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This vignette introduces the generic functions and operations supported by dbMatrix.
dbMatrix objects currently support several statistical matrix operations listed below with support for more coming soon.
✅ - implemented 🟧 - not yet implemented
| dbSparseMatrix | dbDenseMatrix | |
|---|---|---|
| colSums | ✅ | ✅ |
| rowSums | ✅ | ✅ |
| colMeans | ✅ | ✅ |
| rowMeans | ✅ | ✅ |
| colSds | 🟧 | ✅ |
| rowSds | 🟧 | ✅ |
| t | ✅ | ✅ |
| mean | ✅ | ✅ |
| nrow | ✅ | ✅ |
| ncol | ✅ | ✅ |
| dims | ✅ | ✅ |
| head | ✅ | ✅ |
| tail | ✅ | ✅ |
| … |
Let’s create a sparse matrix for demonstration:
set.seed(42)
dgc <- Matrix::rsparsematrix(100, 50, density = 0.1, rand.x = function(n) rpois(n, 5) + 1)
rownames(dgc) <- paste0("gene_", seq_len(100))
colnames(dgc) <- paste0("cell_", seq_len(50))
dplyr::glimpse(dgc)## Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
## ..@ i : int [1:500] 1 7 15 26 32 38 48 81 90 93 ...
## ..@ p : int [1:51] 0 12 22 33 38 46 54 66 80 89 ...
## ..@ Dim : int [1:2] 100 50
## ..@ Dimnames:List of 2
## .. ..$ : chr [1:100] "gene_1" "gene_2" "gene_3" "gene_4" ...
## .. ..$ : chr [1:50] "cell_1" "cell_2" "cell_3" "cell_4" ...
## ..@ x : num [1:500] 4 7 5 7 3 8 4 5 4 7 ...
## ..@ factors : list()# create dbSparseMatrix from the same dgc
con <- DBI::dbConnect(duckdb::duckdb(), ":memory:")
sparse <- dbMatrix(
value = dgc,
con = con,
name = "test_matrix",
class = "dbSparseMatrix",
overwrite = TRUE
)
# preview
# show function aims to emulate the show method for dgCMatrix
head(sparse)## 6 x 50 dbMatrix of class "dbSparseMatrix"## [[ Colnames 'cell_1', 'cell_2', 'cell_3' ... suppressing 44 ...'cell_48', 'cell_49', 'cell_50' ]]##
## gene_1 . . . . . . 5.0000000 . . .
## gene_2 4.0000000 . . . . . . . . .
## gene_3 . 7.0000000 . . . 8.0000000 . 3.0000000 . .
## gene_4 . . . . . . . . . .
## gene_5 . . . . . . 6.0000000 5.0000000 . .
## gene_6 . . . . . . . . . .## 50 x 100 dbMatrix of class "dbSparseMatrix"## [[ Colnames 'gene_1', 'gene_2', 'gene_3' ... suppressing 94 ...'gene_98', 'gene_99', 'gene_100' ]]##
## cell_1 . 4.0000000 . . . . . 7.0000000 . .
## cell_2 . . 7.0000000 . . . . . . 7.0000000
## cell_3 . . . . . . . . . .
##
## ......suppressing 90 columns and 44 rows
##
## cell_48 . . . . . . 8.0000000 . . .
## cell_49 . . . . . 2.0000000 . 8.0000000 . .
## cell_50 . . . . . . . . . .## cell_1 cell_2 cell_3 cell_4 cell_5 cell_6 cell_7 cell_8 cell_9 cell_10
## 0.66 0.63 0.62 0.24 0.48 0.45 0.66 0.69 0.57 0.77
## [ reached 'max' / getOption("max.print") -- omitted 40 entries ]## cell_1 cell_2 cell_3 cell_4 cell_5 cell_6 cell_7 cell_8 cell_9 cell_10
## 66 63 62 24 48 45 66 69 57 77
## [ reached 'max' / getOption("max.print") -- omitted 40 entries ]## gene_1 gene_2 gene_3 gene_4 gene_5 gene_6 gene_7 gene_8 gene_9 gene_10
## 0.50 0.20 0.68 0.08 0.32 0.24 0.58 0.76 0.88 0.62
## [ reached 'max' / getOption("max.print") -- omitted 90 entries ]## gene_1 gene_2 gene_3 gene_4 gene_5 gene_6 gene_7 gene_8 gene_9 gene_10
## 25 10 34 4 16 12 29 38 44 31
## [ reached 'max' / getOption("max.print") -- omitted 90 entries ]Click to expand
## [1] TRUE ## [1] TRUE ## [1] TRUE ## [1] TRUE# Create a dense matrix directly
set.seed(42)
mat <- matrix(rnorm(100), nrow = 10, ncol = 10)
rownames(mat) <- paste0("row_", 1:10)
colnames(mat) <- paste0("col_", 1:10)
# Create dbDenseMatrix
con2 <- DBI::dbConnect(duckdb::duckdb(), ":memory:")
dense <- dbMatrix(
value = mat,
con = con2,
name = "dense_matrix",
class = "dbDenseMatrix",
overwrite = TRUE
)
