update maintainer, minor fixes

grabadam-cal · Jan 26, 2021 · 737d80c · 737d80c
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -4,17 +4,17 @@ Date: 2020-01-24
 Title: Tools for Single Cell Genomics
 Description: A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.
 Authors@R: c(
-  person(given = "Rahul", family = "Satija", email = "[email protected]", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-9448-8833")),
   person(given = "Andrew", family = "Butler", email = "[email protected]", role = "ctb", comment = c(ORCID = "0000-0003-3608-0463")),
   person(given = "Saket", family = "Choudhary", email = "[email protected]", role = "ctb", comment = c(ORCID = "0000-0001-5202-7633")),
   person(given = "Charlotte", family = "Darby", email = "[email protected]", role = "ctb", comment = c(ORCID = "0000-0003-2195-5300")),
   person(given = "Jeff", family = "Farrell", email = "[email protected]", role = "ctb"),
   person(given = "Christoph", family = "Hafemeister", email = "[email protected]", role = "ctb", comment = c(ORCID = "0000-0001-6365-8254")),
   person(given = "Yuhan", family = "Hao", email = "[email protected]", role = "ctb", comment = c(ORCID = "0000-0002-1810-0822")),
-  person(given = "Paul", family = "Hoffman", email = "[email protected]", role = "ctb", comment = c(ORCID = "0000-0002-7693-8957")),
+  person(given = "Paul", family = "Hoffman", email = "[email protected]", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7693-8957")),
   person(given = "Jaison", family = "Jain", email = "[email protected]", role = "ctb", comment = c(ORCID = "0000-0002-9478-5018")),
   person(given = "Efthymia", family = "Papalexi", email = "[email protected]", role = "ctb", comment = c(ORCID = "0000-0001-5898-694X")),
   person(given = "Patrick", family = "Roelli", email = "[email protected]", role = "ctb"),
+  person(given = "Rahul", family = "Satija", email = "[email protected]", role = "ctb", comment = c(ORCID = "0000-0001-9448-8833")),
   person(given = "Karthik", family = "Shekhar", email = "[email protected]", role = "ctb"),
   person(given = "Avi", family = "Srivastava", email = "[email protected]", role = "ctb", comment = c(ORCID = "0000-0001-9798-2079")),
   person(given = "Tim", family = "Stuart", email = "[email protected]", role = "ctb", comment = c(ORCID = "0000-0002-3044-0897")),

diff --git a/vignettes/cell_cycle_vignette.Rmd b/vignettes/cell_cycle_vignette.Rmd
@@ -86,7 +86,7 @@ library(ggplot2)
 plot <- DimPlot(marrow) + 
     theme(axis.title = element_text(size = 18), legend.text = element_text(size = 18)) + 
     guides(colour = guide_legend(override.aes = list(size = 10)))
-ggsave(filename = "../output/images/cell_cycle_vignette.png", height = 7, width = 12, plot = plot)
+ggsave(filename = "../output/images/cell_cycle_vignette.jpg", height = 7, width = 12, plot = plot, quality = 50)
 ```
 
 We score single cells based on the scoring strategy described in [Tirosh *et al*. 2016](http://science.sciencemag.org/content/352/6282/189). See `?AddModuleScore()` in Seurat for more information, this function can be used to calculate supervised module scores for any gene list.

diff --git a/vignettes/conversion_vignette.Rmd b/vignettes/conversion_vignette.Rmd
@@ -99,6 +99,7 @@ Seurat can also read in `loom` files connected via [loomR](https://github.com/mo
 l6.immune <- Connect(filename = '../data/l6_r1_immune_cells.loom', mode = 'r')
 l6.immune
 l6.seurat <- as.Seurat(l6.immune)
+Idents(l6.seurat) <- "ClusterName"
 VlnPlot(l6.seurat, features = c('Sparc', 'Ftl1', 'Junb', 'Ccl4'), ncol = 2)
 # Always remember to close loom files when done
 l6.immune$close_all()

diff --git a/vignettes/dim_reduction_vignette.Rmd b/vignettes/dim_reduction_vignette.Rmd
@@ -99,7 +99,7 @@ FeatureScatter(pbmc, feature1 = "MDS_1", feature2 = "PC_1")
 ```{r save.img, include=FALSE}
 library(ggplot2)
 plot <- DimPlot(pbmc, reduction = "mds", pt.size = 0.5)
-ggsave(filename = "../output/images/pbmc_mds.png", height = 7, width = 12, plot = plot)
+ggsave(filename = "../output/images/pbmc_mds.jpg", height = 7, width = 12, plot = plot, quality = 50)
 ```
 
