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Stats.R
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vkey2 <- function (map, title = NA, side = 2, stretch = 1.4, x, y,
wh)
{
opar <- par(xpd = NA)
on.exit(par(opar))
n <- length(map$breaks) + 1
dy <- strheight("A")
aspect <- diff(grconvertX(1:2, from = "inches"))/diff(grconvertY(1:2,
from = "inches"))
dx <- dy * aspect
if (missing(wh)) {
wh <- 1:(n-1)
}
labs <- format(map$breaks[wh])
maxlabwidth <- max(strwidth(labs))
if (missing(x)) {
x <- grconvertX(1, from = "nfc") - (2 * dx)
if (side == 4)
x <- x - maxlabwidth - dx
}
else {
if (is.list(x)) {
y <- x$y
x <- x$x
}
}
if (missing(y))
y <- par("usr")[3] + dy
ybord <- y + ((0:(n - 1)) * dy * stretch)
rect(x, ybord[-n], x + dx, ybord[-1], col = map$colors, border = NA)
if (side == 4) {
xtext <- x + dx
text(x = x, y = ybord[n] + (1.5 * dy), title, adj = c(0,
0))
}
if (side == 2) {
xtext <- x
text(x = x + dx, y = ybord[n] + (1.5 * dy), title, adj = c(1,
0))
}
text(x = xtext, y = ybord[wh] + 0.5 * dy, labels = labs, pos = side)
}
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
require(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel
# ncol: Number of columns of plots
# nrow: Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
heatmap.3 <- function(x,
Rowv = TRUE, Colv = if (symm) "Rowv" else TRUE,
distfun = dist,
hclustfun = hclust,
dendrogram = c("both","row", "column", "none"),
symm = FALSE,
scale = c("none","row", "column"),
na.rm = TRUE,
revC = identical(Colv,"Rowv"),
add.expr,
breaks,
symbreaks = max(x < 0, na.rm = TRUE) || scale != "none",
col = "heat.colors",
colsep,
rowsep,
sepcolor = "white",
sepwidth = c(0.05, 0.05),
cellnote,
notecex = 1,
notecol = "cyan",
na.color = par("bg"),
trace = c("none", "column","row", "both"),
tracecol = "cyan",
hline = median(breaks),
vline = median(breaks),
linecol = tracecol,
margins = c(5,5),
ColSideColors = NULL,
RowSideColors = NULL,
side.height.fraction=0.3,
cexRow = 0.2 + 1/log10(nr),
cexCol = 0.2 + 1/log10(nc),
labRow = NULL,
labCol = NULL,
key = TRUE,
keysize = 1.5,
density.info = c("none", "histogram", "density"),
denscol = tracecol,
symkey = max(x < 0, na.rm = TRUE) || symbreaks,
densadj = 0.25,
main = NULL,
xlab = NULL,
ylab = NULL,
lmat = NULL,
lhei = NULL,
lwid = NULL,
NumColSideColors = 1,
NumRowSideColors = 1,
KeyValueName="Value",...){
invalid <- function (x) {
if (missing(x) || is.null(x) || length(x) == 0)
return(TRUE)
if (is.list(x))
return(all(sapply(x, invalid)))
else if (is.vector(x))
return(all(is.na(x)))
else return(FALSE)
}
x <- as.matrix(x)
scale01 <- function(x, low = min(x), high = max(x)) {
x <- (x - low)/(high - low)
x
}
retval <- list()
scale <- if (symm && missing(scale))
"none"
else match.arg(scale)
dendrogram <- match.arg(dendrogram)
trace <- match.arg(trace)
density.info <- match.arg(density.info)
if (length(col) == 1 && is.character(col))
col <- get(col, mode = "function")
if (!missing(breaks) && (scale != "none"))
warning("Using scale=\"row\" or scale=\"column\" when breaks are",
"specified can produce unpredictable results.", "Please consider using only one or the other.")
if (is.null(Rowv) || is.na(Rowv))
Rowv <- FALSE
if (is.null(Colv) || is.na(Colv))
Colv <- FALSE
else if (Colv == "Rowv" && !isTRUE(Rowv))
Colv <- FALSE
if (length(di <- dim(x)) != 2 || !is.numeric(x))
stop("`x' must be a numeric matrix")
nr <- di[1]
nc <- di[2]
if (nr <= 1 || nc <= 1)
stop("`x' must have at least 2 rows and 2 columns")
if (!is.numeric(margins) || length(margins) != 2)
stop("`margins' must be a numeric vector of length 2")
if (missing(cellnote))
cellnote <- matrix("", ncol = ncol(x), nrow = nrow(x))
if (!inherits(Rowv, "dendrogram")) {
if (((!isTRUE(Rowv)) || (is.null(Rowv))) && (dendrogram %in%
c("both", "row"))) {
if (is.logical(Colv) && (Colv))
dendrogram <- "column"
else dedrogram <- "none"
warning("Discrepancy: Rowv is FALSE, while dendrogram is `",
dendrogram, "'. Omitting row dendogram.")
