Hi all, I just wanted to know if any of you have experience creating a Seurat object with GSE data. I downloaded the files from GEO, and I'm getting the counts (h5 file), the scale factors (json), the image (png) and the tissue positions (csv). I've been trying to create a Seurat object, so far no success.

Thanks in advance.

You can download dataset of GSE188257 from here. Here I m showing how to create a Seurat object for 1 dataset. You can follow the same process for other three datasets-

    #There are 4 datasets in GSE188257. I m working on only GSM5673398_655 set
        1. Perform these in your terminal.
            mkdir 01-GSM5673398_655
            cd 01-GSM5673398_655
            ls
            GSM5673398_655_counts.csv.gz              
            GSM5673398_655_scalefactors_json.json.gz    
            GSM5673398_655_tissue_positions_list.csv.gz
            GSM5673398_655_filtered_feature_bc_matrix.h5  
            GSM5673398_655_tissue_lowres_image.png

            #create a spatial directory inside 01-GSM5673398_655
            mkdir spatial
            cd spatial
           #Copy spatial data into spatial dir, unzip and rename them as in 10x output
            cp ../GSM5673398_655_scalefactors_json.json.gz .
            cp ../GSM5673398_655_tissue_* .
            gunzip *gz
            ls
            GSM5673398_655_scalefactors_json.json  
            GSM5673398_655_tissue_lowres_image.png  
            GSM5673398_655_tissue_positions_list.csv

            mv GSM5673398_655_scalefactors_json.json scalefactors_json.json
            mv GSM5673398_655_tissue_lowres_image.png tissue_lowres_image.png 
            mv GSM5673398_655_tissue_positions_list.csv tissue_positions_list.csv
            ls
            scalefactors_json.json  
            tissue_lowres_image.png  
            tissue_positions_list.csv

        2. Perform these in R    
            library(Seurat)

            data_dir="/User/01-GSM5673398_655"

            s655 <- Seurat::Load10X_Spatial(
              data.dir = data_dir, 
              filename = "GSM5673398_655_filtered_feature_bc_matrix.h5",
              assay = "Spatial", # specify name of the initial assay
              slice = "slice1", # specify name of the stored image
              filter.matrix = TRUE, 
              to.upper = FALSE
            )
            s655
            An object of class Seurat 
            32285 features across 841 samples within 1 assay 
            Active assay: Spatial (32285 features, 0 variable features)
            1 layer present: counts
            1 image present: slice1

            head(s655@meta.data)
            orig.ident nCount_Spatial nFeature_Spatial
            AAACCGGGTAGGTACC-1 SeuratProject          26570             6507
            AAACCGTTCGTCCAGG-1 SeuratProject          30001             6605
            AAACTGCTGGCTCCAA-1 SeuratProject          45459             6943
            AAAGGCTCTCGCGCCG-1 SeuratProject          20792             5455
            AAAGGGATGTAGCAAG-1 SeuratProject          19440             5338
            AAAGTAGCATTGCTCA-1 SeuratProject          25035             6590

Have you looked at the Seurat vignettes for guidance?

Edit: this vignette in particular goes through analysing Visium data

https://satijalab.org/seurat/articles/spatial_vignette