Merge branch 'GalaxyCLI' of https://github.com/NIDAP-Community/SCWorkflow into GalaxyCLI

Author: phoman14

docs/pkgdown.yml

@@ -10,7 +10,7 @@ articles:
   SCWorkflow-SubsetReclust: SCWorkflow-SubsetReclust.html
   SCWorkflow-Usage: SCWorkflow-Usage.html
   SCWorkflow-Visualizations: SCWorkflow-Visualizations.html
-last_built: 2025-10-31T20:47Z
+last_built: 2025-11-20T16:30Z
 urls:
   reference: https://nidap-community.github.io/SCWorkflow/reference
   article: https://nidap-community.github.io/SCWorkflow/articles

vignettes/SCWorkflow-DEG.Rmd

@@ -19,8 +19,18 @@ knitr::opts_chunk$set(
 library(data.table)
 library(dplyr)
 library(ggplot2)
+
+run_Chunks=F
+```
+
+```{r,include=F,echo=F,eval=run_Chunks}
+
+Anno_SO=readRDS("./images/Anno_SO.rds")
+
 ```
 
+
+
 ## DE with Find Markers 
 
 This function performs a DE (differential expression) analysis on a merged Seurat object to identify expression markers between different groups of cells (contrasts). This analysis uses the FindMarkers() function of the Seurat Workflow. 
@@ -30,15 +40,15 @@ Differential expression analysis (DEG) is a fundamental technique in single-cell
 This function takes a merged Seurat object [1] as input, expected to contain single-cell data from multiple samples, along with relevant metadata and SingleR annotations, which provide information about cell identity. To perform the DEG analysis, the user can choose from various statistical algorithms, such as MAST [2], wilcox [3], bimod [4], and more, which accommodate different types of experimental designs and assumptions about the data. The user can control the sensitivity of the analysis by setting the minimum fold-change in gene expression between the groups to be considered significant. Additionally, users can specify the assay to be used for the analysis, whether it is the scaled data (SCT) or raw RNA counts.
 For best results, it is recommended to use this function with well-curated and preprocessed single-cell data, ensuring that the Seurat object contains relevant metadata and SingleR annotations. Users should carefully select the samples and contrasts based on their experimental design and research questions. Additionally, exploring different statistical algorithms and adjusting the threshold can fine-tune the DEG analysis and reveal more accurate gene expression markers.
 
-```{r,eval=F}
-degGeneExpressionMarkers(object = SO,
-                         samples = samples,
-                         contrasts = contrasts,
-                         parameter.to.test = parameter_to_test,
-                         test.to.use = test_to_use,
-                         log.fc.threshold = log_fc_threshold,
-                         use.spark = use_spark,
-                         assay.to.use = assay_to_use)
+```{r,eval=run_Chunks}
+DEG_SO=degGeneExpressionMarkers(object = Anno_SO$object,
+                                 samples = c("PBS","ENT","NHSIL12","Combo","CD8dep" ),
+                                 contrasts = c("0-1","0-all"),
+                                 parameter.to.test = "SCT_snn_res_2_8",
+                                 test.to.use = test_to_use,
+                                 log.fc.threshold = log_fc_threshold,
+                                 use.spark = use_spark,
+                                 assay.to.use = assay_to_use)
 ```
 
 1. https://satijalab.org/seurat/

vignettes/SCWorkflow-Overview.Rmd

@@ -34,7 +34,7 @@ This tool supports standard scRNAseq, CITE-Seq, and TCR-Seq assays. Samples prep
 
 A corresponding Metadata table can be used to add sample level information to the Seurat object. The table format should have Sample names in the first Column and any  sample metadata in additional columns. The Metadata table can also be used to rename samples by including an alternative sample name Column in the metadata table.
 
-```{r,echo=F,eval=run_Chunks}
+```{r,echo=F,eval=T}
 # data.table(My_Sample_Names=c('CID3586','CID3921',
 #                              'CID4066','CID4290A',
 #                              'CID4398','CID4471',

vignettes/SCWorkflow-Visualizations.Rmd

@@ -103,6 +103,9 @@ library(htmltools)
 # readRDS("./images/Vis_3D.rds")
 # htmltools::includeHTML('./images/Vis_3D.html')
 # shiny::includeHTML('./images/Vis_3D.html')
+
+html_content <- read_html('./images/Vis_3D.html')
+print(html_content)
 ```