Hi, I would like to know which default values (min.cells, min.count,

You should use the defaults in the version available at <a href="https://diseaseneurog

Then, should I use the defaults in the documentation page at <a href="https://diseasen

dreamlet::processAssays() default values changing depending on the package and R version, which ones do you recommend?,about gabrielhoffman/dreamlet

Comments (6)

GabrielHoffman commented on August 22, 2024

You should use the defaults in the version available at https://diseaseneurogenomics.github.io/dreamlet . When a page doesn't change in the updated package version, the older version number can be kept. This just save runtime in generation the documentation pages. So the defaults for the page labeled 1.1.15 are current and also apply to 1.1.18

from dreamlet.

aidarripoll commented on August 22, 2024

Then, should I use the defaults in the documentation page at https://diseaseneurogenomics.github.io/dreamlet/reference/processAssays.html?

processAssays(
  sceObj,
  formula,
  assays = assayNames(sceObj),
  min.cells = 5,
  min.count = 5,
  min.samples = 4,
  min.prop = 0.4,
  isCounts = TRUE,
  normalize.method = "TMM",
  span = "auto",
  quiet = FALSE,
  weightsList = NULL,
  BPPARAM = SerialParam(),
  ...
)

I've realized that if we use dreamlet < v1.0.0(in my case, it was v0.99.16 in R/4.3.0 and v0.99.6 in R/4.2.0) but with BioC v3.17 (+ installing the mentioned dependencies in dreamlet's page), processAssays()'s defaults are the ones in the previous post...

Hence, I assume if we use dreamlet >= v1.0.0 with BioC v3.18 (using R/4.3.0), the processAssays()'s defaults would be the ones in its documentation page at https://diseaseneurogenomics.github.io/dreamlet/reference/processAssays.html. Is it correct?

Thanks again,

Aida

from dreamlet.

GabrielHoffman commented on August 22, 2024

Yes, use the defaults from the most recent version

Gabriel

from dreamlet.

aidarripoll commented on August 22, 2024

Hi again,

when using the last version of dreamlet (1.1.19) installed using BioC v3.18 for R v4.3 following your installation section:

# Install
dreamlet >= 1.0.0 is compatible with BioC v3.18 for R v4.3.

# 1) Make sure Bioconductor is installed
# 2) Install dreamlet and dependencies:
devtools::[install_github](https://remotes.r-lib.org/reference/install_github.html)("DiseaseNeurogenomics/dreamlet")

# 3) Install zellkonverter >= v1.10.1
BiocManager::[install](https://rdrr.io/pkg/BiocManager/man/install.html)("zellkonverter")
dreamlet is also compatible with earlier version of R and Bioconductor after installing these dependencies:

# Dependencies
# In case these aren’t installed automatically:

devtools::[install_github](https://remotes.r-lib.org/reference/install_github.html)("DiseaseNeurogenomics/variancePartition")
devtools::[install_github](https://remotes.r-lib.org/reference/install_github.html)("DiseaseNeurogenomics/zenith")

I get an error when running processAssays(), which I didn't get using R v4.3.0 dreamlet v0.99.16 BioC v3.17:

Overview of the input for processAssays():

> ge_dge
class: SingleCellExperiment
dim: 23269 72
metadata(2): agg_pars aggr_means
assays(1): CD4_TCM
rownames(23269): RP11-34P13.7 AL627309.1 ... RP1-90G24.6 CTA-268H5.14
rowData names(0):
colnames(72): LLDeep_0067 LLDeep_0069 ... LLDeep_1368 LLDeep_1370
colData names(3): date Gender Age
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):
> identical(rownames(colData(ge_dge)), colnames(ge_dge))
[1] TRUE
> head(colData(ge_dge))
DataFrame with 6 rows and 3 columns
                date   Gender       Age
            <factor> <factor> <integer>
LLDeep_0067   181023        F        52
LLDeep_0069   181108        F        51
LLDeep_0078   181003        M        48
LLDeep_0119   181108        F        52
LLDeep_0128   181218        M        42
LLDeep_0166   181023        M        56
> table(colData(ge_dge)$date)

180925 180926 181003 181022 181023 181107 181108 181213 181218
     8      8      8      8      8      8      8      8      8
> form
~Gender + Age + (1 | date)

Error using R v4.3.0, dreamlet v1.1.19, BioC v3.18:

