diff --git a/single-cell-analysis/Sessions_1-2/hands_on_component_plus_art_of_tsne.Rmd b/single-cell-analysis/Sessions_1-2/hands_on_component_plus_art_of_tsne.Rmd new file mode 100644 index 0000000..70c42c6 --- /dev/null +++ b/single-cell-analysis/Sessions_1-2/hands_on_component_plus_art_of_tsne.Rmd @@ -0,0 +1,236 @@ +--- +title: 'Hands-on component of Single-cell RNA-seq workshop: Sessions 1-2' +author: "Krishna Choudhary" +date: "3/27/2021" +output: html_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +##Introduction + +We'll review the current practices for some of the common steps in scRNA-seq data analysis in the Sessions 1-2. We'll discuss different practices for each step and the assumptions underlying various tools and their limitations. This document provides an exposure to one of the popular tools for such analysis. As we'll discuss, the right choice of method in any application depends on a number of factors, including the biological systems under study and the characteristics of the data in hand. For the purpose of our workshop, we'll limit the hands-on component to the Seurat package in R. In general, analysis might require multiple tools in different languages and/or novel development. For more, please see the slide deck in materials. + +The following is based on [this vignette](https://satijalab.org/seurat/articles/pbmc3k_tutorial.html) from the Seurat developers. + +## Setup the working environment + +```{r message=FALSE, warning=FALSE} +library(dplyr) +library(Seurat) +library(patchwork) +``` + +## Load the data + +Please ensure that the directory named "pbmc3k_data" in the workshop materials is in the same directory as this .Rmd file. + +```{r} +data <- CreateSeuratObject(counts = Read10X("data"), + project = "Hello_scWorld", #Name this whatever. + min.cells = 3, # Don't keep genes observed in fewer than 3 cells + min.features = 200, # Don't keep cells with fewer than 200 genes + names.delim = NULL) # Don't try and parse the sample names +``` + +## Filter poor quality or uninteresting cells + +```{r} +#Assess percent of mitochondrial counts in each cell +data[["percent_mt"]] <- PercentageFeatureSet(object = data, + pattern = "^mt-") + +#Violin plot +VlnPlot(object = data, + features = c("nFeature_RNA", + "nCount_RNA", + "percent_mt"), + ncol = 3) + +#Other plotting otions +plot1 <- FeatureScatter(object = data, + feature1 = "nFeature_RNA", + feature2 = "percent_mt") +plot2 <- FeatureScatter(object = data, + feature1 = "nCount_RNA", + feature2 = "nFeature_RNA") +plot1 + plot2 + +#Subset data +data <- subset(x = data, + subset = nFeature_RNA > 200 & + nCount_RNA > 950000 & + percent_mt < 20) + +VlnPlot(object = data, + features = c("nFeature_RNA", + "nCount_RNA", + "percent_mt"), + ncol = 3) + +``` + +## Normalization + +```{r} +data <- NormalizeData(object = data, + normalization.method = "LogNormalize", + scale.factor = 10000) + +``` + +## Feature selection + +```{r} +data <- FindVariableFeatures( object = data, + selection.method = "vst", + nfeatures = 2000) + +#View the 10 most highly variable genes +top10 <- head(x = VariableFeatures(object = data), 10) +print(top10) + +#Seurat allows plotting variable features with and without labels +plot1 <- VariableFeaturePlot(object = data) +plot2 <- LabelPoints(plot = plot1, + points = top10, + repel = TRUE) +plot1 +plot2 + +``` + +## Dimensionality reduction + +### Linear dimensionality reduction +```{r} +#By default Seurat only scales the variable features. +#Explicit input required to rescale all the genes +scale_genes <- rownames(data) +data <- ScaleData(object = data, + features = scale_genes) + +# Use the highly variable genes to find principal components +data <- RunPCA(object = data, + features = VariableFeatures(object = data), + verbose = FALSE) + +#Examine and visualize PCA results a few different ways +print(x = data[["pca"]], dims = 1:5, nfeatures = 5) + +DimPlot(data, reduction = "pca") + +data <- JackStraw(object = data, num.replicate = 100) +data <- ScoreJackStraw(object = data, dims = 1:20) +JackStrawPlot(data, dims = 1:15) + +ElbowPlot(data) + +VizDimLoadings(object = data, dims = 1:2, reduction = "pca") + +``` + +### Nonlinear dimensionality reduction +```{r} +data <- RunUMAP(data, dims = 1:10) +DimPlot(data, reduction = "umap") + +data <- RunTSNE(data, dims=1:10, tsne.method = "Rtsne") +DimPlot(data, reduction = "tsne") + plot_annotation(title = paste0("Rtsne method")) + + +``` + +## Clustering plus art of t-SNE +Note you will need to separtely install FIt-SNE on your computer. Follow the instructions here: https://github.com/KlugerLab/FIt-SNE +```{r} +data <- FindNeighbors(data, dims = 1:10) +data <- FindClusters(data, resolution = 0.5) + +DimPlot(data, reduction = "umap", label = TRUE) +DimPlot(data, reduction = "tsne", label = TRUE) +DimPlot(data, reduction = "pca", label = TRUE) + +data <- RunTSNE(data, dims=1:30, tsne.method = "Rtsne") +DimPlot(data, reduction = "tsne") + plot_annotation(title = paste0("Rtsne method: higher dimension")) + +data <- RunTSNE(data, dims=1:10, verbose =TRUE, + tsne.method="FIt-SNE",initialization="pca", + learning_rate=2400, perplexity=30, df=1, + fast_tsne_path ="/Users/reubenthomas/Dropbox (Gladstone)/scripts/Bioinformatics-Workshops/FIt-SNE/bin/fast_tsne") +DimPlot(data, reduction = "tsne") + plot_annotation(title = paste0("FIt-SNE method: PCA initialization")) + +data <- RunTSNE(data, dims=1:10, verbose =TRUE, + tsne.method="FIt-SNE",initialization="random", + learning_rate=2400, perplexity=30, df=1, + fast_tsne_path ="/Users/reubenthomas/Dropbox (Gladstone)/scripts/Bioinformatics-Workshops/FIt-SNE/bin/fast_tsne") +DimPlot(data, reduction = "tsne") + plot_annotation(title = paste0("FIt-SNE method: Random initialization")) + +data <- RunTSNE(data, dims=1:10, verbose =TRUE, + tsne.method="FIt-SNE",initialization="pca", + learning_rate=2400, perplexity=50, df=1, + fast_tsne_path ="/Users/reubenthomas/Dropbox (Gladstone)/scripts/Bioinformatics-Workshops/FIt-SNE/bin/fast_tsne") +DimPlot(data, reduction = "tsne") + plot_annotation(title = paste0("FIt-SNE method: higher perplexity")) + +data <- RunTSNE(data, dims=1:10, verbose =TRUE, + tsne.method="FIt-SNE",initialization="pca", + learning_rate=2400, perplexity=5, df=1, + fast_tsne_path ="/Users/reubenthomas/Dropbox (Gladstone)/scripts/Bioinformatics-Workshops/FIt-SNE/bin/fast_tsne") +DimPlot(data, reduction = "tsne") + plot_annotation(title = paste0("FIt-SNE method: lower perplexity")) + +data <- RunTSNE(data, dims=1:10, verbose =TRUE, + tsne.method="FIt-SNE",initialization="pca", + learning_rate=2400, perplexity=50, df=0.5, + fast_tsne_path ="/Users/reubenthomas/Dropbox (Gladstone)/scripts/Bioinformatics-Workshops/FIt-SNE/bin/fast_tsne") +DimPlot(data, reduction = "tsne") + plot_annotation(title = paste0("FIt-SNE method: lower df")) + +``` + +### Save the Seurat object +```{r} +test <- data[, 1:10 ] +saveRDS(test, file = "hello_scWorld.rds") +``` + +## Find markers +```{r} +# find all markers of cluster 1 +cluster1.markers <- FindMarkers(data, + ident.1 = 1, + min.pct = 0.25) +head(cluster1.markers, n = 5) + +# find all markers distinguishing cluster 5 from clusters 0 and 3 +cluster5.markers <- FindMarkers(data, + ident.1 = 5, + ident.2 = c(0, 3), + min.pct = 0.25) +head(cluster5.markers, + n = 5) + +# find markers for every cluster compared to all remaining cells, report only the positive ones +data.markers <- FindAllMarkers(data, + only.pos = TRUE, + min.pct = 0.25, + logfc.threshold = 0.25) +data.markers %>% + group_by(., cluster) %>% + top_n(., n = 2, wt = avg_log2FC) + +``` + +## Additional visualizations + +```{r} +VlnPlot(data, features = c("Vps37b", "Tcf7")) + +FeaturePlot(data, features = c("Vps37b", "Tcf7")) +``` + +```{r} +sessionInfo() +``` + +## THE END. diff --git a/single-cell-analysis/Sessions_1-2/hands_on_component_plus_art_of_tsne.html b/single-cell-analysis/Sessions_1-2/hands_on_component_plus_art_of_tsne.html new file mode 100644 index 0000000..a8b27e7 --- /dev/null +++ b/single-cell-analysis/Sessions_1-2/hands_on_component_plus_art_of_tsne.html @@ -0,0 +1,584 @@ + + + + + + + + + + + + + + +Hands-on component of Single-cell RNA-seq workshop: Sessions 1-2 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +

