Fix missing importFrom and vignette issues

.github/workflows/vignette.yml

@@ -85,10 +85,10 @@ jobs:
           Rscript -e 'remotes::install_github("ncborcherding/scRepertoire")'
         shell: bash -l {0}
 
-      - name: Install specific version of pillar to avoid weird error message
-        run: |
-          Rscript -e 'remotes::install_github("r-lib/[email protected]")'
-        shell: bash -l {0}
+      # - name: Install specific version of pillar to avoid weird error message
+      #   run: |
+      #     Rscript -e 'remotes::install_github("r-lib/[email protected]")'
+      #   shell: bash -l {0}
 
       - name: Install package
         run: R CMD INSTALL .

R/projectProbability.R

@@ -6,7 +6,7 @@
 #' @param waypoints Integer vector. Indices of the waypoints used in the Markov chain.
 #' @param probabilities Numeric vector. Probabilities associated with the waypoints, calculated from the Markov chain.
 #' @param t Numeric. The diffusion time to be used in the projection.
-#' @importFrom destiny eigenvalues
+#' @importFrom destiny eigenvectors eigenvalues
 #' @importFrom stats sd
 #' @return each pseudobulk's probabilites
 projectProbability <- function(diffusionmap, waypoints, probabilities, t = 1) {

vignettes/vignette_from_scRepertoire.Rmd

@@ -43,8 +43,6 @@ devtools::install_github("tuonglab/dandelionR", dependencies = TRUE)
 
 In a standard analysis workflow in R, users probably choose to read in their VDJ data with [scRepertoire](https://github.com/ncborcherding/scRepertoire). In this vignette, we will demonstrate how to perform TCR trajectory analysis starting from 'raw' data i.e. just a standard single-cell gene expression data (stored in `SingleCellExperiment`) and VDJ data (in AIRR format).
 
-## Installation
-
 Install `scRepertoire` if you haven't already.
 ```{r, eval = FALSE}
 if (!requireNamespace("scRepertoire", quietly = TRUE)) { # only for the tutorial
@@ -54,8 +52,17 @@ if (!requireNamespace("scRepertoire", quietly = TRUE)) { # only for the tutorial
 devtools::install_github("ncborcherding/scRepertoire")
 ```
 
+## Usage
+
 ### Load the required libraries
-```{r}
+
+```{r, message=FALSE, warning=FALSE, include=FALSE}
+library(destiny)
+# there's a known 'error' with loading a dependency of destiny, pillar:
+# https://github.com/r-lib/pillar/pull/668
+```
+
+```{r, message=FALSE, warning=FALSE}
 library(dandelionR)
 library(scRepertoire)
 library(scater)

vignettes/vignette_reproduce_original.rmd

@@ -18,10 +18,18 @@ library(BiocStyle)
 
 In this vignette, we will demonstrate how to perform TCR trajectory analysis starting from data that has already been processed by `dandelion` in python. This is to demonstrate that the original method/results in the `dandelion` paper can be reproduced.
 
-## Usage
+## Load the required libraries
 
-```{r, message = FALSE, warning = FALSE}
+```{r, message=FALSE, warning=FALSE, include=FALSE}
+library(destiny)
+# there's a known 'error' with loading a dependency of destiny, pillar:
+# https://github.com/r-lib/pillar/pull/668
+```
+
+We will also load `scRepertoire` and `scater` for the analysis.
+```{r, message=FALSE, warning=FALSE}
 library(dandelionR)
+library(scRepertoire)
 library(scater)
 ```