update tutorial

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: scMEGA
 Type: Package
 Title: Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference
-Version: 1.0.2
+Version: 1.1.0
 Author: Zhijian Li
 Maintainer: Zhijian Li <[email protected]>
 Description: Infering gene regulatory network from single-cell multi-omics data.

run.sh

@@ -2,4 +2,6 @@
 
 # R -e "devtools::document()"
 # R -e "pkgdown::build_site(preview = FALSE, lazy=TRUE)"
-R -e "pkgdown::build_article(name='myofibroblast-GRN', quiet=FALSE)"
+# R -e "pkgdown::build_article(name='myofibroblast-GRN', quiet=FALSE)"
+R -e "pkgdown::build_article(name='install', quiet=FALSE)"
+R -e "pkgdown::build_article(name='pbmc_10x_multiome', quiet=FALSE)"

vignettes/cardiomyocyte-GRN.Rmd

@@ -65,11 +65,18 @@ Let's load the data into memory and see how they look like
 obj.rna <- readRDS("./Cardiomyocyte/snRNA.rds")
 obj.atac <- readRDS("./Cardiomyocyte/snATAC.rds")
 gene.activity <- readRDS("./Cardiomyocyte/gene.activity.rds")
+```
+
+We need to convert the assays to an Assay5 for Seuratv5
+```{r to_seuratv5}
+obj.rna[["RNA"]] <- as(obj.rna[["RNA"]], "Assay5")
+obj.atac[["ATAC"]] <- as(obj.atac[["ATAC"]], "Assay5")
 
 obj.rna
 obj.atac
 ```
 
+
 We can observe that there are 45,515 and 6,481 cells in our snRNA-seq and snATAC-seq datasets. We now visualize the data as colored by patients. Note that here we used the UMAP embedding generated from batch-corrected low-dimensional space so that no batch effects are observed from the 2D visualization.
 
 ```{r, fig.height = 5, fig.width = 12, fig.align = "center"}
@@ -111,16 +118,12 @@ So next we use [Harmony](https://www.nature.com/articles/s41592-019-0619-0) to
 perform batch correction and generate a new UMAP embedding.
 
 ```{r, fig.height = 10, fig.width = 12, fig.align = "center"}
-obj.coembed <- RunHarmony(
-  obj.coembed,
-  group.by.vars = c("patient", "region", "tech"),
-  reduction = "pca",
-  max.iter.harmony = 30,
-  dims.use = 1:30,
-  project.dim = FALSE,
-  plot_convergence = FALSE
-)
-
+obj.coembed <- RunHarmony(obj.coembed, 
+                          group.by.vars = c("patient", "region", "tech"),
+                          reduction.use = "pca", 
+                          dims.use = 1:30,
+                          project.dim = FALSE,
+                          plot_convergence = FALSE)
 obj.coembed <- RunUMAP(
   obj.coembed,
   dims = 1:30,

vignettes/install.Rmd

@@ -13,16 +13,8 @@ You can install scMEGA via below commands:
 if (!requireNamespace("devtools", quietly = TRUE))
     install.packages("devtools")
 
-# First install Seurate v5
-devtools::install_github("satijalab/seurat", "seurat5", upgrade='always')
-
-
-# Install Signac
-devtools::install_github("stuart-lab/signac", "seurat5")
-
-
 # Install scMEGA
-devtools::install_github("CostaLab/scMEGA")
+devtools::install_github("CostaLab/scMEGA", force = TRUE)
 ```
 
 

vignettes/pbmc_10x_multiome.Rmd

@@ -233,18 +233,18 @@ meta.data <- [email protected] %>%
 
 # create a Seurat object containing the RNA adata
 pbmc <- CreateSeuratObject(
-  counts = obj.rna@assays$RNA@counts,
+  counts = GetAssayData(obj.rna, layer = "counts"),
   assay = "RNA",
-    meta.data = meta.data
+  meta.data = meta.data
 )
 
 # create ATAC assay and add it to the object
 pbmc[["ATAC"]] <- CreateChromatinAssay(
-  counts = obj.atac@assays$ATAC@counts,
+  counts = GetAssayData(obj.atac, layer = "counts",),
   sep = c(":", "-"),
-    min.cells = 1,
-    genome = 'hg38',
-    fragments = './10x_pbmc/pbmc_granulocyte_sorted_10k_atac_fragments.tsv.gz'
+  min.cells = 1,
+  genome = 'hg38',
+  fragments = './10x_pbmc/pbmc_granulocyte_sorted_10k_atac_fragments.tsv.gz'
 )
 ```
 
@@ -300,9 +300,9 @@ pbmc <- AddTrajectory(object = pbmc,
                       trajectory = c("naive CD4 T cells", 
                                      "memory CD4 T cells"),
                       group.by = "predicted.id", 
-                          reduction = "MOJITOO_UMAP",
-                          dims = 1:2, 
-                          use.all = FALSE)
+                      reduction = "MOJITOO_UMAP",
+                      dims = 1:2, 
+                      use.all = FALSE)
 
 # we only plot the cells that are in this trajectory
 pbmc.t.cells <- pbmc[, !is.na(pbmc$Trajectory)]