##Introduction

+

We’ll review the current practices for some of the common steps in scRNA-seq data analysis in the Sessions 1-2. We’ll discuss different practices for each step and the assumptions underlying various tools and their limitations. This document provides an exposure to one of the popular tools for such analysis. As we’ll discuss, the right choice of method in any application depends on a number of factors, including the biological systems under study and the characteristics of the data in hand. For the purpose of our workshop, we’ll limit the hands-on component to the Seurat package in R. In general, analysis might require multiple tools in different languages and/or novel development. For more, please see the slide deck in materials.

+

The following is based on this vignette from the Seurat developers.

+
+

Setup the working environment

+
library(dplyr)
+library(Seurat)
+library(patchwork)
+
+
+

Load the data

+

Please ensure that the directory named “pbmc3k_data” in the workshop materials is in the same directory as this .Rmd file.

+
data <- CreateSeuratObject(counts = Read10X("data"),
+                           project = "Hello_scWorld",  #Name this whatever.
+                           min.cells = 3,  # Don't keep genes observed in fewer than 3 cells
+                           min.features = 200,  # Don't keep cells with fewer than 200 genes
+                           names.delim = NULL)  # Don't try and parse the sample names
+
+
+

Filter poor quality or uninteresting cells

+
#Assess percent of mitochondrial counts in each cell 
+data[["percent_mt"]] <- PercentageFeatureSet(object = data, 
+                                             pattern = "^mt-")
+
+#Violin plot
+VlnPlot(object = data, 
+        features = c("nFeature_RNA", 
+                   "nCount_RNA", 
+                   "percent_mt"), 
+        ncol = 3)
+

+
#Other plotting otions
+plot1 <- FeatureScatter(object = data, 
+                        feature1 = "nFeature_RNA", 
+                        feature2 = "percent_mt")
+plot2 <- FeatureScatter(object = data,
+                        feature1 = "nCount_RNA", 
+                        feature2 = "nFeature_RNA")
+plot1 + plot2
+

+
#Subset data
+data <- subset(x = data, 
+               subset =  nFeature_RNA > 200 & 
+                 nCount_RNA > 950000 & 
+                 percent_mt < 20)
+
+VlnPlot(object = data, 
+        features = c("nFeature_RNA",
+                   "nCount_RNA",
+                   "percent_mt"),
+        ncol = 3)
+

+
+
+

Normalization

+
data <- NormalizeData(object = data, 
+                      normalization.method = "LogNormalize", 
+                      scale.factor = 10000)
+
+
+

