diff --git a/DESCRIPTION b/DESCRIPTION
index 1d2a99e..7473423 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,28 +1,28 @@
 Package: miaTime
 Type: Package
 Title: Microbiome Time Series Analysis
-Version: 0.1.23
+Version: 0.99.0
 Authors@R:
      c(person(given = "Leo", family = "Lahti", role = c("aut"),
               email = "leo.lahti@iki.fi",
               comment = c(ORCID = "0000-0001-5537-637X")),
-       person(given = "Yagmur", family = "Simsek", role = c("aut", "cre"),
+       person(given = "Tuomas", family = "Borman", role = c("aut", "cre"),
+              email = "tuomas.v.borman@utu.fi",
+              comment = c(ORCID = "0000-0002-8563-8884")),
+       person(given = "Yagmur", family = "Simsek", role = c("aut"),
               email = "yagmur.simsek@hsrw.org"),
        person(given = "Sudarshan", family = "Shetty", role = c("ctb"),
               email = "sudarshanshetty9@gmail.com"),
-       person(given = "Chouaib", family = "Benchraka", role=c("ctb")),
-       person(given = "Tuomas", family = "Borman", role = c("ctb"),
-              email = "tuomas.v.borman@utu.fi",
-              comment = c(ORCID = "0000-0002-8563-8884")),
-      person(given = "Muluh", family = "Muluh", role=c("ctb"))
+       person(given = "Chouaib", family = "Benchraka", role = c("ctb")),
+      person(given = "Muluh", family = "Muluh", role = c("ctb"))
         )
-Description: 
+Description:
     The `miaTime` package provides tools for microbiome time series analysis
     based on (Tree)SummarizedExperiment infrastructure.
-biocViews: Microbiome, Software, Sequencing, Coverage
+biocViews: Microbiome, Software, Sequencing
 License: Artistic-2.0 | file LICENSE
 Depends:
-    R (>= 4.0),
+    R (>= 4.4.0),
     mia
 Imports:
     dplyr,
@@ -31,7 +31,7 @@ Imports:
     SingleCellExperiment,
     SummarizedExperiment,
     TreeSummarizedExperiment
-Suggests: 
+Suggests:
     BiocStyle,
     devtools,
     ggplot2,
@@ -41,17 +41,10 @@ Suggests:
     rmarkdown,
     scater,
     testthat,
-    tidySingleCellExperiment,
-    tidySummarizedExperiment,
-    TreeSummarizedExperiment,
-    vegan,
-    zoo
-Remotes:
-    github::microbiome/mia
-Encoding: UTF-8
+    vegan
 URL: https://github.com/microbiome/miaTime
+BugReports: https://github.com/microbiome/miaTime/issues
+Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.2
 VignetteBuilder: knitr
-LazyData: false
-Config/testthat/edition: 3
diff --git a/NAMESPACE b/NAMESPACE
index eec4d77..1ec2e56 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -8,6 +8,8 @@ exportMethods(addBaselineDivergence)
 exportMethods(addStepwiseDivergence)
 exportMethods(getBaselineDivergence)
 exportMethods(getStepwiseDivergence)
+import(TreeSummarizedExperiment)
+import(mia)
 importFrom(dplyr,arrange)
 importFrom(dplyr,group_by)
 importFrom(dplyr,lag)
diff --git a/NEWS b/NEWS
index 70f0d07..977403d 100755
--- a/NEWS
+++ b/NEWS
@@ -1,3 +1,8 @@
+Changes in version 0.99.0
++ Take into account Bioconductor requirements
+
+Changes in 0.1.23
++ Restructure *BaselineDivergence and *StepwiseDivergence
 
 Changes in version 0.1.19
 + changed assay_name to assay.type
diff --git a/R/AllGenerics.R b/R/AllGenerics.R
new file mode 100644
index 0000000..55b5d16
--- /dev/null
+++ b/R/AllGenerics.R
@@ -0,0 +1,22 @@
+# All generic methods are listed here
+
+#' @rdname addBaselineDivergence
+#' @export
+setGeneric("getBaselineDivergence", signature = "x", function(x, ...)
+    standardGeneric("getBaselineDivergence"))
+
+#' @rdname addBaselineDivergence
+#' @export
+setGeneric("addBaselineDivergence", signature = "x", function(x, ...)
+    standardGeneric("addBaselineDivergence"))
+
+#' @rdname addStepwiseDivergence
+#' @export
+#'
+setGeneric("getStepwiseDivergence", signature = c("x"), function(x, ...)
+    standardGeneric("getStepwiseDivergence"))
+
+#' @rdname addStepwiseDivergence
+#' @export
+setGeneric("addStepwiseDivergence", signature = "x", function(x, ...)
+    standardGeneric("addStepwiseDivergence"))
diff --git a/R/getBaselineDivergence.R b/R/getBaselineDivergence.R
index 0307e6a..cb3f9fb 100644
--- a/R/getBaselineDivergence.R
+++ b/R/getBaselineDivergence.R
@@ -96,11 +96,6 @@
 #' 
 NULL
 
-#' @rdname addBaselineDivergence
-#' @export
-setGeneric("getBaselineDivergence", signature = "x", function(x, ...)
-    standardGeneric("getBaselineDivergence"))
-
 #' @rdname addBaselineDivergence
 #' @export
 setMethod("getBaselineDivergence", signature = c(x = "SummarizedExperiment"),
@@ -163,11 +158,6 @@ setMethod("getBaselineDivergence", signature = c(x = "SummarizedExperiment"),
     }
 )
 
