老师您好，在学习中我发现，KNN补值函数knnImputation函数是按照行而非列进行的缺失值填补，但是蛋白组或者基因组数据一般都是列是样本行是蛋白/基因特征名称。是按照行（特征）进行近邻k个参考的距离计算并进行缺失值填补，而非按照列（样本）进行距离计算的，我想问下您，KNN不是一般是计算样本间距离吗？为什么此处是计算特征间距离进行缺失值填补？两者填补结果都合理吗？

function (data, k = 10, scale = TRUE, meth = "weighAvg", 
    distData = NULL) 
{
    n <- nrow(data)
    if (!is.null(distData)) {
        distInit <- n + 1
        data <- rbind(data, distData)
    }
    else distInit <- 1
    N <- nrow(data)
    ncol <- ncol(data)
    contAttrs <- which(vapply(data, dplyr::type_sum, character(1)) %in% 
        c("dbl", "int"))
    nomAttrs <- setdiff(seq.int(ncol), contAttrs)
    hasNom <- length(nomAttrs)
    dm <- data
    if (scale) 
        dm[, contAttrs] <- scale(dm[, contAttrs])
    if (hasNom) 
        for (i in nomAttrs) dm[[i]] <- as.integer(dm[[i]])
    dm <- as.matrix(dm)
    nas <- which(!complete.cases(dm))
    if (!is.null(distData)) 
        tgt.nas <- nas[nas <= n]
    else tgt.nas <- nas
    if (length(tgt.nas) == 0) 
        warning("No case has missing values. Stopping as there is nothing to do.")
    xcomplete <- dm[setdiff(distInit:N, nas), ]
    if (nrow(xcomplete) < k) 
        stop("Not sufficient complete cases for computing neighbors.")
    for (i in tgt.nas) {
        tgtAs <- which(is.na(dm[i, ]))
        dist <- scale(xcomplete, dm[i, ], FALSE)
        xnom <- setdiff(nomAttrs, tgtAs)
        if (length(xnom)) 
            dist[, xnom] <- ifelse(dist[, xnom] > 0, 1, dist[, 
                xnom])
        dist <- dist[, -tgtAs]
        dist <- sqrt(drop(dist^2 %*% rep(1, ncol(dist))))
        ks <- order(dist)[seq(k)]
        for (j in tgtAs) if (meth == "median") 
            data[i, j] <- centralValue(data[setdiff(distInit:N, 
                nas)[ks], j])
        else data[i, j] <- centralValue(data[setdiff(distInit:N, 
            nas)[ks], j], exp(-dist[ks]))
    }
    data[1:n, ]
}
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