Last updated: 2024-12-31

Checks: 7 0

Knit directory: paed-inflammation-CITEseq/

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(20240216) 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 72fe190. 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:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/obsolete/
    Ignored:    data/C133_Neeland_batch1/
    Ignored:    data/C133_Neeland_merged/
    Ignored:    output/dge_analysis/obsolete/
    Ignored:    renv/library/
    Ignored:    renv/staging/

Untracked files:
    Untracked:  analysis/14.0_DGE_analysis_T-cells.Rmd
    Untracked:  analysis/15.2_proportions_analysis_ann_level_3_macrophages.Rmd
    Untracked:  analysis/17.0_Figure_3.Rmd
    Untracked:  analysis/17.0_Figure_4.Rmd
    Untracked:  analysis/17.0_Figure_5.Rmd
    Untracked:  broad_markers_seurat.csv
    Untracked:  code/background_job.R
    Untracked:  code/reverse_modifier_severity_comparisons.sh
    Untracked:  data/intermediate_objects/CD4 T cells.CF_samples.fit.rds
    Untracked:  data/intermediate_objects/CD4 T cells.all_samples.fit.rds
    Untracked:  data/intermediate_objects/CD8 T cells.CF_samples.fit.rds
    Untracked:  data/intermediate_objects/CD8 T cells.all_samples.fit.rds
    Untracked:  data/intermediate_objects/DC cells.CF_samples.fit.rds
    Untracked:  data/intermediate_objects/DC cells.all_samples.fit.rds
    Untracked:  data/intermediate_objects/T cells.CF_samples.fit.rds
    Untracked:  data/intermediate_objects/T cells.all_samples.fit.rds
    Untracked:  output/dge_analysis/T cells/

Unstaged changes:
    Modified:   .gitignore
    Modified:   analysis/06.0_azimuth_annotation.Rmd
    Modified:   analysis/09.0_integrate_cluster_macro_cells.Rmd
    Modified:   analysis/13.1_DGE_analysis_macro-alveolar.Rmd
    Deleted:    analysis/14.0_proportions_analysis_ann_level_1.Rmd
    Deleted:    analysis/14.1_proportions_analysis_ann_level_3_non-macrophages.Rmd
    Deleted:    analysis/14.2_proportions_analysis_ann_level_3_macrophages.Rmd
    Modified:   analysis/15.0_Figure_1.Rmd
    Modified:   analysis/16.0_Figure_2.Rmd
    Modified:   analysis/17.0_Supplementary_Figure_ADTs.Rmd
    Modified:   code/utility.R
    Modified:   data/cluster_annotations/marker_proteins_TNK_supp.xlsx
    Modified:   data/cluster_annotations/marker_proteins_macrophages_supp.xlsx
    Modified:   data/cluster_annotations/marker_proteins_other_supp.xlsx
    Modified:   data/cluster_annotations/seurat_markers_all_cells.rds
    Modified:   data/intermediate_objects/macro-alveolar.CF_samples.fit.rds
    Modified:   data/intermediate_objects/macro-alveolar.all_samples.fit.rds
    Modified:   output/dge_analysis/macro-alveolar/ORA.FIBROSIS.CF.IVAvCF.NO_MOD.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.FIBROSIS.CF.IVAvNON_CF.CTRL.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.FIBROSIS.CF.NO_MOD.SvCF.NO_MOD.M.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.FIBROSIS.CF.NO_MODvNON_CF.CTRL.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.GO.CF.IVAvCF.NO_MOD.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.GO.CF.IVAvNON_CF.CTRL.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.GO.CF.NO_MOD.SvCF.NO_MOD.M.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.GO.CF.NO_MODvNON_CF.CTRL.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.HALLMARK.CF.IVAvCF.NO_MOD.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.HALLMARK.CF.IVAvNON_CF.CTRL.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.HALLMARK.CF.NO_MOD.SvCF.NO_MOD.M.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.HALLMARK.CF.NO_MODvNON_CF.CTRL.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.REACTOME.CF.IVAvCF.NO_MOD.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.REACTOME.CF.IVAvNON_CF.CTRL.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.REACTOME.CF.NO_MOD.SvCF.NO_MOD.M.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.REACTOME.CF.NO_MODvNON_CF.CTRL.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.WP.CF.IVAvCF.NO_MOD.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.WP.CF.IVAvNON_CF.CTRL.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.WP.CF.NO_MOD.SvCF.NO_MOD.M.csv
    Modified:   output/dge_analysis/macro-alveolar/ORA.WP.CF.NO_MODvNON_CF.CTRL.csv

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/15.0_proportions_analysis_ann_level_1.Rmd) and HTML (docs/15.0_proportions_analysis_ann_level_1.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 72fe190 Jovana Maksimovic 2024-12-31 wflow_publish("analysis/15.0_proportions_analysis_ann_level_1.Rmd")
html 7ae0444 Jovana Maksimovic 2024-12-24 Build site.
Rmd ff4fd99 Jovana Maksimovic 2024-12-24 wflow_publish("analysis/15.0_proportions_analysis_ann_level_1.Rmd")

Load libraries

suppressPackageStartupMessages({
  library(SingleCellExperiment)
  library(edgeR)
  library(tidyverse)
  library(ggplot2)
  library(Seurat)
  library(glmGamPoi)
  library(dittoSeq)
  library(clustree)
  library(AnnotationDbi)
  library(org.Hs.eg.db)
  library(glue)
  library(speckle)
  library(patchwork)
  library(paletteer)
  library(tidyHeatmap)
  library(here)
})

set.seed(42)
options(scipen=999)
options(future.globals.maxSize = 6500 * 1024^2)

