proteomics.scRNAseq_comparison
FloWuenne
2023-06-19
Last updated: 2023-08-24
Checks: 6 1
Knit directory: mi_spatialomics/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of
the R Markdown file created these results, you’ll want to first commit
it to the Git repo. If you’re still working on the analysis, you can
ignore this warning. When you’re finished, you can run
wflow_publish to commit the R Markdown file and build the
HTML.
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(20230612) 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 e8204f0. 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: .DS_Store
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: analysis/.DS_Store
Ignored: analysis/deprecated/.DS_Store
Ignored: analysis/figure/
Ignored: data/.DS_Store
Ignored: data/140623.calcagno_et_al.seurat_object.rds
Ignored: data/molkart_tissue_regions_rois/.DS_Store
Ignored: figures/.DS_Store
Ignored: omnipathr-log/
Ignored: output/.DS_Store
Ignored: output/figure3.pixie_cell_cluster_heatmap.png
Ignored: output/figure3.pixie_pixel_cluster_heatmap.png
Ignored: output/figure3.pixie_pixel_cluster_heatmap.tiff
Ignored: output/harmony.molkart.h5Seurat
Ignored: output/lunaphore_images/
Ignored: output/mol_cart/
Ignored: output/molkart_cell_types/
Ignored: output/proteomics/
Ignored: output/seqIF/
Ignored: plots/.DS_Store
Ignored: plots/Figure1.umap_plot.pdf
Ignored: references/.DS_Store
Ignored: renv/.DS_Store
Ignored: renv/library/
Ignored: renv/staging/
Untracked files:
Untracked: analysis/__pycache__/
Untracked: analysis/deprecated/molkart.quantify_cells_in_regions.ipynb
Untracked: analysis/deprecated/napari_points.ipynb
Untracked: analysis/deprecated/roi.csv
Untracked: analysis/deprecated/roi2.csv
Untracked: analysis/deprecated/test_construct_spatialdata.ipynb
Untracked: analysis/figures.deep_visual_proteomics.Rmd
Untracked: analysis/mol_cart.QC_spots.Rmd
Untracked: analysis/mol_cart.molkart.Figure1.Rmd
Untracked: analysis/mol_cart.molkart.process_quantifications_seurat.Rmd
Untracked: analysis/molecular_cartography_python/
Untracked: analysis/seqIF.heatmaps_pixie.figure3.Rmd
Untracked: analysis/seqIF_python/
Untracked: analysis/spatialMI_functions.py
Untracked: data/Calcagno2022_int_logNorm_annot.h5Seurat
Untracked: data/pixie.cell_table_size_normalized_cell_labels.csv
Untracked: plots/Figure3.cell_types_overtimes.pdf
Untracked: plots/Figure3.pixel_clusters_overtimes.pdf
Untracked: references/mol_cart.heart_regions/
Unstaged changes:
Modified: .gitignore
Modified: analysis/data_analysis.Rmd
Modified: analysis/data_processing.Rmd
Modified: analysis/figures.Rmd
Deleted: analysis/figures.figure_5.Rmd
Modified: analysis/figures.supplementary_figure_X.proteomics_qc.Rmd
Deleted: analysis/molkart.Figure1.Rmd
Deleted: analysis/molkart.QC_spots.Rmd
Deleted: analysis/molkart.process_quantifications_seurat.Rmd
Modified: analysis/proteomics.bulk_de_analysis.Rmd
Modified: analysis/proteomics.filter_proteomic_data.Rmd
Modified: analysis/proteomics.pathway_enrichment_analysis.Rmd
Modified: analysis/proteomics.scRNAseq_comparison.Rmd
