adjust levels of cox analysis (#339)

* improve ui of TPC analysis and navigation bar

* adjust levels of cox analysis

* fix the version of Roxygen2

DESCRIPTION

@@ -72,4 +72,4 @@ VignetteBuilder:
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2

R/vis_pancan_value.R

@@ -265,8 +265,9 @@ vis_unicox_tree <- function(Gene = "TP53", measure = "OS", data_type = "mRNA", u
           variables = c("values"),
           minprop = 0.25, progressbar = TRUE
         ) %>%
-        survminer::surv_categorize(labels = c("Low", "High")) %>%
-        data.frame()
+        survminer::surv_categorize(labels = c("Low", "High")) %>% 
+        data.frame() %>% 
+        dplyr::mutate(values = factor(values, levels = c("Low", "High")))
     }
 
     unicox_res_genes <- ezcox::ezcox(

R/vis_pcawg_value.R

@@ -261,7 +261,8 @@ vis_pcawg_unicox_tree <- function(Gene = "TP53", measure = "OS", data_type = "mR
           minprop = 0.25, progressbar = TRUE
         ) %>%
         survminer::surv_categorize(labels = c("Low", "High")) %>%
-        data.frame()
+        data.frame() %>% 
+        dplyr::mutate(values = factor(values, levels = c("Low", "High")))
     }
 
     unicox_res_genes <- ezcox::ezcox(

inst/shinyapp/helper/analyze_comp_1.md

@@ -1,8 +1,8 @@
 <font size="4.5">In **S3** Step, **individual comparison analysis** can now be performed after data preparation:</font>
 
 - <font size="4.5">Firstly, you can adjust the default analysis and visualization parameters, respectively.</font>
-  - <font size="4">Use **t.test ** method,  if input data is normally distributed (especially for small sample sizes) and meet homogeneity of variances (variances should be similar in both groups).</font>
-  - <font size="4">Use **Wilcoxon test **method, if input data that does not meet the assumptions of the t-test by comparing the ranks of values between two groups.</font>
+  - <font size="4">Use **t.test** method,  if input data is normally distributed (especially for small sample sizes) and meet homogeneity of variances (variances should be similar in both groups).</font>
+  - <font size="4">Use **Wilcoxon test**method, if input data that does not meet the assumptions of the t-test by comparing the ranks of values between two groups.</font>
 - <font size="4">Then, you will obtain the violin&box plot labeled with correlation result after clicking the "**Run**" button.</font>
 - <font size="4">Finally, you can download the 3 types of results.</font>
 

inst/shinyapp/modules/01_general/modules-ga-dim-distribution.R

@@ -234,8 +234,8 @@ server.modules_ga_dim_distribution <- function(
       } else {
         # !!Assume a dense matrix dataset is selected
         return(list(
-          all = if (is.null(custom_file$fData)) all_preload_identifiers else
-            unique(c(custom_file$fData[[1]], all_preload_identifiers)),
+          all = if (is.null(custom_file$fData)) unique(all_preload_identifiers) else
+            unique(c(custom_file$fData[[1]], unique(all_preload_identifiers))),
           selected = c("TP53", "PTEN", "KRAS")
         ))
       }