}
}
if (!inherits(Colv, "dendrogram")) {
if (((!isTRUE(Colv)) || (is.null(Colv))) && (dendrogram %in%
c("both", "column"))) {
if (is.logical(Rowv) && (Rowv))
dendrogram <- "row"
else dendrogram <- "none"
warning("Discrepancy: Colv is FALSE, while dendrogram is `",
dendrogram, "'. Omitting column dendogram.")
}
}
if (inherits(Rowv, "dendrogram")) {
ddr <- Rowv
rowInd <- order.dendrogram(ddr)
}
else if (is.integer(Rowv)) {
hcr <- hclustfun(distfun(x))
ddr <- as.dendrogram(hcr)
ddr <- reorder(ddr, Rowv)
rowInd <- order.dendrogram(ddr)
if (nr != length(rowInd))
stop("row dendrogram ordering gave index of wrong length")
}
else if (isTRUE(Rowv)) {
Rowv <- rowMeans(x, na.rm = na.rm)
hcr <- hclustfun(distfun(x))
ddr <- as.dendrogram(hcr)
ddr <- reorder(ddr, Rowv)
rowInd <- order.dendrogram(ddr)
if (nr != length(rowInd))
stop("row dendrogram ordering gave index of wrong length")
}
else {
rowInd <- nr:1
}
if (inherits(Colv, "dendrogram")) {
ddc <- Colv
colInd <- order.dendrogram(ddc)
}
else if (identical(Colv, "Rowv")) {
if (nr != nc)
stop("Colv = \"Rowv\" but nrow(x) != ncol(x)")
if (exists("ddr")) {
ddc <- ddr
colInd <- order.dendrogram(ddc)
}
else colInd <- rowInd
}
else if (is.integer(Colv)) {
hcc <- hclustfun(distfun(if (symm)
x
else t(x)))
ddc <- as.dendrogram(hcc)
ddc <- reorder(ddc, Colv)
colInd <- order.dendrogram(ddc)
if (nc != length(colInd))
stop("column dendrogram ordering gave index of wrong length")
}
else if (isTRUE(Colv)) {
Colv <- colMeans(x, na.rm = na.rm)
hcc <- hclustfun(distfun(if (symm)
x
else t(x)))
ddc <- as.dendrogram(hcc)
ddc <- reorder(ddc, Colv)
colInd <- order.dendrogram(ddc)
if (nc != length(colInd))
stop("column dendrogram ordering gave index of wrong length")
}
else {
colInd <- 1:nc
}
retval$rowInd <- rowInd
retval$colInd <- colInd
retval$call <- match.call()
x <- x[rowInd, colInd]
x.unscaled <- x
cellnote <- cellnote[rowInd, colInd]
if (is.null(labRow))
labRow <- if (is.null(rownames(x)))
(1:nr)[rowInd]
else rownames(x)
else labRow <- labRow[rowInd]
if (is.null(labCol))
labCol <- if (is.null(colnames(x)))
(1:nc)[colInd]
else colnames(x)
else labCol <- labCol[colInd]
if (scale == "row") {
retval$rowMeans <- rm <- rowMeans(x, na.rm = na.rm)
x <- sweep(x, 1, rm)
retval$rowSDs <- sx <- apply(x, 1, sd, na.rm = na.rm)
x <- sweep(x, 1, sx, "/")
}
else if (scale == "column") {
retval$colMeans <- rm <- colMeans(x, na.rm = na.rm)
x <- sweep(x, 2, rm)
retval$colSDs <- sx <- apply(x, 2, sd, na.rm = na.rm)
x <- sweep(x, 2, sx, "/")
}
if (missing(breaks) || is.null(breaks) || length(breaks) < 1) {
if (missing(col) || is.function(col))
breaks <- 16
else breaks <- length(col) + 1
}
if (length(breaks) == 1) {
if (!symbreaks)
breaks <- seq(min(x, na.rm = na.rm), max(x, na.rm = na.rm),
length = breaks)
else {
extreme <- max(abs(x), na.rm = TRUE)
breaks <- seq(-extreme, extreme, length = breaks)
}
}
nbr <- length(breaks)
ncol <- length(breaks) - 1
if (class(col) == "function")
col <- col(ncol)
min.breaks <- min(breaks)
max.breaks <- max(breaks)
