diff --git a/DESCRIPTION b/DESCRIPTION index fe52c91..9db47eb 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: scigenex Type: Package Title: The scigenex package (Single-Cell Informative GENe Explorer) -Version: 1.4.9 +Version: 1.4.10 Date: 2020-07-22 Author: J. Bavais, Sebastien Nin, Lionel Spinelli and Denis Puthier Maintainer: J. Bavais diff --git a/Makefile b/Makefile index bab8dc5..9938d22 100644 --- a/Makefile +++ b/Makefile @@ -1,5 +1,5 @@ MAKEFILE=Makefile -VERSION=1.4.9 +VERSION=1.4.10 .PHONY: help @@ -87,12 +87,14 @@ release_bump: release @ make install @ git commit -m 'Bumped version $(VER)' +readme: + @ echo "- Rebuilting README.md from README.Rmd" + @ echo "devtools::build_readme()" | R --slave + doc_html: @ echo "#-----------------------------------------------#" @ echo "# Building doc #" @ echo "#-----------------------------------------------#" - @ echo "- Rebuilting README.md from README.Rmd" - @ echo "devtools::build_readme()" | R --slave @ echo "Sys.setenv(RSTUDIO_PANDOC='/Applications/RStudio.app/Contents/Resources/app/quarto/bin/toolslibrary'); library(knitr); pkgdown::build_site()" | R --slave @ git add -u @ git commit -m "Updated html doc to $(VER)." diff --git a/NAMESPACE b/NAMESPACE index 107b1bb..79f2638 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -79,6 +79,7 @@ importFrom(Seurat,NoLegend) importFrom(Seurat,SpatialDimPlot) importFrom(SeuratObject,GetAssayData) importFrom(SparseM,t) +importFrom(WriteXLS,WriteXLS) importFrom(amap,Dist) importFrom(amap,hcluster) importFrom(biomaRt,getBM) @@ -191,5 +192,4 @@ importFrom(utils,object.size) importFrom(utils,read.csv) importFrom(utils,read.table) importFrom(utils,write.table) -importFrom(xlsx,write.xlsx) importMethodsFrom(methods,show) diff --git a/R/ClusterSet_class.R b/R/ClusterSet_class.R index 96598c5..575a910 100644 --- a/R/ClusterSet_class.R +++ b/R/ClusterSet_class.R @@ -639,7 +639,7 @@ setGeneric("cluster_set_to_xls", #' @description Write gene lists from a Cluster-Set object into an excel sheet. #' @param object The ClusterSet object. #' @param file_path The file path. -#' @importFrom xlsx write.xlsx +#' @importFrom WriteXLS WriteXLS #' @examples #' #' Load an example dataset #' load_example_dataset('7871581/files/pbmc3k_medium_clusters') diff --git a/changelog.md b/changelog.md index fc851c6..7b772ed 100644 --- a/changelog.md +++ b/changelog.md @@ -1,6 +1,11 @@ changelog.md # Changelog +## v1.4.10 + +* Fix NAMESPACE (xlxs dependency). +* Rebuilt doc in html format. + ## v1.4.9 * Remove xlxs dependency in favor with WriteXLS dependency (to avoid rJava dependency). diff --git a/docs/404.html b/docs/404.html index 842e6c5..c9be7bf 100644 --- a/docs/404.html +++ b/docs/404.html @@ -32,7 +32,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/articles/index.html b/docs/articles/index.html index 90529f4..2a572ec 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/articles/usage.html b/docs/articles/usage.html index cbee8c8..a9e010a 100644 --- a/docs/articles/usage.html +++ b/docs/articles/usage.html @@ -33,7 +33,7 @@ scigenex - 1.4.8 + 1.4.10 @@ -66,14 +66,14 @@ - +

Guided tutorial

Julie Bavais

-

2023-12-22

+

2024-01-09

Source: vignettes/usage.Rmd @@ -85,21 +85,21 @@

2023-12-22

Guided tutorial

-

The simplest way to use scigenex is to run the following steps using the Seurat R package:

+

The easiest way to use scigenex is to perform the following steps using the Seurat R package:

  • Load data into a Seurat object
    • -
  • Perform quality Control
    • +
  • Perform quality control
  • Perform normalization
-

The subsequent object can be used as input to SciGeneX. Alternatively, you can provide a normalized matrix as input.

+

The resulting object can be used as input to SciGeneX. You can also provide a normalized matrix as input.

The dataset

-

For this tutorial, we will use scRNA-seq dataset of Peripheral Blood Mononuclear Cells (PBMC) freely available on 10x Genomics web site. This dataset contains 2700 single cells sequenced on the Illumina NextSeq 500. This dataset can be downloaded from 10X Genomics or through the SeuratDatalibrary

+

For this tutorial, we’ll be using a peripheral blood mononuclear cell (PBMC) scRNA-seq dataset available from the 10x Genomics website. This dataset contains 2700 individual cells sequenced on the Illumina NextSeq 500 and can be downloaded from 10X Genomics web site or via the SeuratData library.

Preparing the pbmc3k dataset

-

In this step, we will run the classical pre-processing steps from the. Please refer to this tutorial for more informations. If you have already pre-processed your data with Seurat or have a normalized count matrix as input, you can skip this step.

+

In this step, we’ll carry out the classic pre-processing steps of the tutorial. Please refer to this tutorial for more information. If you have already pre-processed your data with Seurat, or if you have a normalized count matrix as input, you can skip this step.

 library(ggplot2)
 library(patchwork)
@@ -139,13 +139,13 @@ 
Preparing the pbmc3k dataset
 
Extracting gene modules using SciGeneX
 

-
In this section, we use the previously generated Seurat object as an input to run SciGeneX main commands. This command run the main algorithm that will:

+
In this section, we use the previously generated Seurat object as input to run the main SciGeneX commands. This command executes the main algorithm which will:

 
    
-
  • Identify and extracts co-expressed genes
    • 
-
  • Partition selected genes into clusters
    • 
-
  • Store the result in a ClusterSet object
    • 
+
  • identify and extract co-expressed genes
    • 
+
  • divide the selected genes into groups
    • 
+
  • store the result in a ClusterSet object.
    • 
 

-
First we will load the Scigenex library. To limit the verbosity of Scigenex function we will set the verbosity level to zero (which will switch off information messages and debugging messages).

+
First, we’ll load the SciGeneX library. To limit the verbosity of the SciGeneX functions, we’ll set the verbosity level to zero (which will disable information and debug messages).

 
@@ -193,7 +193,7 @@ 
Extracting gene modules using Sc
 ##               *  seed  =  123 
 ##               *  keep_nn  =  FALSE 
 ##               *  k_mcl_graph  =  5 
-##               *  output_path  =  /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T//RtmpDSTiJs 
+##               *  output_path  =  /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T//Rtmpl3DiOC 
 ##               *  name  =  s8gZ5uYQ33 
 ##               *  inflation  =  2

There are various methods associated with the ClusterSet objects.

@@ -222,7 +222,7 @@

Extracting gene modules using Sc 
 nclust(pbmc_scigenex)
## [1] 163
-

At this step, several modules need to be filtered out as lots of them may be singletons. Interestingly, the ClusterSet class implements the indexing operator/function (“[”). The first argument/dimension of the indexing function corresponds to the cluster stored in the object. The second dimension correspond to cell names. As an example, one can simply keep gene modules with size (i.e number of genes) greater than 7 using the following code. This leads to an object containing 40 gene modules.

+

At this stage, several modules need to be filtered, as many of them may be singletons. Interestingly, the ClusterSet class implements the indexing operator/function (“[”). The first argument/dimension of the indexing function corresponds to the cluster stored in the object. The second dimension corresponds to the cell/spot names. As an example, we can simply store gene modules whose size (*i.e. number of genes) is greater than 7 using the following code. The result is an object containing gene modules 40.

 pbmc_scigenex <- pbmc_scigenex[clust_size(pbmc_scigenex) > 7, ]
 nclust(pbmc_scigenex)
@@ -266,12 +266,12 @@

Extracting gene modules using Sc ## 18

-

Visualisation using heatmap +

Heatmap visualization

-

Visualization of specific gene modules +

Visualization of specific genetic modules

-

The visualization of a heatmap containing all cells and modules may be time consuming and may require important memory ressources. A first alternative is to look at gene cluster individually or a subset of clusters. The gene modules can be visualized using the plot_heatmap() or the plot_ggheatmap() functions. The first is mainly intented to propose interactive vizualisation based on the iheatmapr library and allows to easily browse the results and zoom on particular regions of the heatmap The second should be mostly used for non interactive vizualisation. This second solution leads to a plot that is more easilly customable as it is bas ed on the ggplot framework. Here we use plot_ggheatmap() to have a look at the 4 first clusters. Note that here, we choose to order columns/cells on Seurat::FindClusters results.