Deleted: analysis/python/lunaphore.figure_3.create_pixie_images.ipynb
Deleted: analysis/python/lunaphore.figure_3.pixie_heatmaps.ipynb
Deleted: analysis/python/molkart.count_spots_on_tissue.ipynb
Deleted: analysis/python/molkart.plot_MC_spots.ipynb
Deleted: analysis/python/molkart.plot_spots_figure1.ipynb
Deleted: analysis/python/molkart.quantify_cells_in_regions.ipynb
Deleted: analysis/python/napari_points.ipynb
Deleted: analysis/python/roi.csv
Deleted: analysis/python/roi2.csv
Deleted: analysis/python/spatialMI_functions.py
Deleted: analysis/python/test_construct_spatialdata.ipynb
Deleted: data/molkart.spots_per_tissue.tsv
Modified: figures/Figure_5.eps
Modified: figures/Figure_5.pdf
Modified: figures/Figure_5.png
Modified: figures/Figure_5.svg
Modified: output/molcart.misty_celltype_table.tsv
Deleted: output/molkart_segmentation_images/sample_control_r1_s1.DAPI_WGA.crop.png
Deleted: output/molkart_segmentation_images/sample_control_r1_s1.DAPI_WGA.crop.scale.png
Deleted: output/molkart_segmentation_images/sample_control_r1_s1.DAPI_WGA.roi.tif
Deleted: output/molkart_segmentation_images/sample_control_r1_s1.DAPI_WGA.tif
Deleted: output/molkart_segmentation_images/sample_control_r1_s1.DAPI_WGA_roi.crop.png
Deleted: output/molkart_segmentation_images/sample_control_r1_s1.DAPI_WGA_roi.crop.scale.png
Deleted: output/molkart_segmentation_images/sample_control_r1_s1.cellpose_full_image.outline.tif
Deleted: output/molkart_segmentation_images/sample_control_r1_s1.cellpose_full_image.roi.outline.tif
Deleted: output/molkart_segmentation_images/sample_control_r1_s1.cellpose_full_image.roi.tif
Deleted: output/molkart_segmentation_images/sample_control_r1_s1.cellpose_full_image.tif
Deleted: output/molkart_segmentation_images/sample_control_r1_s1.cellpose_mask.crop.png
Deleted: output/molkart_segmentation_images/sample_control_r1_s1.cellpose_mask.crop.scale.png
Deleted: output/molkart_segmentation_images/sample_control_r1_s1.cellpose_mask_roi.crop.png
Deleted: output/molkart_segmentation_images/sample_control_r1_s1.cellpose_mask_roi.crop.scale.png
Deleted: output/proteomics.filt_imputed_proteins.tsv
Deleted: output/proteomics.filtered_proteins.tsv
Deleted: output/proteomics.pca_res.rds
Deleted: output/proteomics.protein_missing_stats.tsv
Deleted: output/proteomics.vsn_norm_proteins.tsv
Modified: plots/Figure1.dotplot.pdf
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/proteomics.scRNAseq_comparison.Rmd) and HTML
(docs/proteomics.scRNAseq_comparison.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 |
|---|---|---|---|---|
| html | 67e546d | FloWuenne | 2023-07-23 | Build site. |
| Rmd | ed31d81 | FloWuenne | 2023-07-02 | Finalized proteomics analysis. |
| html | ed31d81 | FloWuenne | 2023-07-02 | Finalized proteomics analysis. |
| html | c1395e6 | FloWuenne | 2023-06-20 | Build site. |
| Rmd | 236130c | FloWuenne | 2023-06-20 | Updating proteomic analysis. |
| html | 236130c | FloWuenne | 2023-06-20 | Updating proteomic analysis. |
Introduction
Load data
We reprocessed the data from Calcagno et al, using the original cell-type annotations from the paper and will load this dataset as a seurat object.
