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reubenthomas 2021-03-30 15:50:56 -07:00
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single-cell-analysis/Session_3/Session3.Rmd
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@ -111,7 +111,7 @@ data <- subset(x=data, subset=nFeature_RNA > 200 & nCount_RNA > quantnCountRNA &
print(sprintf("After filtering outliers: %d cells and %d genes", ncol(data), nrow(data))) print(sprintf("After filtering outliers: %d cells and %d genes", ncol(data), nrow(data)))
``` ```
##Normalization ## Normalization
Now, we will perform sctranform based normalization and visualize the results Now, we will perform sctranform based normalization and visualize the results
```{r} ```{r}
data <- SCTransform(data, method="qpoisson", vars.to.regress = NULL) data <- SCTransform(data, method="qpoisson", vars.to.regress = NULL)
@ -199,9 +199,9 @@ sce <- as(sce, "SingleCellExperiment")
## Prep this object for subsequent aggregation analyses ## Prep this object for subsequent aggregation analyses
(sce <- prepSCE(sce, (sce <- prepSCE(sce,
cluster_id = "seurat_clusters", # subpopulation assignments kid = "seurat_clusters", # subpopulation assignments
group_id = "sTime", # group IDs gid = "sTime", # group IDs
sample_id = "sIndividual", # sample IDs sid = "sIndividual", # sample IDs
drop = TRUE)) # drop all other colData columns drop = TRUE)) # drop all other colData columns
nk <- length(kids <- levels(sce$cluster_id)) nk <- length(kids <- levels(sce$cluster_id))
@ -350,4 +350,7 @@ DimPlot(batch.integrated, reduction = "tsne", group.by = "Time")
DimPlot(batch.integrated, reduction = "tsne", group.by = "batch") DimPlot(batch.integrated, reduction = "tsne", group.by = "batch")
``` ```
```{r}
sessionInfo()
```