+

Visualizing a heatmap containing all cells and modules can be time-consuming and require significant memory resources. A first alternative is to examine gene modules individually or a subset of modules. Gene modules can be visualized using the plot_heatmap() the plot_ggheatmap() functions. The former is primarily intended to provide an interactive visualization based on the iheatmapr library, and allows easy browsing of results and zooming in on particular regions of the heatmap. This second solution leads to a diagram that is more easily customized, as it is based on the ggplot framework. Here, we use plot_ggheatmap() to view the first 4 clusters. Note that here, we choose to order the columns/cells on the results of Seurat::FindClusters.

 plot_ggheatmap(pbmc_scigenex[1:4, ], 
                use_top_genes = FALSE,
@@ -281,7 +281,7 @@ 
Visualization of specific gene m
 

 
Heatmap of representative genes
 

-
However one alternative is to extract the most representative genes of each cluster. This can be achieved using the top_genes() function. This function will store the identifiers of these representative genes inside the top_genes slot of the ClusterSet object. The top_genesslot can be accessed the get_genes() function.

+
However, an alternative is to extract the most representative genes from each group. This can be achieved using the top_genes() function. This function stores the identifiers of these representative genes in the top_genes slot of the ClusterSet object. The get_genes() function is used to access the top_genes slot.

 
 pbmc_scigenex <- top_genes(pbmc_scigenex)
 genes_cl5_top <- get_genes(pbmc_scigenex, cluster = 5, top = TRUE)
@@ -302,12 +302,12 @@ 
Heatmap of representative genes
 
Interactive heatmap
 

-
A very interesting feature of Scigenex is its ability to display gene expression levels across cells through interactive heatmaps. Using this feature, user can interactively assess the expression levels in selected cells or cluster or on the whole dataset. However, it is generally advisable, when using all the clusters to restrict analysis using top_genes=TRUE. Here we will display the expression levels across cells of cluster 3 to 5.

+
A very interesting feature of SciGeneX is its ability to display gene expression levels in cells/spots using interactive heatmaps. With this function, the user can interactively evaluate expression levels in selected cells or groups, or over the whole dataset. However, it is generally advisable, when using all clusters, to restrict the analysis using top_genes=TRUE. Here, we’ll display expression levels in cells from clusters 3 to 5.

 
 plot_heatmap(pbmc_scigenex[3:5, ], 
                use_top_genes = TRUE,
                cell_clusters =Seurat::Idents(pbmc3k))

-

+

 
 

 

@@ -356,7 +356,7 @@ 
Mapping cell pop
 

 
Working with spatial transcriptomics datasets
 

-
The scigenex package offers a certain number of function dedicated to spatial transcriptomic data analysis. At the moment these functions have been mainly developed to analyse VISIUM technology (10X Genomics).

+
The scigenex package offers a certain number of functions dedicated to spatial transcriptomic data analysis. At the moment these functions have been mainly developed to analyse VISIUM technology (10X Genomics).

 

 
Searching for gene modules
 

@@ -432,7 +432,7 @@ 
Interactive heatmapplot_heatmap(gcss_brain[6:9,], 
              use_top_genes = TRUE, 
              cell_clusters = Seurat::Idents(brain1))
-
+
 # Try selecting a subset of columns/rows
 # The 'home' button can be used to reset
@@ -479,7 +479,7 @@ 
Visualizing topological clustersgrep_clust(gcss_brain[Hpca_clust, ], "^Rgs")
## $`3`
 ## [1] "Rgs9"   "Rgs7bp" "Rgs8"
-

To visualize the topological distribution of signals in each cluster we will (i) extract the gene_clusters slot from the gcss object, (ii) compute module score using the Seurat AddModuleScore()function and (iii) store the results in the seurat object. Note that, for each gene module, signal will be scaled from 0 to 1. This will allow us to have a shared legend between all plots

+

To visualize the topological distribution of signals in each cluster, we’ll (i) extract the gene_clusters slot from the gcss object, (ii) calculate the module score using Seurat’s AddModuleScore() function and (iii) store the results in the seurat object. Note that, for each gene module, the signal will be scaled from 0 to 1, allowing us to have a common legend for all plots.

 brain1 <- AddModuleScore(brain1, features = gcss_brain@gene_clusters)
 
@@ -495,7 +495,7 @@ 
Visualizing topological clusters             metadata = paste("Cluster", setNames(Hpca_clust, NULL), sep=""), 
              pt_size=2.5, coord_flip = T) + hull_white

-

We can easily see that the pattern of the gene module containing Hpca is very similar to the Hpca pattern. Moreover this gene module pattern also strongly overlaps the Seurat cell cluster number 0. However, the analysis seems to indicate that a more complex tissue architecture exists as Hpca signal extends beyond Seurat cluster 0.

+

We can easily see that the Hpca-containing gene module pattern is very similar to the Hpca pattern. However, the analysis suggests a more complex tissue architecture than expected, as the Hpca signal extends beyond Seurat’s cluster 0.

We will then display the topological signal of all clusters using the plot_spatial_panel() function.

 p <- plot_spatial_panel(brain1, 
@@ -515,8 +515,8 @@ 
Visualizing topological clusters
 
Comparing FindAllMarkers() and Scigenex gene modules
 

-
It can be interesting to compare the clusters obtained using unsupervised approach (scigenex) to the list of genes obtained using Seurat::FindAllMarkers() that search for differentially expressed genes between the cell populations infered by Seurat::FindClusters(). Here the comparison is performed using results obtained for spatial transcriptomics data.

-
First note that Seurat::FindAllMarkers() will tend to find shared markers between populations.

+
It may be interesting to compare the clusters obtained using an unsupervised approach (SciGeneX) with those obtained from Seurat::FindAllMarkers(), which searches for genes differentially expressed between cell populations deduced by Seurat::FindClusters().

+
As shown using the plot_cmp_genesets() function, Seurat::FindAllMarkers() tends to find markers that are not so specific to cell populations. Thus this markers are shared between gene modules.

 
 seurat_brain_mk <- Seurat::FindAllMarkers(brain1)
 seurat_brain_mk<- split(seurat_brain_mk$gene, seurat_brain_mk$cluster)
@@ -540,7 +540,7 @@ 
Comparing FindAllMar
 

 
Creating a report
 

-
A report can be created using the cluster_set_report()function. The arguments of these function are the processed clusterSet and the corresponding Seurat processed object. Ideally the clusterSet object should contain functionnal annotation (see enrich_go()). Currently, the process of creating a report may be rather long. It may also produce heavy html files that may take some time to load into de web browser.

+
A report can be created using the cluster_set_report() function. The arguments to this function are the processed clusterSet and the corresponding processed Seurat object. Ideally, the clusterSet object should contain functional annotations (see enrich_go()). Currently, the process of creating a report can be quite time consuming It can also produce heavy html files which may take some time to load in the web browser.