## Load reprocessed Calcagno et al seurat object
#calcagno_et_al <- readRDS("./data/140623.calcagno_et_al.seurat_object.rds")
calcagno_et_al <- LoadH5Seurat("./data/Calcagno2022_int_logNorm_annot.h5Seurat")Validating h5Seurat fileInitializing RNA with dataAdding counts for RNAAdding miscellaneous information for RNAInitializing integrated with dataAdding scale.data for integratedAdding variable feature information for integratedAdding miscellaneous information for integratedAdding reduction pcaAdding cell embeddings for pcaAdding feature loadings for pcaAdding miscellaneous information for pcaAdding reduction umapAdding cell embeddings for umapAdding miscellaneous information for umapAdding graph integrated_nnAdding graph integrated_snnAdding command informationAdding cell-level metadataAdding miscellaneous informationAdding tool-specific resultsAdding data that was not associated with an assayWarning: Adding a command log without an assay associated with it## Get only control cells for marker calculation
calcagno_et_al_d0 <- subset(calcagno_et_al,time == "D0")
calcagno_et_al_d1 <- subset(calcagno_et_al,time == "D1")Let’s check the UMAP embedding from our reprocessed object.
## UMAP plot
DimPlot(calcagno_et_al,label = TRUE) + theme(legend.position = "none")
| Version | Author | Date |
|---|---|---|
| ed31d81 | FloWuenne | 2023-07-02 |
Next, let’s quickly verify, that Endocardial cells are expressing the proper markers before we compare them to our proteomic data.
plot_density(calcagno_et_al_d0, features = "Npr3")
| Version | Author | Date |
|---|---|---|
| ed31d81 | FloWuenne | 2023-07-02 |
VlnPlot(calcagno_et_al_d0, features = "Npr3")
| Version | Author | Date |
|---|---|---|
| ed31d81 | FloWuenne | 2023-07-02 |
Expression of the endocardial specific marker Npr3 in this dataset fits with the original authors annotation, suggesting that we can use these endocardial single-cell signature to identify endocardial specific genes in our proteomics data.
Analyze cell-type specific proteins in proteomic data
We will use the snRNAseq data to identify proteins likely differentially expressed in endocardial cells. For this, we will first identify genes specifically expressed in endocardial cells.
Check subclustering of endocard cells at d1
endocard_d1 <- subset(calcagno_et_al, level_2 == "Endocardial" & time == "D1")endocard_d1 <- SCTransform(endocard_d1)Running SCTransform on assay: RNAvst.flavor='v2' set, setting model to use fixed slope and exclude poisson genes.Calculating cell attributes from input UMI matrix: log_umiTotal Step 1 genes: 11133Total overdispersed genes: 8857Excluding 2276 genes from Step 1 because they are not overdispersed.Variance stabilizing transformation of count matrix of size 11133 by 645Model formula is y ~ log_umiGet Negative Binomial regression parameters per geneUsing 2000 genes, 645 cells
|
| | 0%Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
|
|================== | 25%Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
|
|=================================== | 50%Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
|
|==================================================== | 75%Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
or useNames = TRUE.
|
|======================================================================| 100%Setting estimate of 80 genes to inf as theta_mm/theta_mle < 1e-3# of step1 poisson genes (variance < mean): 0# of low mean genes (mean < 0.001): 0Total # of Step1 poisson genes (theta=Inf; variance < mean): 80Total # of poisson genes (theta=Inf; variance < mean): 2276Calling offset model for all 2276 poisson genesFound 85 outliers - those will be ignored in fitting/regularization stepIgnoring theta inf genesReplacing fit params for 2276 poisson genes by theta=InfSetting min_variance based on median UMI: 0.04Second step: Get residuals using fitted parameters for 11133 genes
|
| | 0%
|
|=== | 4%
|
|====== | 9%
|
|========= | 13%
|
|============ | 17%
|
|=============== | 22%
|
|================== | 26%
|
|===================== | 30%
|
|======================== | 35%
|
|=========================== | 39%
|
|============================== | 43%
|
|================================= | 48%
|
|===================================== | 52%
|
|======================================== | 57%