Load Data

files <- list.files(here("data/C133_Neeland_merged"),
                    pattern = "C133_Neeland_full_clean.*(macrophages|t_cells|other_cells)_annotated_diet.SEU.rds",
                    full.names = TRUE)

seuLst <- lapply(files[2:4], function(f) readRDS(f))

seu <- merge(seuLst[[1]], 
             y = c(seuLst[[2]], 
                   seuLst[[3]]))
seu
An object of class Seurat 
21568 features across 194407 samples within 1 assay 
Active assay: RNA (21568 features, 0 variable features)
             used    (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells   12078819   645.1   19478872  1040.3   13738828   733.8
Vcells 1354151361 10331.4 3693734349 28181.0 3551485103 27095.7

Analyse Cell type proportions

# Differences in cell type proportions
props <- getTransformedProps(clusters = seu$ann_level_1,
                             sample = seu$sample.id, transform="asin")
props$Proportions %>% knitr::kable()
sample_1.1 sample_15.1 sample_16.1 sample_17.1 sample_18.1 sample_19.1 sample_2.1 sample_20.1 sample_21.1 sample_22.1 sample_23.1 sample_24.1 sample_25.1 sample_26.1 sample_27.1 sample_28.1 sample_29.1 sample_3.1 sample_30.1 sample_31.1 sample_32.1 sample_33.1 sample_34.1 sample_34.2 sample_34.3 sample_35.1 sample_35.2 sample_36.1 sample_36.2 sample_37.1 sample_37.2 sample_37.3 sample_38.1 sample_38.2 sample_38.3 sample_39.1 sample_39.2 sample_4.1 sample_40.1 sample_41.1 sample_42.1 sample_43.1 sample_5.1 sample_6.1 sample_7.1
B cells 0.0223723 0.0071704 0.0018883 0.0428725 0.0015235 0.0023866 0.0950689 0.0017933 0.0095134 0.0023447 0.2436149 0.0057254 0.0027506 0.0122549 0.0020268 0.0006984 0.0918367 0.0119505 0.0064935 0.0041667 0.0027000 0.0607761 0.0026738 0.0000000 0.0016584 0.0181452 0.0081239 0.0304653 0.0502624 0.0069589 0.0124824 0.0105605 0.0048374 0.0058787 0.0356175 0.0434431 0.0275087 0.0096690 0.1603532 0.0202830 0.0107823 0.0427193 0.0181922 0.0046205 0.0282528
CD4 T cells 0.0092866 0.0275786 0.0129485 0.0176849 0.0082267 0.0023866 0.0364283 0.0087101 0.0192097 0.0082063 0.1650295 0.0262504 0.0174205 0.0323529 0.0133766 0.0060064 0.0299745 0.0061886 0.1911977 0.0722222 0.0155248 0.1547452 0.0113636 0.0051207 0.0035537 0.0173387 0.0164368 0.0113827 0.0425297 0.0153097 0.0163076 0.0268075 0.0056436 0.0102104 0.0274874 0.0250944 0.0585045 0.0106607 0.1119339 0.0521226 0.0361976 0.0390446 0.0801592 0.0137671 0.0381306
CD8 T cells 0.0063318 0.0295091 0.0283248 0.0353698 0.0164534 0.0011933 0.0510884 0.0098629 0.0179290 0.0095252 0.1277014 0.0310036 0.0134474 0.0308824 0.0267531 0.0153653 0.0586735 0.0091763 0.0977633 0.0949074 0.0175498 0.1280972 0.0100267 0.0029261 0.0052120 0.0203629 0.0102022 0.0033478 0.0248550 0.0146138 0.0074492 0.0199025 0.0037624 0.0049505 0.0251645 0.0070157 0.0730337 0.0254122 0.0780404 0.0662736 0.0866982 0.1748966 0.0949403 0.0271570 0.0380244
DC cells 0.0097087 0.0184777 0.0037766 0.0391211 0.0173675 0.0055688 0.0017770 0.0067888 0.0080498 0.0016120 0.0628684 0.0398617 0.0201711 0.0230392 0.0162140 0.0065652 0.0440051 0.0147247 0.0497835 0.0157407 0.0121498 0.0719963 0.0788770 0.0643745 0.0322199 0.0219758 0.0251275 0.0348175 0.0544049 0.0073069 0.0062412 0.0048741 0.0010750 0.0024752 0.0081301 0.0232056 0.0350639 0.0126441 0.0185132 0.0570755 0.0064914 0.0705099 0.0204662 0.0045262 0.0059480
dividing innate cells 0.0000000 0.0002758 0.0002698 0.0024116 0.0006094 0.0003978 0.0093292 0.0002562 0.0000000 0.0000000 0.0108055 0.0012963 0.0006112 0.0000000 0.0000000 0.0001397 0.0031888 0.0006402 0.0003608 0.0000000 0.0003375 0.0032726 0.0013369 0.0000000 0.0002369 0.0002016 0.0001889 0.0016739 0.0008285 0.0000000 0.0000000 0.0004062 0.0000000 0.0003094 0.0011614 0.0035078 0.0017435 0.0006198 0.0034178 0.0023585 0.0003301 0.0026412 0.0000000 0.0000000 0.0001062
epithelial cells 0.0426340 0.0132377 0.0005395 0.0032154 0.0053321 0.0019889 0.1537095 0.0043551 0.0104281 0.0038101 0.2092338 0.0034568 0.0006112 0.0034314 0.0275638 0.0006984 0.0446429 0.0017072 0.0010823 0.0018519 0.0000000 0.0219729 0.0093583 0.0087783 0.0037906 0.0094758 0.0094464 0.0077000 0.0038663 0.0076548 0.0020133 0.0052803 0.0016125 0.0018564 0.0154859 0.0029682 0.0069740 0.0032230 0.0287667 0.0188679 0.0083618 0.0096463 0.0062536 0.0012258 0.0011683