 
 # Uncomment to prepare the report.
 # gcss_brain <- enrich_go(gcss_brain, species = "Mmusculus", ontology = "BP")
@@ -566,8 +566,8 @@ 
Session info## [1] stats     graphics  grDevices utils     datasets  methods   base     
 ## 
 ## other attached packages:
-##  [1] tidyr_1.3.0                dplyr_1.1.3               
-##  [3] scigenex_1.4.8             stxKidney.SeuratData_0.1.0
+##  [1] tidyr_1.3.0                dplyr_1.1.4               
+##  [3] scigenex_1.4.10            stxKidney.SeuratData_0.1.0
 ##  [5] stxBrain.SeuratData_0.1.1  pbmc3k.SeuratData_3.1.4   
 ##  [7] SeuratData_0.2.2           SeuratObject_4.1.3        
 ##  [9] Seurat_4.3.0               patchwork_1.1.2           
@@ -576,19 +576,19 @@ 
Session info## loaded via a namespace (and not attached):
 ##   [1] rappdirs_0.3.3         SparseM_1.81           scattermore_0.8       
 ##   [4] ragg_1.2.5             bit64_4.0.5            knitr_1.45            
-##   [7] irlba_2.3.5.1          data.table_1.14.8      KEGGREST_1.38.0       
+##   [7] irlba_2.3.5.1          data.table_1.14.10     KEGGREST_1.38.0       
 ##  [10] RCurl_1.98-1.10        generics_0.1.3         BiocGenerics_0.44.0   
 ##  [13] org.Mm.eg.db_3.16.0    cowplot_1.1.1          RSQLite_2.2.20        
 ##  [16] shadowtext_0.1.2       RANN_2.6.1             future_1.31.0         
 ##  [19] bit_4.0.5              enrichplot_1.18.3      spatstat.data_3.0-3   
-##  [22] xml2_1.3.3             httpuv_1.6.12          viridis_0.6.4         
+##  [22] xml2_1.3.3             httpuv_1.6.13          viridis_0.6.4         
 ##  [25] amap_0.8-19            xfun_0.41              rJava_1.0-6           
 ##  [28] hms_1.1.3              jquerylib_0.1.4        evaluate_0.23         
-##  [31] promises_1.2.1         fansi_1.0.5            progress_1.2.2        
+##  [31] promises_1.2.1         fansi_1.0.6            progress_1.2.3        
 ##  [34] dbplyr_2.3.2.9000      igraph_1.5.1           DBI_1.1.3             
-##  [37] htmlwidgets_1.6.2      sparsesvd_0.2-2        spatstat.geom_3.2-7   
+##  [37] htmlwidgets_1.6.4      sparsesvd_0.2-2        spatstat.geom_3.2-7   
 ##  [40] stats4_4.2.2           purrr_1.0.2            ellipsis_0.3.2        
-##  [43] biomaRt_2.54.1         deldir_1.0-6           vctrs_0.6.4           
+##  [43] biomaRt_2.54.1         deldir_1.0-6           vctrs_0.6.5           
 ##  [46] Biobase_2.58.0         ROCR_1.0-11            abind_1.4-5           
 ##  [49] cachem_1.0.8           withr_2.5.2            ggforce_0.4.1         
 ##  [52] HDO.db_0.99.1          ggh4x_0.2.4            progressr_0.13.0      
@@ -597,50 +597,50 @@ 
Session info##  [61] ape_5.7-1              lazyeval_0.2.2         crayon_1.5.2          
 ##  [64] clValid_0.7            spatstat.explore_3.2-5 slam_0.1-50           
 ##  [67] pkgconfig_2.0.3        labeling_0.4.3         tweenr_2.0.2          
-##  [70] GenomeInfoDb_1.34.9    nlme_3.1-160           rlang_1.1.1           
-##  [73] globals_0.16.2         lifecycle_1.0.3        miniUI_0.1.1.1        
+##  [70] GenomeInfoDb_1.34.9    nlme_3.1-160           rlang_1.1.2           
+##  [73] globals_0.16.2         lifecycle_1.0.4        miniUI_0.1.1.1        
 ##  [76] downloader_0.4         filelock_1.0.2         BiocFileCache_2.6.1   
-##  [79] rprojroot_2.0.4        polyclip_1.10-4        matrixStats_0.63.0    
+##  [79] rprojroot_2.0.4        polyclip_1.10-4        matrixStats_1.2.0     
 ##  [82] lmtest_0.9-40          Matrix_1.5-4           aplot_0.1.9           
 ##  [85] zoo_1.8-11             pheatmap_1.0.12        ggridges_0.5.4        
 ##  [88] png_0.1-8              viridisLite_0.4.2      bitops_1.0-7          
 ##  [91] gson_0.0.9             KernSmooth_2.23-20     Biostrings_2.66.0     
-##  [94] blob_1.2.3             stringr_1.5.0          qvalue_2.30.0         
+##  [94] blob_1.2.3             stringr_1.5.1          qvalue_2.30.0         
 ##  [97] parallelly_1.34.0      spatstat.random_3.2-2  gridGraphics_0.5-1    
-## [100] S4Vectors_0.36.1       scales_1.2.1           memoise_2.0.1         
-## [103] magrittr_2.0.3         plyr_1.8.8             ica_1.0-3             
+## [100] S4Vectors_0.36.1       scales_1.3.0           memoise_2.0.1         
+## [103] magrittr_2.0.3         plyr_1.8.9             ica_1.0-3             
 ## [106] zlibbioc_1.44.0        compiler_4.2.2         scatterpie_0.1.8      
 ## [109] RColorBrewer_1.1-3     fitdistrplus_1.1-8     ggstar_1.0.4          
-## [112] cli_3.6.1              XVector_0.38.0         listenv_0.9.0         
+## [112] cli_3.6.2              XVector_0.38.0         listenv_0.9.0         
 ## [115] pbapply_1.7-0          MASS_7.3-58.1          tidyselect_1.2.0      
-## [118] stringi_1.7.12         textshaping_0.3.6      highr_0.10            
-## [121] yaml_2.3.7             GOSemSim_2.24.0        ggrepel_0.9.3         
-## [124] grid_4.2.2             sass_0.4.7             fastmatch_1.1-3       
+## [118] stringi_1.8.3          textshaping_0.3.6      highr_0.10            
+## [121] yaml_2.3.8             GOSemSim_2.24.0        ggrepel_0.9.3         
+## [124] grid_4.2.2             sass_0.4.8             fastmatch_1.1-3       
 ## [127] tools_4.2.2            future.apply_1.10.0    parallel_4.2.2        
 ## [130] gridExtra_2.3          farver_2.1.1           Rtsne_0.16            
-## [133] ggraph_2.1.0           digest_0.6.33          shiny_1.7.5.1         
+## [133] ggraph_2.1.0           digest_0.6.33          shiny_1.8.0           
 ## [136] qlcMatrix_0.9.7        Rcpp_1.0.11            xlsx_0.6.5            
-## [139] later_1.3.1            RcppAnnoy_0.0.20       org.Hs.eg.db_3.16.0   
-## [142] httr_1.4.4             AnnotationDbi_1.60.0   colorspace_2.1-0      
-## [145] brio_1.1.3             XML_3.99-0.13          fs_1.6.3              
-## [148] tensor_1.5             reticulate_1.28        IRanges_2.32.0        
-## [151] splines_4.2.2          uwot_0.1.14            yulab.utils_0.0.6     
-## [154] tidytree_0.4.2         spatstat.utils_3.0-4   pkgdown_2.0.7         
-## [157] graphlayouts_0.8.4     xlsxjars_0.6.1         sp_1.6-0              
-## [160] ggplotify_0.1.0        plotly_4.10.1          systemfonts_1.0.4     
-## [163] xtable_1.8-4           jsonlite_1.8.7         ggtree_3.6.2          
-## [166] tidygraph_1.2.3        iheatmapr_0.5.1        testthat_3.1.6        
-## [169] ggfun_0.0.9            R6_2.5.1               pillar_1.9.0          
-## [172] htmltools_0.5.7        mime_0.12              glue_1.6.2            
-## [175] fastmap_1.1.1          clusterProfiler_4.6.0  DT_0.28               
-## [178] BiocParallel_1.32.5    class_7.3-20           codetools_0.2-18      
-## [181] fgsea_1.24.0           utf8_1.2.3             lattice_0.20-45       
-## [184] bslib_0.5.1            spatstat.sparse_3.0-3  tibble_3.2.1          
-## [187] curl_5.0.0             leiden_0.4.3           GO.db_3.16.0          
-## [190] limma_3.54.1           survival_3.4-0         docopt_0.7.1          
-## [193] rmarkdown_2.25         desc_1.4.2             munsell_0.5.0         
-## [196] fastcluster_1.2.3      GenomeInfoDbData_1.2.9 reshape2_1.4.4        
-## [199] gtable_0.3.4

+## [139] later_1.3.2            WriteXLS_6.4.0         RcppAnnoy_0.0.20      
+## [142] org.Hs.eg.db_3.16.0    httr_1.4.4             AnnotationDbi_1.60.0  
+## [145] colorspace_2.1-0       brio_1.1.3             XML_3.99-0.13         
+## [148] fs_1.6.3               tensor_1.5             reticulate_1.28       
+## [151] IRanges_2.32.0         splines_4.2.2          uwot_0.1.14           
+## [154] yulab.utils_0.0.6      tidytree_0.4.2         spatstat.utils_3.0-4  
+## [157] pkgdown_2.0.7          graphlayouts_0.8.4     xlsxjars_0.6.1        
+## [160] sp_1.6-0               ggplotify_0.1.0        plotly_4.10.1         
+## [163] systemfonts_1.0.4      xtable_1.8-4           jsonlite_1.8.8        
+## [166] ggtree_3.6.2           tidygraph_1.2.3        iheatmapr_0.5.1       
+## [169] testthat_3.1.6         ggfun_0.0.9            R6_2.5.1              
+## [172] pillar_1.9.0           htmltools_0.5.7        mime_0.12             
+## [175] glue_1.6.2             fastmap_1.1.1          clusterProfiler_4.6.0 
+## [178] DT_0.31                BiocParallel_1.32.5    class_7.3-20          
+## [181] codetools_0.2-18       fgsea_1.24.0           utf8_1.2.4            
+## [184] lattice_0.20-45        bslib_0.6.1            spatstat.sparse_3.0-3 
+## [187] tibble_3.2.1           curl_5.0.0             leiden_0.4.3          
+## [190] GO.db_3.16.0           limma_3.54.1           survival_3.4-0        
+## [193] docopt_0.7.1           rmarkdown_2.25         desc_1.4.2            
+## [196] munsell_0.5.0          fastcluster_1.2.3      GenomeInfoDbData_1.2.9
+## [199] reshape2_1.4.4         gtable_0.3.4
diff --git a/docs/authors.html b/docs/authors.html index 2a3d53b..d6a7319 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10
@@ -64,15 +64,15 @@

Citation

-

Bavais J, Nin S, Spinelli L, Puthier D (2023). +

Bavais J, Nin S, Spinelli L, Puthier D (2024). scigenex: The scigenex package (Single-Cell Informative GENe Explorer). -R package version 1.4.8, https://github.com/dputhier/scigenex. +R package version 1.4.10, https://github.com/dputhier/scigenex. 

