Update with jan 24 comments

Workflow.Rmd

@@ -624,7 +624,10 @@ The output from `BASiCS_MCMC` is a `BASiCS_Chain` object that contains the
 draws associated to all model parameters. Given that 
 `(N - Burn) / Thin = (30,000 - 15,000) / 15 = 1000`
 the object contains 1,000 draws for each parameter.
-These can be accessed using the `displayChainBASiCS` function.
+The matrices of MCMC draws can be accessed using the `displayChainBASiCS`
+function --- this
+may be useful for estimating and visualising credible intervals using
+packages like `r CRANpkg("bayesplot")` or `r CRANpkg("tidybayes")`.
 For example, the following code displays the first 2 MCMC draws for mean
 expression parameters $\mu_i$ associated to the first 3 genes.
 
@@ -1654,9 +1657,12 @@ posterior summaries provided by Stan [@Carpenter2017], while also being
 fully compatible with the workflow described in this manuscript via the function
 `Stan2BASiCS`, that converts the output of the Stan inference procedure to
 the type of output generated by `BASiCS_MCMC`. However, we note that
-the inference methods provided by Stan are often very slow (Hamiltonian
-Monte Carlo) or unreliable (variational inference, maximum *a posteriori*)
-for BASiCS.
+the Hamiltonian Monte Carlo inference method provided by Stan is
+more computationally intensive than our default implementation.
+Furthermore, the facster approximations provided by Stan, namely
+scalable variational inference and maximum *a posteriori* estimation,
+are often unstable and less accurate.
+
 
 Finally, we also anticipate potential extensions of `r Biocpkg("BASiCS")` to 
 account for the more complex experimental designs. For example, in cohort 
@@ -1702,6 +1708,7 @@ on EBI ArrayExpress under accession number
 The MCMC chains used to generate this article can be found in Zenodo
 under the DOI [10.5281/zenodo.10251224](https://doi.org/10.5281/zenodo.10251224).
 
+
 # Software availability
 
 **Software: All software used in this workflow is available as part of