set variable to run markdown chunks

Author: phoman14

docs/articles/cli.html

@@ -10,7 +10,7 @@ articles:
   SCWorkflow-SubsetReclust: SCWorkflow-SubsetReclust.html
   SCWorkflow-Usage: SCWorkflow-Usage.html
   SCWorkflow-Visualizations: SCWorkflow-Visualizations.html
-last_built: 2025-10-28T17:51Z
+last_built: 2025-10-28T20:10Z
 urls:
   reference: https://nidap-community.github.io/SCWorkflow/reference
   article: https://nidap-community.github.io/SCWorkflow/articles

docs/pkgdown.yml

@@ -7,7 +7,7 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r, include = FALSE}
+```{r, include = FALSE,eval=run_Chunks}
 options(rmarkdown.html_vignette.check_title = FALSE)
 
 knitr::opts_chunk$set(
@@ -32,7 +32,7 @@ This function automates cell type annotation in single-cell RNA sequencing data
 SingleR is an automatic annotation method for single-cell RNA sequencing data that uses a given reference dataset of samples (single-cell or bulk) with known labels to label new cells from a test dataset based on similarity to the reference. Two mouse reference datasets (MouseRNAseqData and ImmGenData) and two human reference datasets (HumanPrimaryCellAtlasData and BlueprintEncodeData) from CellDex R package [2] are currently available.
 
 
-```{r,eval=F}
+```{r,eval=run_Chunks}
 Anno_SO=annotateCellTypes(object=Comb_SO$object,
                   species = "Mouse",
                   reduction.type = "umap",
@@ -43,7 +43,7 @@ Anno_SO=annotateCellTypes(object=Comb_SO$object,
 ```
 
 
-```{r,echo=F,eval=F}
+```{r,echo=F,eval=run_Chunks}
 saveRDS(Anno_SO, file="./images/Anno_SO.rds")
 
 ggsave(Anno_SO$p1, filename = "./images/Anno1.png", width = 10, height = 10)
@@ -62,12 +62,12 @@ ggsave(Anno_SO$p2, filename = "./images/Anno2.png", width = 10, height = 10)
 
 This function will merge an external table of cell annotations into an existing Seurat Object's metadata table. The input external metadata table must have a column named "Barcode" that contains barcodes matching those found in the metadata already present in the input Seurat Object. The output will be a new Seurat Object with metadata that now includes the additional columns from the external table.
 
-```{r,eval=F,echo=T}
+```{r,eval=run_Chunks,echo=T}
 CellType_Anno_Table=read.csv("PerCell_Metadata.csv")
 
 ```
 
-```{r,eval=T,echo=F}
+```{r,eval=run_Chunks,echo=F}
 Anno_SO=readRDS("./images/Anno_SO.rds")
 
 
@@ -77,7 +77,7 @@ [email protected][,'mouseRNAseq_main',drop=F]%>%rename('Cell Type'=mouseR
 
 ```
 
-```{r,eval=F,echo=F}
+```{r,eval=run_Chunks,echo=F}
 
 
 AddExternalAnnotation = function(
@@ -207,7 +207,7 @@ return(NULL)
 
 ```
 
-```{r,eval=F,echo=T}
+```{r,eval=Frun_Chunks,echo=T}
 
 ExtAnno_SO=ExternalAnnotation(object = Anno_SO$object,
                     external_metadata = CellType_Anno_Table,
@@ -228,7 +228,7 @@ This function enables users to visualize the association between two selected ge
 
 Furthermore, the tool allows for the application of filters to the data, setting thresholds, and providing annotations to notify users if cells meet the established thresholds. The visualization can be improved by omitting extreme values. The tool also facilitates the creation of a heatmap to represent the density distribution of cells and exhibit the raw gene/protein expression values.
 