 ```{r save.times, include = FALSE}

diff --git a/vignettes/integration_introduction.Rmd b/vignettes/integration_introduction.Rmd
@@ -158,7 +158,7 @@ DotPlot(immune.combined, features = markers.to.plot, cols = c('blue', 'red'), do
 library(ggplot2)
 plot <- DotPlot(immune.combined, features = markers.to.plot, cols = c('blue', 'red'),
                       dot.scale = 6, split.by = "stim") + RotatedAxis() 
-ggsave(filename = "../output/images/pbmc_alignment.png", height = 7, width = 12, plot = plot)
+ggsave(filename = "../output/images/pbmc_alignment.jpg", height = 7, width = 12, plot = plot, quality = 50)
 ```
 
 ### Identify differential expressed genes across conditions

diff --git a/vignettes/integration_large_datasets.Rmd b/vignettes/integration_large_datasets.Rmd
@@ -102,7 +102,7 @@ library(ggplot2)
 plot <- DimPlot(bm280k.integrated, group.by = "orig.ident") + xlab("UMAP 1") + ylab("UMAP 2") + 
   theme(axis.title = element_text(size = 18), legend.text = element_text(size = 18)) + 
   guides(colour = guide_legend(override.aes = list(size = 10)))
-ggsave(filename = "../output/images/bm280k_integrated.png", height = 7, width = 12, plot = plot)
+ggsave(filename = "../output/images/bm280k_integrated.jpg", height = 7, width = 12, plot = plot, quality = 50)
 ```
 
 ```{r save.times, include = FALSE}

diff --git a/vignettes/integration_mapping.Rmd b/vignettes/integration_mapping.Rmd
@@ -112,7 +112,7 @@ saveRDS(pancreas.integrated, file = "pancreas_integrated.rds")
 plot <- DimPlot(pancreas.integrated, reduction = "umap", label = TRUE, label.size = 4.5) + xlab("UMAP 1") + ylab("UMAP 2") + 
   theme(axis.title = element_text(size = 18), legend.text = element_text(size = 18)) + 
   guides(colour = guide_legend(override.aes = list(size = 10)))
-ggsave(filename = "pancreas_integrated_umap.png", height = 7, width = 12, plot = plot)
+ggsave(filename = "pancreas_integrated_umap.jpg", height = 7, width = 12, plot = plot, quality = 50)
 ```
 
 # Cell type classification using an integrated reference

diff --git a/vignettes/integration_rpca.Rmd b/vignettes/integration_rpca.Rmd
@@ -138,7 +138,7 @@ plot <- DimPlot(immune.combined, group.by = "stim") +
   xlab("UMAP 1") + ylab("UMAP 2") + 
   theme(axis.title = element_text(size = 18), legend.text = element_text(size = 18)) + 
   guides(colour = guide_legend(override.aes = list(size = 10)))
-ggsave(filename = "../output/images/rpca_integration.png", height = 7, width = 12, plot = plot)
+ggsave(filename = "../output/images/rpca_integration.jpg", height = 7, width = 12, plot = plot, quality = 50)
 ```
 
 Now that the datasets have been integrated, you can follow the previous steps in the [introduction to scRNA-seq integration vignette](integration_introduction.html) to identify cell types and cell type-specific responses.

diff --git a/vignettes/mixscape_vignette.Rmd b/vignettes/mixscape_vignette.Rmd
@@ -310,7 +310,7 @@ VlnPlot(
 
