set_Seurat {benchdamic} | R Documentation

## set_Seurat

### Description

Set the parameters for Seurat differential abundance detection method.

### Usage

    set_Seurat(
      pseudo_count = FALSE,
      test.use = c("wilcox", "t"),
      contrast = NULL,
      norm = "TSS",
      expand = TRUE
    )

### Arguments

`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.
`expand` | logical, if TRUE create all combinations of input parameters (default `expand = TRUE`)

### Value

A named list containing the set of parameters for `DA_Seurat` method.

### See Also

`DA_Seurat`

### Examples

    # Set some basic combinations of parameters for Seurat
    base_Seurat <- set_Seurat(contrast = c("group", "B", "A"))
    # Set a specific set of normalization for Seurat (even of other packages!)
    setNorm_Seurat <- set_Seurat(contrast = c("group", "B", "A"), norm = c("TSS",
    "TMM", "poscounts"))
    # Set many possible combinations of parameters for Seurat
    all_Seurat <- set_Seurat(pseudo_count = c(TRUE, FALSE),
        test.use = c("wilcox", "t", "negbinom", "poisson"),
        contrast = c("group", "B", "A"), norm = c("TSS", "TMM"))

[Package benchdamic version 1.0.0 Index]