gamma delta T cells 0.0000000 0.0005516 0.0008093 0.0008039 0.0003047 0.0000000 0.0000000 0.0003843 0.0007318 0.0000000 0.0000000 0.0003241 0.0006112 0.0004902 0.0012161 0.0001397 0.0000000 0.0000000 0.0223665 0.0064815 0.0003375 0.0014025 0.0000000 0.0000000 0.0000000 0.0002016 0.0000000 0.0003348 0.0000000 0.0010438 0.0000000 0.0000000 0.0000000 0.0003094 0.0038715 0.0002698 0.0027121 0.0000000 0.0133865 0.0025943 0.0027506 0.0014929 0.0005685 0.0000000 0.0009559
innate lymphocyte 0.0029548 0.0353006 0.0045859 0.0077706 0.0039610 0.0023866 0.0075522 0.0019214 0.0012806 0.0038101 0.0245580 0.0046451 0.0058068 0.0083333 0.0044589 0.0006984 0.0082908 0.0014938 0.0339105 0.0129630 0.0006750 0.0144928 0.0006684 0.0007315 0.0016584 0.0036290 0.0039675 0.0006696 0.0138083 0.0013918 0.0018120 0.0020309 0.0002687 0.0003094 0.0046458 0.0010793 0.0048431 0.0047105 0.0179436 0.0158019 0.0366377 0.0142398 0.0136441 0.0071664 0.0023367
macrophages 0.8518362 0.8019857 0.9072026 0.7607181 0.8740098 0.9240255 0.6010662 0.9126425 0.8940724 0.9422626 0.1070727 0.7980987 0.8719438 0.8401961 0.8479935 0.9353262 0.6696429 0.9026889 0.5313853 0.7277778 0.8876139 0.4684432 0.8288770 0.8580834 0.8995499 0.8252016 0.8730399 0.7937730 0.7321182 0.9060543 0.8969197 0.9102356 0.9180328 0.9260520 0.8180410 0.8345926 0.7268501 0.8098426 0.5351752 0.7023585 0.7680713 0.5799265 0.7072200 0.8938237 0.8326075
mast cells 0.0000000 0.0000000 0.0000000 0.0005359 0.0001523 0.0000000 0.0008885 0.0000000 0.0000000 0.0000000 0.0098232 0.0001080 0.0012225 0.0014706 0.0008107 0.0000000 0.0063776 0.0000000 0.0007215 0.0023148 0.0000000 0.0112202 0.0000000 0.0000000 0.0004738 0.0004032 0.0003779 0.0010044 0.0000000 0.0000000 0.0000000 0.0004062 0.0002687 0.0000000 0.0007743 0.0002698 0.0001937 0.0021074 0.0000000 0.0000000 0.0000000 0.0000000 0.0005685 0.0000000 0.0000000
monocytes 0.0113972 0.0013789 0.0013488 0.0200965 0.0195003 0.0023866 0.0035540 0.0044832 0.0012806 0.0013189 0.0098232 0.0468834 0.0097800 0.0127451 0.0097284 0.0108954 0.0114796 0.0134443 0.0119048 0.0087963 0.0155248 0.0107527 0.0106952 0.0065838 0.0073442 0.0217742 0.0171925 0.0133914 0.0265120 0.0059151 0.0094625 0.0064988 0.0037624 0.0046411 0.0061943 0.0218564 0.0240217 0.1063592 0.0005696 0.0073113 0.0061613 0.0297428 0.0187607 0.0086752 0.0065852
neutrophils 0.0000000 0.0000000 0.0000000 0.0056270 0.0012188 0.0000000 0.0000000 0.0001281 0.0000000 0.0000000 0.0108055 0.0006482 0.0003056 0.0014706 0.0000000 0.0004191 0.0063776 0.0004268 0.0010823 0.0013889 0.0006750 0.0229079 0.0033422 0.0051207 0.0021322 0.0022177 0.0005668 0.0549046 0.0035902 0.0003479 0.0004027 0.0012185 0.0000000 0.0003094 0.0000000 0.0089045 0.0092987 0.0004958 0.0079749 0.0025943 0.0004401 0.0008039 0.0000000 0.0000000 0.0000000
NK cells 0.0046433 0.0085494 0.0051254 0.0058950 0.0036563 0.0000000 0.0026655 0.0014090 0.0020124 0.0011723 0.0157171 0.0016204 0.0015281 0.0014706 0.0008107 0.0000000 0.0044643 0.0014938 0.0266955 0.0060185 0.0016875 0.0079476 0.0020053 0.0000000 0.0002369 0.0020161 0.0011336 0.0010044 0.0033140 0.0003479 0.0004027 0.0012185 0.0013437 0.0006188 0.0011614 0.0000000 0.0019372 0.0019834 0.0136713 0.0089623 0.0066014 0.0062012 0.0068221 0.0005658 0.0098779
NK-T cells 0.0000000 0.0041368 0.0002698 0.0005359 0.0001523 0.0000000 0.0013327 0.0002562 0.0005488 0.0001465 0.0019646 0.0000000 0.0003056 0.0014706 0.0000000 0.0001397 0.0012755 0.0002134 0.0014430 0.0000000 0.0000000 0.0014025 0.0000000 0.0000000 0.0000000 0.0004032 0.0011336 0.0000000 0.0005523 0.0003479 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0007749 0.0000000 0.0019937 0.0002358 0.0004401 0.0009187 0.0005685 0.0000943 0.0009559
proliferating macrophages 0.0367244 0.0493657 0.0321014 0.0560021 0.0467703 0.0572792 0.0310973 0.0462406 0.0332967 0.0247655 0.0000000 0.0392136 0.0531785 0.0303922 0.0490474 0.0222098 0.0197704 0.0354247 0.0151515 0.0444444 0.0445494 0.0187003 0.0407754 0.0475494 0.0414594 0.0556452 0.0317400 0.0451958 0.0425297 0.0320111 0.0457016 0.0097482 0.0588551 0.0411510 0.0514905 0.0275229 0.0236343 0.0115284 0.0065508 0.0422170 0.0259655 0.0264125 0.0278567 0.0375295 0.0333510
proliferating T/NK 0.0021106 0.0024821 0.0008093 0.0013398 0.0007617 0.0000000 0.0044425 0.0007685 0.0016465 0.0010258 0.0009823 0.0008642 0.0003056 0.0000000 0.0000000 0.0006984 0.0000000 0.0004268 0.0086580 0.0009259 0.0006750 0.0018700 0.0000000 0.0007315 0.0004738 0.0010081 0.0013225 0.0003348 0.0008285 0.0006959 0.0008053 0.0008123 0.0005375 0.0009282 0.0007743 0.0002698 0.0029059 0.0007438 0.0017089 0.0009434 0.0040709 0.0008039 0.0039795 0.0008487 0.0016994