@Manual{,
   title = {scigenex: The scigenex package (Single-Cell Informative GENe Explorer)},
   author = {J. Bavais and Sebastien Nin and Lionel Spinelli and Denis Puthier},
-  year = {2023},
-  note = {R package version 1.4.8},
+  year = {2024},
+  note = {R package version 1.4.10},
   url = {https://github.com/dputhier/scigenex},
 }
diff --git a/docs/changelog.html b/docs/changelog.html index bda981e..017e555 100644 --- a/docs/changelog.html +++ b/docs/changelog.html @@ -1,5 +1,5 @@ -Changelog • scigenexNA • scigenex @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10
@@ -46,10 +46,14 @@
-

Changelog

+

NA

-
+ +

changelog.md # Changelog

+
+

v1.4.9

+
  • Remove xlxs dependency in favor with WriteXLS dependency (to avoid rJava dependency).

v1.4.8

  • Fix an issue with temp dir.
@@ -185,7 +189,7 @@

Code changes

New Features

-
+
diff --git a/docs/index.html b/docs/index.html index ea6b81e..e3a754b 100644 --- a/docs/index.html +++ b/docs/index.html @@ -33,7 +33,7 @@ scigenex - 1.4.8 + 1.4.10
@@ -69,14 +69,14 @@
-

-
## ✔ Setting active project to '/Users/puthier/Documents/git/project_dev/scigenex'
-

Project Status: Active - The project has reached a stable, usable state and is being actively developed. License: MIT

+

+

Project Status: Active - The project has reached a stable, usable state and is being actively developed. License: MIT

SciGeneX repository

-

:arrow_double_down: Installation +

+⏬ Installation

System requirements @@ -90,16 +90,16 @@

Step 1 - Installation of SciGeneXFrom R

The scigenex library is currently not available in CRAN or Bioc. To install from github, use:

-
devtools::install_github("dputhier/scigenex")
-library(scigenex)
+
devtools::install_github("dputhier/scigenex")
+library(scigenex)

From the terminal

Download the tar.gz from github or clone the main branch. Uncompress and run the following command from within the uncompressed scigenex folder:

-
R CMD INSTALL .
+
R CMD INSTALL .

Then load the library from within R.

-
library(scigenex)
+
library(scigenex)
@@ -115,22 +115,22 @@

Installation of MCL using install

Installation of MCL from source

One also can install MCL from source using the following code.

-
# Download the latest version of mcl 
-wget http://micans.org/mcl/src/mcl-latest.tar.gz
-# Uncompress and install mcl
-tar xvfz mcl-latest.tar.gz
-cd mcl-xx-xxx
-./configure
-make
-sudo make install
-# You should get mcl in your path
-mcl -h
+
# Download the latest version of mcl 
+wget http://micans.org/mcl/src/mcl-latest.tar.gz
+# Uncompress and install mcl
+tar xvfz mcl-latest.tar.gz
+cd mcl-xx-xxx
+./configure
+make
+sudo make install
+# You should get mcl in your path
+mcl -h

Installation of MCL from sources

Finally you may install MCL using conda. Importantly, the mcl command should be available in your PATH from within R.

-
conda install -c bioconda mcl
+
conda install -c bioconda mcl
@@ -138,28 +138,28 @@

Installation of MCL from sourcesExample

The scigenex library contains several datasets including the pbmc3k_medium which is a subset from pbmc3k 10X dataset.

-
library(Seurat)
-library(scigenex)
-set_verbosity(1)
-
-# Load a dataset
-load_example_dataset("7871581/files/pbmc3k_medium")
-
-# Select informative genes
-res <- select_genes(pbmc3k_medium,
-                     distance = "pearson",
-                     row_sum=5)
-                     
-# Cluster informative features
- 
-## Construct and partition the graph
-res <- gene_clustering(res, 
-                       inflation = 1.5, 
-                       threads = 4)
-                        
-# Display the heatmap of gene clusters
-res <- top_genes(res)
-plot_heatmap(res, cell_clusters = Seurat::Idents(pbmc3k_medium))
+
library(Seurat)
+library(scigenex)
+set_verbosity(1)
+
+# Load a dataset
+load_example_dataset("7871581/files/pbmc3k_medium")
+
+# Select informative genes
+res <- select_genes(pbmc3k_medium,
+                     distance = "pearson",
+                     row_sum=5)
+                     
+# Cluster informative features
+ 
+## Construct and partition the graph
+res <- gene_clustering(res, 
+                       inflation = 1.5, 
+                       threads = 4)
+                        
+# Display the heatmap of gene clusters
+res <- top_genes(res)
+plot_heatmap(res, cell_clusters = Seurat::Idents(pbmc3k_medium))

diff --git a/docs/news/index.html b/docs/news/index.html index cf07fa8..35e1917 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10

diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index f0501ea..3a1c851 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -3,7 +3,7 @@ pkgdown: 2.0.7 pkgdown_sha: ~ articles: usage: usage.html -last_built: 2023-12-22T15:00Z +last_built: 2024-01-09T06:50Z urls: reference: https://scigenex.r-lib.org/reference article: https://scigenex.r-lib.org/articles diff --git a/docs/reference/ClusterSet-class.html b/docs/reference/ClusterSet-class.html index 03308fd..2ec513e 100644 --- a/docs/reference/ClusterSet-class.html +++ b/docs/reference/ClusterSet-class.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10
@@ -123,7 +123,7 @@

Examples

#> |-- INFO : Computing distances to KNN. #> |-- INFO : Creating the input file for MCL algorithm. #> |-- INFO : Writing the input file. -#> |-- INFO : The input file saved in '/var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/RtmpBuBaBi/4qPd5kES06.input_mcl.txt'. +#> |-- INFO : The input file saved in '/var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/RtmpV73YWY/4qPd5kES06.input_mcl.txt'. #> |-- INFO : Adding results to a ClusterSet object. is(res) #> [1] "ClusterSet" @@ -213,7 +213,7 @@

Examples

#> * seed = 123 #> * keep_nn = FALSE #> * k_mcl_graph = 5 -#> * output_path = /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T//RtmpBuBaBi +#> * output_path = /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T//RtmpV73YWY #> * name = 4qPd5kES06 #> * inflation = 1.6 show_methods(res) diff --git a/docs/reference/check_format_cluster_set.html b/docs/reference/check_format_cluster_set.html index 7a2593c..46de3dc 100644 --- a/docs/reference/check_format_cluster_set.html +++ b/docs/reference/check_format_cluster_set.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10
diff --git a/docs/reference/clust_names-ClusterSet-method.html b/docs/reference/clust_names-ClusterSet-method.html index 3e5fb27..8937197 100644 --- a/docs/reference/clust_names-ClusterSet-method.html +++ b/docs/reference/clust_names-ClusterSet-method.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/clust_names.html b/docs/reference/clust_names.html index fcd8b76..b2c9fe7 100644 --- a/docs/reference/clust_names.html +++ b/docs/reference/clust_names.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/clust_size-ClusterSet-method.html b/docs/reference/clust_size-ClusterSet-method.html index b06cd2d..4128ed3 100644 --- a/docs/reference/clust_size-ClusterSet-method.html +++ b/docs/reference/clust_size-ClusterSet-method.html @@ -19,7 +19,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/clust_size.html b/docs/reference/clust_size.html index fd2d01a..d01a7df 100644 --- a/docs/reference/clust_size.html +++ b/docs/reference/clust_size.html @@ -19,7 +19,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/cluster_set_from_matrix.html b/docs/reference/cluster_set_from_matrix.html index 4a20f10..f70f878 100644 --- a/docs/reference/cluster_set_from_matrix.html +++ b/docs/reference/cluster_set_from_matrix.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/cluster_set_from_seurat.html b/docs/reference/cluster_set_from_seurat.html index e2de18e..f346073 100644 --- a/docs/reference/cluster_set_from_seurat.html +++ b/docs/reference/cluster_set_from_seurat.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/cluster_set_report.html b/docs/reference/cluster_set_report.html index 77a4b51..4139d0d 100644 --- a/docs/reference/cluster_set_report.html +++ b/docs/reference/cluster_set_report.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 @@ -151,10 +151,10 @@

