Last updated: 2025-02-28
Checks: 7 0
Knit directory: muse/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20200712) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 9db5d31. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .Rproj.user/
Ignored: data/1M_neurons_filtered_gene_bc_matrices_h5.h5
Ignored: data/293t/
Ignored: data/293t_3t3_filtered_gene_bc_matrices.tar.gz
Ignored: data/293t_filtered_gene_bc_matrices.tar.gz
Ignored: data/5k_Human_Donor1_PBMC_3p_gem-x_5k_Human_Donor1_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
Ignored: data/5k_Human_Donor2_PBMC_3p_gem-x_5k_Human_Donor2_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
Ignored: data/5k_Human_Donor3_PBMC_3p_gem-x_5k_Human_Donor3_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
Ignored: data/5k_Human_Donor4_PBMC_3p_gem-x_5k_Human_Donor4_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
Ignored: data/Parent_SC3v3_Human_Glioblastoma_filtered_feature_bc_matrix.tar.gz
Ignored: data/brain_counts/
Ignored: data/cl.obo
Ignored: data/cl.owl
Ignored: data/jurkat/
Ignored: data/jurkat:293t_50:50_filtered_gene_bc_matrices.tar.gz
Ignored: data/jurkat_293t/
Ignored: data/jurkat_filtered_gene_bc_matrices.tar.gz
Ignored: data/pbmc20k/
Ignored: data/pbmc20k_seurat/
Ignored: data/pbmc3k/
Ignored: data/pbmc4k_filtered_gene_bc_matrices.tar.gz
Ignored: data/refdata-gex-GRCh38-2020-A.tar.gz
Ignored: data/seurat_1m_neuron.rds
Ignored: data/t_3k_filtered_gene_bc_matrices.tar.gz
Ignored: r_packages_4.4.1/
Untracked files:
Untracked: analysis/bioc_scrnaseq.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/exporting.Rmd) and HTML
(docs/exporting.html) files. If you’ve configured a remote
Git repository (see ?wflow_git_remote), click on the
hyperlinks in the table below to view the files as they were in that
past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 9db5d31 | Dave Tang | 2025-02-28 | HDF5 format |
| html | f8dcdf7 | Dave Tang | 2025-02-28 | Build site. |
| Rmd | f179c68 | Dave Tang | 2025-02-28 | File sizes with different distribution of zeros |
| html | 2574e2e | Dave Tang | 2025-02-28 | Build site. |
| Rmd | 8c6e5d6 | Dave Tang | 2025-02-28 | Exporting files out of R |
Create matrix with 99% zeros.
ngenes <- 16000
ncells <- 10000
matrix(
data = rbinom(n = ngenes*ncells, size = 1, prob = 0.01),
nrow = ngenes,
ncol = ncells
) -> my_mat
# theoretical number of zeros
ngenes*ncells*0.99[1] 158400000# number of zeros
sum(my_mat == 0)[1] 158400459Dense matrix object size.
object.size(my_mat)640000216 bytesExport dense matrix as rds.
dense_matrix <- "my_mat.rds"
saveRDS(object = my_mat, file = dense_matrix)Size of dense matrix file.
paste0(file.size(dense_matrix) / 1024 / 1024, " MBs")[1] "4.30414867401123 MBs"Convert to sparse matrix.
suppressPackageStartupMessages(library(Matrix))
my_mat_sparse <- as(my_mat, "sparseMatrix")
class(my_mat_sparse)[1] "dgCMatrix"
attr(,"package")
[1] "Matrix"Sparse matrix object size.
object.size(my_mat_sparse)19236000 bytesExport sparse matrix as rds.
sparse_matrix <- "my_mat_sparse.rds"
saveRDS(object = my_mat_sparse, file = sparse_matrix)Size of sparse matrix file.
paste0(file.size(sparse_matrix) / 1024 / 1024, " MBs")[1] "3.51174449920654 MBs"Clean up.
file.remove(dense_matrix)[1] TRUEfile.remove(sparse_matrix)[1] TRUEExport as HDF5.
suppressPackageStartupMessages(library(hdf5r))
hdf5_file <- "my_mat.h5"
file.h5 <- H5File$new(hdf5_file, mode="w")
file.h5$create_group("data")Class: H5Group
Filename: /home/rstudio/muse/my_mat.h5
Group: /datafile.h5[["data/matrix"]] <- my_mat
## Close the file at the end
## the 'close' method closes only the file-id, but leaves object inside the file open
## This may prevent re-opening of the file. 'close_all' closes the file and all objects in it
file.h5$close_all()Size of HDF5 file.
paste0(file.size(hdf5_file) / 1024 / 1024, " MBs")[1] "10.5204658508301 MBs"Load.
