microbes: tools for analyzing microbiome data

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

This function classifies each cog into "core" or "variable" categories.

1	cog_cv_ra(df)

`df`	the input is a data-frame with the column names participants and the row names cogs.

"Core" is defined as any cog that is in 100 percent of participants. "Variable" is defined as any cog that is in less than 100 percent of participants.

The output is a data frame with three columns. The first column is the cog name, the second column classifies the cog by "core" or "variable" and the

atomczik

http://www.ncbi.nlm.nih.gov/pubmed/19043404

see also cog.path.ref

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (df) 
{
    df <- normalize_counts(df)
    n <- length(row.names(df))
    m <- length(df)
    k <- m + 1
    for (i in 1:n) {
        df[i, ] <- as.numeric(df[i, ])
    }
    df$core_variable <- "blank"
    for (i in 1:n) {
        df$core_variable[i] <- ((sum((df[i, ]) >= 1e-07)) - 1)
    }
    for (i in 1:n) {
        if (df[i, ]$core_variable >= m) {
            df[i, ]$core_variable <- "core"
        }
        else {
            df[i, ]$core_variable <- "variable"
        }
    }
    df$RA_mean <- "blank"
    for (i in 1:n) {
        df$RA_mean[i] <- mean(as.numeric(df[i, 1:m]))
    }
    df$RA_sd <- "blank"
    for (i in 1:n) {
        df$RA_sd[i] <- sd(as.numeric(df[i, 1:m]))
    }
    cog.cv.ra <- as.data.frame(cbind(row.names(df), df$core_variable, 
        df$RA_mean, df$RA_sd))
    names(cog.cv.ra) <- c("cog.name", "core_variable", "RA_mean", 
        "RA_sd")
    cog.cv.ra$RA_mean <- as.numeric(cog.cv.ra$RA_mean)
    cog.cv.ra$RA_sd <- as.numeric(cog.cv.ra$RA_sd)
    return(cog.cv.ra)
  }