Cell type proportions by sample

Create sample meta data table.

seu@meta.data %>%
  dplyr::select(sample.id,
                Participant,
                Disease,
                Treatment,
                Severity,
                Group,
                Group_severity,
                Batch, 
                Age, 
                Sex) %>%
     left_join(props$Counts %>% 
                 data.frame %>%
                 group_by(sample) %>%
                 summarise(ncells = sum(Freq)),
               by = c("sample.id" = "sample")) %>%
    distinct() -> info

head(info) %>% knitr::kable()
sample.id Participant Disease Treatment Severity Group Group_severity Batch Age Sex ncells
sample_33.1 sample_33 CF treated (ivacaftor) severe CF.IVA CF.IVA.S 1 5.950685 M 2139
sample_25.1 sample_25 CF untreated severe CF.NO_MOD CF.NO_MOD.S 1 4.910000 F 3272
sample_29.1 sample_29 CF untreated severe CF.NO_MOD CF.NO_MOD.S 1 5.989041 F 1568
sample_27.1 sample_27 CF treated (ivacaftor) mild CF.IVA CF.IVA.M 1 4.917808 M 2467
sample_32.1 sample_32 CF untreated mild CF.NO_MOD CF.NO_MOD.M 1 5.926027 F 2963
sample_26.1 sample_26 CF untreated mild CF.NO_MOD CF.NO_MOD.M 1 5.049315 M 2040
props$Proportions %>%
  data.frame %>%
  left_join(info, 
             by = c("sample" = "sample.id")) %>%
ggplot(aes(x = sample, y = Freq, fill = clusters)) +
  geom_bar(stat = "identity", color = "black", size = 0.1) +
  theme(axis.text.x = element_text(angle = 90,
                                   vjust = 0.5,
                                   hjust = 1),
        legend.text = element_text(size = 8),
        legend.position = "bottom") +
  labs(y = "Proportion", fill = "Cell Label") +
  scale_fill_paletteer_d("miscpalettes::pastel", direction = 1) +
  facet_grid(~Group, scales = "free_x", space = "free_x")

Version Author Date
7ae0444 Jovana Maksimovic 2024-12-24

No. cells per sample

info %>%
ggplot(aes(x = sample.id, y = ncells, fill = Disease)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 90,
                                   vjust = 0.5,
                                   hjust = 1),
        legend.text = element_text(size = 8),
        legend.position = "bottom") +
  labs(y = "No. cells", fill = "Disease") +
  facet_grid(~Group, scales = "free_x", space = "free_x") +
  geom_hline(yintercept = 1000, linetype = "dashed")

Version Author Date
7ae0444 Jovana Maksimovic 2024-12-24

Cell proportions by cell type

props$Proportions %>%
  data.frame %>%
  left_join(info, 
             by = c("sample" = "sample.id")) %>%
ggplot(aes(x = clusters, y = Freq, fill = clusters)) +
  geom_boxplot(outlier.size = 0.1, size = 0.25) +
  theme(axis.text.x = element_text(angle = 45,
                                   vjust = 1,
                                   hjust = 1),
        legend.text = element_text(size = 8)) +
  labs(y = "Proportion") +
  scale_fill_paletteer_d("miscpalettes::pastel", direction = 1) +
  NoLegend()

Version Author Date
7ae0444 Jovana Maksimovic 2024-12-24

Explore sources of variation

Cell count data

Look at the sources of variation in the raw cell count level data.

dims <- list(c(1,2), c(2:3), c(3,4), c(4,5))
p <- vector("list", length(dims))

for(i in 1:length(dims)){
  mds <- plotMDS(props$Counts,
    gene.selection = "common",
    plot = FALSE, dim.plot = dims[[i]])
  
  data.frame(x = mds$x, 
             y = mds$y,
             sample = rownames(mds$distance.matrix.squared)) %>%
    left_join(info, 
             by = c("sample" = "sample.id")) %>%
    distinct() -> dat
  
  p[[i]] <- ggplot(dat, aes(x = x, y = y, 
                            shape = as.factor(Disease),
                            color = as.factor(Batch))) +
    geom_point(size = 3) +
    labs(x = glue("Principal Component {dims[[i]][1]}"),
         y = glue("Principal Component {dims[[i]][2]}"),
         colour = "Batch",
         shape = "Disease") +
    theme(legend.direction = "horizontal",
          legend.text = element_text(size = 8),
          legend.title = element_text(size = 9),
          axis.text = element_text(size = 8),
          axis.title = element_text(size = 9)) 
}

wrap_plots(p, cols = 2) + plot_layout(guides = "collect") &
  theme(legend.position = "bottom") 

Version Author Date
7ae0444 Jovana Maksimovic 2024-12-24
dims <- list(c(1,2), c(2:3), c(3,4), c(4,5))
p <- vector("list", length(dims))

for(i in 1:length(dims)){
  mds <- plotMDS(props$Counts, 
                 gene.selection = "common",
                 plot = FALSE, dim.plot = dims[[i]])
  
  data.frame(x = mds$x, 
             y = mds$y,
             sample = rownames(mds$distance.matrix.squared)) %>%
    left_join(info, 
             by = c("sample" = "sample.id")) %>%
    distinct() -> dat
  
  p[[i]] <- ggplot(dat, aes(x = x, y = y, 
                            colour = log2(ncells)))+
    geom_text(aes(label = str_remove_all(sample, "sample_")), size = 2.5) +
    labs(x = glue("Principal Component {dims[[i]][1]}"),
         y = glue("Principal Component {dims[[i]][2]}"),
         colour = "Log2 No. Cells") +
    theme(legend.direction = "horizontal",
          legend.text = element_text(size = 8),
          legend.title = element_text(size = 9),
          axis.text = element_text(size = 8),
          axis.title = element_text(size = 9)) +
    scale_colour_viridis_c(option = "magma")
}

wrap_plots(p, cols = 2) + plot_layout(guides = "collect") &
  theme(legend.position = "bottom") 