-#' @rdname addBaselineDivergence
-#' @export
-setGeneric("addBaselineDivergence", signature = "x", function(x, ...)
-    standardGeneric("addBaselineDivergence"))
-
 #' @rdname addBaselineDivergence
 #' @export
 setMethod("addBaselineDivergence", signature = c(x = "SummarizedExperiment"),
diff --git a/R/getStepwiseDivergence.R b/R/getStepwiseDivergence.R
index adf9c95..6d4bbad 100644
--- a/R/getStepwiseDivergence.R
+++ b/R/getStepwiseDivergence.R
@@ -50,12 +50,6 @@
 #' 
 NULL
 
-#' @rdname addStepwiseDivergence
-#' @export
-#'
-setGeneric("getStepwiseDivergence", signature = c("x"), function(x, ...)
-    standardGeneric("getStepwiseDivergence"))
-
 #' @rdname addStepwiseDivergence
 #' @export
 setMethod("getStepwiseDivergence", signature = c(x = "ANY"),
@@ -113,11 +107,6 @@ setMethod("getStepwiseDivergence", signature = c(x = "ANY"),
     }
 )
 
-#' @rdname addStepwiseDivergence
-#' @export
-setGeneric("addStepwiseDivergence", signature = "x", function(x, ...)
-    standardGeneric("addStepwiseDivergence"))
-
 #' @rdname addStepwiseDivergence
 #' @export
 setMethod("addStepwiseDivergence", signature = c(x = "SummarizedExperiment"),
diff --git a/R/data.R b/R/miaTime.R
similarity index 94%
rename from R/data.R
rename to R/miaTime.R
index fa7df16..2a0b54c 100755
--- a/R/data.R
+++ b/R/miaTime.R
@@ -1,3 +1,16 @@
+#' \code{miaTime} Package.
+#'
+#' \code{miaTime} implements time series methods in \link[mia]{mia} ecosystem.
+#' @name miaTime-package
+#' @aliases miaTime
+#' @seealso \link[TreeSummarizedExperiment:TreeSummarizedExperiment-class]{TreeSummarizedExperiment} class
+"_PACKAGE"
+NULL
+
+#' @import mia
+#' @import TreeSummarizedExperiment
+NULL
+
 #' @title HITChip Atlas with 1006 Western Adults
 #' @description This data set contains genus-level microbiota profiling with
 #' HITChip for 1006 western adults with no reported health complications,
diff --git a/README.md b/README.md
index 9defc3d..ff3cd67 100644
--- a/README.md
+++ b/README.md
@@ -2,14 +2,17 @@
 
 <!-- badges: start -->
 
-[![R-CMD-check-bioc-devel](https://github.com/microbiome/miaTime/workflows/R-CMD-check-bioc-devel/badge.svg)](https://github.com/microbiome/miaTime/actions)
-<!--[![R-CMD-check-bioc](https://github.com/microbiome/mia/actions/workflows/check-bioc.yml/badge.svg)](https://github.com/microbiome/mia/actions/workflows/check-bioc.yml)-->
-[![Codecov test
-coverage](https://codecov.io/gh/microbiome/miaTime/branch/master/graph/badge.svg)](https://codecov.io/gh/microbiome/miaTime?branch=master)
+[![Platforms](http://bioconductor.org/shields/availability/release/miaTime.svg)](https://bioconductor.org/packages/release/bioc/html/miaTime.html)
+[![rworkflows](https://github.com/microbiome/miaTime/actions/workflows/rworkflows.yml/badge.svg?branch=devel)](https://github.com/microbiome/miaTime/actions)
+[![Bioc-release](http://bioconductor.org/shields/build/release/bioc/miaTime.svg)](http://bioconductor.org/packages/release/bioc/html/miaTime.html)
+[![Bioc-age](http://bioconductor.org/shields/years-in-bioc/miaTime.svg)](https://bioconductor.org/packages/release/bioc/html/miaTime.html)
+[![Codecov test coverage](https://codecov.io/gh/microbiome/miaTime/branch/devel/graph/badge.svg)](https://codecov.io/gh/microbiome/miaTime?branch=devel)
+[![Dependencies](http://bioconductor.org//shields/dependencies/release/miaTime.svg)](https://bioconductor.org/packages/release/bioc/html/miaTime.html)
 
 <!-- badges: end -->
 
-# miaTime
+## Using the package
+
 This R package can be used to analyse time series data for microbial
 communities. The package is part of [miaverse](https://microbiome.github.io/), 
 and is based on the `TreeSummarizedExperiment` data container.
@@ -18,16 +21,35 @@ See the [package homepage](https://microbiome.github.io/miaTime) for
 example workflows.
 
 ## Installation
- 
-The package can be directly installed from R command line.
 
+### Bioc-release
 
-```r
-devtools::install_github("microbiome/miaTime")
-library(miaTime)
 ```
+if (!requireNamespace("BiocManager", quietly = TRUE))
+    install.packages("BiocManager")
+
+BiocManager::install("miaTime")
+```
+
+### Bioc-devel
 
-### Contributions and acknowledgments
+```
+if (!requireNamespace("BiocManager", quietly = TRUE))
+    install.packages("BiocManager")
+
+# The following initializes usage of Bioc devel
+BiocManager::install(version='devel')
+
+BiocManager::install("miaTime")
+```
+
+### GitHub
+
+```
+remotes::install_github("microbiome/miaTime")
+```
+
+## Contributions and acknowledgments
 
 You can find us online from [Gitter](https://gitter.im/microbiome/miaverse).
 
@@ -43,22 +65,8 @@ flow kind of approach. Development version should be done against the
 citation("miaTime")  
 ```
 
-```
-## Kindly cite the miaTime R package as follows:
-## 
-##   (C) Yagmur Simsek and Leo Lahti. miaTime R package Version 0.1.21
-##   Package URL: microbiome.github.io/miaTime
-## 
-## A BibTeX entry for LaTeX users is
-## 
-##   @Misc{,
-##     title = {miaTime R package},
-##     author = {Yagmur Simsek and Leo Lahti},
-##     url = {microbiome.github.io/miaTime},
-##     note = {Version 0.1.21},
-##   }
-```
-
-# Code of conduct
+## Code of conduct
 