|
|=========================================== | 61%
|
|============================================== | 65%
|
|================================================= | 70%
|
|==================================================== | 74%
|
|======================================================= | 78%
|
|========================================================== | 83%
|
|============================================================= | 87%
|
|================================================================ | 91%
|
|=================================================================== | 96%
|
|======================================================================| 100%Computing corrected count matrix for 11133 genes
|
| | 0%
|
|=== | 4%
|
|====== | 9%
|
|========= | 13%
|
|============ | 17%
|
|=============== | 22%
|
|================== | 26%
|
|===================== | 30%
|
|======================== | 35%
|
|=========================== | 39%
|
|============================== | 43%
|
|================================= | 48%
|
|===================================== | 52%
|
|======================================== | 57%
|
|=========================================== | 61%
|
|============================================== | 65%
|
|================================================= | 70%
|
|==================================================== | 74%
|
|======================================================= | 78%
|
|========================================================== | 83%
|
|============================================================= | 87%
|
|================================================================ | 91%
|
|=================================================================== | 96%
|
|======================================================================| 100%Calculating gene attributesWall clock passed: Time difference of 2.467093 secsDetermine variable featuresCentering data matrixPlace corrected count matrix in counts slotSet default assay to SCTendocard_d1 <- RunPCA(endocard_d1, verbose = FALSE)
endocard_d1 <- RunUMAP(endocard_d1, dims = 1:10, verbose = FALSE)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per sessionFound more than one class "dist" in cache; using the first, from namespace 'spam'Also defined by 'BiocGenerics'Found more than one class "dist" in cache; using the first, from namespace 'spam'Also defined by 'BiocGenerics'endocard_d1 <- FindNeighbors(endocard_d1, dims = 1:10, verbose = FALSE)
endocard_d1 <- FindClusters(endocard_d1, verbose = FALSE, resolution = 0.2)DimPlot(endocard_d1, label = TRUE) + NoLegend()
| Version | Author | Date |
|---|---|---|
| ed31d81 | FloWuenne | 2023-07-02 |
endo_diff_marker <- FindAllMarkers(endocard_d1, only.pos = TRUE)Calculating cluster 0Calculating cluster 1Calculating cluster 2Calculating cluster 3Calculating cluster 4endo_diff_marker_top <- endo_diff_marker %>%
subset(p_val_adj < 0.05)VlnPlot(endocard_d1, features = c("Selp","Taco1","Serpine1","Prkg1","Nrg1"))
| Version | Author | Date |
|---|---|---|
| ed31d81 | FloWuenne | 2023-07-02 |
Correlate pseudobulk snRNA-seq expression in endocardial cells with proteomic measurements
Let’s load the proteomic data now:
limma_res <- fread("./output/proteomics/proteomics.limma.full_statistics.tsv")
## Extract statistics for different contrasts
miiz_vs_control_signature <- subset(limma_res,analysis == "MI_IZ_vs_control")
miiz_vs_remote_signature <- subset(limma_res,analysis == "MI_IZ_vs_MI_remote")
## Load the normalized protein matrix as well
protein_mat <- fread(file = "./output/proteomics/proteomics.vsn_norm_proteins.tsv")
protein_mat_avg <- protein_mat %>%
mutate(avg_control=rowMeans(.[ , c("control_r1","control_r2","control_r3")], na.rm=TRUE)) %>%
mutate(avg_MI_IZ=rowMeans(.[ , c("MI_IZ_r1","MI_IZ_r2","MI_IZ_r3","MI_IZ_r4")], na.rm=TRUE)) %>%
mutate(avg_MI_remote=rowMeans(.[ , c("MI_remote_r1","MI_remote_r2","MI_remote_r3","MI_remote_r4")], na.rm=TRUE)) %>%
dplyr::select(gene,avg_control,avg_MI_IZ,avg_MI_remote)## Calculate pseudobulk expression profiles for endocardial cells
endocard_seurat <- subset(calcagno_et_al, level_2 == "Endocardial")
sn_endo_bulk <- AverageExpression(endocard_seurat, group.by = c("time"),slot= "data")Warning: `invoke()` is deprecated as of rlang 0.4.0.
Please use `exec()` or `inject()` instead.