Version Author Date
7ae0444 Jovana Maksimovic 2024-12-24

Cell proportion data

Look at the sources of variation in the cell proportions data.

dims <- list(c(1,2), c(2:3), c(3,4), c(4,5))
p <- vector("list", length(dims))

for(i in 1:length(dims)){
  mds <- plotMDS(props$TransformedProps,
    gene.selection = "common",
    plot = FALSE, dim.plot = dims[[i]])
  
  data.frame(x = mds$x, 
             y = mds$y,
             sample = rownames(mds$distance.matrix.squared)) %>%
    left_join(info, 
             by = c("sample" = "sample.id")) %>%
    distinct() -> dat
  
  p[[i]] <- ggplot(dat, aes(x = x, y = y, 
                            shape = as.factor(Disease),
                            color = as.factor(Batch)))+
    geom_point(size = 3) +
    labs(x = glue("Principal Component {dims[[i]][1]}"),
         y = glue("Principal Component {dims[[i]][2]}"),
         colour = "Batch",
         shape = "Disease") +
    theme(legend.direction = "horizontal",
          legend.text = element_text(size = 8),
          legend.title = element_text(size = 9),
          axis.text = element_text(size = 8),
          axis.title = element_text(size = 9))
}

wrap_plots(p, cols = 2) + plot_layout(guides = "collect") &
  theme(legend.position = "bottom") 

Version Author Date
7ae0444 Jovana Maksimovic 2024-12-24
dims <- list(c(1,2), c(2:3), c(3,4), c(4,5))
p <- vector("list", length(dims))

for(i in 1:length(dims)){
  mds <- plotMDS(props$TransformedProps,
    gene.selection = "common",
    plot = FALSE, dim.plot = dims[[i]])
  
  data.frame(x = mds$x, 
             y = mds$y,
             sample = rownames(mds$distance.matrix.squared)) %>%
    left_join(info, 
             by = c("sample" = "sample.id")) %>%
    distinct() -> dat
  
  p[[i]] <- ggplot(dat, aes(x = x, y = y, 
                            shape = as.factor(Disease),
                            color = Sex))+
    geom_point(size = 3) +
    labs(x = glue("Principal Component {dims[[i]][1]}"),
         y = glue("Principal Component {dims[[i]][2]}"),
         colour = "Sex",
         shape = "Disease") +
    theme(legend.direction = "horizontal",
          legend.text = element_text(size = 8),
          legend.title = element_text(size = 9),
          axis.text = element_text(size = 8),
          axis.title = element_text(size = 9))
}

wrap_plots(p, cols = 2) + plot_layout(guides = "collect") &
  theme(legend.position = "bottom") 

Version Author Date
7ae0444 Jovana Maksimovic 2024-12-24
dims <- list(c(1,2), c(2:3), c(3,4), c(4,5))
p <- vector("list", length(dims))

for(i in 1:length(dims)){
  mds <- plotMDS(props$TransformedProps, 
                 gene.selection = "common",
                 plot = FALSE, dim.plot = dims[[i]])
  
   data.frame(x = mds$x, 
             y = mds$y,
             sample = rownames(mds$distance.matrix.squared)) %>%
    left_join(info, 
             by = c("sample" = "sample.id")) %>%
    distinct() -> dat
  
  p[[i]] <- ggplot(dat, aes(x = x, y = y, 
                            colour = log2(Age)))+
    geom_text(aes(label = str_remove_all(sample, "sample_")), size = 2.5) +
    labs(x = glue("Principal Component {dims[[i]][1]}"),
         y = glue("Principal Component {dims[[i]][2]}"),
         colour = "Log2 Age") +
    theme(legend.direction = "horizontal",
          legend.text = element_text(size = 8),
          legend.title = element_text(size = 9),
          axis.text = element_text(size = 8),
          axis.title = element_text(size = 9)) +
    scale_colour_viridis_c(option = "magma")
}

wrap_plots(p, cols = 2) + plot_layout(guides = "collect") &
  theme(legend.position = "bottom") 

Version Author Date
7ae0444 Jovana Maksimovic 2024-12-24
dims <- list(c(1,2), c(2:3), c(3,4), c(4,5))
p <- vector("list", length(dims))

for(i in 1:length(dims)){
  mds <- plotMDS(props$TransformedProps, 
                 gene.selection = "common",
                 plot = FALSE, dim.plot = dims[[i]])
  
  data.frame(x = mds$x, 
             y = mds$y,
             sample = rownames(mds$distance.matrix.squared)) %>%
    left_join(info, 
             by = c("sample" = "sample.id")) %>%
    distinct() -> dat
  
  p[[i]] <- ggplot(dat, aes(x = x, y = y, 
                            colour = log2(ncells)))+
    geom_text(aes(label = str_remove_all(sample, "sample_")), size = 2.5) +
    labs(x = glue("Principal Component {dims[[i]][1]}"),
         y = glue("Principal Component {dims[[i]][2]}"),
         colour = "Log2 No. Cells") +
    theme(legend.direction = "horizontal",
          legend.text = element_text(size = 8),
          legend.title = element_text(size = 9),
          axis.text = element_text(size = 8),
          axis.title = element_text(size = 9)) +
    scale_colour_viridis_c(option = "magma")
}

wrap_plots(p, cols = 2) + plot_layout(guides = "collect") &
  theme(legend.position = "bottom") 

Version Author Date
7ae0444 Jovana Maksimovic 2024-12-24

Principal components versus traits

Principal components analysis (PCA) allows us to mathematically determine the sources of variation in the data. We can then investigate whether these correlate with any of the specifed covariates. First, we calculate the principal components. The scree plot belows shows us that most of the variation in this data is captured by the top 7 principal components.