-The project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.
+The project is released with a
+[Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).
+By contributing to this project, you agree to abide by its terms.
diff --git a/man/SilvermanAGutData.Rd b/man/SilvermanAGutData.Rd
index 4c449d7..0c95473 100644
--- a/man/SilvermanAGutData.Rd
+++ b/man/SilvermanAGutData.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data.R
+% Please edit documentation in R/miaTime.R
 \docType{data}
 \name{SilvermanAGutData}
 \alias{SilvermanAGutData}
diff --git a/man/addBaselineDivergence.Rd b/man/addBaselineDivergence.Rd
index b86ae1c..48a03fd 100644
--- a/man/addBaselineDivergence.Rd
+++ b/man/addBaselineDivergence.Rd
@@ -1,14 +1,16 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/getBaselineDivergence.R
-\name{addBaselineDivergence}
-\alias{addBaselineDivergence}
+% Please edit documentation in R/AllGenerics.R, R/getBaselineDivergence.R
+\name{getBaselineDivergence}
 \alias{getBaselineDivergence}
+\alias{addBaselineDivergence}
 \alias{getBaselineDivergence,SummarizedExperiment-method}
 \alias{addBaselineDivergence,SummarizedExperiment-method}
 \title{Beta diversity between the baseline and later time steps}
 \usage{
 getBaselineDivergence(x, ...)
 
+addBaselineDivergence(x, ...)
+
 \S4method{getBaselineDivergence}{SummarizedExperiment}(
   x,
   time.col,
@@ -19,8 +21,6 @@ getBaselineDivergence(x, ...)
   ...
 )
 
-addBaselineDivergence(x, ...)
-
 \S4method{addBaselineDivergence}{SummarizedExperiment}(x, name = "divergence", name.time = "time_diff", ...)
 }
 \arguments{
diff --git a/man/addStepwiseDivergence.Rd b/man/addStepwiseDivergence.Rd
index cd47736..d8ddd9c 100644
--- a/man/addStepwiseDivergence.Rd
+++ b/man/addStepwiseDivergence.Rd
@@ -1,14 +1,16 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/getStepwiseDivergence.R
-\name{addStepwiseDivergence}
-\alias{addStepwiseDivergence}
+% Please edit documentation in R/AllGenerics.R, R/getStepwiseDivergence.R
+\name{getStepwiseDivergence}
 \alias{getStepwiseDivergence}
+\alias{addStepwiseDivergence}
 \alias{getStepwiseDivergence,ANY-method}
 \alias{addStepwiseDivergence,SummarizedExperiment-method}
 \title{Beta diversity between consecutive time steps}
 \usage{
 getStepwiseDivergence(x, ...)
 
+addStepwiseDivergence(x, ...)
+
 \S4method{getStepwiseDivergence}{ANY}(
   x,
   time.col,
@@ -19,8 +21,6 @@ getStepwiseDivergence(x, ...)
   ...
 )
 
-addStepwiseDivergence(x, ...)
-
 \S4method{addStepwiseDivergence}{SummarizedExperiment}(x, name = "divergence", name.time = "time_diff", ...)
 }
 \arguments{
diff --git a/man/hitchip1006.Rd b/man/hitchip1006.Rd
index f77a34e..255de46 100755
--- a/man/hitchip1006.Rd
+++ b/man/hitchip1006.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data.R
+% Please edit documentation in R/miaTime.R
 \docType{data}
 \name{hitchip1006}
 \alias{hitchip1006}
diff --git a/man/miaTime-package.Rd b/man/miaTime-package.Rd
new file mode 100644
index 0000000..41e150e
--- /dev/null
+++ b/man/miaTime-package.Rd
@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/miaTime.R
+\docType{package}
+\name{miaTime-package}
+\alias{miaTime-package}
+\alias{miaTime}
+\title{\code{miaTime} Package.}
+\description{
+\code{miaTime} implements time series methods in \link[mia]{mia} ecosystem.
+}
+\seealso{
+\link[TreeSummarizedExperiment:TreeSummarizedExperiment-class]{TreeSummarizedExperiment} class
+}
+\author{
+\strong{Maintainer}: Yagmur Simsek \email{yagmur.simsek@hsrw.org}
+
+Authors:
+\itemize{
+  \item Leo Lahti \email{leo.lahti@iki.fi} (\href{https://orcid.org/0000-0001-5537-637X}{ORCID})
+}
+
+Other contributors:
+\itemize{
+  \item Sudarshan Shetty \email{sudarshanshetty9@gmail.com} [contributor]
+  \item Chouaib Benchraka [contributor]
+  \item Tuomas Borman \email{tuomas.v.borman@utu.fi} (\href{https://orcid.org/0000-0002-8563-8884}{ORCID}) [contributor]
+  \item Muluh Muluh [contributor]
+}
+
+}
diff --git a/man/minimalgut.Rd b/man/minimalgut.Rd
index 41ece43..37d0214 100644
--- a/man/minimalgut.Rd
+++ b/man/minimalgut.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data.R
+% Please edit documentation in R/miaTime.R
 \docType{data}
 \name{minimalgut}
 \alias{minimalgut}
diff --git a/man/temporalMicrobiome20.Rd b/man/temporalMicrobiome20.Rd
index 33bd078..30475a3 100644
--- a/man/temporalMicrobiome20.Rd
+++ b/man/temporalMicrobiome20.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data.R
+% Please edit documentation in R/miaTime.R
 \docType{data}
 \name{temporalMicrobiome20}
 \alias{temporalMicrobiome20}
diff --git a/vignettes/articles/manipulation.Rmd b/vignettes/articles/manipulation.Rmd
index abee500..0976d7b 100644
--- a/vignettes/articles/manipulation.Rmd
+++ b/vignettes/articles/manipulation.Rmd
@@ -1,11 +1,6 @@
 ---
 title: "Time series manipulation"
 date: "`r Sys.Date()`"
-author:
-- name: Leo Lahti
-  email: leo.lahti@iki.fi
-- name: Yagmur Simsek
-  email: yagmur.simsek@hsrw.org
 package: 
     miaTime
 output: 
@@ -22,134 +17,144 @@ vignette: >
 bibliography: references.bib
 ---
 
