Seconding this, it really comes down to (a) reading in the mtx file as a sparse matrix, which often only contains the count values which is why you also need to read in the gene names (--> features) and barcodes/cell names. And the metadata will probably just be a data.frame'able table, too, with additional information about the cells.
If you want even more convenience, you could
- Download the files of interest into one directory.
- Use the
read10xCounts()function from the DropletUtils package. This will create aSingleCellExperimentobject.
@Friederike Thank you
PS
Remark: examples how to read "mtx" files by R and Python can be found here: https://www.kaggle.com/reminho/human-glioblastoma-dataset