Examples

#> |-- DEBUG : Report ID is : e5cde0b97ddfd882ccff69e313ef025a #> |-- INFO : Preparing report. #> |-- INFO : Dumping dataset -#> |-- INFO : Writing report into /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T//RtmpBuBaBi/scigenex_report11b2d263f255f.Rmd -#> |-- INFO : Rmd file written at : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T//RtmpBuBaBi/scigenex_report11b2d263f255f.Rmd +#> |-- INFO : Writing report into /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T//RtmpV73YWY/scigenex_report3d0a1e38e5a0.Rmd +#> |-- INFO : Rmd file written at : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T//RtmpV73YWY/scigenex_report3d0a1e38e5a0.Rmd #> |-- INFO : Rendering report. -#> |-- INFO : Creating report at : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T//RtmpBuBaBi/scigenex_report11b2d263f255f.html +#> |-- INFO : Creating report at : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T//RtmpV73YWY/scigenex_report3d0a1e38e5a0.html diff --git a/docs/reference/cluster_set_to_xls-ClusterSet-method.html b/docs/reference/cluster_set_to_xls-ClusterSet-method.html index 3f8cd5d..9601dab 100644 --- a/docs/reference/cluster_set_to_xls-ClusterSet-method.html +++ b/docs/reference/cluster_set_to_xls-ClusterSet-method.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/cluster_set_to_xls.html b/docs/reference/cluster_set_to_xls.html index 92d750a..6d0a169 100644 --- a/docs/reference/cluster_set_to_xls.html +++ b/docs/reference/cluster_set_to_xls.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/cluster_stats-ClusterSet-method.html b/docs/reference/cluster_stats-ClusterSet-method.html index 9500987..9f8e760 100644 --- a/docs/reference/cluster_stats-ClusterSet-method.html +++ b/docs/reference/cluster_stats-ClusterSet-method.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/cluster_stats.html b/docs/reference/cluster_stats.html index 326c9fb..99de7d5 100644 --- a/docs/reference/cluster_stats.html +++ b/docs/reference/cluster_stats.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/col_names-ClusterSet-method.html b/docs/reference/col_names-ClusterSet-method.html index d64be97..82cdc33 100644 --- a/docs/reference/col_names-ClusterSet-method.html +++ b/docs/reference/col_names-ClusterSet-method.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/col_names.html b/docs/reference/col_names.html index c59d73f..bde9459 100644 --- a/docs/reference/col_names.html +++ b/docs/reference/col_names.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/colors_for_gradient.html b/docs/reference/colors_for_gradient.html index c5e8642..ac411df 100644 --- a/docs/reference/colors_for_gradient.html +++ b/docs/reference/colors_for_gradient.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/compare_genesets.html b/docs/reference/compare_genesets.html index ebe1215..9e8d697 100644 --- a/docs/reference/compare_genesets.html +++ b/docs/reference/compare_genesets.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/complex9.html b/docs/reference/complex9.html index 7e1c5e5..9e85243 100644 --- a/docs/reference/complex9.html +++ b/docs/reference/complex9.html @@ -22,7 +22,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/complex9Noisy.html b/docs/reference/complex9Noisy.html index f61ce1d..c5df284 100644 --- a/docs/reference/complex9Noisy.html +++ b/docs/reference/complex9Noisy.html @@ -26,7 +26,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/construct_new_graph.html b/docs/reference/construct_new_graph.html index 82e3496..04af0b2 100644 --- a/docs/reference/construct_new_graph.html +++ b/docs/reference/construct_new_graph.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 @@ -115,7 +115,7 @@

Examples

#> |-- INFO : Computing distances to KNN. #> |-- INFO : Creating the input file for MCL algorithm. #> |-- INFO : Writing the input file. -#> |-- INFO : The input file saved in '/var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/RtmpBuBaBi/06oFCM0di0.input_mcl.txt'. +#> |-- INFO : The input file saved in '/var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/RtmpV73YWY/06oFCM0di0.input_mcl.txt'. diff --git a/docs/reference/create_3_rnd_clust.html b/docs/reference/create_3_rnd_clust.html index 38ddc3a..2ee6dca 100644 --- a/docs/reference/create_3_rnd_clust.html +++ b/docs/reference/create_3_rnd_clust.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/create_4_rnd_clust.html b/docs/reference/create_4_rnd_clust.html index e761361..3463d83 100644 --- a/docs/reference/create_4_rnd_clust.html +++ b/docs/reference/create_4_rnd_clust.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/create_rand_str.html b/docs/reference/create_rand_str.html index 1665475..2337ce1 100644 --- a/docs/reference/create_rand_str.html +++ b/docs/reference/create_rand_str.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/dim-ClusterSet-method.html b/docs/reference/dim-ClusterSet-method.html index 2763e6e..f69552f 100644 --- a/docs/reference/dim-ClusterSet-method.html +++ b/docs/reference/dim-ClusterSet-method.html @@ -19,7 +19,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/discrete_palette.html b/docs/reference/discrete_palette.html index 78a74f7..ff2dcad 100644 --- a/docs/reference/discrete_palette.html +++ b/docs/reference/discrete_palette.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/display_hull.html b/docs/reference/display_hull.html index e003b1a..fa18d37 100644 --- a/docs/reference/display_hull.html +++ b/docs/reference/display_hull.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/enrich_go-ClusterSet-method.html b/docs/reference/enrich_go-ClusterSet-method.html index c1239d4..a413eb9 100644 --- a/docs/reference/enrich_go-ClusterSet-method.html +++ b/docs/reference/enrich_go-ClusterSet-method.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/enrich_go.html b/docs/reference/enrich_go.html index 52736d0..c0c4330 100644 --- a/docs/reference/enrich_go.html +++ b/docs/reference/enrich_go.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/filter_by_dot_prod.html b/docs/reference/filter_by_dot_prod.html index 72c6a28..9cccfb5 100644 --- a/docs/reference/filter_by_dot_prod.html +++ b/docs/reference/filter_by_dot_prod.html @@ -27,7 +27,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/filter_cluster_sd.html b/docs/reference/filter_cluster_sd.html index b2946ad..9b81e8b 100644 --- a/docs/reference/filter_cluster_sd.html +++ b/docs/reference/filter_cluster_sd.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/filter_cluster_size.html b/docs/reference/filter_cluster_size.html index 83e4915..ffa75e0 100644 --- a/docs/reference/filter_cluster_size.html +++ b/docs/reference/filter_cluster_size.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/filter_nb_supporting_cells.html b/docs/reference/filter_nb_supporting_cells.html index 5502e41..550825f 100644 --- a/docs/reference/filter_nb_supporting_cells.html +++ b/docs/reference/filter_nb_supporting_cells.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/gene_cluster-ClusterSet-method.html b/docs/reference/gene_cluster-ClusterSet-method.html index b9d7110..1f7a5a0 100644 --- a/docs/reference/gene_cluster-ClusterSet-method.html +++ b/docs/reference/gene_cluster-ClusterSet-method.html @@ -20,7 +20,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/gene_cluster.html b/docs/reference/gene_cluster.html index c02c3d6..197523d 100644 --- a/docs/reference/gene_cluster.html +++ b/docs/reference/gene_cluster.html @@ -20,7 +20,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/gene_clustering.html b/docs/reference/gene_clustering.html index 4fa4bf1..c739c7e 100644 --- a/docs/reference/gene_clustering.html +++ b/docs/reference/gene_clustering.html @@ -19,7 +19,7 @@ scigenex - 1.4.8 + 1.4.10 @@ -153,7 +153,7 @@

Examples

#> |-- INFO : Computing distances to KNN. #> |-- INFO : Creating the input file for MCL algorithm. #> |-- INFO : Writing the input file. -#> |-- INFO : The input file saved in '/var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/RtmpBuBaBi/R14z6ff8TV.input_mcl.txt'. +#> |-- INFO : The input file saved in '/var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/RtmpV73YWY/R14z6ff8TV.input_mcl.txt'. #> |-- INFO : Adding results to a ClusterSet object. # Display the heatmap of gene clusters @@ -185,7 +185,7 @@