x[x < min.breaks] <- min.breaks
x[x > max.breaks] <- max.breaks
if (missing(lhei) || is.null(lhei))
lhei <- c(keysize, 4)
if (missing(lwid) || is.null(lwid))
lwid <- c(keysize, 4)
if (missing(lmat) || is.null(lmat)) {
lmat <- rbind(4:3, 2:1)
if (!is.null(ColSideColors)) {
#if (!is.matrix(ColSideColors))
#stop("'ColSideColors' must be a matrix")
if (!is.character(ColSideColors) || nrow(ColSideColors) != nc)
stop("'ColSideColors' must be a matrix of nrow(x) rows")
lmat <- rbind(lmat[1, ] + 1, c(NA, 1), lmat[2, ] + 1)
#lhei <- c(lhei[1], 0.2, lhei[2])
lhei=c(lhei[1], side.height.fraction*NumColSideColors, lhei[2])
}
if (!is.null(RowSideColors)) {
#if (!is.matrix(RowSideColors))
#stop("'RowSideColors' must be a matrix")
if (!is.character(RowSideColors) || ncol(RowSideColors) != nr)
stop("'RowSideColors' must be a matrix of ncol(x) columns")
lmat <- cbind(lmat[, 1] + 1, c(rep(NA, nrow(lmat) - 1), 1), lmat[,2] + 1)
#lwid <- c(lwid[1], 0.2, lwid[2])
lwid <- c(lwid[1], side.height.fraction*NumRowSideColors, lwid[2])
}
lmat[is.na(lmat)] <- 0
}
if (length(lhei) != nrow(lmat))
stop("lhei must have length = nrow(lmat) = ", nrow(lmat))
if (length(lwid) != ncol(lmat))
stop("lwid must have length = ncol(lmat) =", ncol(lmat))
op <- par(no.readonly = TRUE)
on.exit(par(op))
layout(lmat, widths = lwid, heights = lhei, respect = FALSE)
if (!is.null(RowSideColors)) {
if (!is.matrix(RowSideColors)){
par(mar = c(margins[1], 0, 0, 0.5))
image(rbind(1:nr), col = RowSideColors[rowInd], axes = FALSE)
box()
} else {
par(mar = c(margins[1], 0, 0, 0.5))
rsc = t(RowSideColors[,rowInd, drop=F])
rsc.colors = matrix()
rsc.names = names(table(rsc))
rsc.i = 1
for (rsc.name in rsc.names) {
rsc.colors[rsc.i] = rsc.name
rsc[rsc == rsc.name] = rsc.i
rsc.i = rsc.i + 1
}
rsc = matrix(as.numeric(rsc), nrow = dim(rsc)[1])
image(t(rsc), col = as.vector(rsc.colors), axes = FALSE)
if (length(rownames(RowSideColors)) > 0) {
axis(1, 0:(dim(rsc)[2] - 1)/(dim(rsc)[2] - 1), rownames(RowSideColors), las = 2, tick = FALSE)
}
box()
}
}
if (!is.null(ColSideColors)) {
if (!is.matrix(ColSideColors)){
par(mar = c(0.5, 0, 0, margins[2]))
image(cbind(1:nc), col = ColSideColors[colInd], axes = FALSE)
box()
} else {
par(mar = c(0.5, 0, 0, margins[2]))
csc = ColSideColors[colInd, , drop=F]
csc.colors = matrix()
csc.names = names(table(csc))
csc.i = 1
for (csc.name in csc.names) {
csc.colors[csc.i] = csc.name
csc[csc == csc.name] = csc.i
csc.i = csc.i + 1
}
csc = matrix(as.numeric(csc), nrow = dim(csc)[1])
image(csc, col = as.vector(csc.colors), axes = FALSE)
if (length(colnames(ColSideColors)) > 0) {
axis(2, 0:(dim(csc)[2] - 1)/max(1,(dim(csc)[2] - 1)), colnames(ColSideColors), las = 2, tick = FALSE)
}
box()
}
}
par(mar = c(margins[1], 0, 0, margins[2]))
x <- t(x)
cellnote <- t(cellnote)
if (revC) {
iy <- nr:1
if (exists("ddr"))
ddr <- rev(ddr)
x <- x[, iy]
cellnote <- cellnote[, iy]
}
else iy <- 1:nr
image(1:nc, 1:nr, x, xlim = 0.5 + c(0, nc), ylim = 0.5 + c(0, nr), axes = FALSE, xlab = "", ylab = "", col = col, breaks = breaks, ...)