> res.proc <- processAssays(sceObj = ge_dge, formula = form, min.cells = 5, min.count = 5, min.samples = 4, min.prop = 0.4)
  CD4_TCM...1.7 secs
Error in processAssays(sceObj = ge_dge, formula = form, min.cells = 5,  :
  All models failed.  Consider changing formula
In addition: Warning messages:
1: In voomWithDreamWeights(y[keep, ], formula, data, weights = precWeights,  :
  The experimental design has no replication. Setting weights to 1.
2: In processAssays(sceObj = ge_dge, formula = form, min.cells = 5,  :
  Not enough samples retained or model fit fails: CD4_TCM

> sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: SUSE Linux Enterprise Server 12 SP2

Matrix products: default
BLAS:   /gpfs/apps/MN4/R/4.3.0-BiocM-3.18/GCC/lib64/R/lib/libRblas.so
LAPACK: /gpfs/apps/MN4/R/4.3.0-BiocM-3.18/GCC/lib64/R/lib/libRlapack.so;  LAPACK version 3.11.0

locale:
[1] C

time zone: Europe/Madrid
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods
[8] base

other attached packages:
 [1] RColorBrewer_1.1-3          stringr_1.5.1
 [3] stringi_1.8.3               reshape2_1.4.4
 [5] dplyr_1.1.4                 plyr_1.8.9
 [7] scater_1.28.0               scuttle_1.10.3
 [9] zenith_1.5.2                dreamlet_1.1.19
[11] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
[13] Biobase_2.62.0              GenomicRanges_1.54.1
[15] GenomeInfoDb_1.38.6         IRanges_2.36.0
[17] S4Vectors_0.40.2            BiocGenerics_0.48.1
[19] MatrixGenerics_1.14.0       matrixStats_1.2.0
[21] variancePartition_1.33.11   BiocParallel_1.36.0
[23] limma_3.58.1                ggplot2_3.5.0
[25] Seurat_5.0.2                SeuratObject_5.0.1
[27] sp_2.1-3                    optparse_1.7.4