-```{r eval=FALSE, include=FALSE}
-library(Cairo)
-knitr::opts_chunk$set(cache = FALSE,
-                      fig.width = 9,
-                      dpi=300,
-                      dev = "png",
-                      dev.args = list(type = "cairo-png"),
-                      message = FALSE,
-                      warning = FALSE)
-```
-
-```{r loadhide, message=FALSE, warning=FALSE, echo=FALSE}
-library(mia)
-library(miaTime)
-library(dplyr)
-library(lubridate)
-library(SummarizedExperiment)
+```{r}
+#| label: setup
+#| include: false
+
+library(knitr)
+knitr::opts_chunk$set(
+    collapse = TRUE,
+    comment = "#>",
+    cache = TRUE
+)
 ```
 
-# Introduction
+## Introduction
 
-`miaTime` implements tools for time series manipulation based on the `TreeSummarizedExperiment` [@TSE] data container. Much of the functionality is also applicable to the `SummarizedExperiment` [@SE] data objects. This tutorial shows how to use `miaTime` methods as well as the broader R/Bioconductor ecosystem to manipulate time series data.
+`miaTime` implements tools for time series manipulation based on the
+`r BiocStyle::Biocpkg("TreeSummarizedExperiment")` (`TreeSE`) data container.
+Much of the functionality is also applicable to the
+`r BiocStyle::Biocpkg("SummarizedExperiment")` data objects. This tutorial
+shows how to use `miaTime` methods as well as the broader R/Bioconductor
+ecosystem to manipulate time series data.
 
-Check also the related package [TimeSeriesExperiment](https://github.com/nlhuong/TimeSeriesExperiment).
+Check also the related package
+[TimeSeriesExperiment](https://github.com/nlhuong/TimeSeriesExperiment).
 
 ## Installation
 
-Installing the latest development version in R.
+`miaTime` is hosted on Bioconductor, and can be installed using via
+`BiocManager`.
+
+```{r}
+#| label: install
+#| eval: false
 
-```{r, eval=FALSE}
-library(devtools)
-devtools::install_github("microbiome/miaTime")
+BiocManager::install("miaTime")
 ```
 
-Loading the package:
+Once installed, `miaTime` is made available in the usual way.
+
+```{r}
+#| label: load_package
 
-```{r load-packages, message=FALSE, warning=FALSE}
 library(miaTime)
 ```
 
-
 ## Sorting samples 
 
-The _tidySingleCellExperiment_ package provides handy functions for (Tree)SE manipulation.
+To sort data based on subject and time point in base R, you can use the
+`order()` function.
+
+```{r}
+#| label: sort_samples
 
-```{r sort samples, message=FALSE, warning=FALSE}
-library("tidySingleCellExperiment")
 data(hitchip1006)
 tse <- hitchip1006
-tse2 <- tse %>% arrange(subject, time)
-```
 
+index <- order(tse[["subject"]], tse[["time"]])
+tse <- tse[ , index]
+```
 
-## Storing time information with Period class
+## Storing time information with `period` class
 
 `miaTime` utilizes the functions available in the package `lubridate`
-to convert time series field to "Period" class object. This gives access to a 
-number of readily available [time series manipulation tools](https://cran.r-project.org/web/packages/lubridate/vignettes/lubridate.html).
+to convert time series field to `period` class object. This gives access to a 
+number of readily available
+[time series manipulation tools](https://cran.r-project.org/web/packages/lubridate/vignettes/lubridate.html).
 
 
 Load example data:
 
 ```{r}
+#| label: lubridate
+
 # Load packages
-library(miaTime)
 library(lubridate)
-library(SummarizedExperiment)
-
-# Load demo data
-data(hitchip1006)
-tse <- hitchip1006
 
 # Time is given in days in the demo data.
 # Convert days to seconds
-time_in_seconds <- 60*60*24*colData(tse)[,"time"]
+time_in_seconds <- 60*60*24*tse[["time"]]
 # Convert the time data to period class
-Seconds <- as.period(time_in_seconds, unit="sec")
+seconds <- as.period(time_in_seconds, unit = "sec")
 # Check the output
-Seconds[1140:1151]
+seconds |> tail()
 ```
 
-
 ## Conversion between time units
 
 The time field in days is now shown in seconds. It can then be
-converted to many different units using the lubridate package.
+converted to many different units using the `lubridate` package.
 
 ```{r}
-Hours <- as.period(Seconds, unit = "hour")
-Hours[1140:1151]
+#| label: hours
+
+hours <- as.period(seconds, unit = "hour")
+hours |> tail()
 ```
 
 The updated time information can then be added to the
 `SummarizedExperiment` data object as a new `colData` (sample data)
 field.
 
-
 ```{r}
-colData(tse)$timeSec <- Seconds
+#| label: add_seconds
+
+colData(tse)$time_sec <- seconds
 colData(tse)
 ```
 
 ## Calculating time differences 
 
-The \link[lubridate]{as.duration} function helps to specify time points as duration.
+The `lubridate::as.duration()` function helps to specify time points as
+duration.
 