retval$carpet <- x
if (exists("ddr"))
retval$rowDendrogram <- ddr
if (exists("ddc"))
retval$colDendrogram <- ddc
retval$breaks <- breaks
retval$col <- col
if (!invalid(na.color) & any(is.na(x))) { # load library(gplots)
mmat <- ifelse(is.na(x), 1, NA)
image(1:nc, 1:nr, mmat, axes = FALSE, xlab = "", ylab = "",
col = na.color, add = TRUE)
}
axis(1, 1:nc, labels = labCol, las = 2, line = -0.5, tick = 0,
cex.axis = cexCol)
if (!is.null(xlab))
mtext(xlab, side = 1, line = margins[1] - 1.25)
axis(4, iy, labels = labRow, las = 2, line = -0.5, tick = 0,
cex.axis = cexRow)
if (!is.null(ylab))
mtext(ylab, side = 4, line = margins[2] - 1.25)
if (!missing(add.expr))
eval(substitute(add.expr))
if (!missing(colsep))
for (csep in colsep) rect(xleft = csep + 0.5, ybottom = 0, xright = csep + 0.5 + sepwidth[1], ytop = ncol(x) + 1, lty = 1, lwd = 1, col = sepcolor, border = sepcolor)
if (!missing(rowsep))
for (rsep in rowsep) rect(xleft = 0, ybottom = (ncol(x) + 1 - rsep) - 0.5, xright = nrow(x) + 1, ytop = (ncol(x) + 1 - rsep) - 0.5 - sepwidth[2], lty = 1, lwd = 1, col = sepcolor, border = sepcolor)
min.scale <- min(breaks)
max.scale <- max(breaks)
x.scaled <- scale01(t(x), min.scale, max.scale)
if (trace %in% c("both", "column")) {
retval$vline <- vline
vline.vals <- scale01(vline, min.scale, max.scale)
for (i in colInd) {
if (!is.null(vline)) {
abline(v = i - 0.5 + vline.vals, col = linecol,
lty = 2)
}
xv <- rep(i, nrow(x.scaled)) + x.scaled[, i] - 0.5
xv <- c(xv[1], xv)
yv <- 1:length(xv) - 0.5
lines(x = xv, y = yv, lwd = 1, col = tracecol, type = "s")
}
}
if (trace %in% c("both", "row")) {
retval$hline <- hline
hline.vals <- scale01(hline, min.scale, max.scale)
for (i in rowInd) {
if (!is.null(hline)) {
abline(h = i + hline, col = linecol, lty = 2)
}
yv <- rep(i, ncol(x.scaled)) + x.scaled[i, ] - 0.5
yv <- rev(c(yv[1], yv))
xv <- length(yv):1 - 0.5
lines(x = xv, y = yv, lwd = 1, col = tracecol, type = "s")
}
}
if (!missing(cellnote))
text(x = c(row(cellnote)), y = c(col(cellnote)), labels = c(cellnote),
col = notecol, cex = notecex)
par(mar = c(margins[1], 0, 0, 0))
if (dendrogram %in% c("both", "row")) {
plot(ddr, horiz = TRUE, axes = FALSE, yaxs = "i", leaflab = "none")
}
else plot.new()
par(mar = c(0, 0, if (!is.null(main)) 5 else 0, margins[2]))
if (dendrogram %in% c("both", "column")) {
plot(ddc, axes = FALSE, xaxs = "i", leaflab = "none")
}
else plot.new()
if (!is.null(main))
title(main, cex.main = 1.5 * op[["cex.main"]])
if (key) {
par(mar = c(5, 4, 2, 1), cex = 0.75)
tmpbreaks <- breaks
if (symkey) {
max.raw <- max(abs(c(x, breaks)), na.rm = TRUE)
min.raw <- -max.raw
tmpbreaks[1] <- -max(abs(x), na.rm = TRUE)
tmpbreaks[length(tmpbreaks)] <- max(abs(x), na.rm = TRUE)
}
else {
min.raw <- min(x, na.rm = TRUE)
max.raw <- max(x, na.rm = TRUE)
}
z <- seq(min.raw, max.raw, length = length(col))
image(z = matrix(z, ncol = 1), col = col, breaks = tmpbreaks,
xaxt = "n", yaxt = "n")
par(usr = c(0, 1, 0, 1))
lv <- pretty(breaks)
xv <- scale01(as.numeric(lv), min.raw, max.raw)
axis(1, at = xv, labels = lv)
if (scale == "row")
mtext(side = 1, "Row Z-Score", line = 2)
else if (scale == "column")
mtext(side = 1, "Column Z-Score", line = 2)
else mtext(side = 1, KeyValueName, line = 2)
if (density.info == "density") {
dens <- density(x, adjust = densadj, na.rm = TRUE)
omit <- dens$x < min(breaks) | dens$x > max(breaks)
dens$x <- dens$x[-omit]
dens$y <- dens$y[-omit]
dens$x <- scale01(dens$x, min.raw, max.raw)
lines(dens$x, dens$y/max(dens$y) * 0.95, col = denscol,
lwd = 1)
axis(2, at = pretty(dens$y)/max(dens$y) * 0.95, pretty(dens$y))
title("Color Key\nand Density Plot")
par(cex = 0.5)
mtext(side = 2, "Density", line = 2)
}
else if (density.info == "histogram") {
h <- hist(x, plot = FALSE, breaks = breaks)
hx <- scale01(breaks, min.raw, max.raw)
hy <- c(h$counts, h$counts[length(h$counts)])
lines(hx, hy/max(hy) * 0.95, lwd = 1, type = "s",
col = denscol)
axis(2, at = pretty(hy)/max(hy) * 0.95, pretty(hy))
title("Color Key\nand Histogram")
par(cex = 0.5)
mtext(side = 2, "Count", line = 2)
}
else title("Color Key")
}
else plot.new()
retval$colorTable <- data.frame(low = retval$breaks[-length(retval$breaks)],
high = retval$breaks[-1], color = retval$col)
invisible(retval)
}
load_package <- function (package.names=c('MASS', 'ggbiplot', 'vegan', 'pscl', 'glmmADMB', 'aod', 'nlme', 'MiRKAT', 'matrixStats', 'gplots', 'scales', 'ggplot2', 'GUniFrac', 'rpart', 'qvalue', 'DESeq2',
'phangorn', 'phyloseq', 'RColorBrewer', 'squash', 'rhdf5', 'biom', 'reshape', 'randomForest', 'Boruta', 'ade4')) {
for (package.name in package.names) {
require(package.name, character.only=T)
}
}
getPermuteMatrixBlock <- function(permutations, strata) {
strata <- factor(strata)
strata <- factor(as.numeric(strata))
res <- sapply(1:permutations, function(i) {
strata1 <- strata
levels(strata1) <- sample(levels(strata1))
order(as.numeric(as.character(strata1)))
})
t(res)
}
# adonis2: permutate the covariate instead of data matrix - validated
adonis2 <- function (formula, data = NULL, permutations = 999, method = "bray",
strata = NULL, block.perm = TRUE, contr.unordered = "contr.sum", contr.ordered = "contr.poly",
...)