Feature selection

+
data <- FindVariableFeatures( object = data, 
+                              selection.method = "vst", 
+                              nfeatures = 2000)
+
+#View the 10 most highly variable genes
+top10 <- head(x = VariableFeatures(object = data), 10)
+print(top10)
+
##  [1] "Gm10800" "Ccl21a"  "Ccl19"   "Gm21541" "Glycam1" "Gm2564"  "Ccl19.1"
+##  [8] "Gm10801" "Ccl20"   "Gzma"
+
#Seurat allows plotting variable features with and without labels
+plot1 <- VariableFeaturePlot(object = data)
+plot2 <- LabelPoints(plot = plot1, 
+                     points = top10, 
+                     repel = TRUE)
+
## When using repel, set xnudge and ynudge to 0 for optimal results
+
plot1
+
## Warning: Transformation introduced infinite values in continuous x-axis
+
## Warning: Removed 33 rows containing missing values (geom_point).
+

+
plot2
+
## Warning: Transformation introduced infinite values in continuous x-axis
+## Removed 33 rows containing missing values (geom_point).
+

+
+
+

Dimensionality reduction

+
+

Linear dimensionality reduction

+
#By default Seurat only scales the variable features.
+#Explicit input required to rescale all the genes
+scale_genes <- rownames(data)
+data <- ScaleData(object = data, 
+                  features = scale_genes)
+
## Centering and scaling data matrix
+
# Use the highly variable genes to find principal components
+data <- RunPCA(object = data,
+               features = VariableFeatures(object = data),  
+               verbose = FALSE)  
+
+#Examine and visualize PCA results a few different ways
+print(x = data[["pca"]], dims = 1:5, nfeatures = 5)
+
## PC_ 1 
+## Positive:  Serping1, Col3a1, Col1a2, Col1a1, Serpinf1 
+## Negative:  Cd74, Tyrobp, H2-Aa, H2-Ab1, Fcer1g 
+## PC_ 2 
+## Positive:  Plvap, Lrg1, Cldn5, Ctla2a, Fabp4 
+## Negative:  Lyz2, Cd68, Fcer1g, Tyrobp, Lgals3 
+## PC_ 3 
+## Positive:  Nkg7, Ctsw, Icos, Gm26917, Cd27 
+## Negative:  Plvap, Lrg1, Ms4a6d, Lyz2, Tmem88 
+## PC_ 4 
+## Positive:  Cfb, Lrg1, Clu, Gm26905, Plvap 
+## Negative:  Slc45a2, Tyrp1, Car14, Dct, Tspan10 
+## PC_ 5 
+## Positive:  Nkg7, Actb, Ctsw, AW112010, Gzmb 
+## Negative:  Gm11168, Gm26870, Gm10717, Gm21738, Gm10719
+
DimPlot(data, reduction = "pca")
+

+
data <- JackStraw(object = data, num.replicate = 100)
+data <- ScoreJackStraw(object = data, dims = 1:20)
+JackStrawPlot(data, dims = 1:15)
+
## Warning: Removed 21000 rows containing missing values (geom_point).
+

+
ElbowPlot(data)
+

+
VizDimLoadings(object = data, dims = 1:2, reduction = "pca")
+

+
+
+

Nonlinear dimensionality reduction

+
data <- RunUMAP(data, dims = 1:10)
+
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
+## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
+## This message will be shown once per session
+
## 12:35:28 UMAP embedding parameters a = 0.9922 b = 1.112
+
## 12:35:28 Read 5438 rows and found 10 numeric columns
+
## 12:35:28 Using Annoy for neighbor search, n_neighbors = 30
+
## 12:35:28 Building Annoy index with metric = cosine, n_trees = 50
+
## 0%   10   20   30   40   50   60   70   80   90   100%
+
## [----|----|----|----|----|----|----|----|----|----|
+
## **************************************************|
+## 12:35:29 Writing NN index file to temp file /var/folders/y2/j9tn1gl54d312h5j4b1m6j3c0000gp/T//RtmpvExSuP/file1304d73c0dfb7
+## 12:35:29 Searching Annoy index using 1 thread, search_k = 3000
+## 12:35:30 Annoy recall = 100%
+## 12:35:30 Commencing smooth kNN distance calibration using 1 thread
+## 12:35:31 Initializing from normalized Laplacian + noise
+## 12:35:31 Commencing optimization for 500 epochs, with 228306 positive edges
+## 12:35:38 Optimization finished
+
DimPlot(data, reduction = "umap")
+

