Many molecular datasets are being generated across cancer types that contain multi-modal data collected from longitudinally- and spatially-related biological specimens. A major roadblock to this goal is that the data is stored in a wide variety of file formats or programming language-specific libraries, classes, or data structures. Although a wide range of experimental protocols and platforms are available, an important commonality across these technologies is that they often produce a matrix of features that are measured in a set of observations. In order to facilitate data sharing across groups and technologies, and assays, and to promote interoperability between down-stream analysis tools, a detailed data schema describing the characteristics of FOMs has been developed and will serve a standard useful for the community.
The rmams package can be installed with the remotes package using the following command:
devtools::install_github("single-cell-mams/rmams")
library(Seurat)
options(Seurat.object.assay.version = "v3")
counts <- matrix(rpois((500*200), 1), nrow = 500, ncol = 200, dimnames = list(paste0("Row", 1:500), paste0("Col", 1:200)))
srt <- CreateSeuratObject(counts = counts)
srt <- NormalizeData(srt)
subset_srt <- srt[, 1:100]
mams <- convert_seurat_to_MAMS(object_list = list(srt = srt, subset_srt = subset_srt),
observation_subsets = c("full", "subset"), dataset_id = "dataset1")
print(mams)
You can access a detailed tutorial on how to use the rmams package here.
Wang Y, Sarfraz I, Teh WK, Sokolov A, Herb BR, Creasy HH, Virshup I, Dries R, Degatano K, Mahurkar A, Schnell DJ, Madrigal P, Hilton J, Gehlenborg N, Tickle T, Campbell JD. Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single cell data. bioRxiv [Preprint]. 2023 Mar 7:2023.03.06.531314. doi: 10.1101/2023.03.06.531314. PMID: 36945543; PMCID: PMC10028847.