View source: R/scmet_differential.R
scmet_differential | R Documentation |
Function for performing differential methylation testing to identify differentially methylted (DM) and differentially variable (DV) features across two groups of pre-specified cell populations.
scmet_differential( obj_A, obj_B, psi_m = log(1.5), psi_e = log(1.5), psi_g = log(1.5), evidence_thresh_m = 0.8, evidence_thresh_e = 0.8, evidence_thresh_g = 0.8, efdr_m = 0.05, efdr_e = 0.05, efdr_g = 0.05, group_label_A = "GroupA", group_label_B = "GroupB", features_selected = NULL, filter_outlier_features = FALSE, outlier_m = 0.05, outlier_g = 0.05 )
obj_A |
The scMET posterior object for group A. |
obj_B |
The scMET posterior object for group B. |
psi_m |
Minimum log odds ratio tolerance threshold for detecting changes
in overall methylation (positive real number). Default value: |
psi_e |
Minimum log odds ratio tolerance threshold for detecting changes in residual over-dispersion (positive real number). |
psi_g |
Minimum log odds ratio tolerance threshold for detecting changes in biological over-dispersion (positive real number). |
evidence_thresh_m |
Optional parameter. Posterior evidence probability
threshold parameter |
evidence_thresh_e |
Optional parameter. Posterior evidence probability
threshold parameter |
evidence_thresh_g |
Optional parameter. Posterior evidence probability
threshold parameter |
efdr_m |
Target for expected false discovery rate related to the
comparison of means. If |
efdr_e |
Target for expected false discovery rate related to the
comparison of residual over-dispersions If |
efdr_g |
Target for expected false discovery rate related to the
comparison of biological over-dispersions If |
group_label_A |
Label assigned to group A. |
group_label_B |
Label assigned to group B. |
features_selected |
User defined list of selected features to perform differential analysis. Should be the same length as the total number of features, with TRUE for features included in the differential analysis, and FALSE for those excluded from further analysis. |
filter_outlier_features |
Logical, whether to filter features that have
either mean methylation levels |
outlier_m |
Value of average mean methylation across both groups so a
feature is considered as outlier. I.e. if set to 0.05, then will remove
features with |
outlier_g |
Value of average overdispersion |
An scmet_differential
object which is a list containing the
following elements:
diff_mu_summary
: A data.frame
containing differential mean methylation output information per feature
(rows), including posterior median parameters for each group and mu_LOR
containing the log odds-ratio between the groups. The mu_tail_prob
column
contains the posterior tail probability of a feature being called as DM.
The mu_diff_test
column informs the outcomes of the test.
diff_epsilon_summary
: Same as above, but for differential
variability based on residual overdispersion.
diff_gamma_summary
: The same as above but for DV analysis based on
overdispersion.
diff_mu_thresh
: Information about optimal
posterior evidence threshold search for mean methylation mu.
diff_epsilon_thresh
: Same as above but for residual
overdispersion epsilon..
diff_gamma_thresh
: Same as above but
for overdispersion gamma.
opts
: The parameters used for
testing. For reproducibility purposes.
C.A.Kapourani C.A.Kapourani@ed.ac.uk
scmet
, scmet_hvf_lvf
## Not run: # Fit scMET for each group fit_A <- scmet(Y = scmet_diff_dt$scmet_dt_A$Y, X = scmet_diff_dt$scmet_dt_A$X, L = 4, iter = 50, seed = 12) fit_B <- scmet(Y = scmet_diff_dt$scmet_dt_B$Y, X = scmet_diff_dt$scmet_dt_B$X, L = 4, iter = 50, seed = 12) # Run differential test diff_obj <- scmet_differential(obj_A = fit_A, obj_B = fit_B) ## End(Not run)
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