Merge pull request #31 from BioBam/develop

Develop

.github/workflows/r.yml

@@ -1,5 +1,3 @@
-# See https://github.com/r-lib/actions/tree/master/examples#readme for
-
 name: R-CMD-Check
 
 on:
@@ -30,6 +28,11 @@ jobs:
     steps:
       - uses: actions/checkout@v3
 
+      - name: Install qpdf (macOS)
+        if: runner.os == 'macOS'
+        run: |
+          brew install qpdf
+
       - uses: r-lib/actions/setup-pandoc@v2
 
       - uses: r-lib/actions/setup-r@v2

R/show_functions.R

@@ -521,7 +521,6 @@ extract_info <- function(data, return_type = "avg_bin_size",
     return(avg_sizes)
   } else if (return_type == "num_bins") {
     bin_counts <- table(data[[path_col]])
-    print("num_bins works")
     return(bin_counts)
   } else {
     stop("Invalid return_type. Choose between 'avg_bin_size' and 'num_bins'.")

vignettes/Basic-Workflow.Rmd

@@ -293,17 +293,17 @@ scmp_ob
 
 With our refined models in hand, we now focus on identifying genes showing 
 significant differences with pseudotime, among paths, or both. For this purpose,
-we use the `sc.get.siggenes()` function. Our aim is to select models with a 
+we use the `sc.filter()` function. Our aim is to select models with a 
 relatively high $R^2$, indicating simple linear relationships. The `vars` 
-parameter in `sc.get.siggenes()` allows us to extract different sets of 
+parameter in `sc.filter()` allows us to extract different sets of 
 significant genes. Setting `vars = 'all'` retrieves all non-flat profiles 
 identified in `sc.p.vector()` with $R^2>=$ the specified threshold. The option
 `vars = 'groups` fetches genes per path, resulting in two gene lists that 
 demonstrate associative significance among paths, helping us identify genes 
 associated with one path or the other along the pseudotime continuum. The 
 `vars = 'each'` option finds significance for each term in the polynomial. 
 In our case, we are interested in genes differentially expressed between paths
-and over pseudotime continum, so we will choose `vars = 'groups`.
+and over pseudotime continuum, so we will choose `vars = 'groups`.
 ```{r, "vars = groups",eval=TRUE, echo=TRUE}
 scmp_ob <- sc.filter(
   scmpObj = scmp_ob,