{
TOL <- 1e-07
Terms <- terms(formula, data = data, keep.order=TRUE)
lhs <- formula[[2]]
lhs <- eval(lhs, data, parent.frame())
formula[[2]] <- NULL
rhs.frame <- model.frame(formula, data, drop.unused.levels = TRUE)
op.c <- options()$contrasts
options(contrasts = c(contr.unordered, contr.ordered))
rhs <- model.matrix(formula, rhs.frame)
options(contrasts = op.c)
grps <- attr(rhs, "assign")
qrhs <- qr(rhs)
rhs <- rhs[, qrhs$pivot, drop = FALSE]
rhs <- rhs[, 1:qrhs$rank, drop = FALSE]
grps <- grps[qrhs$pivot][1:qrhs$rank]
u.grps <- unique(grps)
nterms <- length(u.grps) - 1
st <- ifelse(nterms == 1, 2, nterms)
H.s <- lapply(st:(nterms+1), function(j) {
Xj <- rhs[, grps %in% u.grps[1:j]]
qrX <- qr(Xj, tol = TOL)
Q <- qr.Q(qrX)
tcrossprod(Q[, 1:qrX$rank])
})
if (inherits(lhs, "dist")) {
if (any(lhs < -TOL))
stop("dissimilarities must be non-negative")
dmat <- as.matrix(lhs^2)
}
else {
dist.lhs <- as.matrix(vegdist(lhs, method = method, ...))
dmat <- dist.lhs^2
}
n <- nrow(dmat)
G <- -sweep(dmat, 1, rowMeans(dmat))/2
SS.Exp.comb <- sapply(H.s, function(hat) sum(G * t(hat)))
if (nterms == 1) {
SS.Exp.each <- SS.Exp.comb
} else {
SS.Exp.each <- SS.Exp.comb[2] - SS.Exp.comb[1]
}
H.snterm <- H.s[[length(H.s)]]
tIH.snterm <- t(diag(n) - H.snterm)
# if (length(H.s) > 1) {
# H.s[[2]] <- H.s[[2]] - H.s[[1]]
# }
SS.Res <- sum(G * tIH.snterm)
df.Exp <- sum(grps == u.grps[length(u.grps)])
df.Res <- n - qrhs$rank
if (inherits(lhs, "dist")) {
beta.sites <- qr.coef(qrhs, as.matrix(lhs))
beta.spp <- NULL
}
else {
beta.sites <- qr.coef(qrhs, dist.lhs)
beta.spp <- qr.coef(qrhs, as.matrix(lhs))
}
colnames(beta.spp) <- colnames(lhs)
colnames(beta.sites) <- rownames(lhs)
F.Mod <- (SS.Exp.each/df.Exp)/(SS.Res/df.Res)
f.test <- function(tH, G, df.Exp, df.Res, tIH.snterm) {
(sum(G * tH)/df.Exp)/(sum(G * tIH.snterm)/df.Res)
}
rhs.1 <- rhs[, grps %in% u.grps[1:nterms], drop=F]
rhs.2 <- rhs[, grps %in% u.grps[(nterms+1)], drop=F]
if (missing(strata)) strata <- NULL
if (block.perm == FALSE) {
permute.ind <- vegan:::getPermuteMatrix(permutations, n, strata = strata)
} else {
if (is.null(strata)) stop('Block permutation requires strata!\n')
strata.u <- unique(strata)
reorder.ind <- unlist(lapply(strata.u, function(x) which(strata == x)))
expand.ind <- rep(1:length(strata.u), sapply(strata.u, function(x) sum(strata == x)))
rhs.2.u <- rhs.2[sapply(strata.u, function(x) which(strata == x)[1]), , drop=F]
}
# Not right
f.perms <-
sapply(1:permutations, function(i) {
if (block.perm == FALSE) {
rhs.2 <- rhs[permute.ind[i, ], grps %in% u.grps[(nterms+1)], drop=F]
} else {
rhs.2[reorder.ind, ] <- rhs.2.u[sample(nrow(rhs.2.u)), , drop=F][expand.ind, , drop=F]
}
Xj <- cbind(rhs.1, rhs.2)
qrX <- qr(Xj, tol = TOL)
Q <- qr.Q(qrX)
tH.snterm <- t(tcrossprod(Q[, 1:qrX$rank]))
tIH.snterm <- diag(n) - tH.snterm
if (nterms > 1) {
tH.snterm <- tH.snterm - t(H.s[[1]])
}
f.test(tH.snterm, G, df.Exp, df.Res,
tIH.snterm)
}
)
f.perms <- round(f.perms, 12)
F.Mod <- round(F.Mod, 12)