This warning is displayed once every 8 hours.sn_endo_bulk_df <- as.data.frame(sn_endo_bulk$RNA)
sn_endo_bulk_df$gene <- rownames(sn_endo_bulk_df)## Merge average protein expression values with average RNA expression
rna_protein_avg <- left_join(protein_mat_avg,sn_endo_bulk_df, by = "gene") %>%
drop_na()
corrplot_rna_protein <- ggplot(rna_protein_avg,aes(avg_control,D0, label = gene)) +
geom_point() +
geom_point(data = subset(rna_protein_avg, gene == "Vwf"),color = "red", size =3)
corrplot_rna_protein
| Version | Author | Date |
|---|---|---|
| ed31d81 | FloWuenne | 2023-07-02 |
Get endocard specific genes
endo_marker <- FindMarkers(calcagno_et_al,ident.1 = "Clust_Npr3",
only.pos = TRUE)endo_marker$gene <- rownames(endo_marker)
endo_marker <- endo_marker %>%
mutate("pct_diff" = pct.1 - pct.2) %>% # Only
subset(pct.2 < 0.1) Calculate differentially expressed genes in endocardial cells in snRNA-seq
calcagno_et_al$cell_type_time <- paste(calcagno_et_al$level_2, calcagno_et_al$time,
sep = "_")
Idents(calcagno_et_al) <- "cell_type_time"
endocard_de <- FindMarkers(calcagno_et_al,
ident.1 = "Endocardial_D1",
ident.2 = "Endocardial_D0",
min.diff.pct = 0.1,
logfc.threshold = 0,
verbose = FALSE)
colnames(endocard_de) <- gsub("\\.","_",colnames(endocard_de))
endocard_de <- endocard_de %>%
mutate("gene" = rownames(endocard_de)) %>%
mutate("pct_ratio" = pct_2 /pct_1,
"pct_diff" = pct_2 -pct_1) %>%
arrange(desc(avg_log2FC))merged_protein_rna <- left_join(endo_marker,miiz_vs_remote_signature, by = "gene")merged_protein_rna <- merged_protein_rna %>%
mutate("label_gene" = if_else(gene == "Vwf",gene,""))
endo_proteomic_corr <- ggplot(merged_protein_rna,aes(avg_log2FC,logFC,
label = label_gene)) +
geom_point(data =subset(merged_protein_rna,gene != "Vwf"), size =3, fill = "darkgrey", pch = 21) +
geom_point(data = subset(merged_protein_rna,gene == "Vwf"),size = 4, fill = "red", pch = 21) +
geom_label_repel() +
labs(x = "Average expression in Endocardial cells (snRNA-seq)",
y = "Log-fold change MI_IZ vs control(proteomics)")
endo_proteomic_corrWarning: Removed 156 rows containing missing values (`geom_point()`).Warning: Removed 156 rows containing missing values (`geom_label_repel()`).
write.table(merged_protein_rna,
file = "./output/proteomics/proteomics.snRNAseq_comp.tsv",
sep = "\t",
col.names = TRUE,
row.names = FALSE,
quote = FALSE)
sessionInfo()R version 4.2.3 (2023-03-15)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] RColorBrewer_1.1-3 ggsci_3.0.0 cowplot_1.1.1
[4] SeuratDisk_0.0.0.9020 plotly_4.10.2 ggrepel_0.9.3
[7] data.table_1.14.8 Nebulosa_1.8.0 patchwork_1.1.2
[10] Libra_1.7 nnls_1.4 here_1.0.1
[13] Seurat_4.9.9.9058 SeuratObject_4.9.9.9091 sp_2.0-0
[16] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[19] dplyr_1.1.2 purrr_1.0.1 readr_2.1.4
