,
John Riddell [ctb]| Title: | Bayesian modelling of cell-to-cell DNA methylation heterogeneity |
|---|---|
| Description: | High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. |
| Authors: | Andreas C. Kapourani [aut, cre]
,
John Riddell [ctb] |
| Maintainer: | Andreas C. Kapourani <[email protected]> |
| License: | GPL-3 |
| Version: | 1.9.0 |
| Built: | 2024-11-30 04:21:42 UTC |
| Source: | https://github.com/bioc/scMET |
scMET: Bayesian modelling of DNA methylation at single-cell
resolution.Package for analysing single-cell DNA methylation datasets. scMET performs feature selection, by identifying highly variable features, and also differential testing, based on mean but also more importantly on variability between two groups of cells.
scMET main package documentation.
C.A.Kapourani [email protected]
Stan Development Team (2020). RStan: the R interface to Stan. R package version 2.19.3. https://mc-stan.org
scmet, scmet_differential,
scmet_hvf
Maximum Likelihood Estimate (MLE) of Beta-Binomial (BB) model. Some details about this model can be found on the following tutorial https://rpubs.com/cakapourani/beta-binomial
bb_mle(x, w = NULL, n_starts = 10, lower_thresh = 0.001)bb_mle(x, w = NULL, n_starts = 10, lower_thresh = 0.001)
x |
An n x 2 data.table or matrix, where 1st column keeps total number of trials and 2nd column number of successes, n is the total number of samples. |
w |
Vector with initial values of |
n_starts |
Total number of restarts when optimisation fails. |
lower_thresh |
Threshold when to stop optimisation. |
A list with the following elements:
gamma:
The overdispersion parameter. This is the most important parameter, since
it tells us if and how much overdispersion we observe in the data that
cannot be explained by the Binomial model.
mu: The mean
parameter, i.e. success probability of the beta binomial.
alpha: Alpha parameter, when taking the different
parametrisation of the BB.
beta: Beta parameter, when taking
the different parametrisation of the BB.
is_conv: Logical,
whether or not the optimisation converged.
lrt: The
likelihood ratio test statistic, for testing whether the Binomial or the
Beta-Binomial fit better the data.
chi2_test: The p-value
from the Chi-squared test obtained from the LRT statistics.
Z_score: The Z score statistic proposed by Tarone (1979).
Seems more stable than LRT, in test whether we have overdispersion in our
data.
z_test: The p-value obtain from the Z-score statistic.
bb_ll: Beta binomial log likelihood (used internally to
compute the LRT statistic and the BIC)
BIC_bb: The Bayes
Information Criterion for beta binomial model
bin_ll:
Binomial log likelihood (used internally to compute the LRT statistic and
the BIC.)
BIC_bin|: The Bayes Information Criterion for binomial model
C.A.Kapourani [email protected]
scmet, scmet_differential,
scmet_hvf_lvf
# Extract data from a single Feature x <- scmet_dt$Y[Feature == "Feature_1", c("total_reads", "met_reads")] fit_mle <- bb_mle(x)# Extract data from a single Feature x <- scmet_dt$Y[Feature == "Feature_1", c("total_reads", "met_reads")] fit_mle <- bb_mle(x)
Generic function for crating a radial basis function (RBF) design matrix for input vector X.
create_design_matrix(L, X, c = 1.2)create_design_matrix(L, X, c = 1.2)
L |
Total number of basis functions, including the bias term. |
X |
Vector of covariates |
c |
Scaling parameter for variance of RBFs |
A design matrix object H.
C.A.Kapourani [email protected]
scmet, scmet_differential,
scmet_hvf_lvf
# Extract H <- create_design_matrix(L = 4, X = scmet_dt$X)# Extract H <- create_design_matrix(L = 4, X = scmet_dt$X)
Helper function that converts SCE objects to scmet objects
that can be used as input to the scmet function. The structure of the
SCE object to store single cell methylation data is the following. We
create two sparse assays, met storing methylated CpGs and total storing
total number of CpGs. Rows correspond to features and columns to cells,
similar to scRNA-seq convention.To distinguish between a feature (in a cell)
having zero methylated CpGs vs not having CpG coverage at all (missing value),
we check if the corresponding entry in total is zero as well.