loaded via a namespace (and not attached):
  [1] spatstat.sparse_3.0-3     bitops_1.0-7
  [3] httr_1.4.7                Rgraphviz_2.46.0
  [5] numDeriv_2016.8-1.1       tools_4.3.0
  [7] sctransform_0.4.1         backports_1.4.1
  [9] utf8_1.2.4                R6_2.5.1
 [11] metafor_4.4-0             lazyeval_0.2.2
 [13] uwot_0.1.16               withr_3.0.0
 [15] prettyunits_1.2.0         gridExtra_2.3
 [17] progressr_0.14.0          cli_3.6.2
 [19] spatstat.explore_3.2-6    fastDummies_1.7.3
 [21] KEGGgraph_1.62.0          SQUAREM_2021.1
 [23] mvtnorm_1.2-4             spatstat.data_3.0-4
 [25] ggridges_0.5.6            pbapply_1.7-2
 [27] mixsqp_0.3-54             parallelly_1.37.1
 [29] invgamma_1.1              RSQLite_2.3.5
 [31] generics_0.1.3            gtools_3.9.5
 [33] ica_1.0-3                 spatstat.random_3.2-3
 [35] Matrix_1.6-5              metadat_1.2-0
 [37] ggbeeswarm_0.7.2          fansi_1.0.6
 [39] abind_1.4-5               lifecycle_1.0.4
 [41] edgeR_4.0.16              mathjaxr_1.6-0
 [43] gplots_3.1.3.1            SparseArray_1.2.4
 [45] Rtsne_0.17                grid_4.3.0
 [47] blob_1.2.4                promises_1.2.1
 [49] crayon_1.5.2              miniUI_0.1.1.1
 [51] lattice_0.22-5            beachmat_2.18.1
 [53] msigdbr_7.5.1             cowplot_1.1.3
 [55] annotate_1.80.0           KEGGREST_1.42.0
 [57] pillar_1.9.0              boot_1.3-30
 [59] corpcor_1.6.10            future.apply_1.11.1
 [61] codetools_0.2-19          leiden_0.4.3.1
 [63] glue_1.7.0                data.table_1.15.2
 [65] vctrs_0.6.5               png_0.1-8
 [67] spam_2.10-0               Rdpack_2.6
 [69] gtable_0.3.4              assertthat_0.2.1
 [71] cachem_1.0.8              rbibutils_2.2.16
 [73] S4Arrays_1.2.0            mime_0.12
 [75] Rfast_2.0.7               survival_3.5-8
 [77] iterators_1.0.14          statmod_1.5.0
 [79] ellipsis_0.3.2            fitdistrplus_1.1-11
 [81] ROCR_1.0-11               nlme_3.1-164
 [83] pbkrtest_0.5.2            bit64_4.0.5
 [85] progress_1.2.3            EnvStats_2.8.1
 [87] RcppAnnoy_0.0.22          irlba_2.3.5.1
 [89] vipor_0.4.7               KernSmooth_2.23-22
 [91] colorspace_2.1-0          rmeta_3.0
 [93] DBI_1.2.2                 tidyselect_1.2.0
 [95] bit_4.0.5                 compiler_4.3.0
 [97] graph_1.80.0              BiocNeighbors_1.18.0
 [99] DelayedArray_0.28.0       plotly_4.10.4
[101] scales_1.3.0              caTools_1.18.2
[103] remaCor_0.0.18            lmtest_0.9-40
[105] digest_0.6.34             goftest_1.2-3
[107] spatstat.utils_3.0-4      minqa_1.2.6
[109] aod_1.3.3                 XVector_0.42.0
[111] RhpcBLASctl_0.23-42       htmltools_0.5.7
[113] pkgconfig_2.0.3           lme4_1.1-35.1
[115] sparseMatrixStats_1.14.0  mashr_0.2.79
[117] fastmap_1.1.1             rlang_1.1.3
[119] htmlwidgets_1.6.4         shiny_1.8.0
[121] DelayedMatrixStats_1.24.0 zoo_1.8-12
[123] jsonlite_1.8.8            BiocSingular_1.16.0
[125] RCurl_1.98-1.14           magrittr_2.0.3
[127] GenomeInfoDbData_1.2.11   dotCall64_1.1-1
[129] patchwork_1.2.0           munsell_0.5.0
[131] Rcpp_1.0.12               viridis_0.6.5
[133] babelgene_22.9            reticulate_1.35.0
[135] RcppZiggurat_0.1.6        EnrichmentBrowser_2.32.0
[137] zlibbioc_1.48.0           MASS_7.3-60.0.1
[139] parallel_4.3.0            listenv_0.9.1
[141] ggrepel_0.9.5             deldir_2.0-4
[143] Biostrings_2.70.2         splines_4.3.0
[145] tensor_1.5                hms_1.1.3
[147] locfit_1.5-9.8            igraph_1.5.0
[149] spatstat.geom_3.2-9       RcppHNSW_0.6.0
[151] ScaledMatrix_1.8.1        XML_3.99-0.16.1
[153] nloptr_2.0.3              httpuv_1.6.14
[155] RANN_2.6.1                tidyr_1.3.1
[157] getopt_1.20.4             purrr_1.0.2
[159] polyclip_1.10-6           future_1.33.1
[161] scattermore_1.2           ashr_2.2-63
[163] rsvd_1.0.5                broom_1.0.5
[165] xtable_1.8-4              fANCOVA_0.6-1
[167] RSpectra_0.16-1           later_1.3.2
[169] viridisLite_0.4.2         truncnorm_1.0-9
[171] tibble_3.2.1              lmerTest_3.1-3
[173] memoise_2.0.1             beeswarm_0.4.0
[175] AnnotationDbi_1.64.1      cluster_2.1.6
[177] globals_0.16.2            GSEABase_1.64.0

NO error using R v4.3.0, dreamlet v0.99.16, BioC v3.17:

> res.proc <- processAssays(sceObj = ge_dge, formula = form, min.cells = 5, min.count = 5, min.samples = 4, min.prop = 0.4)
  CD4_TCM...3.1 mins
Warning message:
In processAssays(sceObj = ge_dge, formula = form, min.cells = 5,  :
  Not enough samples retained or model fit fails:

> details(res.proc)
DataFrame with 1 row and 3 columns
        assay n_retained                formula
  <character>  <integer>            <character>
1     CD4_TCM         72 ~Gender + Age + (1 |..

> sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: SUSE Linux Enterprise Server 12 SP2

Matrix products: default
BLAS:   /gpfs/apps/MN4/R/4.3.0/GCC/lib64/R/lib/libRblas.so
LAPACK: /gpfs/apps/MN4/R/4.3.0/GCC/lib64/R/lib/libRlapack.so;  LAPACK version 3.11.0

locale:
[1] C

time zone: Europe/Madrid
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods
[8] base

other attached packages:
 [1] RColorBrewer_1.1-3          stringr_1.5.0
 [3] stringi_1.7.12              reshape2_1.4.4
 [5] dplyr_1.1.2                 plyr_1.8.8
 [7] scater_1.28.0               scuttle_1.10.1
 [9] SingleCellExperiment_1.22.0 SummarizedExperiment_1.30.1
[11] Biobase_2.60.0              GenomicRanges_1.52.0
[13] GenomeInfoDb_1.36.0         IRanges_2.34.0
[15] S4Vectors_0.38.1            BiocGenerics_0.46.0
[17] MatrixGenerics_1.12.2       matrixStats_1.0.0
[19] zenith_1.2.0                dreamlet_0.99.16
[21] variancePartition_1.31.9    BiocParallel_1.34.2
[23] limma_3.56.1                ggplot2_3.4.2
[25] SeuratObject_4.1.3          Seurat_4.3.0
[27] optparse_1.7.3

