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Merged
14zac2 merged 2 commits into from
Mar 18, 2023
Merged

First #5

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cbae18a
Updated to current packages and originalexp assay
Mar 18, 2023
15778c0
Updated package list on README
Mar 18, 2023
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21 changes: 12 additions & 9 deletions CodingBlocks.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -7,11 +7,11 @@ Before beginning this tutorial, make sure you have the following packages instal

R version | Packages | |
----------|-----------------------------|----------------|-----------------
4.0.3 |SingleCellExperiment_1.12.0* |dplyr_1.0.4* |ggplot2_3.3.3
.|Seurat_3.2.2 |scmap_1.12.0* |Harmony_1.0**
.|scater_1.18.3* |celldex_1.0.0* |cerebroApp_1.3.0**
.|SCINA_1.2.0 |SingleR_1.4.0* |msigdb_0.2.0**
.|devtools_2.3.2 | |
4.0.3 |SingleCellExperiment_1.12.0* |dplyr_1.0.9* |ggplot2_3.3.6
.|Seurat_4.1.1 |scmap_1.12.0* |harmony_1.0**
.|scater_1.18.6* |celldex_1.0.0* |cerebroApp_1.3.0**
.|SCINA_1.2.0 |SingleR_1.4.1* |msigdb_0.2.0**
.|devtools_2.4.3 | |
"*" = packages must be installed by running `BiocManager::install("package")` instead of `install.packages("package")`.
"**" = packages must be installed by from github using devtools, see code block below.

Expand Down Expand Up @@ -285,7 +285,7 @@ [email protected] <- factor(rep("query", ncol(query_seur)))

# Downsample the reference to be similar to query in terms of total UMIs
totalUMI <- median([email protected]$nCount_RNA)
ref_seur@assays$RNA@counts <- Seurat::SampleUMI(ref_seur@assays$RNA@counts,
ref_seur@assays$originalexp@counts <- Seurat::SampleUMI(ref_seur@assays$originalexp@counts,
max.umi=totalUMI, upsample=FALSE)

# Merge the datasets together into a single Seurat object
Expand All @@ -305,7 +305,10 @@ merged_seur <- Seurat::RunPCA(merged_seur)
merged_seur <- Seurat::RunUMAP(merged_seur, dims=1:15)
Seurat::DimPlot(merged_seur, reduction="umap") + ggplot2::ggtitle("Before Integration")
# Run Harmony to remove batch effects
merged_seur <- harmony::RunHarmony(merged_seur, "source", dims.use=1:15)
merged_seur <- harmony::RunHarmony(merged_seur,
"source",
dims.use=1:15,
assay.use = "originalexp")
merged_seur <- Seurat::RunUMAP(merged_seur, dims=1:15, reduction="harmony")
# Plot the data
Seurat::DimPlot(merged_seur, reduction="umap") + ggplot2::ggtitle("After Integration")
Expand Down Expand Up @@ -343,7 +346,7 @@ cluster_labs <- c("0"="ambiguous",

# Assign cluster label to the associated query cells
# (the query cells that had been assigned the same cluster label)
[email protected]$annotation <- cluster_labs[[email protected]$RNA_snn_res.0.5]
[email protected]$annotation <- cluster_labs[[email protected]$originalexp_snn_res.0.5]

# Add the results to the SingleCellExperiment Object and plot
query_sce$Harmony_lab <- [email protected]$annotation[[email protected]$source =="query"]
Expand Down Expand Up @@ -488,7 +491,7 @@ Pathway analysis can also be done for each cluster to determine significantly up
# First get marker genes through cerebro
query_seur <- cerebroApp::getMarkerGenes(query_seur,
groups = c('seurat_clusters'),
assay = "RNA",
assay = "originalexp",
organism = "hg")

# Get enriched pathways through cerebro
Expand Down
444 changes: 192 additions & 252 deletions ExpectedOutput.html
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12 changes: 6 additions & 6 deletions README.md
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Expand Up @@ -13,15 +13,15 @@ To make recommendations by the tutorial more accessible, we have provided an R N
This code has been successfully run using `R 4.0.3` and the following packages:
```
SingleCellExperiment_1.12.0*
Seurat_3.2.2
scater_1.18.3*
Seurat_4.1.1
scater_1.18.6*
SCINA_1.2.0
devtools_2.3.2
dplyr_1.0.4*
devtools_2.4.3
dplyr_1.0.9*
scmap_1.12.0*
celldex_1.0.0*
SingleR_1.4.0*
ggplot2_3.3.3
SingleR_1.4.1*
ggplot2_3.3.6
Harmony_1.0**
cerebroApp_1.3.0**
msigdb_0.2.0**
Expand Down