# preview
dense## 10 x 10 dbMatrix of class "dbDenseMatrix"##
## row_1 1.3709584 1.3048697 -0.3066386 0.4554501 0.2059986 0.3219253
## row_2 -0.5646982 2.2866454 -1.7813084 0.7048373 -0.3610573 -0.7838389
## row_3 0.3631284 -1.3888607 -0.1719174 1.0351035 0.7581632 1.5757275
##
## ...... suppressing 4 rows ......
##
## row_8 -0.0946590 -2.6564554 -1.7631631 -0.8509076 1.4441013 0.0898329
## row_9 2.0184237 -2.4404669 0.4600974 -2.4142076 -0.4314462 -2.9930901
## row_10 -0.0627141 1.3201133 -0.6399949 0.0361226 0.6556479 0.2848830## 10 x 10 dbMatrix of class "dbDenseMatrix"## [[ Colnames 'row_1', 'row_2', 'row_3' ... suppressing 4 ...'row_8', 'row_9', 'row_10' ]]##
## col_1 1.3709584 -0.5646982 0.3631284 0.6328626 0.4042683 -0.1061245
## col_2 1.3048697 2.2866454 -1.3888607 -0.2787888 -0.1333213 0.6359504
## col_3 -0.3066386 -1.7813084 -0.1719174 1.2146747 1.8951935 -0.4304691
##
## ...... suppressing 4 rows ......
##
## col_8 -1.0431189 -0.0901864 0.6235182 -0.9535234 -0.5428288 0.5809965
## col_9 1.5127070 0.2579214 0.0884402 -0.1208965 -1.1943289 0.6119969
## col_10 1.3921164 -0.4761739 0.6503486 1.3911105 -1.1107889 -0.8607926## col_1 col_2 col_3 col_4 col_5 col_6
## 0.54729677 -0.16345673 -0.17807953 -0.36390406 -0.02021535 0.01839391
## col_7 col_8 col_9 col_10
## 0.53907680 -0.21787537 0.25110630 -0.08719458## col_1 col_2 col_3 col_4 col_5 col_6 col_7
## 5.4729677 -1.6345673 -1.7807953 -3.6390406 -0.2021535 0.1839391 5.3907680
## col_8 col_9 col_10
## -2.1787537 2.5110630 -0.8719458## row_1 row_2 row_3 row_4 row_5 row_6
## 0.48470333 -0.06226284 0.41154753 0.25924440 -0.21826635 0.07264603
## row_7 row_8 row_9 row_10
## -0.01914153 -0.39709480 -0.47524086 0.26901325## row_1 row_2 row_3 row_4 row_5 row_6 row_7
## 4.8470333 -0.6226284 4.1154753 2.5924440 -2.1826635 0.7264603 -0.1914153
## row_8 row_9 row_10
## -3.9709480 -4.7524086 2.6901325## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: AlmaLinux 8.10 (Cerulean Leopard)
##
## Matrix products: default
## BLAS/LAPACK: FlexiBLAS NETLIB; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] Matrix_1.7-4 dbMatrix_0.1.0
##
## loaded via a namespace (and not attached):
## [1] bit_4.6.0 jsonlite_2.0.0 dplyr_1.1.4
## [4] compiler_4.5.2 tidyselect_1.2.1 Rcpp_1.1.0
## [7] blob_1.2.4 nanoarrow_0.7.0-1 pins_1.4.1
## [10] assertthat_0.2.1 dbProject_0.0.0.9002 jquerylib_0.1.4
## [13] arrow_22.0.0 yaml_2.3.10 fastmap_1.2.0
## [16] lattice_0.22-7 R6_2.6.1 generics_0.1.4
## [19] knitr_1.50 tibble_3.3.0 MatrixGenerics_1.22.0
## [22] DBI_1.2.3 bslib_0.9.0 pillar_1.11.1
## [25] connections_0.2.1 rlang_1.1.6 cachem_1.1.0
## [28] xfun_0.54 sass_0.4.10 bit64_4.6.0-1
## [31] cli_3.6.5 withr_3.0.2 magrittr_2.0.4
## [34] digest_0.6.38 grid_4.5.2 rscontract_0.1.2
## [37] dbplyr_2.5.1 lifecycle_1.0.4 vctrs_0.6.5
## [40] evaluate_1.0.5 glue_1.8.0 data.table_1.17.8
## [43] duckdb_1.4.2 rmarkdown_2.30 purrr_1.2.0
## [46] matrixStats_1.5.0 tools_4.5.2 pkgconfig_2.0.3
## [49] htmltools_0.5.8.1These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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