Description Usage Arguments Details Value
For each observation (e.g. SNP), estimates the posterior probability of
each association pattern. This version of the main Primo
function uses moderated
t-statistics and parameters previously calculated under the 'limma' framework
(i.e. using estimate_densities_modT
).
It is useful for cases where the same statistic from one study
(e.g. gene-SNP pair) may be mapped to multiple statistics from
another study (e.g. multiple gene-CpG pairings).
Utilizes parallel computing, when available.
1 | Primo_modT(Tstat_mod, mdfs, V_mat, Gamma, tol = 0.001, par_size = 1)
|
Tstat_mod |
matrix of moderated t-statistics. |
mdfs |
matrix of moderated degrees of freedom. |
V_mat |
matrix of scaling factors. |
Gamma |
correlation matrix. |
tol |
numeric value specifying the tolerance threshold for convergence. |
par_size |
numeric value specifying the number of workers for parallel computing (1 for sequential processing). |
The following are additional details describing the input arguments (for m SNPs/observations measured in d studies):
Tstat_mod | m x d matrix. |
mdfs | m x d matrix. |
V_mat | m x d matrix. |
Gamma | d x d matrix. |
A list with the following elements:
post_prob | matrix of posterior probabilities (each column corresponds to an association pattern). |
pis | vector of estimated proportion of observations belonging to each association pattern. |
D_mat | matrix of densities under each association pattern. |
Gamma | correlation matrix. |
Tstat_mod | matrix of moderated t-statistics. |
V_mat | matrix of scaling factors under the alternative distribution. |
mdf_sd_mat | matrix of standard deviation adjustment according to moderated degrees of freedom: df/(df-2). |
The main element of interest for inference is the posterior probabilities matrix, post_prob
.
The estimated proportion of observations belonging to each association pattern, pis
, may
also be of interest. The remaining elements are returned primarily for use by other functions.
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