RestoreNet (version 1.0.1)

get.scatterpie: Clonal pie-chart

Description

Draw a clonal pie-chart of a random-effects reaction network.

Usage

get.scatterpie(re.res, txt = FALSE, legend = FALSE)

Value

No return value.

Arguments

re.res

output list returned by fit.re().

txt

logical (defaults to FALSE). If TRUE, barcode names will be printed on the pies.

legend

logical (defaults to FALSE). If TRUE, the legend of the pie-chart will be printed.

Details

This function generates a clonal pie-chart given a previously fitted random-effects model. In this representation each clone \(k\) is identified with a pie whose slices are lineage-specific and weighted with \(w_k\), defined as the difference between the conditional expectations of the random-effects on duplication and death parameters, that is $$w_k = E_{u\vert \Delta Y; \hat{\psi}}[u^k_{\alpha_{lin}}] - E_{u\vert \Delta Y; \hat{\psi}}[u^k_{\delta_{lin}}]$$, where \(\texttt{lin}\) is a cell lineage. The diameter of the \(k\)-th pie is proportional to the euclidean 2-norm of \(w_k\). Therefore, the larger the diameter, the more the corresponding clone is expanding into the lineage associated to the largest slice.

Examples

rcts <- c("A->1", "B->1", "C->1", "D->1",
          "A->0", "B->0", "C->0", "D->0",
          "A->B", "A->C", "C->D") ## set of reactions
ctps <- head(LETTERS,4)
nC <- 3 ## number of clones
S <- 10 ## trajectory length
tau <- 1 ## for tau-leaping algorithm
u_1 <- c(.2, .15, .17, .09*5,
         .001, .007, .004, .002,
         .13, .15, .08)
u_2 <- c(.2, .15, .17, .09,
         .001, .007, .004, .002,
         .13, .15, .08)
u_3 <- c(.2, .15, .17*3, .09,
         .001, .007, .004, .002,
         .13, .15, .08)
theta_allcls <- cbind(u_1, u_2, u_3) ## clone-specific parameters
rownames(theta_allcls) <- rcts
s20 <- 1 ## additional noise
Y <- array(data = NA,
           dim = c(S + 1, length(ctps), nC),
           dimnames = list(seq(from = 0, to = S*tau, by = tau),
                           ctps,
                           1:nC)) ## empty array to store simulations
Y0 <- c(100,0,0,0) ## initial state
names(Y0) <- ctps
for (cl in 1:nC) { ## loop over clones
  Y[,,cl] <- get.sim.tl(Yt = Y0,
                        theta = theta_allcls[,cl],
                        S = S,
                        s2 = s20,
                        tau = tau,
                        rct.lst = rcts,
                        verbose = TRUE)
}
null.res <- fit.null(Y = Y,
                     rct.lst = rcts,
                     maxit = 0, ## needs to be increased (>=100) for real applications
                     lmm = 0, ## needs to be increased (>=5) for real applications
) ## null model fitting

re.res <- fit.re(theta_0 = null.res$fit$par,
                 Y = Y,
                 rct.lst = rcts,
                 maxit = 0, ## needs to be increased (>=100) for real applications
                 lmm = 0, ## needs to be increased (>=5) for real applications
                 maxemit = 1 ## needs to be increased (>= 100) for real applications
) ## random-effects model fitting

get.scatterpie(re.res, txt = TRUE)