 ```{r save.img, include=FALSE}
 p <- VlnPlot(object = eccite, features = "adt_PDL1", idents = c("NT","JAK2","STAT1","IFNGR1","IFNGR2", "IRF1"), group.by = "gene", pt.size = 0.2, sort = T, split.by = "mixscape_class.global", cols = c("coral3","grey79","grey39")) +ggtitle("PD-L1 protein") +theme(axis.text.x = element_text(angle = 0, hjust = 0.5))
-ggsave(filename = "../output/images/mixscape_vignette.png", height = 7, width = 12, plot = p)
+ggsave(filename = "../output/images/mixscape_vignette.jpg", height = 7, width = 12, plot = p, quality = 50)
 ```
 
 # Visualizing perturbation responses with Linear Discriminant Analysis (LDA)

diff --git a/vignettes/multimodal_reference_mapping.Rmd b/vignettes/multimodal_reference_mapping.Rmd
@@ -169,7 +169,7 @@ FeaturePlot(pbmc3k, features = c("pDC", "CD16 Mono", "Treg"),  reduction = "ref.
 library(ggplot2)
 plot <- FeaturePlot(pbmc3k, features = "CD16 Mono",  reduction = "ref.umap", cols = c("lightgrey", "darkred"))  + ggtitle("CD16 Mono") + theme(plot.title = element_text(hjust = 0.5, size = 30)) + labs(color = "Prediction Score") +  xlab("UMAP 1") + ylab("UMAP 2") + 
   theme(axis.title = element_text(size = 18), legend.text = element_text(size = 18), legend.title = element_text(size = 25)) 
-ggsave(filename = "../output/images/multimodal_reference_mapping.png", height = 7, width = 12, plot = plot)
+ggsave(filename = "../output/images/multimodal_reference_mapping.jpg", height = 7, width = 12, plot = plot, quality = 50)
 ```
 
 We can verify our predictions by exploring the expression of canonical marker genes. For example, CLEC4C and LIRA4 have been [reported](https://pubmed.ncbi.nlm.nih.gov/30395816/) as markers of pDC identity, consistent with our predictions. Similarly, if we perform differential expression to identify markers of Tregs, we identify a set of [canonical markers](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4761514/) including RTKN2, CTLA4, FOXP3, and IL2RA.

diff --git a/vignettes/multimodal_vignette.Rmd b/vignettes/multimodal_vignette.Rmd
@@ -207,7 +207,7 @@ plot3 <- FeatureScatter(pbmc10k, feature1 = 'adt_CD3', feature2 = 'CD3E', pt.siz
 ```{r save.img, include = FALSE}
 plot <- FeatureScatter(cbmc, feature1 = "adt_CD19", feature2 = "adt_CD3") + NoLegend() +
   theme(axis.title = element_text(size = 18), legend.text = element_text(size = 18))
-ggsave(filename = "../output/images/citeseq_plot.png", height = 7, width = 12, plot = plot)
+ggsave(filename = "../output/images/citeseq_plot.jpg", height = 7, width = 12, plot = plot, quality = 50)
 ```
 
 ```{r save.times, include = FALSE}

diff --git a/vignettes/pbmc3k_tutorial.Rmd b/vignettes/pbmc3k_tutorial.Rmd
@@ -365,7 +365,7 @@ library(ggplot2)
 plot <- DimPlot(pbmc, reduction = "umap", label = TRUE, label.size = 4.5) + xlab("UMAP 1") + ylab("UMAP 2") + 
   theme(axis.title = element_text(size = 18), legend.text = element_text(size = 18)) + 
   guides(colour = guide_legend(override.aes = list(size = 10)))
-ggsave(filename = "../output/images/pbmc3k_umap.png", height = 7, width = 12, plot = plot)
+ggsave(filename = "../output/images/pbmc3k_umap.jpg", height = 7, width = 12, plot = plot, quality = 50)
 ```
 
 ```{r save.rds, eval=FALSE}

diff --git a/vignettes/spatial_vignette.Rmd b/vignettes/spatial_vignette.Rmd
@@ -144,7 +144,7 @@ SpatialFeaturePlot(brain, features = c("Hpca", "Ttr"))
 library(ggplot2)
 plot <- SpatialFeaturePlot(brain, features = c("Ttr")) + 
   theme(legend.text = element_text(size = 0), legend.title = element_text(size = 20), legend.key.size = unit(1, "cm")) 
-ggsave(filename = "../output/images/spatial_vignette_ttr.png", height = 7, width = 12, plot = plot)
+ggsave(filename = "../output/images/spatial_vignette_ttr.jpg", height = 7, width = 12, plot = plot, quality = 50)
 ```
 
 The default parameters in Seurat emphasize the visualization of molecular data. However, you can also adjust the size of the spots (and their transparency) to improve the visualization of the histology image, by changing the following parameters:

diff --git a/vignettes/vignettes.yaml b/vignettes/vignettes.yaml
@@ -4,19 +4,19 @@
       name: pbmc3k_tutorial
       summary: |
         A basic overview of Seurat that includes an introduction to common analytical workflows.
-      image: pbmc3k_umap.png
+      image: pbmc3k_umap.jpg
 