loaded via a namespace (and not attached):
  [1] spatstat.sparse_3.0-1     bitops_1.0-7
  [3] httr_1.4.6                Rgraphviz_2.44.0
  [5] numDeriv_2016.8-1.1       tools_4.3.0
  [7] sctransform_0.3.5         backports_1.4.1
  [9] utf8_1.2.3                R6_2.5.1
 [11] lazyeval_0.2.2            uwot_0.1.14
 [13] withr_2.5.0               sp_1.6-1
 [15] prettyunits_1.1.1         gridExtra_2.3
 [17] progressr_0.13.0          cli_3.6.1
 [19] spatstat.explore_3.2-1    KEGGgraph_1.60.0
 [21] SQUAREM_2021.1            mvtnorm_1.1-3
 [23] spatstat.data_3.0-1       ggridges_0.5.4
 [25] pbapply_1.7-0             mixsqp_0.3-48
 [27] parallelly_1.36.0         invgamma_1.1
 [29] RSQLite_2.3.1             generics_0.1.3
 [31] gtools_3.9.4              ica_1.0-3
 [33] spatstat.random_3.1-5     Matrix_1.6-1
 [35] ggbeeswarm_0.7.2          fansi_1.0.4
 [37] abind_1.4-5               lifecycle_1.0.3
 [39] edgeR_3.42.4              gplots_3.1.3
 [41] Rtsne_0.16                grid_4.3.0
 [43] blob_1.2.4                promises_1.2.0.1
 [45] crayon_1.5.2              miniUI_0.1.1.1
 [47] lattice_0.21-8            beachmat_2.16.0
 [49] msigdbr_7.5.1             cowplot_1.1.1
 [51] annotate_1.78.0           KEGGREST_1.40.0
 [53] pillar_1.9.0              boot_1.3-28.1
 [55] corpcor_1.6.10            future.apply_1.11.0
 [57] codetools_0.2-19          leiden_0.4.3
 [59] glue_1.6.2                data.table_1.14.8
 [61] vctrs_0.6.2               png_0.1-8
 [63] Rdpack_2.4                gtable_0.3.3
 [65] assertthat_0.2.1          cachem_1.0.8
 [67] rbibutils_2.2.13          S4Arrays_1.0.4
 [69] mime_0.12                 Rfast_2.0.7
 [71] survival_3.5-5            iterators_1.0.14
 [73] ellipsis_0.3.2            fitdistrplus_1.1-11
 [75] ROCR_1.0-11               nlme_3.1-162
 [77] pbkrtest_0.5.2            bit64_4.0.5
 [79] progress_1.2.2            EnvStats_2.8.0
 [81] RcppAnnoy_0.0.20          irlba_2.3.5.1
 [83] vipor_0.4.5               KernSmooth_2.23-20
 [85] colorspace_2.1-0          rmeta_3.0
 [87] DBI_1.1.3                 tidyselect_1.2.0
 [89] bit_4.0.5                 compiler_4.3.0
 [91] graph_1.78.0              BiocNeighbors_1.18.0
 [93] DelayedArray_0.26.3       plotly_4.10.2
 [95] scales_1.2.1              caTools_1.18.2
 [97] remaCor_0.0.16            lmtest_0.9-40
 [99] digest_0.6.31             goftest_1.2-3
[101] spatstat.utils_3.0-3      minqa_1.2.5
[103] aod_1.3.2                 XVector_0.40.0
[105] RhpcBLASctl_0.23-42       htmltools_0.5.5
[107] pkgconfig_2.0.3           lme4_1.1-33
[109] sparseMatrixStats_1.12.0  mashr_0.2.69
[111] fastmap_1.1.1             rlang_1.1.1
[113] htmlwidgets_1.6.2         shiny_1.7.4
[115] DelayedMatrixStats_1.22.1 zoo_1.8-12
[117] jsonlite_1.8.4            BiocSingular_1.16.0
[119] RCurl_1.98-1.12           magrittr_2.0.3
[121] GenomeInfoDbData_1.2.10   patchwork_1.1.2
[123] munsell_0.5.0             Rcpp_1.0.10
[125] viridis_0.6.4             babelgene_22.9
[127] reticulate_1.30           EnrichmentBrowser_2.30.1
[129] RcppZiggurat_0.1.6        zlibbioc_1.46.0
[131] MASS_7.3-58.4             parallel_4.3.0
[133] listenv_0.9.0             ggrepel_0.9.3
[135] deldir_1.0-9              Biostrings_2.68.1
[137] splines_4.3.0             tensor_1.5
[139] hms_1.1.3                 locfit_1.5-9.7
[141] igraph_1.5.0              spatstat.geom_3.2-1
[143] ScaledMatrix_1.8.1        XML_3.99-0.14
[145] nloptr_2.0.3              httpuv_1.6.11
[147] RANN_2.6.1                tidyr_1.3.0
[149] getopt_1.20.3             purrr_1.0.1
[151] polyclip_1.10-4           future_1.32.0
[153] scattermore_1.2           ashr_2.2-54
[155] rsvd_1.0.5                broom_1.0.5
[157] xtable_1.8-4              later_1.3.1
[159] viridisLite_0.4.2         truncnorm_1.0-9
[161] tibble_3.2.1              lmerTest_3.1-3
[163] memoise_2.0.1             beeswarm_0.4.0
[165] AnnotationDbi_1.62.1      cluster_2.1.4
[167] globals_0.16.2            GSEABase_1.62.0

