R/method-mp_cal_alpha.R

In xiangpin/MicrobiotaProcess: A comprehensive R package for managing and analyzing microbiome and other ecological data within the tidy framework

#' @title alpha index
#' 
#' @description
#' calculate the alpha index (Obseve,Chao1,Shannon,Simpson) of sample
#' with \code{\link[vegan]{diversity}}
#' @param obj object, data.frame of (nrow sample * ncol taxonomy(feature)) 
#' or phyloseq.
#' @param mindepth numeric, Subsample size for rarefying community.
#' @param sampleda data.frame,sample information, row sample * column factors.
#' @param force logical whether calculate the alpha index even the count of otu is
#' not rarefied, default is FALSE. If it is TRUE, meaning the rarefaction is not be
#' performed automatically.
#' @param ... additional arguments.
#' @return data.frame contained alpha Index.
#' @author Shuangbin Xu
#' @rdname get_alphaindex
#' @export
#' @examples
#' \dontrun{
#' otudafile <- system.file("extdata", "otu_tax_table.txt", 
#'                         package="MicrobiotaProcess")
#' otuda <- read.table(otudafile, sep="\t", 
#'              header=TRUE, row.names=1, 
#'              check.names=FALSE, skip=1, comment.char="")
#' otuda <- otuda[sapply(otuda, is.numeric)] %>% t() %>% 
#'           data.frame(check.names=FALSE)
#' set.seed(1024)
#' alphatab <- get_alphaindex(otuda)
#' head(as.data.frame(alphatab))
#' data(test_otu_data)
#' class(test_otu_data)
#' test_otu_data %<>% as.phyloseq()
#' class(test_otu_data)
#' set.seed(1024)
#' alphatab2 <- get_alphaindex(test_otu_data)
#' head(as.data.frame(alphatab2))
#' }
setGeneric("get_alphaindex", function(obj, ...){standardGeneric("get_alphaindex")})

#' @aliases get_alphaindex,matrix
#' @rdname get_alphaindex
#' @importFrom vegan estimateR diversity specnumber
#' @export
setMethod("get_alphaindex", "matrix", function(obj, mindepth, sampleda, force=FALSE, ...){
    #if (!identical(all.equal(obj, round(obj)),TRUE)){
    #    stop("the data should be integer (counts)!")
    #}
    if (missing(mindepth) || is.null(mindepth)){
        mindepth <- min(rowSums(obj))
    }
    if (obj %>% rowSums %>% var != 0 && !force){
        obj <- vegan::rrarefy(obj, mindepth)
    }
    Shannon <- diversity(obj)
    Simpson <- diversity(obj, index="simpson")
    Pielou <- Shannon/log(specnumber(obj))
    if (identical(all.equal(obj, round(obj)),TRUE)){
        spn <- estimateR(obj)
        Observe <- spn[1,]
        Chao1 <- spn[2, ] 
        ACE <- spn[4, ]
        alpha <- data.frame(Observe=Observe, Chao1=Chao1, ACE=ACE, Shannon, Simpson, Pielou)
    }else{
        message_wrap("Note: The Chao1 and ACE accept only integers (counts), but the input 
                     is not integer, so the two metrics cannot be calculated.")
        Observe <- apply(obj, 1, function(x)sum(x>0))
        Chao1 <- NULL
        ACE <- NULL
        alpha <- data.frame(Observe=Observe, Shannon, Simpson, Pielou)
    }
    if (missing(sampleda)){
        sampleda <- NULL
    }
    alpha <- new("alphasample",
                 alpha=alpha,
		 sampleda=sampleda)
    return(alpha)
})     

#' @aliases get_alphaindex,data.frame
#' @rdname get_alphaindex
#' @export
setMethod("get_alphaindex", "data.frame", function(obj, ...){
    obj <- obj[,colSums(obj)>0,drop=FALSE]
    obj <- as.matrix(obj)
    alpha <- get_alphaindex(obj, ...)
    return(alpha)
})

#' @aliases get_alphaindex,integer
#' @rdname get_alphaindex
#' @export
setMethod("get_alphaindex", "integer", function(obj, ...){
    obj <- obj[obj>0]
    obj <- as.matrix(obj)
    alpha <- get_alphaindex(obj, ...)
    alpha <- alpha@alpha
    return(alpha)
})

#' @aliases get_alphaindex,numeric
#' @rdname get_alphaindex
#' @export
setMethod("get_alphaindex", "numeric",function(obj, ...){
    obj <- obj[obj>0]
    obj <- as.matrix(obj)
    alpha <- get_alphaindex(obj, ...)
    alpha <- alpha@alpha
    return(alpha)
})