+
data <- RunTSNE(data, dims=1:10, tsne.method = "Rtsne")
+DimPlot(data, reduction = "tsne") + plot_annotation(title = paste0("Rtsne method"))
+

+
+
+
+

Clustering plus art of t-SNE

+

Note you will need to separtely install FIt-SNE on your computer. Follow the instructions here: https://github.com/KlugerLab/FIt-SNE

+
data <- FindNeighbors(data, dims = 1:10)
+
## Computing nearest neighbor graph
+
## Computing SNN
+
data <- FindClusters(data, resolution = 0.5)
+
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
+## 
+## Number of nodes: 5438
+## Number of edges: 177873
+## 
+## Running Louvain algorithm...
+## Maximum modularity in 10 random starts: 0.9331
+## Number of communities: 18
+## Elapsed time: 0 seconds
+
DimPlot(data, reduction = "umap", label = TRUE)
+

+
DimPlot(data, reduction = "tsne", label = TRUE)
+

+
DimPlot(data, reduction = "pca", label = TRUE)
+

+
data <- RunTSNE(data, dims=1:30, tsne.method = "Rtsne")
+DimPlot(data, reduction = "tsne") + plot_annotation(title = paste0("Rtsne method: higher dimension"))
+

+
data <- RunTSNE(data, dims=1:10, verbose =TRUE,
+                tsne.method="FIt-SNE",initialization="pca",
+                learning_rate=2400, perplexity=30, df=1,
+                fast_tsne_path ="/Users/reubenthomas/Dropbox (Gladstone)/scripts/Bioinformatics-Workshops/FIt-SNE/bin/fast_tsne")
+
## Warning: The following arguments are not used: verbose
+
## Using rsvd() to compute the top PCs for initialization.
+
DimPlot(data, reduction = "tsne") + plot_annotation(title = paste0("FIt-SNE method: PCA initialization"))
+

+
data <- RunTSNE(data, dims=1:10, verbose =TRUE,
+                tsne.method="FIt-SNE",initialization="random",
+                learning_rate=2400, perplexity=30, df=1,
+                fast_tsne_path ="/Users/reubenthomas/Dropbox (Gladstone)/scripts/Bioinformatics-Workshops/FIt-SNE/bin/fast_tsne")
+
## Warning: The following arguments are not used: verbose
+
## Random initialization
+
DimPlot(data, reduction = "tsne") + plot_annotation(title = paste0("FIt-SNE method: Random initialization"))
+

+
data <- RunTSNE(data, dims=1:10, verbose =TRUE,
+                tsne.method="FIt-SNE",initialization="pca",
+                learning_rate=2400, perplexity=50, df=1,
+                fast_tsne_path ="/Users/reubenthomas/Dropbox (Gladstone)/scripts/Bioinformatics-Workshops/FIt-SNE/bin/fast_tsne")
+
## Warning: The following arguments are not used: verbose
+
## Using rsvd() to compute the top PCs for initialization.
+
DimPlot(data, reduction = "tsne") + plot_annotation(title = paste0("FIt-SNE method: higher perplexity"))
+

+
data <- RunTSNE(data, dims=1:10, verbose =TRUE,
+                tsne.method="FIt-SNE",initialization="pca",
+                learning_rate=2400, perplexity=5, df=1,
+                fast_tsne_path ="/Users/reubenthomas/Dropbox (Gladstone)/scripts/Bioinformatics-Workshops/FIt-SNE/bin/fast_tsne")
+
## Warning: The following arguments are not used: verbose
+
## Using rsvd() to compute the top PCs for initialization.
+
DimPlot(data, reduction = "tsne") + plot_annotation(title = paste0("FIt-SNE method: lower perplexity"))
+

+
data <- RunTSNE(data, dims=1:10, verbose =TRUE,
+                tsne.method="FIt-SNE",initialization="pca",
+                learning_rate=2400, perplexity=50, df=0.5,
+                fast_tsne_path ="/Users/reubenthomas/Dropbox (Gladstone)/scripts/Bioinformatics-Workshops/FIt-SNE/bin/fast_tsne")
+
## Warning: The following arguments are not used: verbose
+
## Using rsvd() to compute the top PCs for initialization.
+
DimPlot(data, reduction = "tsne") + plot_annotation(title = paste0("FIt-SNE method: lower df"))
+