In this last case, although I get the warning message, I can still run dreamlet() later and the results seem to be OK.

Let me know if you need an anonymized version of the input ('ge_dge') to figure out what's going on.

Also, could it be the case there are some conflicts between other R packages/dependencies between BioC versions? I noticed other problems when using BioC v3.18 and Seurat::as.SingleCellExperiment():

> sce <- as.SingleCellExperiment(pbmc_datasets)
Error in .set_internal_all(x, value, getfun = int_colData, setfun = `int_colData<-`,  :
  invalid 'value' in 'altExps(<SingleCellExperiment>) <- value'
each element of 'value' should have number of columns equal to 'ncol(x)'
In addition: Warning message:
In .check_altexp_columns(x, value[[v]], withDimnames = withDimnames,  :
  'colnames(value[[3]])' are not the same as 'colnames(x)' for
'altExps<-'. This will be an error in the next release of Bioconductor.

To make things easier, I was thinking of two options:

Use your recommended parameters for processAssays() in dreamlet v1.1.19 but using dreamlet v0.99.16 BioC v3.17? Or are there other functions that has significantly changed in dreamlet v1.1.19 that I will miss?
Use your latest stable release, instead of the Bioconductor release, as pointed out in the [installation section] (https://diseaseneurogenomics.github.io/dreamlet/articles/dreamlet.html):

# Installation
# To install this package, start R (version “4.3”) and enter:

if (![require](https://rdrr.io/r/base/library.html)("BiocManager", quietly = TRUE)) {
  [install.packages](https://rdrr.io/r/utils/install.packages.html)("BiocManager")
}

# Select release #1 or #2

# 1) Bioconductor release
BiocManager::[install](https://rdrr.io/pkg/BiocManager/man/install.html)("dreamlet")

# 2) Latest stable release
devtools::[install_github](https://remotes.r-lib.org/reference/install_github.html)("DiseaseNeurogenomics/dreamlet")

Ideally, I would like to keep working with dreamlet v0.99.16 BioC v3.17 (but using the most updated and recommended functions/parameters) since I'm using dreamlet in some custom DEA pipelines, which are part of the Singularity container of a big consortium, which has R/4.3.1 BioC v3.17 and many other packages installed. Then, changing to BioC v3.18 might affect pipelines from other users.

Thanks a lot!
Aida

from dreamlet.

aidarripoll commented on August 22, 2024

Also, an even weird observation was when I compared the output from the exact same script using the same R version (R v4.3.0) but a bit different dreamlet versions (0.99.6 vs. 0.99.16). I'm interested in this since ~1 year ago I started using dreamlet and the version I run at that moment was 0.99.6 (I think I was using R/4.2.0). With 0.99.16 I find a lot more DEGs than with the previous version. However, the logFC and the adj.P.Val highly correlates. Please, find attach the sessionInfo() and some meaningful plots.