-```{r,eval=F,echo=T,results='hide'}
+```{r,eval=run_Chunks,echo=T,results='hide'}
 grep('Cd4',rownames(Anno_SO$object@assays$RNA),ignore.case = T,value=T)
 
 DLAnno_SO=dualLabeling(object = Anno_SO$object, 
@@ -261,7 +261,7 @@ plot(DLAnno_SO$plot_densityHM)
 plot(DLAnno_SO$plot_umap)
 ```
 
-```{r,echo=F,eval=F}
+```{r,echo=F,eval=run_Chunks}
 png(filename="./images/DL1.png")
 plot(DLAnno_SO$plot_densityHM)
 dev.off()
@@ -285,19 +285,19 @@ The code also makes sure to display your chosen samples, creates a caption for t
 If you haven't selected specific samples, it will use all the available samples from your data. It also checks for the presence of your chosen genes in the data and processes them to ensure uniformity across different cell types.
 The output of this function is a detailed figure showing the activity of chosen genes across different cell types. This is useful for identifying distinct groups of cells based on gene activity levels.
 
-```{r,eval=F}
+```{r,eval=run_Chunks}
 Marker_Table <- read.csv("Marker_Table_demo.csv")
 ```
 
-```{r,eval=T,echo=F}
+```{r,eval=run_Chunks,echo=F}
 Marker_Table_demo <- read.csv("../tests/testthat/fixtures/Marker_Table_demo.csv")
 Marker_Table_demo%>%head()%>%
   knitr::kable()
 
 Marker_Table=Marker_Table_demo[1:4,]
 ```
 
-```{r,eval=F,echo=T,results='hide'}
+```{r,eval=Frun_Chunks,echo=T,results='hide'}
     
 FigOut=colorByMarkerTable(object=Anno_SO$object,
                    samples.subset=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
@@ -315,7 +315,7 @@ plot(FigOut)
 
 ```
 
-```{r,echo=F,eval=F}
+```{r,echo=F,eval=run_Chunks}
 png(filename="./images/CBM1.png",width=1000,height=700)
 plot(FigOut)
 dev.off()
@@ -343,7 +343,7 @@ Subclass Identification: If desired, the program can identify subclasses within
 **Updating Cell Type Labels:** The program appends a "Likely_CellType" column to the metadata of the scRNA-seq object, based on the results of the module score analysis.
 **Output:** An updated scRNA-seq object with new cell type labels.
 
-```{r,eval=F}
+```{r,eval=run_Chunks}
 
 MS_object=modScore(object=Anno_SO$object, 
                    marker.table=Marker_Table[,c("Macrophages","M1","M2" )], 
@@ -377,7 +377,7 @@ This function creates a dot plot visualization of cell types by metadata categor
 It also generates a dot plot using Seurat's Dotplot function [3], providing a visual representation of the percentage of various cell types within each cluster.  Typically, a cluster can be more distinctively named by the predominant cell type as seen in the dotplot.  The plot's order can be customized for the clusters and cell types. If no specific order is provided, the function uses a default order.
 An optional parameter allows the user to make the plot interactive. The function returns the updated Seurat object and the plot.
 
-```{r,eval=F}
+```{r,eval=run_Chunks}
 
 RNC_object=nameClusters(object=Anno_SO$object,
                          cluster.numbers= c(0,1,2,3,4,5,6,7),
@@ -392,7 +392,7 @@ RNC_object=nameClusters(object=Anno_SO$object,
 
 ```
 
-```{r,eval=F,echo=T,results='hide'}
+```{r,eval=run_Chunks,echo=T,results='hide'}
 
 # DimPlot(MS_object, group.by = "SCT_snn_res.0.2", label = T, reduction = 'umap')
 # DimPlot(MS_object, group.by = "mouseRNAseq_main", label = T, reduction = 'umap')
@@ -421,7 +421,7 @@ In addition to the plot, the function provides the tabular format of the dot plo
 This function can be useful for exploratory data analysis and visualizing the differences in gene expression across different conditions or groups of cells.
 
 
-```{r,eval=F}
+```{r,eval=run_Chunks}
 
 FigOut=dotPlotMet(object=Anno_SO$object,
                    metadata="orig.ident",
@@ -435,7 +435,7 @@ FigOut=dotPlotMet(object=Anno_SO$object,
 