#' @aliases get_alphaindex,phyloseq
#' @rdname get_alphaindex
#' @export
setMethod("get_alphaindex", "phyloseq", function(obj, ...){
    otuda <- checkotu(obj)
    sampleda <- checksample(obj)
    alpha <- get_alphaindex(obj=otuda, sampleda=sampleda,...)
    return(alpha)
})

#' @title calculate the alpha index with MPSE or tbl_mpse
#' @docType methods
#' @rdname mp_cal_alpha-methods
#' @param .data MPSE or tbl_mpse object
#' @param .abundance The column name of OTU abundance column to be calculate
#' @param action character it has three options, "add" joins the new information 
#' to the input tbl (default), "only" return a non-redundant tibble with the just 
#' new information, ang 'get' return a 'alphasample' object.
#' @param force logical whether calculate the alpha index even the '.abundance' is 
#' not rarefied, default is FALSE.
#' @param ... additional arguments
#' @return update object or other (refer to action)
#' @seealso [mp_plot_alpha()]
#' @export
#' @author Shuangbin Xu
#' @examples
#' data(mouse.time.mpse)
#' mpse <- mouse.time.mpse %>% 
#'         mp_rrarefy() %>%
#'         mp_cal_alpha(.abundance=RareAbundance)
#' mpse
#' p <- mpse %>% mp_plot_alpha(.group=time, .alpha=c(Observe, Shannon, Pielou))
#' p
#' # Or you can extract the result and visualize it with ggplot2 and ggplot2-extensions
#' \dontrun{
#' tbl <- mpse %>% 
#'        mp_extract_sample
#' tbl
#' tbl %<>% 
#'   tidyr::pivot_longer(cols=!c("Sample", "time"), names_to="measure", values_to="alpha")
#' tbl
#' library(ggplot2)
#' library(ggsignif)
#' library(gghalves)
#' p <- ggplot(data=tbl, aes(x=time, y=alpha, fill=time)) + 
#'      geom_half_violin(color=NA, side="l", trim=FALSE) + 
#'      geom_boxplot(aes(color=time), fill=NA, position=position_nudge(x=.22), width=0.2) + 
#'      geom_half_point(side="r", shape=21) + 
#'      geom_signif(comparisons=list(c("Early", "Late")), test="wilcox.test", textsize=2) + 
#'      facet_wrap(facet=vars(measure), scales="free_y", nrow=1) +
#'      scale_fill_manual(values=c("#00A087FF", "#3C5488FF")) + 
#'      scale_color_manual(values=c("#00A087FF", "#3C5488FF"))
#' p
#' }
setGeneric("mp_cal_alpha", function(.data, .abundance=NULL, 
                                    action=c("add", "only", "get"), 
                                    force=FALSE, ...){
    standardGeneric("mp_cal_alpha")
})


#' @rdname mp_cal_alpha-methods
#' @aliases mp_cal_alpha,MPSE
#' @exportMethod mp_cal_alpha 
setMethod("mp_cal_alpha", signature(.data="MPSE"),
          function(.data, .abundance=NULL, action="add", force=FALSE, ...){
    action %<>% match.arg(c("add", "only", "get"))
    
    .abundance <- rlang::enquo(.abundance)
    if (rlang::quo_is_null(.abundance)){
        .abundance <- as.symbol("RareAbundance")
    }

    if (!valid_rare(.data, .abundance=.abundance) && !force){
        trash <- try(silent = TRUE,
                     expr = {
                         .data <- mp_rrarefy(.data = .data, ...)
                     }
                 )
        if (inherits(trash, "try-error")){
            stop_wrap("The 'Abundance' column cannot be rarefied, please check whether it is integer (count).
                       Or you can set 'force=TRUE' to calculate the alpha index without rarefaction.
                      ")
        }

        message_wrap("The rarefied abundance of species might not be provided. Rarefaction of all
                      observations is performed automatically using 'Abundance' column. 
                      If you still want to calculate the alpha index with the specified '.abundance', 
                      you can set 'force=TRUE'. ")
        .abundance <- as.symbol("RareAbundance")
    }
    
    alphada <- SummarizedExperiment::assays(.data)@listData[[rlang::as_name(.abundance)]] %>% 
               t() %>% 
               get_alphaindex(force=TRUE)
    if (action=="get"){
        alphada@sampleda <- .data@colData %>% data.frame(check.names=FALSE)
        return(alphada)
    }
    da <- alphada@alpha %>% as_tibble(rownames="Sample")
    da <- .data %>%
          mp_extract_sample() %>%
          left_join(da, by="Sample", suffix=c("", ".y")) 
    if (action=="add"){
        .data@colData <- da %>% 
                         column_to_rownames(var="Sample") %>% 
                         S4Vectors::DataFrame(check.names=FALSE)
        return(.data)
    }else if (action=="only"){
        return(da)
    }else{
        stop("Note: The action must be either \"add\" for adding this information to your data frame or \"get\" to just get the information")
    }
})

