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]