## -------------------------------------------------------------------------- library(CATALYST) data(raw_data) ff <- concatFCS(raw_data) ## ----fig.width=17, fig.height=8.5------------------------------------------ normCytof(x=ff, y="dvs", k=300) ## ----results='hide'-------------------------------------------------------- data(sample_ff) sample_ff ## -------------------------------------------------------------------------- data(sample_key) sample_key ## ----messages=FALSE-------------------------------------------------------- re <- assignPrelim(x=sample_ff, y=sample_key, verbose=FALSE) re ## -------------------------------------------------------------------------- re <- estCutoffs(x=re, verbose=FALSE) re ## ----results = 'hide'------------------------------------------------------ # use global separation cutoff applyCutoffs(x=re, sep_cutoffs=0.35) # use population-specific cutoffs re <- applyCutoffs(x=re) ## ----results='hide'-------------------------------------------------------- outFCS(x=re) ## ----fig.width=9, fig.height=5--------------------------------------------- plotYields(x=re, which="C1") ## ----fig.width=9, fig.height=5--------------------------------------------- plotYields(x=re, which=0, legend=FALSE) ## ----fig.width=9, fig.height=5--------------------------------------------- # event plot for unassigned events plotEvents(x=re, which=0, n_events=1000) ## ----fig.width=9, fig.height=5--------------------------------------------- plotEvents(x=re, which="D1", n_events=500) ## ----warning=FALSE, fig.width=6, fig.height=6.5---------------------------- plotMahal(x=re, which="A5") ## -------------------------------------------------------------------------- # get single-stained control samples data(ss_exp) # specify mass channels stained for bc_ms <- c(139, 141:156, 158:176) # debarcode re <- assignPrelim(x=ss_exp, y=bc_ms, verbose=FALSE) re <- estCutoffs(x=re, verbose=FALSE) re <- applyCutoffs(x=re) # compute spillover matrix spillMat <- computeSpillmat(x=re) ## ----fig.width=8, fig.height=6--------------------------------------------- # estimate trim value minimizing sum of squared # population- and channel-wise medians upon compensation estTrim(x=re, min=0.05, max=0.11) ## ----fig.width=7.5, fig.height=7.5----------------------------------------- spillMat <- computeSpillmat(x=re, trim=0.08) plotSpillmat(bc_ms=bc_ms, SM=spillMat) ## -------------------------------------------------------------------------- data(mp_cells) comped_cells <- compCytof(x=mp_cells, y=spillMat) ## ----echo=FALSE, message=FALSE, results='hide'----------------------------- cf <- 20 ss_t <- asinh(exprs(ss_exp)/cf) ss_comped_t <- asinh(exprs(compCytof(ss_exp, spillMat))/cf) cells_t <- asinh(exprs(mp_cells)/cf) comped_cells_t <- asinh(exprs(comped_cells)/cf) ## ----echo=FALSE, fig.width=9, fig.height=9--------------------------------- par(mfrow=c(2,2), pty="s") which <- c("La139Di", "Gd155Di") #which <- c("Yb171Di", "Yb172Di") which <- c("Er167Di", "Er168Di") cols <- colorRampPalette(rev(RColorBrewer::brewer.pal(10, "Spectral"))) bw <- .25; n <- 64 smoothScatter(ss_t[, which], nrpoints=0, nbin=n, bandwidth=bw, colramp=cols, main='Single stains') smoothScatter(ss_comped_t[, which], nrpoints=0, nbin=n, bandwidth=bw, colramp=cols, main='Compensated') smoothScatter(cells_t[, which], nrpoints=0, nbin=n, bandwidth=bw, colramp=cols, main='Multiplexed cells') smoothScatter(comped_cells_t[, which], nrpoints=0, nbin=n, bandwidth=bw, colramp=cols, main='Compensated')