# remove outlying sample
info <- info[info$sample.id != "sample_23.1",]
props$TransformedProps <- props$TransformedProps[, colnames(props$TransformedProps) != "sample_23.1"]

PCs <- prcomp(t(props$TransformedProps), center = TRUE, 
              scale = TRUE, retx = TRUE)
loadings = PCs$x # pc loadings
plot(PCs, type="lines") # scree plot

Version Author Date
7ae0444 Jovana Maksimovic 2024-12-24

Collect all of the known sample traits.

nGenes = nrow(props$TransformedProps)
nSamples = ncol(props$TransformedProps)

m <- match(colnames(props$TransformedProps), info$sample.id)
info <- info[m,]

datTraits <- info %>% dplyr::select(Participant, Batch, Disease, Treatment,
                                    Group, Severity, Age, Sex, ncells) %>%
  mutate(Age = log2(Age),
         ncells = log2(ncells),
    Donor = factor(Participant),
         Batch = factor(Batch),
         Disease = factor(Disease, 
                          labels = 1:length(unique(Disease))),
         Group = factor(Group, 
                        labels = 1:length(unique(Group))),
         Treatment = factor(Treatment, 
                            labels = 1:length(unique(Treatment))),
         Sex = factor(Sex, labels = length(unique(Sex))),
         Severity = factor(Severity, labels = length(unique(Severity)))) %>%
  mutate(across(everything(), as.numeric)) %>%
  dplyr::select(-Participant)

datTraits %>% 
  knitr::kable()
Batch Disease Treatment Group Severity Age Sex ncells Donor
19 4 2 1 4 1 -0.2590872 2 11.21006 1
18 4 1 4 3 2 -0.0939001 2 11.82416 2
16 4 1 4 3 2 -0.1151479 1 11.85604 3
24 5 1 4 3 2 -0.0441471 1 11.86573 4
27 5 1 4 3 2 0.1428834 2 12.68036 5
33 6 1 4 3 2 -0.0729608 1 11.29577 6
17 4 2 1 4 1 0.1464588 2 11.13635 7
32 6 1 4 3 3 0.5597097 2 12.93055 8
22 4 1 4 3 3 1.5743836 1 12.41627 9
23 4 1 2 1 2 1.5993830 2 12.73640 10
28 6 1 2 1 2 2.3883594 2 13.17633 11
2 2 1 4 3 3 2.2957230 1 11.67596 12
6 2 1 4 3 2 2.3360877 2 10.99435 13
4 2 1 2 1 2 2.2980155 2 11.26854 14
29 6 1 4 3 2 2.5790214 1 12.80554 15
3 2 1 4 3 3 2.5823250 1 10.61471 16
30 6 2 1 4 1 0.1321035 2 12.19414 17
7 2 1 4 3 3 2.5889097 1 11.43671 18
8 2 1 4 3 2 2.5583683 1 11.07682 19
5 2 1 4 3 2 2.5670653 1 11.53284 20
1 2 1 2 1 3 2.5730557 2 11.06272 21
40 7 1 4 3 2 -0.9343238 1 10.54689 22
41 7 1 4 3 2 0.0918737 1 10.41680 22
34 7 1 4 3 2 1.0409164 1 12.04337 22
35 7 1 4 3 2 0.0807044 2 12.27612 23
39 7 1 4 3 2 0.9940589 2 12.36987 23
38 7 1 4 3 3 -0.0564254 1 11.54448 24
37 7 1 3 2 3 1.1764977 1 11.82217 24
10 3 1 4 3 2 1.5597097 1 11.48884 25
9 3 1 3 2 2 2.1930156 1 12.27816 25
11 3 1 3 2 2 2.2980155 1 11.26562 25
14 3 1 2 1 2 1.5703964 2 11.86147 26
15 3 1 2 1 2 2.0206033 2 11.65821 26
13 3 1 2 1 2 2.3485584 2 11.33483 26
26 5 1 4 3 2 1.9730702 1 11.85565 27
25 5 1 3 2 2 2.6297159 1 12.33371 27
36 7 2 1 4 1 0.2923784 2 12.97782 28
42 1 1 4 3 3 1.5801455 2 11.77767 29
43 1 1 4 3 2 1.5801455 2 12.04985 30
45 1 1 2 1 3 1.5993178 2 13.14991 31
44 1 2 1 4 1 1.5849625 2 13.08813 32
12 3 2 1 4 1 3.0699187 1 10.78054 33
20 4 2 1 4 1 2.4204621 2 13.37246 34
21 4 2 1 4 1 2.2356012 1 13.20075 35

Correlate known sample traits with the top 10 principal components. This can help us determine which traits are potentially contributing to the main sources of variation in the data and should thus be included in our statistical analysis.

moduleTraitCor <- suppressWarnings(cor(loadings[, 1:10], datTraits, use = "p"))
moduleTraitPvalue <- WGCNA::corPvalueStudent(moduleTraitCor, (nSamples - 2))

textMatrix <- paste(signif(moduleTraitCor, 2), "\n(", 
                    signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) <- dim(moduleTraitCor)

## Display the correlation values within a heatmap plot
par(cex=0.75, mar = c(6, 8.5, 3, 3))
WGCNA::labeledHeatmap(Matrix = t(moduleTraitCor),
                      xLabels = colnames(loadings)[1:10],
                      yLabels = names(datTraits),
                      colorLabels = FALSE,
                      colors = WGCNA::blueWhiteRed(6),
                      textMatrix = t(textMatrix),
                      setStdMargins = FALSE,
                      cex.text = 1,
                      zlim = c(-1,1),
                      main = paste("PCA-trait relationships: Top 10 PCs"))

Version Author Date
7ae0444 Jovana Maksimovic 2024-12-24

Statistical analysis using propeller and limma

Create the design matrix.