.internal_mp_cal_alpha <- function(.data, .abundance=NULL, action="add", force=FALSE, ...){
    action %<>% match.arg(c("add", "only", "get"))
    
    .abundance <- rlang::enquo(.abundance)

    if (rlang::quo_is_null(.abundance)){
        .abundance <- as.symbol("RareAbundance")
    }

    if (!valid_rare(.data, .abundance=.abundance) && !force){
        trash <- try(silent = TRUE,
                     expr = {
                         .data <- mp_rrarefy(.data = .data, ...)
                     }
                 )
        if (inherits(trash, "try-error")){
            stop_wrap("The 'Abundance' column cannot be rarefied, please check whether it is integer (count).
                       Or you can set 'force=TRUE' to calculate the alpha index without rarefaction.
                      ")
        }

        message_wrap("The rarefied abundance of species might not be provided. Rarefaction of all
                      observations is performed automatically using 'Abundance' column.
                      If you still want to calculate the alpha index with the specified '.abundance',
                      you can set 'force=TRUE'. ")
        .abundance <- as.symbol("RareAbundance")        
    }

    alphada <- .data %>% 
               select(c("Sample", "OTU", rlang::as_name(.abundance))) %>%
               tidyr::pivot_wider(id_cols="Sample", 
                                  names_from="OTU", 
                                  values_from=rlang::as_name(.abundance)) %>%
               column_to_rownames(var="Sample") %>%
               get_alphaindex(force=TRUE)

    if (action=="get"){
        samplevar <- attr(.data, "samplevar")
        alphada@sampleda <- .data %>% 
                            select(samplevar) %>% 
                            column_to_rownames(var="Sample")
        return(alphada)
    }
    da <- alphada@alpha %>% as_tibble(rownames="Sample")
    if (action=="add"){
        samplevar <- .data %>% attr("samplevar")
        assaysvar <- .data %>% attr("assaysvar")
        othernm <- colnames(.data)[!colnames(.data) %in% c("OTU", "Sample", assaysvar, samplevar)]
        .data %<>% left_join(da, by="Sample", suffix=c("", ".y")) %>%
                   select(c("OTU", "Sample", assaysvar, samplevar, colnames(da), othernm))
        return(.data)
    }else if (action=="only"){
        return(da)
    }
}

#' @rdname mp_cal_alpha-methods
#' @aliases mp_cal_alpha,tbl_mpse
#' @exportMethod mp_cal_alpha
setMethod("mp_cal_alpha", signature(.data="tbl_mpse"), .internal_mp_cal_alpha)


#' @rdname mp_cal_alpha-methods
#' @aliases mp_cal_alpha,grouped_df_mpse
#' @exportMethod mp_cal_alpha
setMethod("mp_cal_alpha", signature(.data="grouped_df_mpse"), .internal_mp_cal_alpha)

#' @title calculate the divergence with MPSE or tbl_mpse
#' @docType methods
#' @rdname mp_cal_divergence-methods
#' @param .data MPSE or tbl_mpse object
#' @param .abundance The column name of OTU abundance column to be calculate.
#' @param .name the colname name of the divergence results, default is 'divergence'.
#' @param reference a no-empty character, either 'median' or 'mean' or the sample 
#' name, or a numeric vector which has length equal to the number of features,
#' default is 'mean'. 
#' @param distFUN the function to calculate the distance between the reference and
#' samples, default is 'vegan::vegdist'. 
#' @param method the method to calculate the distance, which will pass to the function
#' that is specified in 'distFUN', default is 'bray'.
#' @param action character it has three options, "add" joins the new information
#' to the input tbl (default), "only" return a non-redundant tibble with the just
#' new information, ang 'get' return a 'alphasample' object.
#' @param ... additional arguments, see also the arguments of 'distFUN' function.
#' @return update object or other (refer to action)
#' @seealso [mp_plot_alpha()]
#' @export
#' @author Shuangbin Xu
#' @examples
#' \dontrun{
#' # example(mp_cal_divergence, run.dontrun = TRUE) to run the example.
#' data(mouse.time.mpse)
#' mouse.time.mpse %>% 
#'   mp_cal_divergence(
#'     .abundance = Abundance,
#'     .name = 'divergence.mean',
#'     distFUN = vegan::vegdist,
#'     method = 'bray'
#'   ) %>% 
#'   mp_plot_alpha(
#'     .alpha = divergence.mean,
#'     .group = time,
#'   )
#' }
setGeneric('mp_cal_divergence', 
           function(.data,
                    .abundance, 
                    .name = 'divergence',
                    reference = 'mean', 
                    distFUN = vegan::vegdist, 
                    method = 'bray',
                    action = 'add', ...)
    standardGeneric('mp_cal_divergence')
)