R v4.3.0, dreamlet v0.99.16, BioC v3.17:

> sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: SUSE Linux Enterprise Server 12 SP2

Matrix products: default
BLAS:   /gpfs/apps/MN4/R/4.3.0/GCC/lib64/R/lib/libRblas.so
LAPACK: /gpfs/apps/MN4/R/4.3.0/GCC/lib64/R/lib/libRlapack.so;  LAPACK version 3.11.0

locale:
[1] C

time zone: Europe/Madrid
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods
[8] base

other attached packages:
 [1] RColorBrewer_1.1-3          scater_1.28.0
 [3] scuttle_1.10.1              zenith_1.2.0
 [5] dreamlet_0.99.16            variancePartition_1.31.9
 [7] BiocParallel_1.34.2         limma_3.56.1
 [9] ggplot2_3.4.2               stringr_1.5.0
[11] stringi_1.7.12              reshape2_1.4.4
[13] dplyr_1.1.2                 plyr_1.8.8
[15] MAST_1.26.0                 SingleCellExperiment_1.22.0
[17] SummarizedExperiment_1.30.1 Biobase_2.60.0
[19] GenomicRanges_1.52.0        GenomeInfoDb_1.36.0
[21] IRanges_2.34.0              S4Vectors_0.38.1
[23] BiocGenerics_0.46.0         MatrixGenerics_1.12.2
[25] matrixStats_1.0.0           SeuratObject_4.1.3
[27] Seurat_4.3.0                optparse_1.7.3

loaded via a namespace (and not attached):
  [1] spatstat.sparse_3.0-1     bitops_1.0-7
  [3] httr_1.4.6                Rgraphviz_2.44.0
  [5] numDeriv_2016.8-1.1       tools_4.3.0
  [7] sctransform_0.3.5         backports_1.4.1
  [9] utf8_1.2.3                R6_2.5.1
 [11] lazyeval_0.2.2            uwot_0.1.14
 [13] withr_2.5.0               sp_1.6-1
 [15] prettyunits_1.1.1         gridExtra_2.3
 [17] progressr_0.13.0          cli_3.6.1
 [19] spatstat.explore_3.2-1    KEGGgraph_1.60.0
 [21] SQUAREM_2021.1            mvtnorm_1.1-3
 [23] spatstat.data_3.0-1       ggridges_0.5.4
 [25] pbapply_1.7-0             mixsqp_0.3-48
 [27] parallelly_1.36.0         invgamma_1.1
 [29] RSQLite_2.3.1             generics_0.1.3
 [31] gtools_3.9.4              ica_1.0-3
 [33] spatstat.random_3.1-5     Matrix_1.6-1
 [35] ggbeeswarm_0.7.2          fansi_1.0.4
 [37] abind_1.4-5               lifecycle_1.0.3
 [39] edgeR_3.42.4              gplots_3.1.3
 [41] Rtsne_0.16                grid_4.3.0
 [43] blob_1.2.4                promises_1.2.0.1
 [45] crayon_1.5.2              miniUI_0.1.1.1
 [47] lattice_0.21-8            beachmat_2.16.0
 [49] msigdbr_7.5.1             cowplot_1.1.1
 [51] annotate_1.78.0           KEGGREST_1.40.0
 [53] pillar_1.9.0              boot_1.3-28.1
 [55] corpcor_1.6.10            future.apply_1.11.0
 [57] codetools_0.2-19          leiden_0.4.3
 [59] glue_1.6.2                data.table_1.14.8
 [61] vctrs_0.6.2               png_0.1-8
 [63] Rdpack_2.4                gtable_0.3.3
 [65] assertthat_0.2.1          cachem_1.0.8
 [67] rbibutils_2.2.13          S4Arrays_1.0.4
 [69] mime_0.12                 Rfast_2.0.7
 [71] survival_3.5-5            iterators_1.0.14
 [73] ellipsis_0.3.2            fitdistrplus_1.1-11
 [75] ROCR_1.0-11               nlme_3.1-162
 [77] pbkrtest_0.5.2            bit64_4.0.5
 [79] progress_1.2.2            EnvStats_2.8.0
 [81] RcppAnnoy_0.0.20          irlba_2.3.5.1
 [83] vipor_0.4.5               KernSmooth_2.23-20
 [85] colorspace_2.1-0          rmeta_3.0
 [87] DBI_1.1.3                 tidyselect_1.2.0
 [89] bit_4.0.5                 compiler_4.3.0
 [91] graph_1.78.0              BiocNeighbors_1.18.0
 [93] DelayedArray_0.26.3       plotly_4.10.2
 [95] scales_1.2.1              caTools_1.18.2
 [97] remaCor_0.0.16            lmtest_0.9-40
 [99] digest_0.6.31             goftest_1.2-3
[101] spatstat.utils_3.0-3      minqa_1.2.5
[103] aod_1.3.2                 XVector_0.40.0
[105] RhpcBLASctl_0.23-42       htmltools_0.5.5
[107] pkgconfig_2.0.3           lme4_1.1-33
[109] sparseMatrixStats_1.12.0  mashr_0.2.69
[111] fastmap_1.1.1             rlang_1.1.1
[113] htmlwidgets_1.6.2         shiny_1.7.4
[115] DelayedMatrixStats_1.22.1 zoo_1.8-12
[117] jsonlite_1.8.4            BiocSingular_1.16.0
[119] RCurl_1.98-1.12           magrittr_2.0.3
[121] GenomeInfoDbData_1.2.10   patchwork_1.1.2
[123] munsell_0.5.0             Rcpp_1.0.10
[125] viridis_0.6.4             babelgene_22.9
[127] reticulate_1.30           EnrichmentBrowser_2.30.1
[129] RcppZiggurat_0.1.6        zlibbioc_1.46.0
[131] MASS_7.3-58.4             parallel_4.3.0
[133] listenv_0.9.0             ggrepel_0.9.3
[135] deldir_1.0-9              Biostrings_2.68.1
[137] splines_4.3.0             tensor_1.5
[139] hms_1.1.3                 locfit_1.5-9.7
[141] igraph_1.5.0              spatstat.geom_3.2-1
[143] ScaledMatrix_1.8.1        XML_3.99-0.14
[145] nloptr_2.0.3              httpuv_1.6.11
[147] RANN_2.6.1                tidyr_1.3.0
[149] getopt_1.20.3             purrr_1.0.1
[151] polyclip_1.10-4           future_1.32.0
[153] scattermore_1.2           ashr_2.2-54
[155] rsvd_1.0.5                broom_1.0.5
[157] xtable_1.8-4              later_1.3.1
[159] viridisLite_0.4.2         truncnorm_1.0-9
[161] tibble_3.2.1              lmerTest_3.1-3
[163] memoise_2.0.1             beeswarm_0.4.0
[165] AnnotationDbi_1.62.1      cluster_2.1.4
[167] globals_0.16.2            GSEABase_1.62.0