## now re-open it
file.h5 <- H5File$new(hdf5_file, mode="r+")
my_mat_import <- file.h5[["data/matrix"]][,]
class(my_mat_import)[1] "matrix" "array" identical(my_mat, my_mat_import)[1] TRUEfile.h5$close_all()Clean up.
file.remove(hdf5_file)[1] TRUEAs a workflow to check file sizes as we change the number of zeros.
file_size_wf <- function(prob, ngenes = 16000, ncells = 10000){
matrix(
data = rbinom(n = ngenes*ncells, size = 1, prob = prob),
nrow = ngenes,
ncol = ncells
) -> my_mat
my_mat_sparse <- as(my_mat, "sparseMatrix")
dense_matrix <- paste0("my_mat_", prob, ".rds")
saveRDS(object = my_mat, file = dense_matrix)
sparse_matrix <- paste0("my_mat_sparse_", prob, ".rds")
saveRDS(object = my_mat_sparse, file = sparse_matrix)
hdf5_file <- paste0("my_mat_", prob, ".h5")
file.h5 <- H5File$new(hdf5_file, mode="w")
file.h5$create_group("data")
file.h5[["data/matrix"]] <- my_mat
file.h5$close_all()
list(
prob = prob,
dense_size = paste0(file.size(dense_matrix) / 1024 / 1024, " MBs"),
sparse_size = paste0(file.size(sparse_matrix) / 1024 / 1024, " MBs"),
hdf5_size = paste0(file.size(hdf5_file) / 1024 / 1024, " MBs")
) -> res
file.remove(dense_matrix)
file.remove(sparse_matrix)
file.remove(hdf5_file)
return(res)
}
purrr::map_df(.x = c(0.01, 0.05, 0.25, 0.5), \(x) file_size_wf(x))# A tibble: 4 × 4
prob dense_size sparse_size hdf5_size
<dbl> <chr> <chr> <chr>
1 0.01 4.30162048339844 MBs 3.50793647766113 MBs 10.5156774520874 MBs
2 0.05 12.7624349594116 MBs 16.807599067688 MBs 23.2547760009766 MBs
3 0.25 32.0998592376709 MBs 79.5521965026855 MBs 46.7966976165771 MBs
4 0.5 38.3305673599243 MBs 144.26503276825 MBs 51.6437215805054 MBs
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
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: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] hdf5r_1.3.11 Matrix_1.7-0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] bit_4.5.0 jsonlite_1.8.9 dplyr_1.1.4 compiler_4.4.1
[5] promises_1.3.0 tidyselect_1.2.1 Rcpp_1.0.13 stringr_1.5.1
[9] git2r_0.35.0 callr_3.7.6 later_1.3.2 jquerylib_0.1.4
[13] yaml_2.3.10 fastmap_1.2.0 lattice_0.22-6 R6_2.5.1
[17] generics_0.1.3 knitr_1.48 tibble_3.2.1 rprojroot_2.0.4
[21] bslib_0.8.0 pillar_1.9.0 rlang_1.1.4 utf8_1.2.4
[25] cachem_1.1.0 stringi_1.8.4 httpuv_1.6.15 xfun_0.48
[29] getPass_0.2-4 fs_1.6.4 sass_0.4.9 bit64_4.5.2
[33] cli_3.6.3 magrittr_2.0.3 ps_1.8.1 grid_4.4.1
[37] digest_0.6.37 processx_3.8.4 rstudioapi_0.17.1 lifecycle_1.0.4
[41] vctrs_0.6.5 evaluate_1.0.1 glue_1.8.0 whisker_0.4.1
[45] fansi_1.0.6 purrr_1.0.2 rmarkdown_2.28 httr_1.4.7
[49] tools_4.4.1 pkgconfig_2.0.3 htmltools_0.5.8.1