Description Usage Arguments Details Value Examples
Perform Gibbs sampling inference for a hierarchical Bayesian mixture of factor analysers to identify bifurcations in singlecell expression data.
1 2 3 4 5 6  mfa(y, iter = 2000, thin = 1, burn = iter/2, b = 2,
zero_inflation = FALSE, pc_initialise = 1, prop_collapse = 0,
scale_input = !zero_inflation, lambda = NULL, eta_tilde = NULL,
alpha = 0.1, beta = 0.1, theta_tilde = 0, tau_eta = 1,
tau_theta = 1, tau_c = 1, alpha_chi = 0.01, beta_chi = 0.01,
w_alpha = 1/b, clamp_pseudotimes = FALSE)

y 
A cellbygene singlecell expression matrix or an ExpressionSet object 
iter 
Number of MCMC iterations 
thin 
MCMC samples to thin 
burn 
Number of MCMC samples to throw away 
b 
Number of branches to model 
zero_inflation 
Logical, should zero inflation be enabled? 
pc_initialise 
Which principal component to initialise pseudotimes to 
prop_collapse 
Proportion of Gibbs samples which should marginalise over c 
scale_input 
Logical. If true, input is scaled to have mean 0 variance 1 
lambda 
The dropout parameter  by default estimated using the function 
eta_tilde 
Hyperparameter 
alpha 
Hyperparameter 
beta 
Hyperparameter 
theta_tilde 
Hyperparameter 
tau_eta 
Hyperparameter 
tau_theta 
Hyperparameter 
tau_c 
Hyperparameter 
alpha_chi 
Hyperparameter 
beta_chi 
Hyperparameter 
w_alpha 
Hyperparameter 
clamp_pseudotimes 
This clamps the pseudotimes to their initial values and doesn't perform sampling. Should be FALSE except for diagnostics. 
The column names of Y are used as feature (gene/transcript) names while the row names are used as cell names. If either of these is undefined then the corresponding names are set to cell_x or feature_y.
It is recommended the form of Y is analogous to logexpression to mitigate the impact of outliers.
In the absence of prior information, three valid local maxima in the posterior likelihood exist (see manuscript). Setting the initial values to a principal component typically fixes sampling to one of them, analogous to specifying a root cell in similar methods.
The hyperparameter eta_tilde
represents the expected expression in the absence of
any actual expression measurements. While a Bayesian purist might reason this based on
knowledge of the measurement technology, simply taking the mean of the input matrix in
an Empirical Bayes style seems reasonable.
The degree of shrinkage of the factor loading matrices to a common value is given by the
gamma prior on chi
. The mean of this is alpha_chi / beta_chi
while the variance
alpha_chi / beta_chi^2
. Therefore, to obtain higher levels of shrinkage increase
alpha_chi
with respect to beta_chi
.
The collapsed Gibbs sampling option given by collapse
involves marginalising out
c
(the factor loading intercepts) when updating the branch assignment parameters
gamma
which tends to soften the branch assignments.
If zero inflation is enabled using the zero_inflation
parameter then scaling should
*not* be enabled.
An S3 structure with the following entries:
traces
A list of iterationbydim trace matrices for
several important variables
iter
Number of iterations
thin
Thinning applied
burn
Burn period at the start of MCMC
b
Number of branches modelled
prop_collapse
Proportion of updates for gamma that are collapsed
N
Number of cells
G
Number of features (genes/transcripts)
feature_names
Names of features
cell_names
Names of cells
1 2  synth < create_synthetic(C = 20, G = 5)
m < mfa(synth$X)

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