R v4.3.0, dreamlet v0.99.6, BioC v3.17:

> sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 8.4 (Ootpa)

Matrix products: default
BLAS:   /.statelite/tmpfs/gpfs/apps/NORD3/R/4.3.0/GCC/IMPI/lib64/R/lib/libRblas.so
LAPACK: /.statelite/tmpfs/gpfs/apps/NORD3/R/4.3.0/GCC/IMPI/lib64/R/lib/libRlapack.so;  LAPACK version 3.11.0

locale:
[1] C

time zone: Europe/Madrid
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods
[8] base

other attached packages:
 [1] RColorBrewer_1.1-3          scater_1.28.0
 [3] scuttle_1.10.1              zenith_1.2.0
 [5] dreamlet_0.99.6             variancePartition_1.30.2
 [7] BiocParallel_1.34.2         limma_3.56.2
 [9] ggplot2_3.4.2               stringr_1.5.0
[11] stringi_1.7.12              reshape2_1.4.4
[13] dplyr_1.1.2                 plyr_1.8.8
[15] MAST_1.26.0                 SingleCellExperiment_1.22.0
[17] SummarizedExperiment_1.30.2 Biobase_2.60.0
[19] GenomicRanges_1.52.0        GenomeInfoDb_1.36.0
[21] IRanges_2.34.0              S4Vectors_0.38.1
[23] BiocGenerics_0.46.0         MatrixGenerics_1.12.0
[25] matrixStats_1.0.0           SeuratObject_4.1.3
[27] Seurat_4.3.0                optparse_1.7.3

