scmet_plot_estimated_vs_true  R Documentation 
Function for plotting true on xaxis and inferred parameter estimates on yaxis (either mean methylation or overdispersion). Along with posterior medians, the 80 high posterior density is shown as error bars. Wehn MLE estimates are provided, a plot showing the shrinkage introduced by scMET is shown as arrows.
scmet_plot_estimated_vs_true( obj, sim_dt, param = "mu", mle_fit = NULL, diff_feat_idx = NULL, hpd_thresh = 0.8, title = NULL, nfeatures = NULL )
obj 
The scMET object after calling the 
sim_dt 
The simulated data object. E.g. after calling the

param 
The parameter to plot posterior estimates, either "mu" or "gamma". 
mle_fit 
A three column matrix of betabinomial maximum likelihood estimates. First column feature name, second column mean methylation and third column overdispersion estimates. Number of features should match the ones used by scMET. 
diff_feat_idx 
Vector with locations of features that were simulated to
be differentially variable or methylated. This is stored in the object
after calling the 
hpd_thresh 
The high posterior density threshold, as computed by the

title 
Optional title, default NULL. 
nfeatures 
Optional parameter, denoting a subset of number of features to plot. Mostly to reduce overplotting. 
A ggplot2 object.
C.A.Kapourani C.A.Kapourani@ed.ac.uk
scmet
, scmet_simulate_diff
,
scmet_simulate
, scmet_plot_mean_var
,
scmet_plot_vf_tail_prob
,
scmet_plot_efdr_efnr_grid
, scmet_plot_volcano
,
scmet_plot_ma
# Fit scMET obj < scmet(Y = scmet_dt$Y, X = scmet_dt$X, L = 4, iter = 100) scmet_plot_estimated_vs_true(obj = obj, sim_dt = scmet_dt, param = "mu") # BB MLE fit to compare with scMET mle_fit < scmet_dt$Y[, bb_mle(cbind(total_reads, met_reads)) [c("mu", "gamma")], by = c("Feature")] scmet_plot_estimated_vs_true(obj = obj, sim_dt = scmet_dt, param = "mu", mle_fit = mle_fit)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.