Examples

method="reciprocal_neighborhood") #> |-- INFO : Creating the input file for MCL algorithm. #> |-- INFO : Writing the input file. -#> |-- INFO : The input file saved in '/var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/RtmpBuBaBi/65vDYGyn24.input_mcl.txt'. +#> |-- INFO : The input file saved in '/var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/RtmpV73YWY/65vDYGyn24.input_mcl.txt'. #> |-- INFO : Adding results to a ClusterSet object. # Display the heatmap of gene clusters diff --git a/docs/reference/getFlippedTissueCoordinates.html b/docs/reference/getFlippedTissueCoordinates.html index 672cac0..7364531 100644 --- a/docs/reference/getFlippedTissueCoordinates.html +++ b/docs/reference/getFlippedTissueCoordinates.html @@ -23,7 +23,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/get_data_for_scigenex.html b/docs/reference/get_data_for_scigenex.html index 01730ac..72d05c2 100644 --- a/docs/reference/get_data_for_scigenex.html +++ b/docs/reference/get_data_for_scigenex.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/get_genes.html b/docs/reference/get_genes.html index 0f2e95c..ace11fb 100644 --- a/docs/reference/get_genes.html +++ b/docs/reference/get_genes.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/get_verbosity.html b/docs/reference/get_verbosity.html index cbee8f1..b65823b 100644 --- a/docs/reference/get_verbosity.html +++ b/docs/reference/get_verbosity.html @@ -19,7 +19,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/grapes-in-grapes-character-ClusterSet-method.html b/docs/reference/grapes-in-grapes-character-ClusterSet-method.html index e88e05a..416f064 100644 --- a/docs/reference/grapes-in-grapes-character-ClusterSet-method.html +++ b/docs/reference/grapes-in-grapes-character-ClusterSet-method.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/grep_clust-ClusterSet-method.html b/docs/reference/grep_clust-ClusterSet-method.html index 1480892..2948d06 100644 --- a/docs/reference/grep_clust-ClusterSet-method.html +++ b/docs/reference/grep_clust-ClusterSet-method.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/grep_clust.html b/docs/reference/grep_clust.html index 7e51886..c69f083 100644 --- a/docs/reference/grep_clust.html +++ b/docs/reference/grep_clust.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/index.html b/docs/reference/index.html index dc4cee3..ac3ab42 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/install_mcl.html b/docs/reference/install_mcl.html index f49f79f..e2e8253 100644 --- a/docs/reference/install_mcl.html +++ b/docs/reference/install_mcl.html @@ -19,7 +19,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/keep_dbf_graph.html b/docs/reference/keep_dbf_graph.html index 67fe070..b7f0f8f 100644 --- a/docs/reference/keep_dbf_graph.html +++ b/docs/reference/keep_dbf_graph.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 @@ -108,7 +108,7 @@

Examples

keep_dbf_graph(object = res) #> |-- INFO : Creating the input file for MCL algorithm. #> |-- INFO : Writing the input file. -#> |-- INFO : The input file saved in '/var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/RtmpBuBaBi/06oFCM0di0.input_mcl.txt'. +#> |-- INFO : The input file saved in '/var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/RtmpV73YWY/06oFCM0di0.input_mcl.txt'. #> An object of class ClusterSet #> Name: 06oFCM0di0 #> Memory used: 1743336 @@ -132,7 +132,7 @@

Examples

#> * no_dknn_filter = FALSE #> * seed = 123 #> * keep_nn = TRUE -#> * output_path = /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T//RtmpBuBaBi +#> * output_path = /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T//RtmpV73YWY #> * name = 06oFCM0di0 diff --git a/docs/reference/load_example_dataset.html b/docs/reference/load_example_dataset.html index 9b42ab6..e911919 100644 --- a/docs/reference/load_example_dataset.html +++ b/docs/reference/load_example_dataset.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/lymph_node_selected_spot.html b/docs/reference/lymph_node_selected_spot.html index a03b745..789a23f 100644 --- a/docs/reference/lymph_node_selected_spot.html +++ b/docs/reference/lymph_node_selected_spot.html @@ -22,7 +22,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/lymph_node_selected_spot_2.html b/docs/reference/lymph_node_selected_spot_2.html index b338fd7..4a40f81 100644 --- a/docs/reference/lymph_node_selected_spot_2.html +++ b/docs/reference/lymph_node_selected_spot_2.html @@ -22,7 +22,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/mcl_system_cmd.html b/docs/reference/mcl_system_cmd.html index 6cea6fe..6dc74ec 100644 --- a/docs/reference/mcl_system_cmd.html +++ b/docs/reference/mcl_system_cmd.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/module_quality_scores-ClusterSet-method.html b/docs/reference/module_quality_scores-ClusterSet-method.html index ed81eb7..352d32d 100644 --- a/docs/reference/module_quality_scores-ClusterSet-method.html +++ b/docs/reference/module_quality_scores-ClusterSet-method.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/module_quality_scores.html b/docs/reference/module_quality_scores.html index 9fff9ff..f97e3f4 100644 --- a/docs/reference/module_quality_scores.html +++ b/docs/reference/module_quality_scores.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/nclust-ClusterSet-method.html b/docs/reference/nclust-ClusterSet-method.html index 1864be0..ccb172f 100644 --- a/docs/reference/nclust-ClusterSet-method.html +++ b/docs/reference/nclust-ClusterSet-method.html @@ -19,7 +19,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/nclust.html b/docs/reference/nclust.html index 22b2057..f440198 100644 --- a/docs/reference/nclust.html +++ b/docs/reference/nclust.html @@ -19,7 +19,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/ncol.ClusterSet.html b/docs/reference/ncol.ClusterSet.html index 5e62b08..180a634 100644 --- a/docs/reference/ncol.ClusterSet.html +++ b/docs/reference/ncol.ClusterSet.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/nrow.ClusterSet.html b/docs/reference/nrow.ClusterSet.html index 9b85fc6..1ee8996 100644 --- a/docs/reference/nrow.ClusterSet.html +++ b/docs/reference/nrow.ClusterSet.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/plot_clust_enrichments-ClusterSet-method.html b/docs/reference/plot_clust_enrichments-ClusterSet-method.html index dad33dc..e2da31a 100644 --- a/docs/reference/plot_clust_enrichments-ClusterSet-method.html +++ b/docs/reference/plot_clust_enrichments-ClusterSet-method.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/plot_clust_enrichments.html b/docs/reference/plot_clust_enrichments.html index 6823947..1088d6f 100644 --- a/docs/reference/plot_clust_enrichments.html +++ b/docs/reference/plot_clust_enrichments.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/plot_cluster_stats.html b/docs/reference/plot_cluster_stats.html index 4e3fed8..da0ac1b 100644 --- a/docs/reference/plot_cluster_stats.html +++ b/docs/reference/plot_cluster_stats.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/plot_cmp_genesets.html b/docs/reference/plot_cmp_genesets.html index f9121ea..5b466f3 100644 --- a/docs/reference/plot_cmp_genesets.html +++ b/docs/reference/plot_cmp_genesets.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/plot_dist.html b/docs/reference/plot_dist.html index 4426007..2ac3af7 100644 --- a/docs/reference/plot_dist.html +++ b/docs/reference/plot_dist.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/plot_ggheatmap-ClusterSet-method.html b/docs/reference/plot_ggheatmap-ClusterSet-method.html index 77daa4d..862ecfe 100644 --- a/docs/reference/plot_ggheatmap-ClusterSet-method.html +++ b/docs/reference/plot_ggheatmap-ClusterSet-method.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/plot_ggheatmap.html b/docs/reference/plot_ggheatmap.html index 9676c3c..ca31648 100644 --- a/docs/reference/plot_ggheatmap.html +++ b/docs/reference/plot_ggheatmap.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/plot_heatmap.html b/docs/reference/plot_heatmap.html index 61b045f..5f99733 100644 --- a/docs/reference/plot_heatmap.html +++ b/docs/reference/plot_heatmap.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/plot_markers_to_clusters-ClusterSet-method.html b/docs/reference/plot_markers_to_clusters-ClusterSet-method.html index ce2d7a8..7d6fdfc 100644 --- a/docs/reference/plot_markers_to_clusters-ClusterSet-method.html +++ b/docs/reference/plot_markers_to_clusters-ClusterSet-method.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/plot_markers_to_clusters.html b/docs/reference/plot_markers_to_clusters.html index d1d3658..1627da4 100644 --- a/docs/reference/plot_markers_to_clusters.html +++ b/docs/reference/plot_markers_to_clusters.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/plot_profiles.html b/docs/reference/plot_profiles.html index 0a51475..0a5d007 100644 --- a/docs/reference/plot_profiles.html +++ b/docs/reference/plot_profiles.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/plot_spatial.html b/docs/reference/plot_spatial.html index e5a6da0..f1e631d 100644 --- a/docs/reference/plot_spatial.html +++ b/docs/reference/plot_spatial.html @@ -21,7 +21,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/plot_spatial_panel.html b/docs/reference/plot_spatial_panel.html index db07c89..3b1a663 100644 --- a/docs/reference/plot_spatial_panel.html +++ b/docs/reference/plot_spatial_panel.html @@ -19,7 +19,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/plot_stratum.html b/docs/reference/plot_stratum.html index fec2f95..722bb4b 100644 --- a/docs/reference/plot_stratum.html +++ b/docs/reference/plot_stratum.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/print_msg.html b/docs/reference/print_msg.html index 6d040d4..d80d5f1 100644 --- a/docs/reference/print_msg.html +++ b/docs/reference/print_msg.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/print_stat.html b/docs/reference/print_stat.html index 757e0a5..86ed1cf 100644 --- a/docs/reference/print_stat.html +++ b/docs/reference/print_stat.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/rename_clust-ClusterSet-method.html b/docs/reference/rename_clust-ClusterSet-method.html index 106e9e7..4637662 100644 --- a/docs/reference/rename_clust-ClusterSet-method.html +++ b/docs/reference/rename_clust-ClusterSet-method.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/rename_clust.html b/docs/reference/rename_clust.html index 4388b8b..70d3bc4 100644 --- a/docs/reference/rename_clust.html +++ b/docs/reference/rename_clust.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/reorder_clust-ClusterSet-method.html b/docs/reference/reorder_clust-ClusterSet-method.html index 3d71f87..2cc599f 100644 --- a/docs/reference/reorder_clust-ClusterSet-method.html +++ b/docs/reference/reorder_clust-ClusterSet-method.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/reorder_clust.html b/docs/reference/reorder_clust.html index 038422f..ac8d2c8 100644 --- a/docs/reference/reorder_clust.html +++ b/docs/reference/reorder_clust.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/reorder_genes-ClusterSet-method.html b/docs/reference/reorder_genes-ClusterSet-method.html index 385bb00..b2d7be0 100644 --- a/docs/reference/reorder_genes-ClusterSet-method.html +++ b/docs/reference/reorder_genes-ClusterSet-method.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/reorder_genes.html b/docs/reference/reorder_genes.html index 8a7c7bd..9343f06 100644 --- a/docs/reference/reorder_genes.html +++ b/docs/reference/reorder_genes.html @@ -18,7 +18,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/row_names-ClusterSet-method.html b/docs/reference/row_names-ClusterSet-method.html index 1a8b364..b1801c7 100644 --- a/docs/reference/row_names-ClusterSet-method.html +++ b/docs/reference/row_names-ClusterSet-method.html @@ -19,7 +19,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/row_names.html b/docs/reference/row_names.html index ccdf5c1..e0e50f4 100644 --- a/docs/reference/row_names.html +++ b/docs/reference/row_names.html @@ -19,7 +19,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/select_genes.html b/docs/reference/select_genes.html index 9a257bc..43cdf24 100644 --- a/docs/reference/select_genes.html +++ b/docs/reference/select_genes.html @@ -20,7 +20,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/set_verbosity.html b/docs/reference/set_verbosity.html index e52c354..dd2c3ef 100644 --- a/docs/reference/set_verbosity.html +++ b/docs/reference/set_verbosity.html @@ -21,7 +21,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/show-ClusterSet-method.html b/docs/reference/show-ClusterSet-method.html index 7016337..c8a1c6a 100644 --- a/docs/reference/show-ClusterSet-method.html +++ b/docs/reference/show-ClusterSet-method.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/show_methods.html b/docs/reference/show_methods.html index 7b858ee..665a3ce 100644 --- a/docs/reference/show_methods.html +++ b/docs/reference/show_methods.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/stratify_seurat.html b/docs/reference/stratify_seurat.html index 8f86847..aa371ab 100644 --- a/docs/reference/stratify_seurat.html +++ b/docs/reference/stratify_seurat.html @@ -20,7 +20,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/sub-ClusterSet-ANY-ANY-ANY-method.html b/docs/reference/sub-ClusterSet-ANY-ANY-ANY-method.html index fc2ba52..8b10518 100644 --- a/docs/reference/sub-ClusterSet-ANY-ANY-ANY-method.html +++ b/docs/reference/sub-ClusterSet-ANY-ANY-ANY-method.html @@ -20,7 +20,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/top_by_go-ClusterSet-method.html b/docs/reference/top_by_go-ClusterSet-method.html index 2899418..5dd31b4 100644 --- a/docs/reference/top_by_go-ClusterSet-method.html +++ b/docs/reference/top_by_go-ClusterSet-method.html @@ -19,7 +19,7 @@ scigenex - 1.4.8 + 1.4.10 @@ -114,16 +114,10 @@

