deva {blacksheepr} | R Documentation

## Run the entire blacksheep Function from Start to finish

### Description

Run the entire blacksheep Function from Start to finish

### Usage

    deva(se, analyze_negative_outliers = FALSE,
        aggregate_features = FALSE, feature_delineator = "\\.",
        fraction_samples_cutoff = 0.3, fdrcutoffvalue = 0.1)

### Arguments

`se` | The SummarizedExperiment object containing the countdata and the associated annotation data with comparisons in the colData object.
`analyze_negative_outliers` | DEFAULT: FALSE; Toggle the analysis of outliers in the negative direction as well. Will lead to the output of the outlier table containing "-1" values, in addition to negative outputs for boundaries and aggregate tables (if applicable)
`aggregate_features` | DEFAULT: FALSE; Toggle the Aggregate feature, which will aggregate features in your table based on the given delineator. Aggregation will output an aggregate table that counts the number of outliers per feature, and also a fraction table that show the number of outliers / number of candidates (which excludes missing values)
`feature_delineator` | DEFAULT: <"\."> What character delineates the separation between primary and secondary features. NOTE: to use proper R syntax with escape characters if necessary Ex) Protein1.Phosphosite1 uses "\." to aggregate on Protein1
`fraction_samples_cutoff` | DEFAULT: 0.3; Input a fractional cut off for the of samples that need to have an outlier for feature to be considered. ex) 10 samples in ingroup - 3 need to have an outlier for feature to be considered significant
`fdrcutoffvalue` | DEFAULT: 0.1; The FDR value for significance

### Value

outputs the full output of deva, including the analysis tables, the heatmaps for the analyses, the fraction table showing the fraction of outliers per sample, and the median and boundary values that together comprise the outlier boundary

### Examples

    suppressPackageStartupMessages(library(SummarizedExperiment))
    data("sample_phosphodata")
    data("sample_annotationdata")

    se <- SummarizedExperiment(
        assays = list(counts = as.matrix(sample_phosphodata[1:1000,])),
        colData = DataFrame(sample_annotationdata))

    deva(se = se,
        analyze_negative_outliers = FALSE, aggregate_features = FALSE,
        feature_delineator = "-", fraction_samples_cutoff = 0.3,
        fdrcutoffvalue = 0.1)

[Package blacksheepr version 1.1.0 Index]