DiffFiltLoss: Difference in filtering loss
In PERFect: Permutation filtration for microbiome data

Description Usage Arguments Details Value Author(s) References See Also Examples

This function calculates differences in filtering loss due to removing a set of J taxa sequentially.

1	DiffFiltLoss(X, Order_Ind, Plot = TRUE, Taxa_Names = NULL)

`X`	OTU table, where taxa are columns and samples are rows of the table. It should be a in dataframe format with columns corresponding to taxa names.
`Order_Ind`	Numeric column order corresponding to taxa importance arrangement.
`Plot`	A binary TRUE/FALSE value. If TRUE, the function returns plot of sequential differences in filtering loss.
`Taxa_Names`	Optional taxa labels corresponding to the columns ordering given by Order_Ind.

This function calculates and plots (if Plot = TRUE) differences in filtering loss sequentially for removing the first j taxa as DFL(j+1) = FL(J_j+1) - FL(J_j) for taxa j=1, ..., p.

`DFL`	Differences in filtering loss values.
`p_FL`	Plot of the differences in filtering loss.

Ekaterina Smirnova

Smirnova, E., Huzurbazar, H., Jafari, F. “PERFect: permutation filtration of microbiome data', to be submitted.

FiltLoss

data(mock2)

# Proportion data matrix
Prop <- mock2$Prop

# Counts data matrix
Counts <- mock2$Counts

#arrange counts in order of increasing number of samples taxa are present in
NP <- NP_Order(Counts)

#obtain numeric column order corresponding to taxa importance arrangment
Order_Ind <- match(NP, names(Prop))
DFL <- DiffFiltLoss(X=Prop, Order_Ind = Order_Ind, Plot = TRUE, Taxa_Names = NP)

#Differences in filtering loss values
DFL$DFL

#Plot of the differences in filtering loss
DFL$p_FL