createConcordance: createConcordance

In mcalgaro93/benchdamic: Benchmark of differential abundance methods on microbiome data

createConcordance

Description

Compute the between and within method concordances comparing the lists of extracted statistics from the outputs of the differential abundance detection methods.

Usage

createConcordance(object, slot = "pValMat", colName = "rawP", type = "pvalue")

Arguments

object	Output of differential abundance detection methods. pValMat, statInfo matrices, and method's name must be present (See vignette for detailed information).
slot	A character vector with 1 or number-of-methods-times repeats of the slot names where to extract values for each method (default slot = "pValMat").
colName	A character vector with 1 or number-of-methods-times repeats of the column name of the slot where to extract values for each method (default colName = "rawP").
type	A character vector with 1 or number-of-methods-times repeats of the value type of the column selected where to extract values for each method. Two values are possible: "pvalue" or "logfc" (default type = "pvalue").

Value

A long format data.frame object with several columns:

• comparison which indicates the comparison number;

• n_features which indicates the total number of taxa in the comparison dataset;

• method1 which contains the first method name;

• method2 which contains the first method name;

• rank;

• concordance which is defined as the cardinality of the intersection of the top rank elements of each list, divided by rank, i.e. , (L_{1:rank} \bigcap M_{1:rank})/(rank), where L and M represent the lists of the extracted statistics of method1 and method2 respectively (averaged values between subset1 and subset2).

See Also

extractStatistics and areaCAT.

Examples

data(ps_plaque_16S)

# Balanced design
my_splits <- createSplits(
    object = ps_plaque_16S, varName = "HMP_BODY_SUBSITE", balanced = TRUE,
    paired = "RSID", N = 10 # N = 100 suggested
)

# Make sure the subject ID variable is a factor
phyloseq::sample_data(ps_plaque_16S)[, "RSID"] <- as.factor(
    phyloseq::sample_data(ps_plaque_16S)[["RSID"]])

# Initialize some limma based methods
my_limma <- set_limma(design = ~ RSID + HMP_BODY_SUBSITE, 
    coef = "HMP_BODY_SUBSITESupragingival Plaque",
    norm = c("TMM", "CSS"))

# Set the normalization methods according to the DA methods
my_norm <- setNormalizations(fun = c("norm_edgeR", "norm_CSS"),
    method = c("TMM", "CSS"))

# Run methods on split datasets
results <- runSplits(split_list = my_splits, method_list = my_limma,
    normalization_list = my_norm, object = ps_plaque_16S)

# Concordance for p-values
concordance_pvalues <- createConcordance(
    object = results, slot = "pValMat", colName = "rawP", type = "pvalue"
)

# Concordance for log fold changes
concordance_logfc <- createConcordance(
    object = results, slot = "statInfo", colName = "logFC", type = "logfc"
)

# Concordance for log fold changes in the first method and p-values in the
# other
concordance_logfc_pvalues <- createConcordance(
    object = results, slot = c("statInfo", "pValMat"),
    colName = c("logFC", "rawP"), type = c("logfc", "pvalue")
)

mcalgaro93/benchdamic documentation built on March 10, 2024, 10:40 p.m.