Usually from publicly available 10X spatial/scRnaseq, you end up with 4 files barcodes/counts/features/tissuesMap(for spatial only).

In my workflow, I generally use CreateSeuratObject and apply min.cells and min.features thresholds.

Sometimes features file is missing, I have only h5 file.
I was wondering (notably for spatial) how to apply min.cells and min.features thresholds. Load10X_Spatial doesn't permit it.

# barcodes
file_barcodes <- file.path(outdir_matrix_dir, glue("filtered_feature_bc_matrix"), "barcodes.tsv.gz")
df_barcodes <- read.csv(file_barcodes, sep = "\t", header = FALSE, 
                        col.names = c("barcode_id"))

# counts
file_counts <- file.path(outdir_matrix_dir, glue("filtered_feature_bc_matrix"), "matrix.mtx.gz")
counts <- readMM(file = file_counts)

# tissue map
file_tisspos <- file.path(image_spatial_dir, glue("spatial/"), "tissue_positions_list.csv")
tisspos <- read.csv(file_tisspos, header = FALSE, 
                       col.names=c("barcode_id", "in_tissue", "array_row", "array_col", 
                                   "pxl_row_in_fullres", "pxl_col_in_fullres"))


#########################################################################################
### Worklow with feature.tsv.gz available missing and filtered_feature_bc_matrix.h5 only
#########################################################################################

 # Load into seurat 
  my_image <- Read10X_Image(
   as.character(dirname(file_tisspos)),
   image.name = "tissue_lowres_image.png",
   filter.matrix = TRUE
 )

 seurat.object <- Load10X_Spatial(
  as.character(dirname(file_barcodes)),
  filename = "filtered_feature_bc_matrix.h5",
  assay = "Spatial",
  slice = "slice1",
  filter.matrix = TRUE,
  to.upper = FALSE,
  image = my_image,
)

##################################################################################
### Worklow with feature.tsv.gz available : apply threshods over cells and features
##################################################################################

#  features
file_features <- file.path(outdir_matrix_dir, glue("filtered_feature_bc_matrix"), "features.tsv.gz")
 df_features <- read.csv(file_features, sep = "\t", header = FALSE, 
                         col.names = c("gene_id", "gene_name", "feature_type"))

 # Load into seurat 
 seurat.object <- CreateSeuratObject(
      counts = Read10X( data.dir = as.character(dirname(file_features)) ,  gene.column = 2),
      assay = 'Spatial', min.cells = 3, min.features = 200
 )

 # Add image
image <- my_image[Cells(x = seurat.object)] 

DefaultAssay(object = image) <- 'Spatial'

seurat.object[['Slice1']] <- image