I'm trying to turn a long formatted tibble/data frame into a Seurat object. So, my input has one row per cell per gene:
library(tibble)
library(Seurat)
n_rows <- 10000
cnt_frame <- tibble(cell_id = sprintf("sample%02d_cell%03d",
sample(1:10, n_rows, replace=T),
sample(1:1000, n_rows, replace=T)),
gene_id = sprintf("TLA%03d", sample(1:500, n_rows, replace=T)),
read_count = rnbinom(n_rows, size=10, mu=10))
head(cnt_frame)
# A tibble: 6 x 3
cell_id gene_id read_count
<chr> <chr> <dbl>
1 sample08_cell813 TLA092 6
2 sample10_cell481 TLA167 10
3 sample03_cell173 TLA029 14
4 sample07_cell140 TLA358 10
5 sample03_cell021 TLA314 9
6 sample08_cell091 TLA228 8
I now wish to turn this into a Seurat object.
cnt_mat <- cnt_frame %>%
pivot_wider(names_from=cell_id, values_from=read_count, values_fill=0) %>% ## how to do without pivot?
column_to_rownames("gene_id") %>%
Seurat::as.sparse()
seur <- CreateSeuratObject(cntMat, meta.data=my_metadata)
My question is how to do this without the pivot_wider. My actual data frame is much bigger than this toy example and the pivot step is reaching integer overflow. Given that a sparse matrix is, in principle, quite close to a long table, pivoting to a full matrix seems like it should be unnecessary, but I can't figure out how to get from here to there.
1 answer
Slightly modified example data.
cnt_mat <- structure(list(cell_id = c("sample08_cell813", "sample10_cell481",
"sample03_cell173", "sample07_cell140", "sample03_cell021", "sample08_cell091",
"sample08_cell091"), gene_id = c("TLA092", "TLA167", "TLA029",
"TLA358", "TLA314", "TLA228", "TLA029"), read_count = c(6, 10,
14, 10, 9, 8, 2)), row.names = c(NA, -7L), class = "data.frame")
> cnt_mat
cell_id gene_id read_count
1 sample08_cell813 TLA092 6
2 sample10_cell481 TLA167 10
3 sample03_cell173 TLA029 14
4 sample07_cell140 TLA358 10
5 sample03_cell021 TLA314 9
6 sample08_cell091 TLA228 8
7 sample08_cell091 TLA029 2
You can construct the sparse matrix directly. This is assuming that there are no 0 counts in the long data (might want to double check to be sure).
library("Matrix")
sparse_mat <- sparseMatrix(
i=match(cnt_mat$gene_id, unique(cnt_mat$gene_id)),
j=match(cnt_mat$cell_id, unique(cnt_mat$cell_id)),
x=cnt_mat$read_count,
dims=c(length(unique(cnt_mat$gene_id)), length(unique(cnt_mat$cell_id))),
dimnames=list(unique(cnt_mat$gene_id), unique(cnt_mat$cell_id))
)
> sparse_mat
6 x 6 sparse Matrix of class "dgCMatrix"
sample08_cell813 sample10_cell481 sample03_cell173 sample07_cell140
TLA092 6 . . .
TLA167 . 10 . .
TLA029 . . 14 .
TLA358 . . . 10
TLA314 . . . .
TLA228 . . . .
sample03_cell021 sample08_cell091
TLA092 . .
TLA167 . .
TLA029 . 2
TLA358 . .
TLA314 9 .
TLA228 . 8
No worries! It's not immediately obvious how to generate a sparse matrix by hand like this, so don't beat yourself up.
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