[22] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.2
[25] tidyverse_2.0.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] utf8_1.2.3 spatstat.explore_3.2-1
[3] reticulate_1.30 ks_1.14.0
[5] tidyselect_1.2.0 htmlwidgets_1.6.2
[7] grid_4.2.3 Rtsne_0.16
[9] munsell_0.5.0 codetools_0.2-19
[11] ica_1.0-3 future_1.33.0
[13] miniUI_0.1.1.1 withr_2.5.0
[15] spatstat.random_3.1-5 colorspace_2.1-0
[17] progressr_0.13.0 Biobase_2.58.0
[19] highr_0.10 knitr_1.43
[21] rstudioapi_0.15.0 stats4_4.2.3
[23] SingleCellExperiment_1.20.1 ROCR_1.0-11
[25] tensor_1.5 listenv_0.9.0
[27] labeling_0.4.2 MatrixGenerics_1.10.0
[29] git2r_0.32.0 GenomeInfoDbData_1.2.9
[31] polyclip_1.10-4 farver_2.1.1
[33] bit64_4.0.5 rprojroot_2.0.3
[35] parallelly_1.36.0 vctrs_0.6.3
[37] generics_0.1.3 xfun_0.39
[39] timechange_0.2.0 R6_2.5.1
[41] GenomeInfoDb_1.34.9 hdf5r_1.3.8
[43] bitops_1.0-7 spatstat.utils_3.0-3
[45] cachem_1.0.8 DelayedArray_0.24.0
[47] promises_1.2.0.1 scales_1.2.1
[49] gtable_0.3.3 globals_0.16.2
[51] processx_3.8.2 goftest_1.2-3
[53] spam_2.9-1 rlang_1.1.1
[55] splines_4.2.3 lazyeval_0.2.2
[57] spatstat.geom_3.2-4 BiocManager_1.30.21.1
[59] yaml_2.3.7 reshape2_1.4.4
[61] abind_1.4-5 httpuv_1.6.11
[63] tools_4.2.3 ellipsis_0.3.2
[65] jquerylib_0.1.4 BiocGenerics_0.44.0
[67] ggridges_0.5.4 Rcpp_1.0.11
[69] plyr_1.8.8 sparseMatrixStats_1.10.0
[71] zlibbioc_1.44.0 RCurl_1.98-1.12
[73] ps_1.7.5 deldir_1.0-9
[75] pbapply_1.7-2 S4Vectors_0.36.2
[77] zoo_1.8-12 SummarizedExperiment_1.28.0
[79] cluster_2.1.4 fs_1.6.3
[81] magrittr_2.0.3 glmGamPoi_1.10.2
[83] RSpectra_0.16-1 scattermore_1.2
[85] lmtest_0.9-40 RANN_2.6.1
[87] mvtnorm_1.2-2 whisker_0.4.1
[89] fitdistrplus_1.1-11 matrixStats_1.0.0
[91] hms_1.1.3 mime_0.12
[93] evaluate_0.21 xtable_1.8-4
[95] mclust_6.0.0 fastDummies_1.7.3
[97] IRanges_2.32.0 gridExtra_2.3
[99] compiler_4.2.3 KernSmooth_2.23-20
[101] crayon_1.5.2 htmltools_0.5.5
[103] later_1.3.1 tzdb_0.4.0
[105] MASS_7.3-58.2 Matrix_1.5-3
[107] cli_3.6.1 parallel_4.2.3
[109] dotCall64_1.0-2 igraph_1.5.0.1
[111] GenomicRanges_1.50.2 pkgconfig_2.0.3
[113] getPass_0.2-2 spatstat.sparse_3.0-2
[115] bslib_0.5.0 XVector_0.38.0
[117] callr_3.7.3 digest_0.6.33
[119] sctransform_0.3.5 RcppAnnoy_0.0.21
[121] pracma_2.4.2 spatstat.data_3.0-1
[123] rmarkdown_2.23 leiden_0.4.3
[125] uwot_0.1.16 DelayedMatrixStats_1.20.0
[127] shiny_1.7.4.1 lifecycle_1.0.3
[129] nlme_3.1-162 jsonlite_1.8.7
[131] limma_3.54.2 viridisLite_0.4.2
[133] fansi_1.0.4 pillar_1.9.0
[135] lattice_0.20-45 fastmap_1.1.1
[137] httr_1.4.6 survival_3.5-3
[139] glue_1.6.2 png_0.1-8
[141] bit_4.0.5 stringi_1.7.12
[143] sass_0.4.7 RcppHNSW_0.4.1
[145] renv_1.0.0 irlba_2.3.5.1
[147] future.apply_1.11.0