 ```{r}
-Duration <- as.duration(Seconds)
-Duration[1140:1151]
+#| label: duration
+
+duration <- as.duration(seconds)
+duration |> tail()
 ```
 
 The difference between subsequent time points can then be calculated.
 
 ```{r}
-Timediff <- diff(Duration)
-Timediff <- c(NA, Timediff)
-Timediff[1140:1151]
+#| label: time_diff
+
+time_diff <- diff(duration)
+time_diff <- c(NA, time_diff)
+time_diff |> tail()
 ```
 
 The time difference from a selected point to the other time points
 can be calculated as follows.
 
 ```{r}
-base <- Hours - Hours[1] #distance from starting point
-base[1140:1151]
+#| label: time_diff2
 
-base_1140 <- Seconds - Seconds[1140]
-base_1140[1140:1151]
+# Difference from second time point
+time_diff <- hours - sort(unique(hours))[[2]]
+time_diff |> tail()
 ```
 
 ## Time point rank
@@ -157,52 +162,47 @@ base_1140[1140:1151]
 Rank of the time points can be calculated by `rank` function provided in base R.
 
 ```{r}
-colData(tse)$rank <- rank(colData(tse)$time)
+#| label: rank
+
+tse[["time"]] <- rank(tse[["time"]])
 colData(tse)
 ```
 
-
 ## Operations per unit
 
-Sometimes we need to operate on time series per unit (subject,
-reaction chamber, sampling location, ...).
+Sometimes we need to operate on time series per unit (subject, reaction chamber,
+sampling location, ...).
 
 Add time point rank per subject.
 
 ```{r}
+#| label: rank_for_subject
+
 library(dplyr)
-colData(tse) <- colData(tse) %>%
-   as.data.frame() %>%
-   group_by(subject) %>%
-   mutate(rank = rank(time, ties.method="average")) %>%
+
+colData(tse) <- colData(tse) |>
+   as.data.frame() |>
+   group_by(subject) |>
+   mutate(rank = rank(time, ties.method = "average")) |>
    DataFrame()
 ```
 
-
 ## Subset to baseline samples
 
-```{r}
-library(tidySummarizedExperiment)
-
-# Pick samples with time point 0
-tse <- hitchip1006 |> filter(time == 0)
-# Or: tse <- tse[, tse$time==0]
+`TreeSE` consists of rows for features and columns for samples. If we are
+specifically interested in baseline samples, we can easily subset the data as
+follows.
 
-# Sample with the smallest time point within each subject
-colData(tse) <- colData(tse) %>%
-   as.data.frame() %>%
-   group_by(subject) %>%
-   mutate(rank = rank(time, ties.method="average")) %>%
-   DataFrame
+```{r}
+#| label: subset
 
-# Pick the subset including first time point per subject
-tse1 <- tse[, tse$rank == 1]
+tse <- tse[, tse$time==0]
 ```
 
-
-
 # Session info
 
 ```{r}
+#| label: session_info
+
 sessionInfo()
 ```
diff --git a/vignettes/articles/minimalgut.Rmd b/vignettes/articles/minimalgut.Rmd
index 9e99cde..bee4822 100644
--- a/vignettes/articles/minimalgut.Rmd
+++ b/vignettes/articles/minimalgut.Rmd
@@ -1,9 +1,6 @@
 ---
 title: "Minimal gut bioreactor examples"
 date: "`r Sys.Date()`"
-author:
-- name: Sudarshan Shetty
-- name: Leo Lahti
 package: 
     miaTime
 output: 
@@ -20,58 +17,49 @@ vignette: >
 bibliography: references.bib
 ---
 
-```{r eval=FALSE, include=FALSE}
-library(Cairo)
-knitr::opts_chunk$set(cache = FALSE,
-                      fig.width = 9,
-                      dpi=300,
-                      dev = "png",
-                      dev.args = list(type = "cairo-png"),
-                      message = FALSE,
-                      warning = FALSE)
-```
+```{r}
+#| label: setup
+#| include: false
 
-```{r loadhide, message=FALSE, warning=FALSE, echo=FALSE}
-library(mia)
-library(miaTime)
-library(dplyr)
-library(lubridate)
 library(knitr)
-library(TreeSummarizedExperiment)
+knitr::opts_chunk$set(
+    collapse = TRUE,
+    comment = "#>",
+    cache = TRUE
+)
 ```
 
-
 # Minimal gut microbiome
 
-Dense samples of the minimal gut microbiome. In the initial hours, MDb-MM was grown under batch condition and 24 h onwards, continuous feeding of media with pulse feeding cycles. This information is stored in the `colData`.   
+Dense samples of the minimal gut microbiome. In the initial hours, MDb-MM was
+grown under batch condition and 24 h onwards, continuous feeding of media with
+pulse feeding cycles. This information is stored in the `colData`.   
+
+```{r}
+#| label: sample_table
 
-```{r fig.width=8, fig.height=3}
 library(miaTime)
+
 data(minimalgut)
 tse <- minimalgut
 
-# quick check of number of samples 
-kable(table(colData(tse)$StudyIdentifier,colData(tse)$condition_1))
+# Quick check of number of samples 
+table(tse[["StudyIdentifier"]], tse[["condition_1"]])
 ```
 
 Visualize samples available for each of the bioreactors. This allows
 to identify if there are any missing samples for specific times.
 
-
 ```{r}
+#| label: show_timepoints
+
 library(ggplot2)
-library(tidySummarizedExperiment)
-tse |> 
-    ggplot(aes(as.factor(Time.hr), StudyIdentifier)) +
-    geom_tile(aes(fill=condition_1), color="white") +
-    scale_fill_manual("Condition Sampled", 
-                      values = c("#ff006e", "#e07a5f", "#457b9d")) +
-    theme_minimal() +
-    theme(axis.text.x = element_text(size=8, angle = 90),
-          legend.position = "top") +
-    labs(x="Time (h)", y="")
-```
 
+colData(tse) |>
+    ggplot() +
+    geom_tile(
+        aes(x = as.factor(Time.hr), y = StudyIdentifier, fill = condition_1))
+```
 
 ## Community dynamics   
 
@@ -81,229 +69,150 @@ can investigate the succession of mdbMM16 from the start of experiment
 here hour zero until the end of the experiment.
 
