CRAN Package Check Results for Package tidyseurat

Last updated on 2026-09-06 05:50:28 CEST.

Flavor	Version	Tinstall	Tcheck	Ttotal	Status	Flags
r-devel-linux-x86_64-debian-clang	0.8.10	26.46	288.02	314.48	OK
r-devel-linux-x86_64-debian-gcc	0.8.10	17.49	196.40	213.89	OK
r-devel-linux-x86_64-fedora-clang	0.8.10	20.00	191.39	211.39	OK
r-devel-linux-x86_64-fedora-gcc	0.8.10	20.00	200.23	220.23	OK
r-devel-windows-x86_64	0.8.10	32.00	268.00	300.00	ERROR
r-patched-linux-x86_64	0.8.10	27.77	286.18	313.95	OK
r-release-linux-x86_64	0.8.10	23.56	282.97	306.53	OK
r-release-macos-arm64	0.8.10	7.00	66.00	73.00	OK
r-release-macos-x86_64	0.8.10	19.00	277.00	296.00	OK
r-release-windows-x86_64	0.8.10	31.00	274.00	305.00	OK
r-oldrel-macos-arm64	0.8.10	7.00	78.00	85.00	OK
r-oldrel-macos-x86_64	0.8.10	22.00	410.00	432.00	OK
r-oldrel-windows-x86_64	0.8.10	42.00	378.00	420.00	OK