+
+

Save the Seurat object

+
test <- data[, 1:10 ]
+saveRDS(test, file = "hello_scWorld.rds")
+
+
+
+

Find markers

+
# find all markers of cluster 1
+cluster1.markers <- FindMarkers(data, 
+                                ident.1 = 1, 
+                                min.pct = 0.25)
+head(cluster1.markers, n = 5)
+
##       p_val avg_log2FC pct.1 pct.2 p_val_adj
+## Spi1      0   1.672875 0.914 0.208         0
+## Cybb      0   1.862620 0.857 0.147         0
+## Fcgr1     0   1.924539 0.572 0.034         0
+## Cd68      0   3.266696 0.923 0.191         0
+## Ccl6      0   4.016911 0.819 0.091         0
+
# find all markers distinguishing cluster 5 from clusters 0 and 3
+cluster5.markers <- FindMarkers(data, 
+                                ident.1 = 5, 
+                                ident.2 = c(0, 3), 
+                                min.pct = 0.25)
+head(cluster5.markers,
+     n = 5)
+
##                p_val avg_log2FC pct.1 pct.2     p_val_adj
+## Sparc   0.000000e+00   4.614014 0.927 0.019  0.000000e+00
+## Igfbp7 5.582004e-293   5.709793 0.850 0.021 1.191870e-288
+## Ptrf   3.510770e-272   3.342912 0.774 0.009 7.496196e-268
+## Clu    1.710715e-265   3.763025 0.784 0.016 3.652719e-261
+## Plvap  1.616214e-262   4.448397 0.740 0.004 3.450939e-258
+
# find markers for every cluster compared to all remaining cells, report only the positive ones
+data.markers <- FindAllMarkers(data, 
+                               only.pos = TRUE, 
+                               min.pct = 0.25, 
+                               logfc.threshold = 0.25)
+
## Calculating cluster 0
+
## Calculating cluster 1
+
## Calculating cluster 2
+
## Calculating cluster 3
+
## Calculating cluster 4
+
## Calculating cluster 5
+
## Calculating cluster 6
+
## Calculating cluster 7
+
## Calculating cluster 8
+
## Calculating cluster 9
+
## Calculating cluster 10
+
## Calculating cluster 11
+
## Calculating cluster 12
+
## Calculating cluster 13
+
## Calculating cluster 14
+
## Calculating cluster 15
+
## Calculating cluster 16
+
## Calculating cluster 17
+
data.markers %>%
+  group_by(., cluster) %>% 
+  top_n(., n = 2, wt = avg_log2FC)
+
## # A tibble: 36 × 7
+## # Groups:   cluster [18]
+##        p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene   
+##        <dbl>      <dbl> <dbl> <dbl>     <dbl> <fct>   <chr>  
+##  1 0               2.73 0.735 0.139 0         0       Tcf7   
+##  2 3.68e-180       2.45 0.907 0.55  7.85e-176 0       Vps37b 
+##  3 0               5.82 0.984 0.332 0         1       Lyz2   
+##  4 2.35e-202       4.02 0.407 0.049 5.01e-198 1       Chil3  
+##  5 5.55e-239       2.82 0.811 0.24  1.19e-234 2       Plbd1  
+##  6 1.13e-150       3.19 0.938 0.789 2.42e-146 2       Cst3   
+##  7 0               3.76 0.745 0.112 0         3       Cxcr6  
+##  8 0               3.40 0.838 0.155 0         3       Icos   
+##  9 0               4.46 0.935 0.038 0         4       Gpihbp1
+## 10 0               3.68 0.938 0.104 0         4       Esam   
+## # … with 26 more rows
+
+
+