 ```{r}
-## Divergence from baseline i.e from hour zero.
-tse <- transformAssay(minimalgut, method = "relabundance") # get relative abundance
-tse <- addBaselineDivergence(tse,
-                             group = "StudyIdentifier",
-                             time.col = "Time.hr",
-                             name = "divergence_from_baseline",
-                             name.time = "time_from_baseline",
-                             assay.type = "relabundance",
-                             method = "bray")
-
+#| label: calculate_divergence
+
+# Transform data to relativeS
+tse <- transformAssay(tse, method = "relabundance")
+# Divergence from baseline i.e from hour zero
+tse <- addBaselineDivergence(
+    tse,
+    assay.type = "relabundance",
+    method = "bray",
+    group = "StudyIdentifier",
+    time.col = "Time.hr",
+    )
 ```
 
+Let's then visualize the divergence.
+
+```{r}
+#| label: show_divergence
+
+library(scater)
 
-Visualize the divergence
-
-```{r fig.height=4, fig.width=8}
-# First define nice colors for bioreactors
-bioreac_cols <- c(`Bioreactor A`= "#b2182b",
-                  `Bioreactor B`= "#2166ac",
-                  `Bioreactor C` = "#35978f")
-
-tse |>
-    ggplot(aes(x=Time.hr, y=divergence_from_baseline))+
-    geom_point(aes(color=StudyIdentifier), size=2, alpha=0.5) +
-    geom_line(aes(color=StudyIdentifier)) +
-    theme_minimal() +
-    scale_color_manual(values = bioreac_cols) +
-    labs(x="Time (h)", y="Divergence \nfrom baseline") +
-    # highlight specific timepoints
-    geom_vline(xintercept = 152, lty=2, color="#991720") + 
-    geom_vline(xintercept = 248, lty=2, color= "#0963bd")+
-    annotate("text",x=c(152, 248),y=c(0.8, 0.8),
-             label=c("Addition of\nB.hydrogenotrophica","Acetate Discontinued"),
-             hjust=c(1.05,-0.05))
+# Create a time series plot for divergence
+p <- plotColData(
+    tse, x = "Time.hr", y = "divergence", colour_by = "StudyIdentifier") +
+    # Add line between points
+    geom_line(aes(group = .data[["colour_by"]], colour =  .data[["colour_by"]]))
+p
 ```
 
 ## Visualizing selected taxa
 
-Now visualize abundance of *Blautia hydrogenotrophica* using the `miaViz::plotSeries` function.
+Now visualize abundance of _Blautia hydrogenotrophica_ using the
+`miaViz::plotSeries()` function.
 
-```{r fig.height=4, fig.width=8}
-library(miaViz)
-plotSeries(transformAssay(minimalgut, method = "relabundance"),
-           x = "Time.hr",
-           y = "Blautia_hydrogenotrophica",
-           colour_by = "Species",
-           assay.type = "relabundance")+
-    geom_vline(xintercept = 152, lty=2, color="#991720") + 
-    geom_vline(xintercept = 248, lty=2, color= "#0963bd")+
-    annotate("text",x=c(152, 248),y=c(0.2, 0.15),
-             label=c("Addition of\nB.hydrogenotrophica","Acetate Discontinued"),
-             hjust=c(1.05,-0.05))+
-    labs(x="Time (h)", y="B.hydrogenotrophica\nRelative Abundance") +
-    theme(legend.position = "none") 
-```
+```{r}
+#| label: plot_series
 
+library(miaViz)
 
+# Plot certain feature by time
+p <- plotSeries(
+    tse,
+    x = "Time.hr", y = "Blautia_hydrogenotrophica", colour_by = "Species",
+    assay.type = "relabundance")
+p
+```
 
 ## Visualize the rate (slope) of divergence 
 
 Sample dissimilarity between consecutive time steps(step size n >= 1) within
-a group(subject, age, reaction chamber, etc.) can be calculated by `addStepwiseDivergence`. If we normalize this by the time interval, this gives an approximate slope for the change.
-
-
-```{r addStepwiseDivergence, fig.height=4, fig.width=8, warning=FALSE}
-# Load libraries
-library(miaTime)
-library(dplyr)
+a group(subject, age, reaction chamber, etc.) can be calculated by
+`addStepwiseDivergence`.
 
-# Sort samples by time (necessary for addStepwiseDivergence)
-tse <- tse[, order(colData(tse)$Time_hr_num)]
+```{r}
+#| label: stepwise_divergence
 
