Usage
mca_2cond(
data_c1,
data_c2,
metadata_info_c1 = c(ValueCol = "Log2FC", StatCol = "p.adj", cutoff_stat = 0.05,
ValueCutoff = 1),
metadata_info_c2 = c(ValueCol = "Log2FC", StatCol = "p.adj", cutoff_stat = 0.05,
ValueCutoff = 1),
feature = "Metabolite",
save_table = "csv",
method_background = "C1&C2",
path = NULL
)Arguments
- data_c1
DF for your data (results from e.g. dma) containing metabolites in rows with corresponding Log2FC and stat (p-value, p.adjusted) value columns.
- data_c2
DF for your data (results from e.g. dma) containing metabolites in rows with corresponding Log2FC and stat (p-value, p.adjusted) value columns.
- metadata_info_c1
Optional: Pass ColumnNames and Cutoffs for condition 1 including the value column (e.g. Log2FC, Log2Diff, t.val, etc) and the stats column (e.g. p.adj, p.val). This must include: c(ValueCol=ColumnName_data_c1,StatCol=ColumnName_data_c1, cutoff_stat= NumericValue, ValueCutoff=NumericValue) Default=c(ValueCol="Log2FC",StatCol="p.adj", cutoff_stat= 0.05, ValueCutoff=1)
- metadata_info_c2
Optional: Pass ColumnNames and Cutoffs for condition 2 includingthe value column (e.g. Log2FC, Log2Diff, t.val, etc) and the stats column (e.g. p.adj, p.val). This must include: c(ValueCol=ColumnName_data_c2,StatCol=ColumnName_data_c2, cutoff_stat= NumericValue, ValueCutoff=NumericValue)Default=c(ValueCol="Log2FC",StatCol="p.adj", cutoff_stat= 0.05, ValueCutoff=1)
- feature
Optional: Column name of Column including the Metabolite identifiers. This MUST BE THE SAME in each of your Input files. Default="Metabolite"
- save_table
Optional: File types for the analysis results are: "csv", "xlsx", "txt" Default = "csv"
- method_background
Optional: Background method C1|C2, C1&C2, C2, C1 or * Default="C1&C2"
- path
Optional: Path to the folder the results should be saved at. Default = NULL
Value
List of two DFs: 1. summary of the cluster count and 2. the detailed information of each metabolites in the clusters.
Examples
Intra <- intracell_raw%>%tibble::column_to_rownames("Code")
Input <- MetaProViz::dma(data=Intra[-c(49:58) ,-c(1:3)], metadata_sample=Intra[-c(49:58) , c(1:3)], metadata_info = c(Conditions = "Conditions", Numerator = NULL, Denominator = "HK2"))
#> In `Numerator` 786-O, 786-M1A, 786-M2A, NA/0 values exist in 5 Metabolite(s). and in `denominator`HK2 2 Metabolite(s).. Those metabolite(s) might return p.val= NA, p.adj.= NA, t.val= NA. The Log2FC = Inf, if all replicates are 0/NA.
#> There are NA's/0s in the data. This can impact the output of the SHapiro-Wilk test for all metabolites that include NAs/0s.
#> Error occurred during shapiro that performs the shapiro-Wilk test. Message: all 'x' values are identical
#> For 32.97% of metabolites the group variances are equal.
#> No condition was specified as numerator and HK2 was selected as a denominator. Performing multiple testing `all-vs-one` using lmFit.
#> Warning: Partial NA coefficients for 1 probe(s)
Res <- MetaProViz::mca_2cond(data_c1 = Input[["dma"]][["786-O_vs_HK2"]],
data_c2 = Input[["dma"]][["786-M1A_vs_HK2"]])
#> Warning: data_c1 includes NAs in Log2FC and/or in p.adj. 1 metabolites containing NAs are removed.
#> Warning: data_c2 includes NAs in Log2FC and/or inp.adj. 1 metabolites containing NAs are removed.