Additional visualizations

+
VlnPlot(data, features = c("Vps37b", "Tcf7"))
+

+
FeaturePlot(data, features = c("Vps37b", "Tcf7"))
+

+
sessionInfo()
+
## R version 4.1.3 (2022-03-10)
+## Platform: x86_64-apple-darwin17.0 (64-bit)
+## Running under: macOS Catalina 10.15.7
+## 
+## Matrix products: default
+## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
+## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
+## 
+## locale:
+## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+## 
+## attached base packages:
+## [1] stats     graphics  grDevices utils     datasets  methods   base     
+## 
+## other attached packages:
+## [1] patchwork_1.1.1    SeuratObject_4.0.4 Seurat_4.1.0       dplyr_1.0.8       
+## 
+## loaded via a namespace (and not attached):
+##   [1] Rtsne_0.15            colorspace_2.0-2      deldir_1.0-6         
+##   [4] ellipsis_0.3.2        ggridges_0.5.3        rstudioapi_0.13      
+##   [7] spatstat.data_2.1-2   farver_2.1.0          leiden_0.3.9         
+##  [10] listenv_0.8.0         ggrepel_0.9.1         RSpectra_0.16-0      
+##  [13] fansi_1.0.2           codetools_0.2-18      splines_4.1.3        
+##  [16] knitr_1.37            polyclip_1.10-0       jsonlite_1.7.3       
+##  [19] ica_1.0-2             cluster_2.1.2         png_0.1-7            
+##  [22] uwot_0.1.11           shiny_1.7.1           sctransform_0.3.3    
+##  [25] spatstat.sparse_2.1-0 compiler_4.1.3        httr_1.4.2           
+##  [28] assertthat_0.2.1      Matrix_1.4-0          fastmap_1.1.0        
+##  [31] lazyeval_0.2.2        limma_3.50.0          cli_3.2.0            
+##  [34] later_1.3.0           htmltools_0.5.2       tools_4.1.3          
+##  [37] rsvd_1.0.5            igraph_1.2.11         gtable_0.3.0         
+##  [40] glue_1.6.1            RANN_2.6.1            reshape2_1.4.4       
+##  [43] Rcpp_1.0.8            scattermore_0.8       jquerylib_0.1.4      
+##  [46] vctrs_0.3.8           nlme_3.1-155          lmtest_0.9-39        
+##  [49] spatstat.random_2.1-0 xfun_0.29             stringr_1.4.0        
+##  [52] globals_0.14.0        mime_0.12             miniUI_0.1.1.1       
+##  [55] lifecycle_1.0.1       irlba_2.3.5           goftest_1.2-3        
+##  [58] future_1.23.0         MASS_7.3-55           zoo_1.8-9            
+##  [61] scales_1.1.1          spatstat.core_2.4-0   promises_1.2.0.1     
+##  [64] spatstat.utils_2.3-0  parallel_4.1.3        RColorBrewer_1.1-2   
+##  [67] yaml_2.2.2            reticulate_1.24       pbapply_1.5-0        
+##  [70] gridExtra_2.3         ggplot2_3.3.5         sass_0.4.0           
+##  [73] rpart_4.1.16          stringi_1.7.6         highr_0.9            
+##  [76] rlang_1.0.1           pkgconfig_2.0.3       matrixStats_0.61.0   
+##  [79] evaluate_0.14         lattice_0.20-45       ROCR_1.0-11          
+##  [82] purrr_0.3.4           tensor_1.5            labeling_0.4.2       
+##  [85] htmlwidgets_1.5.4     cowplot_1.1.1         tidyselect_1.1.1     
+##  [88] parallelly_1.30.0     RcppAnnoy_0.0.19      plyr_1.8.6           
+##  [91] magrittr_2.0.2        R6_2.5.1              generics_0.1.2       
+##  [94] DBI_1.1.2             withr_2.4.3           mgcv_1.8-39          
+##  [97] pillar_1.7.0          fitdistrplus_1.1-6    survival_3.2-13      
+## [100] abind_1.4-5           tibble_3.1.6          future.apply_1.8.1   
+## [103] crayon_1.5.0          KernSmooth_2.23-20    utf8_1.2.2           
+## [106] spatstat.geom_2.3-2   plotly_4.10.0         rmarkdown_2.11       
+## [109] grid_4.1.3            data.table_1.14.2     digest_0.6.29        
+## [112] xtable_1.8-4          tidyr_1.2.0           httpuv_1.6.5         
+## [115] munsell_0.5.0         viridisLite_0.4.0     bslib_0.3.1
+
+
+

THE END.

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