The rownames and colnames slots should store the feature and cell names,
respectively. Covariates X that might explain variability in mean
(methylation) should be stored in metadata(rowData(sce)X.
sce_to_scmet(sce)sce_to_scmet(sce)
sce |
SummarizedExperiment object |
A named list containing the matrix Y (methylation data in format
required by the scmet function) and the covariates X.
C.A.Kapourani [email protected]
scmet, scmet_differential,
scmet_hvf_lvf
# Extract sce <- scmet_to_sce(Y = scmet_dt$Y, X = scmet_dt$X) df <- sce_to_scmet(sce)# Extract sce <- scmet_to_sce(Y = scmet_dt$Y, X = scmet_dt$X) df <- sce_to_scmet(sce)
Compute posterior of scMET model. This is the main function
which infers model parameters and corrects for the mean-overdispersion
relationship. The most important parameters the user should focus are X,
L, user_mcmc and iter. Advanced users may want to optimise the model
by changing the prior parameters. For small datasets, we recommend using
MCMC implementation of scMET since it is more stable.
scmet( Y, X = NULL, L = 4, use_mcmc = FALSE, use_eb = TRUE, iter = 5000, algorithm = "meanfield", output_samples = 2000, chains = 4, m_wmu = rep(0, NCOL(X)), s_wmu = 2, s_mu = 1.5, m_wgamma = rep(0, L), s_wgamma = 2, a_sgamma = 2, b_sgamma = 3, rbf_c = 1, init_using_eb = TRUE, tol_rel_obj = 1e-04, n_cores = 2, lambda = 4, seed = sample.int(.Machine$integer.max, 1), ... )scmet( Y, X = NULL, L = 4, use_mcmc = FALSE, use_eb = TRUE, iter = 5000, algorithm = "meanfield", output_samples = 2000, chains = 4, m_wmu = rep(0, NCOL(X)), s_wmu = 2, s_mu = 1.5, m_wgamma = rep(0, L), s_wgamma = 2, a_sgamma = 2, b_sgamma = 3, rbf_c = 1, init_using_eb = TRUE, tol_rel_obj = 1e-04, n_cores = 2, lambda = 4, seed = sample.int(.Machine$integer.max, 1), ... )
Y |
Observed data (methylated reads and total reads) for each feature
and cell, in a long format |
X |
Covariates which might explain variability in mean (methylation). If
X = NULL, then we do not perform any correction on the mean estimates. NOTE
that if X is provided, |
L |
Total number of basis function to fit the mean-overdispersion trend. For L = 1, this reduces to a model that does not correct for the mean-overdispersion relationship. |
use_mcmc |
Logical, whether to use the MCMC implementation for posterior inference. If FALSE, we run the VB implementation (default). For small datasets, we recommend using MCMC implementation since it is more stable. |
use_eb |
Logical, whether to use 'Empirical Bayes' for parameter
initialization. If |
iter |
Total number of iterations, either MCMC or VB algorithm. NOTE: The STAN implementation of VB relies on black-box variational inference and potentially with relatively small sample sizes sometimes tends to 'search' around the local/global minima. We've seen that with larger sample sizes (thousands of cells), it tends to converge much faster, e.g. around 2-3k iterations. |
algorithm |
Stan algorithm to be used by Stan. If MCMC: Possible values are: "NUTS", "HMC". If VB: Possible values are: "meanfield" and "fullrank". |
output_samples |
If VB algorithm, the number of posterior samples to draw and save. |
chains |
Total number of chains. |
m_wmu |
Prior mean of regression coefficients for covariates X. |
s_wmu |
Prior standard deviation of regression coefficients for covariates X. |
s_mu |
Prior standard deviation for mean parameter |
m_wgamma |
Prior mean of regression coefficients of the basis functions. |
s_wgamma |
Prior standard deviation of regression coefficients of the basis functions. |
a_sgamma |
Gamma prior (shape) for standard deviation for dispersion
parameter |
b_sgamma |
Gamma prior (rate) for standard deviation for dispersion
parameter |
rbf_c |
Scale parameter for empirically computing the variance of the RBFs. |
init_using_eb |
Logical, initial values of parameters for STAN posterior inference. Preferably this should be set always to TRUE, to lower the chances of VB/MCMC initialisations being far away from posterior mass. |
tol_rel_obj |
If VB algorithm, the convergence tolerance on the relative norm of the objective. |
n_cores |
Total number of cores. |
lambda |
The penalty term to fit the RBF coefficients for the mean-overdispersion trend when initialising hyper-parameter with EB. |
seed |
The seed for random number generation. |
... |
Additional parameters passed to |
An object of class scmet_mcmc or scmet_vb with the
following elements:
posterior: A list of matrices
containing the samples from the posterior. Each matrix corresponds to a
different parameter returned from scMET.