SumsOfSqs = c(SS.Exp.each, SS.Res, SS.Exp.comb[length(SS.Exp.comb)] + SS.Res)
tab <- data.frame(Df = c(df.Exp, df.Res, n - 1), SumsOfSqs = SumsOfSqs,
MeanSqs = c(SS.Exp.each/df.Exp, SS.Res/df.Res, NA), F.Model = c(F.Mod,
NA, NA), R2 = SumsOfSqs/SumsOfSqs[length(SumsOfSqs)],
P = c((sum(f.perms >= F.Mod) + 1)/(permutations +
1), NA, NA))
rownames(tab) <- c(attr(attr(rhs.frame, "terms"), "term.labels")[u.grps[length(u.grps)]],
"Residuals", "Total")
colnames(tab)[ncol(tab)] <- "Pr(>F)"
attr(tab, "heading") <- "Terms added sequentially (first to last)\n"
class(tab) <- c("anova", class(tab))
out <- list(aov.tab = tab, call = match.call(), coefficients = beta.spp,
coef.sites = beta.sites, f.perms = as.matrix(f.perms), model.matrix = rhs,
terms = Terms)
class(out) <- "adonis"
out
}
# Test 1 - validated
# pv1 <- pv2 <- numeric(100)
# for ( i in 1:100) {
# x1 <- rnorm(20); x2 <- x1 + rnorm(20); y <- 0.5*x1 + x2 + rnorm(20);
# pv1[i] <- adonis(dist(y) ~ x1 + x2)$aov.tab[2, 6]
# pv2[i] <- adonis2(dist(y) ~ x1 + x2, block.perm=F)$aov.tab[1, 6]
# }
# hist(pv1)
# hist(pv2)
# plot(-log10(pv1), -log10(pv2))
#
# # Test 2 - validated
# pv1 <- pv2 <- numeric(100)
# for ( i in 1:100) {
# x2 <- factor(sample(1:2, 20, repl=T)); x1 <- rnorm(20) + 0.25*as.numeric(x2); y <- 0.5*x1 + rnorm(20);
# pv1[i] <- adonis(dist(y) ~ x1 + x2)$aov.tab[2, 6]
# ID <- c(1:20, 1:20)
# x1 <- c(x1, x1); x2 <- factor(c(x2, x2)); y <- c(y, y)
# pv2[i] <- adonis2(dist(y) ~ x1 + x2, strata=ID)$aov.tab[1, 6]
# }
# hist(pv1)
# hist(pv2)
# plot(pv1, pv2)
#
# pv1 <- pv2 <- numeric(100)
# for ( i in 1:100) {
# x2 <- factor(sample(1:2, 20, repl=T)); x1 <- rnorm(20) + 0.25*as.numeric(x2); y <- 0.5*x1 + 1*as.numeric(x2) + rnorm(20);
# pv1[i] <- adonis(dist(y) ~ x1 + x2)$aov.tab[2, 6]
# ID <- c(1:20, 1:10)
# x1 <- c(x1, x1[1:10]); x2 <- c(x2, x2[1:10]); z <- 0.2*rnorm(10);
# y <- c(y[1:10] - z, y[11:20], y[1:10] + z)
# ind <- sample(30)
# y <- y[ind]; x1 <- x1[ind]; x2 <- x2[ind]; ID <- ID[ind]
# pv2[i] <- adonis2(dist(y) ~ x1 + x2, strata=ID)$aov.tab[1, 6]
# }
# hist(pv1)
# hist(pv2)
# plot(-log10(pv1), -log10(pv2))
# abline(0, 1, col='red')
########################################
# This generates the matrix columns-wise
# From JnPaulson
generate_matrix <- function(x){
indptr = x$sample$matrix$indptr+1
indices = x$sample$matrix$indices+1
data = x$sample$matrix$data
nr = length(x$observation$ids)
counts = sapply(2:length(indptr),function(i){
x = rep(0,nr)
seq = indptr[i-1]:(indptr[i]-1)
x[indices[seq]] = data[seq]
x
})
rownames(counts) = x$observation$ids
colnames(counts) = x$sample$ids
# I wish this next line wasn't necessary
lapply(1:nrow(counts),function(i){
counts[i,]
})
}
generate_metadata <- function(x){
metadata = x$metadata
metadata = lapply(1:length(x$ids),function(i){
id_metadata = lapply(metadata,function(j){
if(length(dim(j))>1){ as.vector(j[,i,drop=FALSE]) }
else{ j[i] }
})
list(id = x$ids[i],metadata=id_metadata)
})
return(metadata)
}
namedList <- function(...) {
L <- list(...)
