vignettes

Signed-off-by: Daena Rys <[email protected]>

vignettes/articles/manipulation.Rmd

@@ -55,9 +55,8 @@ BiocManager::install("miaTime")
 
 Once installed, `miaTime` is made available in the usual way.
 
-```{r}
+```{r, message = FALSE, warning = FALSE}
 #| label: load_package
-
 library(miaTime)
 ```
 
@@ -86,7 +85,7 @@ number of readily available
 
 Load example data:
 
-```{r}
+```{r, message = FALSE, warning = FALSE}
 #| label: lubridate
 
 # Load packages
@@ -175,7 +174,7 @@ sampling location, ...).
 
 Add time point rank per subject.
 
-```{r}
+```{r, message = FALSE, warning = FALSE}
 #| label: rank_for_subject
 
 library(dplyr)

vignettes/articles/minimalgut.Rmd

@@ -35,9 +35,8 @@ 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}
+```{r, message = FALSE, warning = FALSE}
 #| label: sample_table
-
 library(miaTime)
 
 data(minimalgut)
@@ -50,7 +49,7 @@ 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}
+```{r, message = FALSE, warning = FALSE}
 #| label: show_timepoints
 
 library(ggplot2)
@@ -68,7 +67,7 @@ The `minimalgut` dataset, mucus-diet based minimal microbiome
 can investigate the succession of mdbMM16 from the start of experiment
 here hour zero until the end of the experiment.
 
-```{r}
+```{r, message = FALSE, warning = FALSE}
 #| label: calculate_divergence
 
 # Transform data to relativeS
@@ -85,7 +84,7 @@ tse <- addBaselineDivergence(
 
 Let's then visualize the divergence.
 
-```{r}
+```{r, message = FALSE, warning = FALSE}
 #| label: show_divergence
 
 library(scater)
@@ -103,7 +102,7 @@ p
 Now visualize abundance of _Blautia hydrogenotrophica_ using the
 `miaViz::plotSeries()` function.
 
-```{r}
+```{r, message = FALSE, warning = FALSE}
 #| label: plot_series
 
 library(miaViz)
@@ -122,7 +121,7 @@ Sample dissimilarity between consecutive time steps(step size n >= 1) within
 a group(subject, age, reaction chamber, etc.) can be calculated by
 `addStepwiseDivergence`.
 
-```{r}
+```{r, message = FALSE, warning = FALSE}
 #| label: stepwise_divergence
 
 # Divergence between consecutive time points
@@ -142,7 +141,7 @@ 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.
 
-```{r}
+```{r, message = FALSE, warning = FALSE}
 #| label: show_stepwise
 
 # Calculate slope for the change
@@ -166,7 +165,7 @@ p
 This shows how to calculate and plot moving average for the variable of
 interest (here: slope).
 
-```{r}
+```{r, message = FALSE, warning = FALSE}
 #| label: moving_average
 
 library(dplyr)
@@ -194,7 +193,7 @@ tse[["sliding_divergence"]] <- colData(tse) |>
 After calculating the moving average of divergences, we can visualize the
 result in a similar way to our previous approach.
 
-```{r}
+```{r, message = FALSE, warning = FALSE}
 #| label: show_moving_average
 
 # Create a time series plot for divergence