.internal_cal_divergence <- function(.data, 
                                     .abundance, 
                                     .name = 'divergence',
                                     reference = 'mean', 
                                     distFUN = vegan::vegdist, 
                                     method = 'bray', 
                                     action = 'add', 
                                     ...){
    .abundance <- rlang::enquo(.abundance)
    .name <- rlang::enquo(.name)

    
    stop_msg <- "The reference must be one of 'median' or 'mean' meaning 
                 the 'median' or 'mean' of all sample. Or it must a numeric 
                 vector which has length equal to the number of features.
                 Or it must be a integer value smaller than the number of 
                 sample. Or it must be a character of sample name. Or it
                 can be a custom function to calculate the reference community.
                 "
    
    dat <- .data %>% mp_extract_assays(!!.abundance)

    if (is.character(reference)){
        if (!(tryCatch(inherits(rlang::as_function(reference), 'function'), error=function(e)FALSE) || 
              reference %in% colnames(dat) && length(reference)==1)){
            stop_wrap(stop_msg)
        }
    }else if(is.numeric(reference)){
        if (!(length(reference) == nrow(dat) || (length(reference)==1 && reference <= ncol(dat)))){
            stop_wrap(stop_msg)
        }
    }else if (!inherits(reference, 'function')){
        stop_wrap(stop_msg)
    }

    if (is.numeric(reference) && length(reference)==1 && reference <= ncol(dat)){
        reference <- dat[, reference]
    }else if (is.character(reference)){
        if (reference %in% colnames(dat)){
            reference <- dat[,colnames(dat) %in% reference]    
        }else if (inherits(rlang::as_function(reference), 'function')){
            reference <- apply(dat, 1, reference)
        }
    }else if (is.numeric(reference) && length(reference) == nrow(dat)){
        reference <- reference
    }else{
        reference <- apply(dat, 1, reference)
    }
    
    res <- unlist(lapply(seq_len(ncol(dat)), 
                  function(i) distFUN(rbind(dat[,i], reference), method = method, ...)
             ))
 
    res <- tibble::tibble(Sample=colnames(dat), !!.name:=res)
    
    if (action == 'get'){
        return(res)
    }else if (action == 'only'){
        sample.da <- .data %>% mp_extract_sample()
        res <- sample.da %>% left_join(res, by = 'Sample')
    }else if (action == 'add'){
        res <- .data %>% left_join(res, by = 'Sample')
    }
    return(res)
}

#' @rdname mp_cal_divergence-methods
#' @aliases mp_cal_divergence,MPSE
#' @exportMethod mp_cal_divergence
setMethod('mp_cal_divergence', signature(.data = 'MPSE'), .internal_cal_divergence)

#' @rdname mp_cal_divergence-methods
#' @aliases mp_cal_divergence,tbl_mpse
#' @exportMethod mp_cal_divergence
setMethod('mp_cal_divergence', signature(.data = 'tbl_mpse'), .internal_cal_divergence)

#' @rdname mp_cal_divergence-methods
#' @aliases mp_cal_divergence,grouped_df_mpse
#' @exportMethod mp_cal_divergence
setMethod('mp_cal_divergence', signature(.data = 'grouped_df_mpse'), .internal_cal_divergence)

valid_rare <- function(.data, ...){
    UseMethod("valid_rare")
}

valid_rare.MPSE <- function(.data, .abundance=NULL){
    assaysvar <- names(.data@assays)
    #.abundance <- ifelse(is.null(.abundance), "RareAbundance", .abundance)
    .abundance <- rlang::as_name(.abundance)
    if (.abundance %in% assaysvar){
        flag <- SummarizedExperiment::assay(.data, .abundance) %>% colSums() %>% var() == 0
        return(flag)
        #return(TRUE)
    }else{
        return(FALSE)
    }
}

.internal_valid_rare <- function(.data, .abundance=NULL){
    assaysvar <- attr(.data, "assaysvar")
    .abundance <- rlang::as_name(.abundance)
    #.abundance <- ifelse(is.null(.abundance), "RareAbundance", .abundance)
    if (.abundance %in% assaysvar){
        flag <- .data %>%
            group_by(!!as.symbol("Sample")) %>%
            dplyr::summarize(Total=sum(!!as.symbol(.abundance))) %>%
            select("Total") %>% unlist() %>% var == 0
        return(flag)
        #return(TRUE)
    }else{
        return(FALSE)
    }
}

valid_rare.tbl_mpse <- function(.data, .abundance=NULL){
    .internal_valid_rare(.data=.data, .abundance=.abundance)
}

valid_rare.grouped_df_mpse <- function(.data, .abundance=NULL){
    .internal_valid_rare(.data=.data, .abundance=.abundance)
}