Y: The observed
data Y.
feature_names: A vector of feature names.
theta_priors: A list with all prior parameter values, for
reproducibility purposes.
opts: A list of all additional
parameters when running scMET. For reproducibility purposes.
C.A.Kapourani [email protected]
scmet_differential, scmet_hvf_lvf
# Fit scMET (in practice 'iter' should be much larger) obj <- scmet(Y = scmet_dt$Y, X = scmet_dt$X, L = 4, iter = 300)# Fit scMET (in practice 'iter' should be much larger) obj <- scmet(Y = scmet_dt$Y, X = scmet_dt$X, L = 4, iter = 300)
Small synthetic data for quick analysis, mostly useful for showing the differential analysis one can perform using scMET.
scmet_diff_dtscmet_diff_dt
An object of class scmet_simulate_diff of length 9.
A list object with simulated data.
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 )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 [email protected]
## 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)## 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)
Small synthetic data for quick analysis, mostly useful for performing feature selection and capturing mean-variance relationship with scMET.
scmet_dtscmet_dt
An object of class scmet_simulate of length 5.
A list object with simulated data.
Function for calling features as highly (or lowly) variable
within a datasert or cell population. This can be thought as a feature
selection step, where the highly variable features (HVF) can be used for
diverse downstream tasks, such as clustering or visualisation. Two
approaches for identifying HVFs (or LVFs): (1) If we correct for
mean-dispersion relationship, then we work directly on residual dispersions
epsilon, and define a percentile threshold delta_e. This is the
preferred option since the residual overdispersion is not confounded by
mean methylation levels. (2) Work directly with the overdispersion
parameter gamma and define an overdispersion contribution threshold
delta_g, above (below) of which we call HVFs (LVFs).
scmet_hvf( scmet_obj, delta_e = 0.9, delta_g = NULL, evidence_thresh = 0.8, efdr = 0.1 ) scmet_lvf( scmet_obj, delta_e = 0.1, delta_g = NULL, evidence_thresh = 0.8, efdr = 0.1 )scmet_hvf( scmet_obj, delta_e = 0.9, delta_g = NULL, evidence_thresh = 0.8, efdr = 0.1 ) scmet_lvf( scmet_obj, delta_e = 0.1, delta_g = NULL, evidence_thresh = 0.8, efdr = 0.1 )
scmet_obj |
The scMET posterior object after performing inference, i.e.
after calling |
delta_e |
Percentile threshold for residual overdispersion to detect variable features (between 0 and 1). Default: 0.9 for HVF and 0.1 for LVF (top 10%). NOTE: This parameter should be used when correcting for mean-dispersion relationship. |
delta_g |
Overdispersion contribution threshold (between 0 and 1). |
evidence_thresh |
Optional parameter. Posterior evidence probability
threshold parameter |
efdr |
Target for expected false discovery rate related to HVF/LVF detection (default = 0.1). |
The scMET posterior object with an additional element named hvf or
lvf according to the analysis performed. This is a list object containing
the following elements:
summary: A data.frame
containing HVF or LVF analysis output information per feature, including
posterior medians for mu, gamma, and epsilon. The tail_prob column
contains the posterior tail probability of a feature being called as HVF or
LVF. The logical is_variable column informs whether the feature is called
as variable or not.
evidence_thresh: The optimal evidence
threshold.
efdr: The EFDR value.
efnr: The
EFNR value.
efdr_grid: The EFDR values for the grid search.
efnr_grid: The EFNR values for the grid search.
evidence_thresh_grid: The grid where we searched for optimal
evidence threshold.