group <- factor(info$Group_severity)
participant <- factor(info$Participant)
age <- log2(info$Age)
batch <- factor(info$Batch)
sex <- factor(info$Sex)

design <- model.matrix(~ 0 + group + batch + age + sex)
colnames(design)[1:7] <- levels(group) 
design
   CF.IVA.M CF.IVA.S CF.LUMA_IVA.M CF.LUMA_IVA.S CF.NO_MOD.M CF.NO_MOD.S
1         0        0             0             0           0           0
2         0        0             0             0           1           0
3         0        0             0             0           1           0
4         0        0             0             0           1           0
5         0        0             0             0           1           0
6         0        0             0             0           1           0
7         0        0             0             0           0           0
8         0        0             0             0           0           1
9         0        0             0             0           0           1
10        1        0             0             0           0           0
11        1        0             0             0           0           0
12        0        0             0             0           0           1
13        0        0             0             0           1           0
14        1        0             0             0           0           0
15        0        0             0             0           1           0
16        0        0             0             0           0           1
17        0        0             0             0           0           0
18        0        0             0             0           0           1
19        0        0             0             0           1           0
20        0        0             0             0           1           0
21        0        1             0             0           0           0
22        0        0             0             0           1           0
23        0        0             0             0           1           0
24        0        0             0             0           1           0
25        0        0             0             0           1           0
26        0        0             0             0           1           0
27        0        0             0             0           0           1
28        0        0             0             1           0           0
29        0        0             0             0           1           0
30        0        0             1             0           0           0
31        0        0             1             0           0           0
32        1        0             0             0           0           0
33        1        0             0             0           0           0
34        1        0             0             0           0           0
35        0        0             0             0           1           0
36        0        0             1             0           0           0
37        0        0             0             0           0           0
38        0        0             0             0           0           1
39        0        0             0             0           1           0
40        0        1             0             0           0           0
41        0        0             0             0           0           0
42        0        0             0             0           0           0
43        0        0             0             0           0           0
44        0        0             0             0           0           0
   NON_CF.CTRL batch1 batch2 batch3 batch4 batch5 batch6         age sexM
1            1      0      0      1      0      0      0 -0.25908722    1
2            0      0      0      1      0      0      0 -0.09390014    1
3            0      0      0      1      0      0      0 -0.11514787    0
4            0      0      0      0      1      0      0 -0.04414710    0
5            0      0      0      0      1      0      0  0.14288337    1
6            0      0      0      0      0      1      0 -0.07296080    0
7            1      0      0      1      0      0      0  0.14645883    1
8            0      0      0      0      0      1      0  0.55970971    1
9            0      0      0      1      0      0      0  1.57438357    0
10           0      0      0      1      0      0      0  1.59938302    1
11           0      0      0      0      0      1      0  2.38835941    1
12           0      1      0      0      0      0      0  2.29572302    0
13           0      1      0      0      0      0      0  2.33608770    1
14           0      1      0      0      0      0      0  2.29801547    1
15           0      0      0      0      0      1      0  2.57902140    0
16           0      1      0      0      0      0      0  2.58232503    0
17           1      0      0      0      0      1      0  0.13210354    1
18           0      1      0      0      0      0      0  2.58890969    0
19           0      1      0      0      0      0      0  2.55836829    0
20           0      1      0      0      0      0      0  2.56706530    0
21           0      1      0      0      0      0      0  2.57305573    1
22           0      0      0      0      0      0      1 -0.93432383    0
23           0      0      0      0      0      0      1  0.09187369    0
24           0      0      0      0      0      0      1  1.04091644    0
25           0      0      0      0      0      0      1  0.08070438    1
26           0      0      0      0      0      0      1  0.99405890    1
27           0      0      0      0      0      0      1 -0.05642543    0
28           0      0      0      0      0      0      1  1.17649766    0
29           0      0      1      0      0      0      0  1.55970971    0
30           0      0      1      0      0      0      0  2.19301559    0
31           0      0      1      0      0      0      0  2.29801547    0
32           0      0      1      0      0      0      0  1.57039639    1
33           0      0      1      0      0      0      0  2.02060327    1
34           0      0      1      0      0      0      0  2.34855840    1
35           0      0      0      0      1      0      0  1.97307024    0
36           0      0      0      0      1      0      0  2.62971590    0
37           1      0      0      0      0      0      1  0.29237837    1
38           0      0      0      0      0      0      0  1.58014548    1
39           0      0      0      0      0      0      0  1.58014548    1
40           0      0      0      0      0      0      0  1.59931779    1
41           1      0      0      0      0      0      0  1.58496250    1
42           1      0      1      0      0      0      0  3.06991870    0
43           1      0      0      1      0      0      0  2.42046210    1
44           1      0      0      1      0      0      0  2.23560118    0
attr(,"assign")
 [1] 1 1 1 1 1 1 1 2 2 2 2 2 2 3 4
attr(,"contrasts")
attr(,"contrasts")$group
[1] "contr.treatment"

attr(,"contrasts")$batch
[1] "contr.treatment"

attr(,"contrasts")$sex
[1] "contr.treatment"

Create the contrast matrix.

