bioinformatics, 2013)
"roc" : Identifies 'markers' of gene expression using ROC analysis.
For each gene, evaluates (using AUC) a classifier built on that gene alone,
to classify between two groups of cells. An AUC value of 1 means that
expression values for this gene alone can perfectly classify the two
groupings (i.e. Each of the cells in cells.1 exhibit a higher level than
each of the cells in cells.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 gene 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 expressed genes between two groups of
cells using the Student's t-test.
"negbinom" : Identifies differentially expressed genes between two
groups of cells using a negative binomial generalized linear model.
Use only for UMI-based datasets
"poisson" : Identifies differentially expressed genes between two
groups of cells using a poisson generalized linear model.
Use only for UMI-based datasets
"LR" : Uses a logistic regression framework to determine differentially
expressed genes. 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 expressed genes between two groups
of cells based on a model using DESeq2 which uses a negative binomial
distribution (Love et al, Genome Biology, 2014).This test does not support
pre-filtering of genes based on average difference (or percent detection rate)
between cell groups. However, genes may be pre-filtered based on their
minimum detection rate (min.pct) across both cell groups. To use this method,
please install DESeq2, using the instructions at
https://bioconductor.org/packages/release/bioc/html/DESeq2.html
|
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.
|
norm |
name of the normalization method used to compute the
normalization factors to use in the differential abundance analysis. If
norm is equal to "TMM", "TMMwsp", "RLE", "upperquartile",
"posupperquartile", "CSSmedian", "CSSdefault", "TSS" the scaling factors are
automatically transformed into normalization factors.
|
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))
# No use of scaling factors
ps_NF <- norm_edgeR(object = ps, method = "none")
# The phyloseq object now contains the scaling factors:
scaleFacts <- phyloseq::sample_data(ps_NF)[, "NF.none"]
head(scaleFacts)
# Differential abundance
DA_Seurat(object = ps_NF, contrast = c("group","B","A"), norm = "none")
[Package
benchdamic version 1.0.0
Index]