C.A.Kapourani [email protected]
# Fit scMET obj <- scmet(Y = scmet_dt$Y, X = scmet_dt$X, L = 4, iter = 100) # Run HVF analysis obj <- scmet_hvf(scmet_obj = obj) # Run LVF analysis obj <- scmet_lvf(scmet_obj = obj)# Fit scMET obj <- scmet(Y = scmet_dt$Y, X = scmet_dt$X, L = 4, iter = 100) # Run HVF analysis obj <- scmet_hvf(scmet_obj = obj) # Run LVF analysis obj <- scmet_lvf(scmet_obj = obj)
Function for plotting the grid search performed to obtain the optimal posterior evidence threshold to achieve a specific EFDR.
scmet_plot_efdr_efnr_grid(obj, task = "hvf")scmet_plot_efdr_efnr_grid(obj, task = "hvf")
obj |
Either the scMET object after calling the
|
task |
String. When calling variable features, i.e. output of
|
A ggplot2 object.
C.A.Kapourani [email protected]
scmet, scmet_differential,
scmet_hvf_lvf, scmet_plot_mean_var,
scmet_plot_vf_tail_prob, scmet_plot_volcano,
scmet_plot_ma
# Fit scMET obj <- scmet(Y = scmet_dt$Y, X = scmet_dt$X, L = 4, iter = 100) obj <- scmet_hvf(scmet_obj = obj, delta_e = 0.7) scmet_plot_vf_tail_prob(obj = obj, task = "hvf")# Fit scMET obj <- scmet(Y = scmet_dt$Y, X = scmet_dt$X, L = 4, iter = 100) obj <- scmet_hvf(scmet_obj = obj, delta_e = 0.7) scmet_plot_vf_tail_prob(obj = obj, task = "hvf")
Function for plotting true on x-axis and inferred parameter estimates on y-axis (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 )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 beta-binomial 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 over-plotting. |
A ggplot2 object.
C.A.Kapourani [email protected]
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$Y[, bb_mle(cbind(total_reads, met_reads)) [c("mu", "gamma")], by = c("Feature")] scmet_plot_estimated_vs_true(obj = obj, sim_dt = scmet_dt, param = "mu", mle_fit = mle_fit)# Fit scMET obj <- scmet(Y = scmet_dt$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)
Function showing MA plots for differential analysis. The y-axis shows difference between measurements across two groups and the x-axis shows the average measurements across the two groups.
scmet_plot_ma( diff_obj, task = "diff_epsilon", x = "mu", xlab = NULL, ylab = NULL, title = NULL, nfeatures = NULL )scmet_plot_ma( diff_obj, task = "diff_epsilon", x = "mu", xlab = NULL, ylab = NULL, title = NULL, nfeatures = NULL )
diff_obj |
The differential scMET object after calling the
|
task |
The differential test to plot. For differential mean methylation:
|
x |
The average parameter across the two populations to plot on the
x-axis. Can be either |
xlab |
Optional x-axis label. |
ylab |
Optional y-axis label. |
title |
Optional title, default NULL. |
nfeatures |
Optional parameter, denoting a subset of number of features to plot (only for non-differential features). Mostly to reduce over-plotting. |
A ggplot2 object.