Check Details

Version: 0.8.10
Check: re-building of vignette outputs
Result: ERROR Error(s) in re-building vignettes: --- re-building 'figures_article.Rmd' using knitr --- finished re-building 'figures_article.Rmd' --- re-building 'introduction.Rmd' using knitr **Brings Seurat to the tidyverse!** website: [stemangiola.github.io/tidyseurat/](https://stemangiola.github.io/tidyseurat/) Please also have a look at - [tidyseurat](https://stemangiola.github.io/tidyseurat/) for tidy single-cell RNA sequencing analysis - [tidySummarizedExperiment](https://tidyomics.github.io/tidySummarizedExperiment/) for tidy bulk RNA sequencing analysis - [tidybulk](https://tidyomics.github.io/tidybulk/) for tidy bulk RNA-seq analysis - [tidygate](https://github.com/stemangiola/tidygate/) for adding custom gate information to your tibble - [tidyHeatmap](https://stemangiola.github.io/tidyHeatmap/) for heatmaps produced with tidy principles  # Introduction tidyseurat provides a bridge between the Seurat single-cell package [@butler2018integrating; @stuart2019comprehensive] and the tidyverse [@wickham2019welcome]. It creates an invisible layer that enables viewing the Seurat object as a tidyverse tibble, and provides Seurat-compatible *dplyr*, *tidyr*, *ggplot* and *plotly* functions. ## Functions/utilities available Seurat-compatible Functions | Description ------------ | ------------- `all` | tidyverse Packages | Description ------------ | ------------- `dplyr` | All `dplyr` APIs like for any tibble `tidyr` | All `tidyr` APIs like for any tibble `ggplot2` | `ggplot` like for any tibble `plotly` | `plot_ly` like for any tibble Utilities | Description ------------ | ------------- `tidy` | Add `tidyseurat` invisible layer over a Seurat object `as_tibble` | Convert cell-wise information to a `tbl_df` `join_features` | Add feature-wise information, returns a `tbl_df` `aggregate_cells`| Aggregate cell gene-transcription abundance as pseudobulk tissue | ## Installation From CRAN ``` r install.packages("tidyseurat") ``` From Github (development) ``` r devtools::install_github("stemangiola/tidyseurat") ``` ``` r library(dplyr) library(tidyr) library(purrr) library(magrittr) library(ggplot2) library(Seurat) library(tidyseurat) ``` ## Create `tidyseurat`, the best of both worlds! This is a seurat object but it is evaluated as tibble. So it is fully compatible both with Seurat and tidyverse APIs. ``` r pbmc_small = SeuratObject::pbmc_small ``` **It looks like a tibble** ``` r pbmc_small ``` ``` ## # A Seurat-tibble abstraction: 80 × 15 ## # <1b>[90mFeatures=230 | Cells=80 | Active assay=RNA | Assays=RNA<1b>[0m ## .cell orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups ## <chr> <fct> <dbl> <int> <fct> <fct> <chr> ## 1 ATGC… SeuratPro… 70 47 0 A g2 ## 2 CATG… SeuratPro… 85 52 0 A g1 ## 3 GAAC… SeuratPro… 87 50 1 B g2 ## 4 TGAC… SeuratPro… 127 56 0 A g2 ## 5 AGTC… SeuratPro… 173 53 0 A g2 ## 6 TCTG… SeuratPro… 70 48 0 A g1 ## 7 TGGT… SeuratPro… 64 36 0 A g1 ## 8 GCAG… SeuratPro… 72 45 0 A g1 ## 9 GATA… SeuratPro… 52 36 0 A g1 ## 10 AATG… SeuratPro… 100 41 0 A g1 ## # ℹ 70 more rows ## # ℹ 8 more variables: RNA_snn_res.1 <fct>, PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>, ## # PC_4 <dbl>, PC_5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl> ``` **But it is a Seurat object after all** ``` r pbmc_small@assays ``` ``` ## $RNA ## Assay data with 230 features for 80 cells ## Top 10 variable features: ## PPBP, IGLL5, VDAC3, CD1C, AKR1C3, PF4, MYL9, GNLY, TREML1, CA2 ``` # Preliminary plots Set colours and theme for plots. ``` r # Use colourblind-friendly colours friendly_cols <- c("#88CCEE", "#CC6677", "#DDCC77", "#117733", "#332288", "#AA4499", "#44AA99", "#999933", "#882255", "#661100", "#6699CC") # Set theme my_theme <- list( scale_fill_manual(values = friendly_cols), scale_color_manual(values = friendly_cols), theme_bw() + theme( panel.border = element_blank(), axis.line = element_line(), panel.grid.major = element_line(size = 0.2), panel.grid.minor = element_line(size = 0.1), text = element_text(size = 12), legend.position = "bottom", aspect.ratio = 1, strip.background = element_blank(), axis.title.x = element_text(margin = margin(t = 10, r = 10, b = 10, l = 10)), axis.title.y = element_text(margin = margin(t = 10, r = 10, b = 10, l = 10)) ) ) ``` We can treat `pbmc_small` effectively as a normal tibble for plotting. Here we plot number of features per cell. ``` r pbmc_small %>% ggplot(aes(nFeature_RNA, fill = groups)) + geom_histogram() + my_theme ```  Here we plot total features per cell. ``` r pbmc_small %>% ggplot(aes(groups, nCount_RNA, fill = groups)) + geom_boxplot(outlier.shape = NA) + geom_jitter(width = 0.1) + my_theme ```  Here we plot abundance of two features for each group. ``` r pbmc_small %>% join_features(features = c("HLA-DRA", "LYZ"), shape = "long") %>% ggplot(aes(groups, .abundance_RNA + 1, fill = groups)) + geom_boxplot(outlier.shape = NA) + geom_jitter(aes(size = nCount_RNA), alpha = 0.5, width = 0.2) + scale_y_log10() + my_theme ```  # Preprocess the dataset Also you can treat the object as Seurat object and proceed with data processing. ``` r pbmc_small_pca <- pbmc_small %>% SCTransform(verbose = FALSE) %>% FindVariableFeatures(verbose = FALSE) %>% RunPCA(verbose = FALSE) pbmc_small_pca ``` ``` ## # A Seurat-tibble abstraction: 80 × 17 ## # <1b>[90mFeatures=220 | Cells=80 | Active assay=SCT | Assays=RNA, SCT<1b>[0m ## .cell orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups ## <chr> <fct> <dbl> <int> <fct> <fct> <chr> ## 1 ATGC… SeuratPro… 70 47 0 A g2 ## 2 CATG… SeuratPro… 85 52 0 A g1 ## 3 GAAC… SeuratPro… 87 50 1 B g2 ## 4 TGAC… SeuratPro… 127 56 0 A g2 ## 5 AGTC… SeuratPro… 173 53 0 A g2 ## 6 TCTG… SeuratPro… 70 48 0 A g1 ## 7 TGGT… SeuratPro… 64 36 0 A g1 ## 8 GCAG… SeuratPro… 72 45 0 A g1 ## 9 GATA… SeuratPro… 52 36 0 A g1 ## 10 AATG… SeuratPro… 100 41 0 A g1 ## # ℹ 70 more rows ## # ℹ 10 more variables: RNA_snn_res.1 <fct>, nCount_SCT <dbl>, ## # nFeature_SCT <int>, PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>, PC_4 <dbl>, ## # PC_5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl> ``` If a tool is not included in the tidyseurat collection, we can use `as_tibble` to permanently convert `tidyseurat` into tibble. Quitting from ./../man/fragments/intro.Rmd:170-176 [pc_plot] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <error/rlang_error> Error: ! object 'ggcoef_multinom' is not exported by 'namespace:ggstats' --- Backtrace: ▆ 1. ├─... %>% ... 2. └─base::loadNamespace(x) 3. └─base::namespaceImportFrom(...) 4. └─base::importIntoEnv(impenv, impnames, ns, impvars) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Quitting from introduction.Rmd:30-31 [unnamed-chunk-2] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <error/rlang_error> Error: ! object 'ggcoef_multinom' is not exported by 'namespace:ggstats' --- Backtrace: ▆ 1. ├─... %>% ... 2. └─base::loadNamespace(x) 3. └─base::namespaceImportFrom(...) 4. └─base::importIntoEnv(impenv, impnames, ns, impvars) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'introduction.Rmd' failed with diagnostics: object 'ggcoef_multinom' is not exported by 'namespace:ggstats' --- failed re-building 'introduction.Rmd' SUMMARY: processing the following file failed: 'introduction.Rmd' Error: Vignette re-building failed. Execution halted Flavor: r-devel-windows-x86_64