     - title: Multimodal analysis
       name: multimodal_vignette
       summary: |
         An introduction to working with multi-modal datasets in Seurat.
-      image: citeseq_plot.png
+      image: citeseq_plot.jpg
 
     - title: Analysis of spatial datasets
       name: spatial_vignette
       summary: |
         Learn to explore spatially-resolved transcriptomic data with examples from 10x Visium and Slide-seq v2.
-      image: spatial_vignette_ttr.png
+      image: spatial_vignette_ttr.jpg
 
 - category: Data Integration
   vignettes:
@@ -36,13 +36,13 @@
       name: integration_rpca
       summary: |
         Example workflow for integrating dataset with RPCA.
-      image: rpca_integration.png
+      image: rpca_integration.jpg
 
     - title: Tips for integrating large datasets
       name: integration_large_datasets
       summary: |
         Example workflow for integrating large datasets
-      image: bm280k_integrated.png
+      image: bm280k_integrated.jpg
 
     - title: Integrating scRNA-seq and scATAC-seq data
       name: atacseq_integration_vignette
@@ -54,21 +54,21 @@
       name: multimodal_reference_mapping
       summary: |
         Analyze query data in the context of multimodal reference atlases.
-      image: multimodal_reference_mapping.png
+      image: multimodal_reference_mapping.jpg
 
 - category: New Statistical Approaches
   vignettes:
     - title: Weighted Nearest Neighbor Analysis
       name: weighted_nearest_neighbor_analysis
       summary: |
         Analyze multimodal single-cell data with weighted nearest neighbor analysis in Seurat v4
-      image: weighted_nearest_neighbor_analysis.png
+      image: weighted_nearest_neighbor_analysis.jpg
 
     - title: Mixscape
       name: mixscape_vignette
       summary: |
         Explore new methods to analyze pooled single-celled perturbation screens.
-      image: mixscape_vignette.png
+      image: mixscape_vignette.jpg
 
     - title: SCTransform
       name: sctransform_vignette
@@ -82,13 +82,13 @@
       name: visualization_vignette
       summary: |
         An overview of the major visualization functionality within Seurat.
-      image: visualization_vignette.png
+      image: visualization_vignette.jpg
 
     - title: Cell Cycle Regression
       name: cell_cycle_vignette
       summary: |
         Mitigate the effects of cell cycle heterogeneity by computing cell cycle phase scores based on marker genes
-      image: cell_cycle_vignette.png
+      image: cell_cycle_vignette.jpg
 
     - title: Differential Expression Testing
       name: de_vignette

diff --git a/vignettes/visualization_vignette.Rmd b/vignettes/visualization_vignette.Rmd
@@ -144,7 +144,7 @@ library(ggplot2)
 plot <- baseplot + DarkTheme() + 
     theme(axis.title = element_text(size = 18), legend.text = element_text(size = 18)) + 
     guides(colour = guide_legend(override.aes = list(size = 10)))
-ggsave(filename = "../output/images/visualization_vignette.png", height = 7, width = 12, plot = plot)
+ggsave(filename = "../output/images/visualization_vignette.jpg", height = 7, width = 12, plot = plot, quality = 50)
 ```
 
 # Interactive plotting features

diff --git a/vignettes/weighted_nearest_neighbor_analysis.Rmd b/vignettes/weighted_nearest_neighbor_analysis.Rmd
@@ -138,10 +138,9 @@ library(ggplot2)
 plot <-  VlnPlot(bm, features = "RNA.weight", group.by = 'celltype.l2', sort = TRUE, pt.size = 0.1) +
   NoLegend() + xlab("") + ggtitle("RNA Modality Weights") + theme(plot.title = element_text(hjust = 0.5, size = 30), axis.text = element_text(size = 20))
  
-ggsave(filename = "../output/images/weighted_nearest_neighbor_analysis.png", height = 7, width = 12, plot = plot)
+ggsave(filename = "../output/images/weighted_nearest_neighbor_analysis.jpg", height = 7, width = 12, plot = plot, quality = 50)
 ```
 
-
 # PBMC - RNA & ATAC
 
 Here, we demonstrate the use of WNN analysis to a second multimodal technology, the 10x multiome RNA+ATAC kit. We use a dataset that is publicly available on the 10x website, where paired transcriptomes and ATAC-seq profiles are measured in 10,412 PBMCs.