C.A.Kapourani [email protected]
scmet, scmet_differential,
scmet_hvf_lvf, scmet_plot_mean_var,
scmet_plot_vf_tail_prob,
scmet_plot_efdr_efnr_grid, scmet_plot_volcano
## 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 = 100, seed = 12) fit_B <- scmet(Y = scmet_diff_dt$scmet_dt_B$Y, X = scmet_diff_dt$scmet_dt_B$X, L = 4, iter = 100, seed = 12) # Run differential test diff_obj <- scmet_differential(obj_A = fit_A, obj_B = fit_B) # Create volcano plot scmet_plot_ma(diff_obj, task = "diff_epsilon") ## End(Not run)## 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 = 100, seed = 12) fit_B <- scmet(Y = scmet_diff_dt$scmet_dt_B$Y, X = scmet_diff_dt$scmet_dt_B$X, L = 4, iter = 100, seed = 12) # Run differential test diff_obj <- scmet_differential(obj_A = fit_A, obj_B = fit_B) # Create volcano plot scmet_plot_ma(diff_obj, task = "diff_epsilon") ## End(Not run)
Function for plotting mean methylation on x-axis and variability on y-axis (either overdispersion or residual overdispersion). If HVF/LVF analysis is performed, points will be also coloured accordingly.
scmet_plot_mean_var( obj, y = "gamma", task = NULL, show_fit = TRUE, title = NULL, nfeatures = NULL, n = 80 )scmet_plot_mean_var( obj, y = "gamma", task = NULL, show_fit = TRUE, title = NULL, nfeatures = NULL, n = 80 )
obj |
The scMET object after calling the |
y |
The parameter to plot on the y-axis. Values can be |
task |
If NULL (default) the mean-variability relationship is plotted. If set to "hvf" or "lvf", points are coloured according the HVF/LVF analysis task. |
show_fit |
Logical, whether to show the fitted mean-overdispersion
trend. Applicable only when |
title |
Optional title, default NULL. |
nfeatures |
Optional parameter, denoting a subset of number of features
to plot. Mostly to reduce over-plotting. When |
n |
Optional integer denoting the number of grid points to colour them
by density. Used by |
A ggplot2 object.
C.A.Kapourani [email protected]
scmet, scmet_differential,
scmet_hvf_lvf, 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_mean_var(obj = obj, y = "gamma")# Fit scMET obj <- scmet(Y = scmet_dt$Y, X = scmet_dt$X, L = 4, iter = 100) scmet_plot_mean_var(obj = obj, y = "gamma")
Function for plotting the tail probabilities associated with the
HVF/LVF analysis. The tail probabilities are plotted on the y-axis, and the
user can choose which parameter can be plotted on the x-axis, using the x
parameter.
scmet_plot_vf_tail_prob( obj, x = "mu", task = "hvf", title = NULL, nfeatures = NULL )scmet_plot_vf_tail_prob( obj, x = "mu", task = "hvf", title = NULL, nfeatures = NULL )
obj |
The scMET object after calling the |
x |
The parameter to plot on the x-axis. Values can be |
task |
The task for identifying variable, either "hvf" or "lvf". |
title |
Optional title, default NULL. |
nfeatures |
Optional parameter, denoting a subset of number of features to plot (only for non HVF/LVF features). Mostly to reduce over-plotting. |
A ggplot2 object.
C.A.Kapourani [email protected]
scmet, scmet_differential,
scmet_hvf_lvf, scmet_plot_mean_var,
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) obj <- scmet_hvf(scmet_obj = obj, delta_e = 0.7) scmet_plot_vf_tail_prob(obj = obj, x = "mu")# Fit scMET obj <- scmet(Y = scmet_dt$Y, X = scmet_dt$X, L = 4, iter = 100) obj <- scmet_hvf(scmet_obj = obj, delta_e = 0.7) scmet_plot_vf_tail_prob(obj = obj, x = "mu")
Function showing volcano plots for differential analysis. The
posterior tail probabilities are ploteted on the y-axis, and depending on
the differential test to plot the effect size will be plotted on the
x-axis. For differential variability (DV) analysis we recommend using the
epsilon parameter.
scmet_plot_volcano( diff_obj, task = "diff_epsilon", xlab = NULL, ylab = "Posterior tail probability", title = NULL, nfeatures = NULL )scmet_plot_volcano( diff_obj, task = "diff_epsilon", xlab = NULL, ylab = "Posterior tail probability", title = NULL, nfeatures = NULL )
diff_obj |
The differential scMET object after calling the
|
task |
The differential test to plot. For differential mean methylation:
|
xlab |
Optional x-axis label. |
ylab |
Optional y-axis label. |
title |
Optional title, default NULL. |
nfeatures |
Optional parameter, denoting a subset of number of features to plot (only for non-differential features). Mostly to reduce over-plotting. |
A ggplot2 object.
