Fetch data from the hdf5 file and returns a Seurat object
Usage
FetchH5(
data_file,
meta_file,
name = NULL,
features = NULL,
layer = NULL,
assay = NULL,
metanames = NULL,
reduction = NULL
)Arguments
- data_file
The path to the hdf5 file containing the data.
- meta_file
The path to the hdf5 file containing the metadata.
- name
The name of the dataset in the hdf5 file. If not specified, the function will attempt to find the shared group name in both files.
- features
The names of the genes or features to fetch. If specified, only these features will be fetched.
- layer
The layer for the counts in the hdf5 file. If not specified, the first layer will be used.
- assay
The name of the assay to use. If not specified, the default assay in the hdf5 file will be used.
- metanames
The names of the metadata columns to fetch.
- reduction
The name of the reduction to fetch.
Examples
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-05-14 06:12:05] Start standard processing workflow...
#> ℹ [2026-05-14 06:12:06] Checking a list of <Seurat>...
#> ! [2026-05-14 06:12:06] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-05-14 06:12:06] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-05-14 06:12:08] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-05-14 06:12:08] Use the separate HVF from `srt_list`
#> ℹ [2026-05-14 06:12:08] Number of available HVF: 2000
#> ℹ [2026-05-14 06:12:08] Finished check
#> ℹ [2026-05-14 06:12:08] Perform `Seurat::ScaleData()`
#> ℹ [2026-05-14 06:12:09] Perform pca linear dimension reduction
#> ℹ [2026-05-14 06:12:09] Use stored estimated dimensions 1:20 for Standardpca
#> ℹ [2026-05-14 06:12:09] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-05-14 06:12:10] Reorder clusters...
#> ℹ [2026-05-14 06:12:10] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-05-14 06:12:10] Perform umap nonlinear dimension reduction
#> ℹ [2026-05-14 06:12:10] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ℹ [2026-05-14 06:12:14] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ✔ [2026-05-14 06:12:19] Standard processing workflow completed
PrepareSCExplorer(pancreas_sub, base_dir = "./SCExplorer")
#> ℹ [2026-05-14 06:12:19] Create SCExplorer base directory: ./SCExplorer
#> ℹ [2026-05-14 06:12:19] Set the project name of each <Seurat> to their dataset name
#> ℹ [2026-05-14 06:12:19] Prepare data for object: "SeuratProject"
#> ℹ [2026-05-14 06:12:19] Write the expression matrix to: ./SCExplorer/data.hdf5
#> ℹ [2026-05-14 06:12:22] Write the meta information to: ./SCExplorer/meta.hdf5
srt <- FetchH5(
data_file = "./SCExplorer/data.hdf5",
meta_file = "./SCExplorer/meta.hdf5",
features = c("Ins1", "Ghrl"),
metanames = c("SubCellType", "Phase"),
reduction = "UMAP"
)
CellDimPlot(
srt,
group.by = c("SubCellType", "Phase"),
reduction = "UMAP"
)
FeatureDimPlot(
srt,
features = c("Ins1", "Ghrl"),
reduction = "UMAP"
)