Examples

#' # DNA Binding: "GO:0003677" pbmc3k_medium_clusters <- top_by_go(pbmc3k_medium_clusters[1:2, ], go_id = "GO:0003677") #> Ensembl site unresponsive, trying useast mirror -#> |-- INFO : Connecting to Biomart... -#> |-- INFO : Processing cluster 1 -#> |-- INFO : Processing cluster 2 +#> Ensembl site unresponsive, trying asia mirror +#> Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': Timeout was reached: [asia.ensembl.org:443] Operation timed out after 10005 milliseconds with 0 bytes received pbmc3k_medium_clusters@top_genes -#> [[1]] -#> [1] "NACA" "RPL6" "RPS3" -#> -#> [[2]] -#> [1] "APP" -#> +#> list() diff --git a/docs/reference/top_by_go.html b/docs/reference/top_by_go.html index 22e468b..45171fe 100644 --- a/docs/reference/top_by_go.html +++ b/docs/reference/top_by_go.html @@ -19,7 +19,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/top_genes-ClusterSet-method.html b/docs/reference/top_genes-ClusterSet-method.html index 86bd597..53ebb3f 100644 --- a/docs/reference/top_genes-ClusterSet-method.html +++ b/docs/reference/top_genes-ClusterSet-method.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/top_genes.html b/docs/reference/top_genes.html index f415c1d..be9c63a 100644 --- a/docs/reference/top_genes.html +++ b/docs/reference/top_genes.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/viz_enrich-ClusterSet-method.html b/docs/reference/viz_enrich-ClusterSet-method.html index 6e30618..26125a8 100644 --- a/docs/reference/viz_enrich-ClusterSet-method.html +++ b/docs/reference/viz_enrich-ClusterSet-method.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/viz_enrich.html b/docs/reference/viz_enrich.html index c174c1f..d5d98ee 100644 --- a/docs/reference/viz_enrich.html +++ b/docs/reference/viz_enrich.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/which_clust-ClusterSet-method.html b/docs/reference/which_clust-ClusterSet-method.html index 40fa7f9..7c6ba8a 100644 --- a/docs/reference/which_clust-ClusterSet-method.html +++ b/docs/reference/which_clust-ClusterSet-method.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/docs/reference/which_clust.html b/docs/reference/which_clust.html index 94f4117..4a3f226 100644 --- a/docs/reference/which_clust.html +++ b/docs/reference/which_clust.html @@ -17,7 +17,7 @@ scigenex - 1.4.8 + 1.4.10 diff --git a/vignettes/usage.Rmd b/vignettes/usage.Rmd index 0f8e73d..edd9049 100644 --- a/vignettes/usage.Rmd +++ b/vignettes/usage.Rmd @@ -11,24 +11,24 @@ vignette: > # Guided tutorial -The simplest way to use scigenex is to run the following steps using the Seurat R package: +The easiest way to use scigenex is to perform the following steps using the Seurat R package: * Load data into a Seurat object -* Perform quality Control +* Perform quality control * Perform normalization -The subsequent object can be used as input to SciGeneX. Alternatively, you can provide a normalized matrix as input. +The resulting object can be used as input to SciGeneX. You can also provide a normalized matrix as input. ## The dataset -For this tutorial, we will use scRNA-seq dataset of Peripheral Blood Mononuclear Cells (PBMC) freely available on 10x Genomics web site. This dataset contains 2700 single cells sequenced on the Illumina NextSeq 500. This dataset can be downloaded [**from 10X Genomics**](https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz) or through the `SeuratData`library +For this tutorial, we'll be using a peripheral blood mononuclear cell (PBMC) scRNA-seq dataset available from the 10x Genomics website. This dataset contains 2700 individual cells sequenced on the Illumina NextSeq 500 and can be downloaded [**from 10X Genomics**](https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz) web site or via the `SeuratData` library. ### Preparing the pbmc3k dataset -In this step, we will run the classical pre-processing steps from the. Please refer to [this tutorial](https://satijalab.org/seurat/articles/pbmc3k_tutorial.html) for more informations. -If you have already pre-processed your data with Seurat or have a normalized count matrix as input, you can skip this step. +In this step, we'll carry out the classic pre-processing steps of the tutorial. Please refer to [this tutorial](https://satijalab.org/seurat/articles/pbmc3k_tutorial.html) for more information. +If you have already pre-processed your data with Seurat, or if you have a normalized count matrix as input, you can skip this step. ```{r loading1, echo=FALSE, eval=TRUE, warning=FALSE, results='hide'} @@ -82,13 +82,13 @@ DimPlot(pbmc3k, reduction = "umap") ## Extracting gene modules using SciGeneX -In this section, we use the previously generated Seurat object as an input to run SciGeneX main commands. This command run the main algorithm that will: +In this section, we use the previously generated Seurat object as input to run the main SciGeneX commands. This command executes the main algorithm which will: -* Identify and extracts co-expressed genes -* Partition selected genes into clusters -* Store the result in a ClusterSet object +* identify and extract co-expressed genes +* divide the selected genes into groups +* store the result in a ClusterSet object. -First we will load the Scigenex library. To limit the verbosity of Scigenex function we will set the verbosity level to zero (which will switch off information messages and debugging messages). +First, we'll load the SciGeneX library. To limit the verbosity of the SciGeneX functions, we'll set the verbosity level to zero (which will disable information and debug messages). ```{r loadlib1, warning=FALSE, results='hide', echo = FALSE, eval=TRUE} @@ -141,7 +141,7 @@ ncol(pbmc_scigenex) nclust(pbmc_scigenex) ``` -At this step, several modules need to be filtered out as lots of them may be singletons. Interestingly, the `ClusterSet` class implements the indexing operator/function ("["). The first argument/dimension of the indexing function corresponds to the cluster stored in the object. The second dimension correspond to cell names. As an example, one can simply keep gene modules with size (*i.e* number of genes) greater than 7 using the following code. This leads to an object containing `r nclust(pbmc_scigenex[clust_size(pbmc_scigenex) > 7, ])` gene modules. +At this stage, several modules need to be filtered, as many of them may be singletons. Interestingly, the `ClusterSet` class implements the indexing operator/function ("["). The first argument/dimension of the indexing function corresponds to the cluster stored in the object. The second dimension corresponds to the cell/spot names. As an example, we can simply store gene modules whose size (*i.e. number of genes) is greater than 7 using the following code. The result is an object containing gene modules `r nclust(pbmc_scigenex[clust_size(pbmc_scigenex) > 7, ])`. ```{r} pbmc_scigenex <- pbmc_scigenex[clust_size(pbmc_scigenex) > 7, ] @@ -190,11 +190,11 @@ tail(gene_cluster(pbmc_scigenex)) ``` -## Visualisation using heatmap +## Heatmap visualization -### Visualization of specific gene modules +### Visualization of specific genetic modules -The visualization of a heatmap containing all cells and modules may be time consuming and may require important memory ressources. A first alternative is to look at gene cluster individually or a subset of clusters. The gene modules can be visualized using the `plot_heatmap()` or the `plot_ggheatmap()` functions. The first is mainly intented to propose interactive vizualisation based on the `iheatmapr` library and allows to easily browse the results and zoom