C.A.Kapourani [email protected]
scmet, scmet_differential,
scmet_hvf_lvf, scmet_plot_mean_var,
scmet_plot_vf_tail_prob,
scmet_plot_efdr_efnr_grid, scmet_plot_ma
## 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 = 100, seed = 12) fit_B <- scmet(Y = scmet_diff_dt$scmet_dt_B$Y, X = scmet_diff_dt$scmet_dt_B$X, L = 4, iter = 100, seed = 12) # Run differential test diff_obj <- scmet_differential(obj_A = fit_A, obj_B = fit_B) # Create volcano plot scmet_plot_volcano(diff_obj, task = "diff_epsilon") ## End(Not run)## 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 = 100, seed = 12) fit_B <- scmet(Y = scmet_diff_dt$scmet_dt_B$Y, X = scmet_diff_dt$scmet_dt_B$X, L = 4, iter = 100, seed = 12) # Run differential test diff_obj <- scmet_differential(obj_A = fit_A, obj_B = fit_B) # Create volcano plot scmet_plot_volcano(diff_obj, task = "diff_epsilon") ## End(Not run)
General function for simulating datasets with diverse proprties. This for instance include, adding covariates X that explain differences in mean methylation levels. Or also defining the trend for the mean - overdispersion relationship.
scmet_simulate( N_feat = 100, N_cells = 50, N_cpgs = 15, L = 4, X = NULL, w_mu = c(-0.5, -1.5), s_mu = 1, w_gamma = NULL, s_gamma = 0.3, rbf_c = 1, cells_range = c(0.4, 0.8), cpgs_range = c(0.4, 0.8) )scmet_simulate( N_feat = 100, N_cells = 50, N_cpgs = 15, L = 4, X = NULL, w_mu = c(-0.5, -1.5), s_mu = 1, w_gamma = NULL, s_gamma = 0.3, rbf_c = 1, cells_range = c(0.4, 0.8), cpgs_range = c(0.4, 0.8) )
N_feat |
Total number of features (genomics regions). |
N_cells |
Maximum number of cells. |
N_cpgs |
Maximum number of CpGs per cell and feature. |
L |
Total number of radial basis functions (RBFs) to fit the mean-overdispersion trend. For L = 1, this reduces to a model that does not correct for the mean-overdispersion relationship. |
X |
Covariates which might explain variability in mean (methylation). If X = NULL, a 2-dim matrix will be generated, first column containing intercept term (all values = 1), and second colunn random generated covariates. |
w_mu |
Regression coefficients for covariates X. Should match number of columns of X. |
s_mu |
Standard deviation for mean parameter |
w_gamma |
Regression coefficients of the basis functions. Should match the value of L. If NULL, random coefficients will be generated. |
s_gamma |
Standard deviation of dispersion parameter |
rbf_c |
Scale parameter for empirically computing the variance of the RBFs. |
cells_range |
Range (betwen 0 and 1) to randomly (sub)sample the number of cells per feature. |
cpgs_range |
Range (betwen 0 and 1) to randomly (sub)sample the number of CpGs per cell and feature. |
A simulated dataset and additional information for reproducibility purposes.
sim <- scmet_simulate(N_feat = 150, N_cells = 50, N_cpgs = 15, L = 4)sim <- scmet_simulate(N_feat = 150, N_cells = 50, N_cpgs = 15, L = 4)
General function for simulating two methylation datasets for
performing differential methylation analysis. Differential analysis can be
either performed in detecting changes in mean or variability of methylation
patterns between the two groups. Similar to scmet_simulate,
the function allows inclusion of covariates X that explain differences in
mean methylation levels. Or also defining the trend for the mean -
overdispersion relationship.