snm <- sapply(substitute(list(...)),deparse)[-1]
if (is.null(nm <- names(L))) nm <- snm
if (any(nonames <- nm=="")) nm[nonames] <- snm[nonames]
setNames(L,nm)
}
read_hdf5_biom<-function(file_input){
x = h5read(file_input,"/",read.attributes = TRUE)
data = generate_matrix(x)
rows = generate_metadata(x$observation)
columns = generate_metadata(x$sample)
shape = c(length(data),length(data[[1]])) # dim(data)
# Experimental -- need to actually load these from file
id = attr(x,"id")
vs = attr(x,"format-version")
format = sprintf("Biological Observation Matrix %s.%s",vs[1],vs[2])
format_url = attr(x,"format-url")
type = "OTU table"
#type=attr(x,"type")
generated_by = attr(x,"generated-by")
date = attr(x,"creation-date")
matrix_type = "dense"
matrix_element_type = "int"
namedList(id,format,format_url,type,generated_by,date,matrix_type,matrix_element_type,
rows,columns,shape,data)
}
############################################
# comm are otu counts, row: otus, column samples
# intersect.no: Pairwise ratio calculated on pairs with at least 'intersect.no' common taxa
GMPR <- function (comm, intersect.no=4) {
sapply(1:ncol(comm), function(i) {
x <- comm[, i]
pr <- sapply(1:ncol(comm), function(j) {
y <- comm[, j]
ind <- x != 0 & y != 0
if (sum(ind) >= intersect.no) {
res <- median(x[ind] / y[ind])
} else {
res <- NA
}
})
if (sum(is.na(pr)) != 0) warning(paste0('Sample '), i, ' does not have any common taxa with some other samples!\n')
exp(mean(log(pr[!is.na(pr)])))
}
)
}
uniquefy_taxa_names <- function (data.obj) {
for (level in names(data.obj$abund.list)) {
obj <- data.obj$abund.list[[level]]
rownames(obj) <- gsub('unclassified', paste0('Unclassified', substr(level, 1, 1)), rownames(obj))
data.obj$abund.list[[level]] <- obj
}
return(data.obj)
}
load_data <- function (otu.file, map.file, tree.file=NULL, parseFunction=parse_taxonomy_greengenes, version='Old', species=TRUE, filter.no=1, rep.seq=NULL,
norm='TSS', intersect.no=4, winsor=FALSE, winsor.qt=0.97,
ko.file=NULL, cog.file=NULL, ko.ann.file=NULL,
meta.sep='', quote="", comment="", read.gg=FALSE, rff=FALSE, dep=NULL, seed=1234) {
# ko and cog file are not rarefied
# filter.no: filter the OTUs with read support less than filter.no (default is filtering singleton); singleton will not be filtered after rarefaction
# winsorization and GMPR should be further studied. Current default is false and GMPR is on the genus level
set.seed(seed)
cat("Load meta file...\n")
if (grepl("csv$", map.file)) {
meta.dat <- read.csv(map.file, header=T, check.names=F, row.names=1, comment=comment, quote=quote)
} else {
meta.dat <- read.table(map.file, header=T, check.names=F, row.names=1, comment=comment, sep=meta.sep, quote=quote)
}
# Load Tree
if (!is.null(tree.file)) {
cat("Load tree file ...\n")
if (read.gg == F) {
tree.12 <- read.tree(tree.file)
} else {
tree.12 <- read_tree_greengenes(tree.file)
}
if (is.rooted(tree.12) == F) {
tree.12 <- midpoint(tree.12)
}
} else {
tree.12 <- NULL
}
cat("Load OTU file...\n")
if (version != 'New') {
biom.obj <- import_biom(otu.file, parseFunction = parseFunction)
} else {
temp <- read_hdf5_biom(otu.file)
otu.file <- paste0(otu.file, '.old')
write_biom(temp, otu.file)
biom.obj <- import_biom(otu.file, parseFunction = parseFunction)
}
otu.tab.12 <- otu_table(biom.obj)@.Data
otu.ind <- rowSums(otu.tab.12 != 0) > filter.no
otu.tab.12 <- otu.tab.12[otu.ind, ]
# OTU names
otu.name.full <- as.matrix(biom.obj@tax_table[, c('Kingdom', 'Phylum', 'Class', 'Order', 'Family', 'Genus', 'Species')])
otu.name.full <- otu.name.full[otu.ind, ]
otu.name.12 <- otu.name.full[, c('Kingdom', 'Phylum', 'Class', 'Order', 'Family', 'Genus', 'Species')]
otu.name.12[is.na(otu.name.12)] <- 'unclassified'
otu.name.12 <- [email protected]