 # Divergence between consecutive time points
-tse <- addStepwiseDivergence(tse, group = "StudyIdentifier",
-                               time_interval = 1,
-                               time.col = "Time_hr_num",
-                               name = "divergence_from_previous_step",
-                               name.time = "time_from_previous_step",
-                               assay.type = "relabundance",
-                               method = "bray")
-
-# We have now new fields added in the colData:
-# time_from_previous_step, divergence_from_previous_step
-# print(colData(tse))
-
-# Visualize the slope of dissimilarity between consecutive time points as a function of time (from baseline)
-library(ggplot2)
-theme_set(theme_bw(10))
-p <- tse |> ggplot(aes(x=time_from_baseline,
-	               y=divergence_from_previous_step/time_from_previous_step,
-	               color=StudyIdentifier)) +
-       geom_point() +
-       geom_line() +
-       labs(x="Time (hours)", y="Slope of dissimilarity (Bray-Curtis)") +
-       geom_hline(aes(yintercept=0), linetype=2)
-
-print(p)
+tse <- addStepwiseDivergence(
+    tse,
+    assay.type = "relabundance",
+    method = "bray",
+    group = "StudyIdentifier",
+    time.interval = 1,
+    time.col = "Time.hr",
+    name = "divergence_from_previous_step",
+    name.time = "time_from_previous_step"
+    )
 ```
 
-## Moving average of the slope
+The results are again stored in `colData`. We calculate the speed of divergence
+change by dividing each divergence change by the corresponding change in time.
+Then we use similar plotting methods as previously.
 
-This shows how to calculate and plot moving average for the variable of interest (here: slope).
-
-```{r rollmean, fig.height=4, fig.width=8, warning=FALSE}
-# Add slope explicitly in colData
-colData(tse)$slope <- colData(tse)$divergence_from_previous_step / colData(tse)$time_from_previous_step
-
-# Split by group and perform operation
-tselist <- splitOn(tse, "StudyIdentifier")
-
-# colData(tse)$divergence_from_previous_step
-addmean <- function (x, k, field, field_name) {
-  # Calculate rolling mean
-  m <- zoo::rollmean(x[[field]], k = k)
-  # Initialize empty field
-  colData(x)[[field_name]] <- rep(NA, ncol(x))
-  # Fill in the rolling mean (length does not match with original data in rolling mean)
-  colData(x)[1:length(m), field_name] <- m
-  # Return the object with a new field added
-  x
-}
-
-# Calculate sliding average for the field "divergence_from_previous_step"
-# and store the result in a new field with the name "sliding_average"
-tselist2 <- lapply(tselist, function (x) {addmean(x, k=3, field = "slope", field_name = "sliding_average")})
-
-# Merge back
-tse <- mergeSEs(tselist2)
-
-# Visualize
-theme_set(theme_bw(10))
-p <- tse |> ggplot(aes(x = time_from_baseline,
-	               y = sliding_average,
-	               color=StudyIdentifier)) +
-     geom_point() +
-     geom_line() +
-     labs(x="Time (hours)", y="Mean slope of dissimilarity (Bray-Curtis)") +
-     geom_hline(aes(yintercept=0), linetype=2)
-print(p)
+```{r}
+#| label: show_stepwise
+
+# Calculate slope for the change
+tse[["divergence_change"]] <- tse[["divergence_from_previous_step"]] /
+    tse[["time_from_previous_step"]]
+
+# Create a time series plot for divergence
+p <- plotColData(
+    tse,
+    x = "Time.hr",
+    y = "divergence_change",
+    colour_by = "StudyIdentifier"
+    ) +
+    # Add line between points
+    geom_line(aes(group = .data[["colour_by"]], colour =  .data[["colour_by"]]))
+p
 ```
 
+## Moving average of the slope
 
+This shows how to calculate and plot moving average for the variable of
+interest (here: slope).
 
-## Error bars
-
-This shows how to visualize error bars on top of time series. In this
-example we create artificial replicates and variation for a brief example.
-
-
-```{r errorbars, fig.height=4, fig.width=8, warning=FALSE, message=FALSE}
-## Source: https://www.geeksforgeeks.org/adding-error-bars-to-a-line-graph-with-ggplot2-in-r/
-## Gives count, mean, standard deviation, standard error of the mean,
-## and confidence interval (default 95%).
-## data: a data frame.
-## measurevar: the name of a column that contains the variable to be summariezed
-## groupvars: a vector containing names of columns that contain grouping variables
-## na.rm: a boolean that indicates whether to ignore NA's
-## conf.interval: the percent range of the confidence interval (default is 95%)
-summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
-                      conf.interval=.95, .drop=TRUE) {
-    library(plyr)
-
-    # New version of length which can handle NA's: if na.rm==T, don't count them
-    length2 <- function (x, na.rm=FALSE) {
-        if (na.rm) sum(!is.na(x))
-        else       length(x)
-    }
-
-    # This does the summary. For each group's data frame, return a vector with
-    # N, mean, and sd
-    datac <- ddply(data, groupvars, .drop=.drop,
-      .fun = function(xx, col) {
-        c(N    = length2(xx[[col]], na.rm=na.rm),
-          mean = mean   (xx[[col]], na.rm=na.rm),
-          sd   = sd     (xx[[col]], na.rm=na.rm)
-        )
-      },
-      measurevar
-    )
+```{r}
+#| label: moving_average
 