scmet_simulate_diff( N_feat = 100, N_cells = 50, N_cpgs = 15, L = 4, diff_feat_prcg_mu = 0, diff_feat_prcg_gamma = 0.2, OR_change_mu = 3, OR_change_gamma = 3, X = NULL, w_mu = c(-0.5, -1.5), s_mu = 1, w_gamma = NULL, s_gamma = 0.3, rbf_c = 1, cells_range = c(0.4, 0.8), cpgs_range = c(0.4, 0.8) )scmet_simulate_diff( N_feat = 100, N_cells = 50, N_cpgs = 15, L = 4, diff_feat_prcg_mu = 0, diff_feat_prcg_gamma = 0.2, OR_change_mu = 3, OR_change_gamma = 3, X = NULL, w_mu = c(-0.5, -1.5), s_mu = 1, w_gamma = NULL, s_gamma = 0.3, rbf_c = 1, cells_range = c(0.4, 0.8), cpgs_range = c(0.4, 0.8) )
N_feat |
Total number of features (genomics regions). |
N_cells |
Maximum number of cells. |
N_cpgs |
Maximum number of CpGs per cell and feature. |
L |
Total number of radial basis functions (RBFs) to fit the mean-overdispersion trend. For L = 1, this reduces to a model that does not correct for the mean-overdispersion relationship. |
diff_feat_prcg_mu |
Percentage of features (betwen 0 and 1) that show differential mean methylation between the two groups. |
diff_feat_prcg_gamma |
Percentage of features (betwen 0 and 1) that show differential variability between the two groups. |
OR_change_mu |
Effect size change (in terms of odds ratio) of mean methylation between the two groups. |
OR_change_gamma |
Effect size change (in terms of odds ratio) of methylation variability between the two groups. |
X |
Covariates which might explain variability in mean (methylation). If X = NULL, a 2-dim matrix will be generated, first column containing intercept term (all values = 1), and second colunn random generated covariates. |
w_mu |
Regression coefficients for covariates X. Should match number of columns of X. |
s_mu |
Standard deviation for mean parameter |
w_gamma |
Regression coefficients of the basis functions. Should match the value of L. If NULL, random coefficients will be generated. |
s_gamma |
Standard deviation of dispersion parameter |
rbf_c |
Scale parameter for empirically computing the variance of the RBFs. |
cells_range |
Range (betwen 0 and 1) to randomly (sub)sample the number of cells per feature. |
cpgs_range |
Range (betwen 0 and 1) to randomly (sub)sample the number of CpGs per cell and feature. |
Methylation data from two cell populations/conditions.
sim_diff <- scmet_simulate_diff(N_feat = 150, N_cells = 100, N_cpgs = 15, L = 4)sim_diff <- scmet_simulate_diff(N_feat = 150, N_cells = 100, N_cpgs = 15, L = 4)
Helper function that converts an scmet to SCE object. The
structure of the SCE object to store single cell methylation data is the
following. We create two assays, met storing methylated CpGs and total
storing total number of CpGs. Rows correspond to features and columns to
cells, similar to scRNA-seq convention. The rownames and colnames slots
should store the feature and cell names, respectively. Covariates X
that might explain variability in mean (methylation) should be stored
in metadata(rowData(sce)$X.
scmet_to_sce(Y, X = NULL)scmet_to_sce(Y, X = NULL)
Y |
Methylation data in data.table format. |
X |
(Optional) Matrix of covariates. |
An SCE object with the structure described above.
C.A.Kapourani [email protected]
scmet, scmet_differential,
scmet_hvf_lvf
# Extract sce <- scmet_to_sce(Y = scmet_dt$Y, X = scmet_dt$X)# Extract sce <- scmet_to_sce(Y = scmet_dt$Y, X = scmet_dt$X)