otu.name.12[, ] <- gsub('\\[', '', otu.name.12)
otu.name.12[, ] <- gsub('\\]', '', otu.name.12)
if (rff == TRUE) {
if (is.null(dep)) {
otu.tab.12 <- t(Rarefy(t(otu.tab.12))$otu.tab.rff)
} else {
otu.tab.12 <- t(Rarefy(t(otu.tab.12), dep)$otu.tab.rff)
}
}
samIDs <- intersect(rownames(meta.dat), colnames(otu.tab.12))
if (length(samIDs) == 0) stop('Sample names in the meta file and biom file differ?\n')
meta.dat <- meta.dat[samIDs, ]
otu.tab.12 <- otu.tab.12[, samIDs]
# Create abundance list
cat("Create taxa abundance list ...\n")
abund.list.12 <- list()
hierachs <- c('Phylum', 'Class', 'Order', 'Family', 'Genus')
for (hierach in hierachs) {
if (hierach != 'Phylum') {
single.names <- otu.name.12[, hierach]
# single.names[grepl('unclassified', single.names, ignore.case=T)] <- paste0('Unclassified',substr(hierach, 1, 1))
tax.family <- paste(otu.name.12[, 'Phylum'], single.names, sep=";")
tax.family[grepl('unclassified', tax.family, ignore.case=T)] <- paste0('Unclassified_', hierach)
} else {
tax.family <- otu.name.12[, 'Phylum']
tax.family[grepl('unclassified', tax.family, ignore.case=T)] <- 'Unclassified_Phylum'
}
family <- aggregate(otu.tab.12, by=list(tax.family), FUN=sum)
rownames(family) <- family[, 1]
family <- as.matrix(family[, -1])
abund.list.12[[hierach]] <- family
}
if (species) {
abund.list.12[['Species']] <- otu.tab.12
rownames(abund.list.12[['Species']]) <- paste0("OTU", rownames(otu.tab.12), ":", otu.name.12[, 'Phylum'], ";", otu.name.12[, 'Genus'])
}
if (norm == 'GMPR') {
sf <- GMPR(abund.list.12[['Genus']], intersect.no)
names(sf) <- samIDs
warning('GMPR is only suitable for samples from a same body location!\n')
} else {
if (norm == 'TSS') {
sf <- colSums(otu.tab.12)
} else {
sf <- NULL
}
}
if (winsor == TRUE) {
# Addressing the outlier (97% percent) or at least one outlier
abund.list.12 <- sapply(abund.list.12, function(genus) {
genus.p <- t(t(genus) / sf)
genus.p <- apply(genus.p, 1, function(x) {
cutoff <- quantile(x, winsor.qt)
x[x >= cutoff] <- cutoff
x
}
)
# column/row switch
genus.w <- t(round(genus.p * sf))
})
# OTU table
otu.tab.12.p <- t(t(otu.tab.12) / sf)
otu.tab.12.p <- apply(otu.tab.12.p, 1, function(x) {
cutoff <- quantile(x, winsor.qt)
x[x >= cutoff] <- cutoff
x
}
)
# column/row switch
otu.tab.12 <- t(round(otu.tab.12.p * sf))
}
# Rarefaction/Normalizing factors are not calculated for functional data
if (!is.null(ko.file)) {
cat("Load kegg file...\n")
ko <- read_biom(ko.file)
ko.dat <- as.matrix(biom_data(ko))
ko.dat <- ko.dat[, intersect(colnames(ko.dat), samIDs)]
# Rarefaction?
if (is.null(ko.ann.file)) {
# Old - back compatability
ko.ann <- observation_metadata(ko)
ko.ann <- cbind(KEGG_Pathways1=sapply(ko.ann, function(x) x['KEGG_Pathways1']),
KEGG_Pathways2=sapply(ko.ann, function(x) x['KEGG_Pathways2']),
KEGG_Pathways3=sapply(ko.ann, function(x) x['KEGG_Pathways3']))
rownames(ko.ann) <- rownames(ko.dat)
ko.ann[is.na(ko.ann)] <- 'Unclassified'
hierachs <- c("KEGG_Pathways1", "KEGG_Pathways2", "KEGG_Pathways3")
for (hierach in hierachs) {
tax.family <- ko.ann[, hierach]
family <- aggregate(ko.dat, by=list(tax.family), FUN=sum)
rownames(family) <- family[, 1]
family <- as.matrix(family[, -1])
abund.list.12[[hierach]] <- family
}
} else {
# New
load(ko.ann.file)
#
kos <- rownames(ko.dat)
abund.list.12[["KEGG_Pathways3"]] <- NULL
kos.id <- NULL
for (ko.item in names(kegg.map)) {
kos.common <- intersect(kos, kegg.map[[ko.item]])
if (length(kos.common) != 0) {
abund.list.12[["KEGG_Pathways3"]] <- rbind(abund.list.12[["KEGG_Pathways3"]], colSums(ko.dat[kos.common, , drop=F]))
kos.id <- c(kos.id, ko.item)
}
}
rownames(abund.list.12[["KEGG_Pathways3"]]) <- kos.id
abund.list.12[["KEGG_Metabolism"]] <- abund.list.12[["KEGG_Pathways3"]][intersect(kos.id, unlist(kegg.ann[['Metabolism']])), ]