-    # Rename the "mean" column    
-    datac <- rename(datac, c("mean" = measurevar))
-
-    datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean
-
-    # Confidence interval multiplier for standard error
-    # Calculate t-statistic for confidence interval: 
-    # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
-    ciMult <- qt(conf.interval/2 + .5, datac$N-1)
-    datac$ci <- datac$se * ciMult
-
-    return(datac)
-}
-
-# Create artificial example on replicates because we don't have any in this demo data
-set.seed(3422)
-df <- as.data.frame(colData(tse))
-sdlevel <- 0.1*mean(df$sliding_average, na.rm=TRUE)
-df1 <- df
-df2 <- df; df2$sliding_average <- rnorm(mean=df1$sliding_average, sd = sdlevel, n=nrow(df1)) 
-df3 <- df; df3$sliding_average <- rnorm(mean=df1$sliding_average, sd = sdlevel, n=nrow(df1))
-df <- bind_rows(list(df1, df2, df3))
-
-# Calculate deviations
-df <- summarySE(df, measurevar="sliding_average", groupvars=c("StudyIdentifier", "time_from_baseline"))
-
-# Visualize
-theme_set(theme_bw(10))
-p <- ggplot(df,
-         aes(x = time_from_baseline, y = sliding_average, color=StudyIdentifier)) +
-     geom_point() +
-     geom_line() +
-     geom_errorbar(aes(ymin=sliding_average-sd, ymax=sliding_average+sd), width=.2,
-                 position=position_dodge(0.05)) +    
-     labs(x="Time (hours)", y="Mean slope of dissimilarity (Bray-Curtis)") +
-     geom_hline(aes(yintercept=0), linetype=2)
-print(p)
+library(dplyr)
+
+# Calculate moving average with time window of 3 time points
+tse[["sliding_divergence"]] <- colData(tse) |>
+    as.data.frame() |>
+    # Group based on reactor
+    group_by(StudyIdentifier) |>
+    # Calculate moving average
+    mutate(sliding_avg = (
+        # We get the previous 2 samples
+        lag(divergence_change, 2) +
+        lag(divergence_change, 1) +
+        # And the current sample
+        divergence_change
+        # And take average
+        ) / 3
+    ) |>
+    # Get only the values as vector
+    ungroup() |>
+    pull(sliding_avg)
 ```
 
+After calculating the moving average of divergences, we can visualize the
+result in a similar way to our previous approach.
 
+```{r}
+#| label: show_moving_average
+
+# Create a time series plot for divergence
+p <- plotColData(
+    tse,
+    x = "Time.hr",
+    y = "sliding_divergence",
+    colour_by = "StudyIdentifier"
+    ) +
+    # Add line between points
+    geom_line(aes(group = .data[["colour_by"]], colour =  .data[["colour_by"]]))
+p
+```
 
 # Session info
 
 ```{r}
+#| label: session_info
+
 sessionInfo()
 ```
diff --git a/vignettes/miaTime.Rmd b/vignettes/miaTime.Rmd
index 11e3ce5..9524e70 100755
--- a/vignettes/miaTime.Rmd
+++ b/vignettes/miaTime.Rmd
@@ -1,11 +1,6 @@
 ---
 title: "miaTime: Microbiome Time Series Analysis"
 date: "`r Sys.Date()`"
-author:
-- name: Leo Lahti
-  email: leo.lahti@iki.fi
-- name: Yagmur Simsek
-  email: yagmur.simsek@hsrw.org
 package: 
     miaTime
 output: 
@@ -21,45 +16,102 @@ vignette: >
     %\VignetteEncoding{UTF-8}
 ---
 
-```{r eval=FALSE, include=FALSE}
-library(Cairo)
-knitr::opts_chunk$set(cache = FALSE,
-                      fig.width = 9,
-                      dpi=300,
-                      dev = "png",
-                      dev.args = list(type = "cairo-png"),
-                      message = FALSE,
-                      warning = FALSE)
+```{r}
+#| label: setup
+#| include: false
+
+library(knitr)
+knitr::opts_chunk$set(
+    collapse = TRUE,
+    comment = "#>",
+    cache = TRUE
+)
 ```
 
+## Introduction
 
+`miaTime` is a package in the `r BiocStyle::Biocpkg("mia")` family, providing
+tools for time series manipulation using the
+`r BiocStyle::Biocpkg("TreeSummarizedExperiment")` data container.
 
+## Installation
 
-# Introduction
+`miaTime` is hosted on Bioconductor, and can be installed using via
+`BiocManager`.
 
-`miaTime` implements tools for time series manipulation based on the `TreeSummarizedExperiment` data container.
+```{r}
+#| label: install
+#| eval: false
 
-## Installation
+BiocManager::install("miaTime")
+```
 
-Installing the latest development version in R.
+## Load the package
 
-```{r, eval=FALSE}
-library(devtools)
-devtools::install_github("microbiome/miaTime")
-```
+Once installed, `miaTime` is made available in the usual way.
 
-Loading the package:
+```{r}
+#| label: load_package
 
-```{r load-packages, message=FALSE, warning=FALSE}
 library(miaTime)
 ```
 
+## Divergence between time points
+
+`miaTime` offers functions to calculate divergences. These can be calculated
+based on samples and their corresponding base time point, e.g., first sample of
+time series. Moreover, divergences can be calculated in rolling basis meaning
+that a sample is compared to previous ith sample.
+
+Divergences can be calculated with `get*Divergence()` functions. In the example
+below, for each subject, we calculate the divergence of their samples by
+comparing them to the first time point.
+
+```{r}
+#| label: base_divergence
+
+data(hitchip1006)
+tse <- hitchip1006
+
+res <- getBaselineDivergence(
+    tse, time.col = "time", group = "sample", name = "baseline")
+res |> head()
+```
+
+A more convenient and preferred approach is to store the values directly in
+`colData` using the `get*Divergence()` functions. In the example below, we
+calculate stepwise divergences with a lag of 1, meaning that for each sample,
+the divergence is calculated by comparing it to the previous time point for
+the same subject.
 
-See [articles](https://microbiome.github.io/miaTime/articles/) for more detailed example workflows.
+```{r}
+#| label: time_divergence
 
+tse <- addStepwiseDivergence(tse, time.col = "time")
+colData(tse)
+```
+
+## Visualize time series
+
+We can visualize time series data with `r BiocStyle::Biocpkg("miaViz")`. Below,
+we visualize 2 most abundant taxa.
+
+```{r}
+#| label: plot_series
+
+library(miaViz)
+
+p <- plotSeries(tse, x = "time", y = getTop(tse, 5))
+p
+```
+
+See [articles](https://microbiome.github.io/miaTime/articles/) for more detailed
+example workflows.
 
 ## Session info
 
 ```{r}
+#| label: session_info
+
 sessionInfo()
 ```