Data Preparation

Data Preparation

SeuratExplorer needs a processed Seurat object.

Required

• At least one assay with data (RNA, ATAC, …).
• A dimensional reduction matching the configured keywords (umap, tsne, pca by default). Without one, the Dim Reduction / Feature / Rename-Clusters pages cannot work.
• Cell metadata (meta.data), normally including cluster assignments.

Recommended

• Multiple cluster-resolution columns.
• Sample / batch information.
• QC metrics (nCount_RNA, nFeature_RNA, percent.mt, …).
• Cell-type annotations (these can also be added interactively in the app).

File formats

• .rds — standard R format.
• .qs2 — better compression for large objects (qs2::qs_save()).

Maximum upload size is 20 GB by default (configurable with MaxInputFileSize).

Example preprocessing

library(Seurat)

seurat_obj <- Read10X(data.dir = "path/to/data")
seurat_obj <- CreateSeuratObject(counts = seurat_obj)

seurat_obj <- NormalizeData(seurat_obj)
seurat_obj <- FindVariableFeatures(seurat_obj)
seurat_obj <- ScaleData(seurat_obj)
seurat_obj <- RunPCA(seurat_obj)
seurat_obj <- RunUMAP(seurat_obj, dims = 1:30)

seurat_obj <- FindNeighbors(seurat_obj, dims = 1:30)
seurat_obj <- FindClusters(seurat_obj, resolution = 0.4)
seurat_obj <- FindClusters(seurat_obj, resolution = 0.8)

saveRDS(seurat_obj, "my_seurat_object.rds")
# or
qs2::qs_save(seurat_obj, "my_seurat_object.qs2")

Assays and slots

SeuratExplorer supports assay switching across scRNA-seq, scATAC-seq, spatial, CITE-seq and custom assays. Different features can read different slots:

Feature	Supported slots
Feature Plot	counts, data, scale.data
Violin Plot	counts, data, scale.data
Dot Plot	data
Heatmap (Cell Level)	data, scale.data
Heatmap (Group Averaged)	data
Ridge Plot	counts, data, scale.data
DEGs Analysis	counts, data
Top Expressed Features	counts
Feature Summary	data
Feature Correlation	data
Gene Based Cluster	counts, data
Module Score	counts, data