 ```
 
-```{r,eval=F,echo=F,results='hide'}
+```{r,eval=run_Chunks,echo=F,results='hide'}
 
       c("Itgam", "Ly6g_neg","Adgre1", "Cd68",  "Siglec1", "Csf1r", "Apoe",  "Mafb",  "Cx3cr1", "Nr4a1","F13a1",
                                  "Lgals3", "Cxcl9", "Cxcl10", "Sell", "Arg1", "Mmp13", "Mmp12")

docs/search.json

@@ -9,6 +9,7 @@ vignette: >
 
 ```{r, include = FALSE}
 options(rmarkdown.html_vignette.check_title = FALSE)
+run_Chunks=FALSE
 
 knitr::opts_chunk$set(
   collapse = TRUE,
@@ -32,7 +33,7 @@ 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}
+```{r,echo=F,eval=run_Chunks}
 # data.table(My_Sample_Names=c('CID3586','CID3921',
 #                              'CID4066','CID4290A',
 #                              'CID4398','CID4471',
@@ -63,7 +64,7 @@ Samples can also be excluded from the final Seurat object using a REGEX strategy
 
 The final Seurat Object will contain an assay slot with log2 normalized counts. QC figures for individual samples will also be produced to help evaluate samples quality.
 
-```{r,eval=F,echo=T,results='hide'}
+```{r,eval=run_Chunks,echo=T,results='hide'}
 
 SampleMetadataTable <- read.table(file = "./images/Sample_Metadata.txt",  sep = '\t',header = T)
 files=list.files(path="../tests/testthat/fixtures/Chariou/h5files",full.names = T)
@@ -81,7 +82,7 @@ SOlist=processRawData(input=files,
 )
 ```
 
-```{r,echo=F,eval=F}
+```{r,echo=F,eval=run_Chunks}
 ggsave(SOlist$plots[[1]], filename = "./images/ProcessInputData1.png", width = 10, height = 10)
 ggsave(SOlist$plots[[2]], filename = "./images/ProcessInputData2.png", width = 10, height = 10)
 
@@ -109,7 +110,7 @@ The individual filtering criteria used in this tool are listed below.
 The function will return a filtered Seurat Object and various figures showing metrics before and after filtering. These figures can be used to help evaluate the effects of filtering criteria and whether filtering limits need to be adjusted. 
 
 
-```{r,eval=F,echo=T,results='hide'}
+```{r,eval=run_Chunks,echo=T,results='hide'}
 
 SO_filtered=filterQC(object=SOlist$object,
                      ## Filter Genes
@@ -135,7 +136,7 @@ SO_filtered=filterQC(object=SOlist$object,
 
 ```
 
-```{r,echo=F,eval=F}
+```{r,echo=F,eval=run_Chunks}
 ggsave(SO_filtered$plots$PostFilterCombined, filename = "./images/QC1.png", width = 10, height = 10)
 ggsave(SO_filtered$plots$ViolinPlotCombine, filename = "./images/QC2.png", width = 10, height = 10)
 ggsave(SO_filtered$plots$ScatterPlotCombine, filename = "./images/QC3.png", width = 10, height = 10)
@@ -151,7 +152,7 @@ ggsave(SO_filtered$plots$ScatterPlotCombine, filename = "./images/QC3.png", widt
 
 This functions combines multiple sample level Seurat Objects into a single Seurat Object and normalizes the combined dataset. The multi-dimensionality of the data will be summarized into a set of "principal components" and visualized in both UMAP and tSNE projections.  A graph-based clustering approach will identify cell clusters with in the data.
 
-```{r,eval=F,echo=T,results='hide'}
+```{r,eval=run_Chunks,echo=T,results='hide'}
 
 Comb_SO=combineNormalize(
                          object=SO_filtered$object,
@@ -194,7 +195,7 @@ Comb_SO=combineNormalize(
 
 ```
 
-```{r,echo=F,eval=F}
+```{r,echo=F,eval=run_Chunks}
 saveRDS(Comb_SO$object, file="./images/CN_SO.rds")
 
 ggsave(Comb_SO$plots$UMAP, filename = "./images/CN1.png", width = 10, height = 10)