on particular regions of the heatmap The second should be mostly used for non interactive vizualisation. This second solution leads to a plot that is more easilly customable as it is bas ed on the ggplot framework. Here we use `plot_ggheatmap()` to have a look at the 4 first clusters. Note that here, we choose to order columns/cells on `Seurat::FindClusters` results. +Visualizing a heatmap containing all cells and modules can be time-consuming and require significant memory resources. A first alternative is to examine gene modules individually or a subset of modules. Gene modules can be visualized using the `plot_heatmap()` the `plot_ggheatmap()` functions. The former is primarily intended to provide an interactive visualization based on the `iheatmapr` library, and allows easy browsing of results and zooming in on particular regions of the heatmap. This second solution leads to a diagram that is more easily customized, as it is based on the ggplot framework. Here, we use `plot_ggheatmap()` to view the first 4 clusters. Note that here, we choose to order the columns/cells on the results of `Seurat::FindClusters`. ```{r} @@ -205,7 +205,7 @@ plot_ggheatmap(pbmc_scigenex[1:4, ], ### Heatmap of representative genes -However one alternative is to extract the most representative genes of each cluster. This can be achieved using the `top_genes()` function. This function will store the identifiers of these representative genes inside the `top_genes` slot of the `ClusterSet` object. The `top_genes`slot can be accessed the `get_genes()` function. +However, an alternative is to extract the most representative genes from each group. This can be achieved using the `top_genes()` function. This function stores the identifiers of these representative genes in the `top_genes` slot of the `ClusterSet` object. The `get_genes()` function is used to access the `top_genes` slot. ```{r} pbmc_scigenex <- top_genes(pbmc_scigenex) @@ -225,7 +225,7 @@ plot_ggheatmap(pbmc_scigenex, ### Interactive heatmap -A very interesting feature of Scigenex is its ability to display gene expression levels across cells through interactive heatmaps. Using this feature, user can interactively assess the expression levels in selected cells or cluster or on the whole dataset. However, it is generally advisable, when using all the clusters to restrict analysis using `top_genes=TRUE`. Here we will display the expression levels across cells of cluster 3 to 5. +A very interesting feature of SciGeneX is its ability to display gene expression levels in cells/spots using interactive heatmaps. With this function, the user can interactively evaluate expression levels in selected cells or groups, or over the whole dataset. However, it is generally advisable, when using all clusters, to restrict the analysis using `top_genes=TRUE`. Here, we'll display expression levels in cells from clusters 3 to 5. ```{r} plot_heatmap(pbmc_scigenex[3:5, ], @@ -293,7 +293,7 @@ plot_markers_to_clusters(pbmc_scigenex, ## Working with spatial transcriptomics datasets -The scigenex package offers a certain number of function dedicated to spatial transcriptomic data analysis. At the moment these functions have been mainly developed to analyse VISIUM technology (10X Genomics). +The scigenex package offers a certain number of functions dedicated to spatial transcriptomic data analysis. At the moment these functions have been mainly developed to analyse VISIUM technology (10X Genomics). ### Searching for gene modules @@ -413,7 +413,7 @@ Note that cluster `r setNames(which_clust( gcss_brain, "Hpca"), NULL)` also cont grep_clust(gcss_brain[Hpca_clust, ], "^Rgs") ``` -To visualize the topological distribution of signals in each cluster we will *(i)* extract the `gene_clusters` slot from the `gcss` object, *(ii)* compute module score using the Seurat `AddModuleScore()`function and *(iii)* store the results in the seurat object. Note that, for each gene module, signal will be scaled from 0 to 1. This will allow us to have a shared legend between all plots +To visualize the topological distribution of signals in each cluster, we'll *(i)* extract the `gene_clusters` slot from the `gcss` object, *(ii)* calculate the module score using Seurat's `AddModuleScore()` function and *(iii)* store the results in the seurat object. Note that, for each gene module, the signal will be scaled from 0 to 1, allowing us to have a common legend for all plots. ```{r AddModuleScore, echo=TRUE, eval=TRUE, warning=FALSE} brain1 <- AddModuleScore(brain1, features = gcss_brain@gene_clusters) @@ -435,7 +435,7 @@ plot_spatial(seurat_obj = brain1, pt_size=2.5, coord_flip = T) + hull_white ``` -We can easily see that the pattern of the gene module containing Hpca is very similar to the Hpca pattern. Moreover this gene module pattern also strongly overlaps the Seurat cell cluster number 0. However, the analysis seems to indicate that a more complex tissue architecture exists as Hpca signal extends beyond Seurat cluster 0. +We can easily see that the Hpca-containing gene module pattern is very similar to the Hpca pattern. However, the analysis suggests a more complex tissue architecture than expected, as the Hpca signal extends beyond Seurat's cluster 0. We will then display the topological signal of all clusters using the `plot_spatial_panel()` function. @@ -455,9 +455,9 @@ print(p + guide_area()) ## Comparing FindAllMarkers() and Scigenex gene modules -It can be interesting to compare the clusters obtained using unsupervised approach (scigenex) to the list of genes obtained using `Seurat::FindAllMarkers()` that search for differentially expressed genes between the cell populations infered by `Seurat::FindClusters()`. Here the comparison is performed using results obtained for spatial transcriptomics data. +It may be interesting to compare the clusters obtained using an unsupervised approach (SciGeneX) with those obtained from `Seurat::FindAllMarkers()`, which searches for genes differentially expressed between cell populations deduced by `Seurat::FindClusters()`. -First note that `Seurat::FindAllMarkers()` will tend to find shared markers between populations. +As shown using the `plot_cmp_genesets()` function, `Seurat::FindAllMarkers()` tends to find markers that are not so specific to cell populations. Thus this markers are shared between gene modules. ```{r seurat_classes, echo=TRUE, eval=TRUE, warning=FALSE, message=FALSE, results='hide'} seurat_brain_mk <- Seurat::FindAllMarkers(brain1) @@ -485,7 +485,7 @@ plot_cmp_genesets(gcss_brain@gene_clusters, seurat_brain_mk, layout = "square", ## Creating a report -A report can be created using the `cluster_set_report()`function. The arguments of these function are the processed clusterSet and the corresponding Seurat processed object. Ideally the clusterSet object should contain functionnal annotation (see `enrich_go()`). Currently, the process of creating a report may be rather long. It may also produce heavy html files that may take some time to load into de web browser. +A report can be created using the `cluster_set_report()` function. The arguments to this function are the processed clusterSet and the corresponding processed Seurat object. Ideally, the clusterSet object should contain functional annotations (see `enrich_go()`). Currently, the process of creating a report can be quite time consuming It can also produce heavy html files which may take some time to load in the web browser. ```{r report, echo=TRUE, eval=FALSE, warning=FALSE, results='hide'} # Uncomment to prepare the report.