loaded via a namespace (and not attached):
  [1] spatstat.sparse_3.0-1     bitops_1.0-7
  [3] httr_1.4.6                doParallel_1.0.17
  [5] Rgraphviz_2.44.0          tools_4.3.0
  [7] sctransform_0.3.5         backports_1.4.1
  [9] utf8_1.2.3                R6_2.5.1
 [11] lazyeval_0.2.2            uwot_0.1.14
 [13] withr_2.5.0               sp_1.6-1
 [15] prettyunits_1.1.1         gridExtra_2.3
 [17] progressr_0.13.0          cli_3.6.1
 [19] spatstat.explore_3.2-1    KEGGgraph_1.60.0
 [21] SQUAREM_2021.1            mvtnorm_1.2-2
 [23] spatstat.data_3.0-1       ggridges_0.5.4
 [25] pbapply_1.7-0             mixsqp_0.3-48
 [27] parallelly_1.36.0         invgamma_1.1
 [29] RSQLite_2.3.1             generics_0.1.3
 [31] gtools_3.9.4              ica_1.0-3
 [33] spatstat.random_3.1-5     Matrix_1.6-1.1
 [35] ggbeeswarm_0.7.2          fansi_1.0.4
 [37] abind_1.4-5               lifecycle_1.0.3
 [39] edgeR_3.42.4              clusterGeneration_1.3.7
 [41] gplots_3.1.3              Rtsne_0.16
 [43] grid_4.3.0                blob_1.2.4
 [45] promises_1.2.0.1          crayon_1.5.2
 [47] miniUI_0.1.1.1            lattice_0.21-8
 [49] beachmat_2.16.0           msigdbr_7.5.1
 [51] cowplot_1.1.1             annotate_1.78.0
 [53] KEGGREST_1.40.0           pillar_1.9.0
 [55] boot_1.3-28.1             future.apply_1.11.0
 [57] codetools_0.2-19          leiden_0.4.3
 [59] glue_1.6.2                data.table_1.14.8
 [61] vctrs_0.6.2               png_0.1-8
 [63] Rdpack_2.4                gtable_0.3.3
 [65] assertthat_0.2.1          cachem_1.0.8
 [67] rbibutils_2.2.13          S4Arrays_1.2.0
 [69] mime_0.12                 Rfast_2.0.7
 [71] survival_3.5-5            iterators_1.0.14
 [73] ellipsis_0.3.2            fitdistrplus_1.1-11
 [75] ROCR_1.0-11               nlme_3.1-162
 [77] pbkrtest_0.5.2            bit64_4.0.5
 [79] progress_1.2.2            RcppAnnoy_0.0.20
 [81] irlba_2.3.5.1             vipor_0.4.5
 [83] KernSmooth_2.23-20        colorspace_2.1-0
 [85] rmeta_3.0                 DBI_1.1.3
 [87] tidyselect_1.2.0          bit_4.0.5
 [89] compiler_4.3.0            graph_1.78.0
 [91] BiocNeighbors_1.18.0      DelayedArray_0.26.3
 [93] plotly_4.10.2             scales_1.2.1
 [95] caTools_1.18.2            remaCor_0.0.11
 [97] lmtest_0.9-40             digest_0.6.31
 [99] goftest_1.2-3             spatstat.utils_3.0-3
[101] minqa_1.2.5               aod_1.3.2
[103] XVector_0.40.0            RhpcBLASctl_0.23-42
[105] htmltools_0.5.5           pkgconfig_2.0.3
[107] lme4_1.1-33               sparseMatrixStats_1.12.0
[109] mashr_0.2.69              fastmap_1.1.1
[111] rlang_1.1.1               htmlwidgets_1.6.2
[113] shiny_1.7.4               DelayedMatrixStats_1.22.0
[115] zoo_1.8-12                jsonlite_1.8.5
[117] BiocSingular_1.16.0       RCurl_1.98-1.12
[119] magrittr_2.0.3            GenomeInfoDbData_1.2.10
[121] patchwork_1.1.2           munsell_0.5.0
[123] Rcpp_1.0.10               viridis_0.6.3
[125] babelgene_22.9            reticulate_1.30
[127] EnrichmentBrowser_2.30.1  RcppZiggurat_0.1.6
[129] zlibbioc_1.46.0           MASS_7.3-58.4
[131] parallel_4.3.0            listenv_0.9.0
[133] ggrepel_0.9.3             deldir_1.0-9
[135] Biostrings_2.68.1         splines_4.3.0
[137] tensor_1.5                hms_1.1.3
[139] locfit_1.5-9.7            igraph_1.4.3
[141] RUnit_0.4.32              spatstat.geom_3.2-1
[143] ScaledMatrix_1.8.1        XML_3.99-0.14
[145] nloptr_2.0.3              foreach_1.5.2
[147] httpuv_1.6.11             RANN_2.6.1
[149] tidyr_1.3.0               getopt_1.20.3
[151] purrr_1.0.1               polyclip_1.10-4
[153] future_1.32.0             scattermore_1.1
[155] ashr_2.2-54               rsvd_1.0.5
[157] broom_1.0.4               xtable_1.8-4
[159] later_1.3.1               viridisLite_0.4.2
[161] truncnorm_1.0-9           tibble_3.2.1
[163] beeswarm_0.4.0            memoise_2.0.1
[165] AnnotationDbi_1.62.1      cluster_2.1.4
[167] globals_0.16.2            GSEABase_1.62.0

Thanks again!
Aida

from dreamlet.

GabrielHoffman commented on August 22, 2024

I have fixed some bugs. You should use version > 1.0 either from https://bioconductor.org/packages/dreamlet/ with BioC 3.18 or https://diseaseneurogenomics.github.io/dreamlet/index.html

from dreamlet.

dreamlet::processAssays() default values changing depending on the package and R version, which ones do you recommend? about dreamlet HOT 6 CLOSED

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