contr <- makeContrasts(CF.NO_MODvNON_CF.CTRL = 0.5*(CF.NO_MOD.M + CF.NO_MOD.S) - NON_CF.CTRL,
                       CF.IVAvCF.NO_MOD = 0.5*(CF.IVA.S + CF.IVA.M) - 0.5*(CF.NO_MOD.S + CF.NO_MOD.M),
                       CF.LUMA_IVAvCF.NO_MOD = 0.5*(CF.LUMA_IVA.S + CF.LUMA_IVA.M) - 0.5*(CF.NO_MOD.S + CF.NO_MOD.M),
                       CF.NO_MOD.SvCF.NO_MOD.M = CF.NO_MOD.S - CF.NO_MOD.M,
                       levels = design)
contr
               Contrasts
Levels          CF.NO_MODvNON_CF.CTRL CF.IVAvCF.NO_MOD CF.LUMA_IVAvCF.NO_MOD
  CF.IVA.M                        0.0              0.5                   0.0
  CF.IVA.S                        0.0              0.5                   0.0
  CF.LUMA_IVA.M                   0.0              0.0                   0.5
  CF.LUMA_IVA.S                   0.0              0.0                   0.5
  CF.NO_MOD.M                     0.5             -0.5                  -0.5
  CF.NO_MOD.S                     0.5             -0.5                  -0.5
  NON_CF.CTRL                    -1.0              0.0                   0.0
  batch1                          0.0              0.0                   0.0
  batch2                          0.0              0.0                   0.0
  batch3                          0.0              0.0                   0.0
  batch4                          0.0              0.0                   0.0
  batch5                          0.0              0.0                   0.0
  batch6                          0.0              0.0                   0.0
  age                             0.0              0.0                   0.0
  sexM                            0.0              0.0                   0.0
               Contrasts
Levels          CF.NO_MOD.SvCF.NO_MOD.M
  CF.IVA.M                            0
  CF.IVA.S                            0
  CF.LUMA_IVA.M                       0
  CF.LUMA_IVA.S                       0
  CF.NO_MOD.M                        -1
  CF.NO_MOD.S                         1
  NON_CF.CTRL                         0
  batch1                              0
  batch2                              0
  batch3                              0
  batch4                              0
  batch5                              0
  batch6                              0
  age                                 0
  sexM                                0

Add random effect for samples from the same individual.

dupcor <- duplicateCorrelation(props$TransformedProps, design=design,
                                block=participant)
dupcor
$consensus.correlation
[1] 0.6815988

$cor
[1] 0.6815988

$atanh.correlations
 [1]  0.91280819  1.07781785 -0.05536504  0.85854344  0.52144441  1.11093619
 [7]  0.20770523  1.17122725  0.94099741  0.54921399  1.71581017  1.63069725
[13]  1.14397325  0.51267513 -0.53606034  0.97778570

Fit the model.

fit <- lmFit(props$TransformedProps, design=design, block=participant, 
                correlation=dupcor$consensus)
fit2 <- contrasts.fit(fit, contr)
fit2 <- eBayes(fit2, robust=TRUE, trend=FALSE)
pvalue <- 0.05
summary(decideTests(fit2, p.value = pvalue))
       CF.NO_MODvNON_CF.CTRL CF.IVAvCF.NO_MOD CF.LUMA_IVAvCF.NO_MOD
Down                       1                0                     1
NotSig                    15               16                    15
Up                         0                0                     0
       CF.NO_MOD.SvCF.NO_MOD.M
Down                         0
NotSig                      16
Up                           0

Results

p <- vector("list", ncol(contr))

for(i in 1:ncol(contr)){
  print(knitr::kable(topTable(fit2, coef = i, number = Inf),
                     caption = colnames(contr)[i]))
  
  props$Proportions %>% data.frame %>%
    left_join(info,
              by = c("sample" = "sample.id")) %>%
    mutate(Group = Group_severity) %>%
    dplyr::filter(Group %in% names(contr[,i])[abs(contr[, i]) > 0]) -> dat
  
  if(length(unique(dat$Group)) > 2) dat$Group <- str_remove(dat$Group, ".(M|S)$")
  ggplot(dat, aes(x = Group,
                  y = Freq,
                  colour = Group,
                  group = Group)) +
    geom_jitter(stat = "identity",
                width = 0.15,
                size = 2) +
    stat_summary(geom = "point",
      fun.y = "mean",
      col = "black",
      shape = "_",
      size = 14) +
    theme_classic() +
    theme(axis.text.x = element_text(angle = 90,
                                     hjust = 1,
                                     vjust = 0.5),
          legend.position = "bottom",
          legend.direction = "horizontal") +
    labs(x = "Group", y = "Proportion",
         colour = "Condition") +
    facet_wrap(~clusters, scales = "free_y", ncol = 4) +
    ggtitle(colnames(contr)[i]) -> p[[i]]
  
  print(p[[i]])
}
CF.NO_MODvNON_CF.CTRL
logFC AveExpr t P.Value adj.P.Val B
monocytes -0.0866804 0.1046134 -4.5306909 0.0000812 0.0012991 1.413145
CD8 T cells -0.0741143 0.1671905 -1.8914421 0.0679821 0.5438572 -4.779980
macrophages 0.0887124 1.1311489 1.3773538 0.1783099 0.7581057 -5.557175
NK-T cells 0.0121502 0.0158486 1.3412624 0.1895264 0.7581057 -5.603834
gamma delta T cells 0.0183142 0.0242845 1.1081569 0.2762713 0.8449842 -5.878127
neutrophils 0.0195569 0.0365714 1.0017495 0.3241777 0.8449842 -5.987338
B cells -0.0286482 0.1209003 -0.6654712 0.5106920 0.8449842 -6.263453
DC cells -0.0196543 0.1376617 -0.6424225 0.5253424 0.8449842 -6.278446
dividing innate cells -0.0073307 0.0232467 -0.5587345 0.5803349 0.8449842 -6.328590
innate lymphocyte 0.0095538 0.0733282 0.4797679 0.6347417 0.8449842 -6.369579
mast cells -0.0047191 0.0158193 -0.4753958 0.6378193 0.8449842 -6.371669
NK cells -0.0065650 0.0505933 -0.4046685 0.6884847 0.8449842 -6.402857
epithelial cells -0.0139599 0.0868234 -0.3768585 0.7088574 0.8449842 -6.413742
proliferating macrophages 0.0077459 0.1876178 0.3356832 0.7393612 0.8449842 -6.428493
proliferating T/NK -0.0018288 0.0310578 -0.1712652 0.8651221 0.9227969 -6.470429
CD4 T cells -0.0008403 0.1587382 -0.0216559 0.9828618 0.9828618 -6.484976

Version Author Date
7ae0444 Jovana Maksimovic 2024-12-24
CF.IVAvCF.NO_MOD
logFC AveExpr t P.Value adj.P.Val B
NK-T cells -0.0178018 0.0158486 -1.6771544 0.1035136 0.9866568 -4.880636
mast cells 0.0125230 0.0158193 1.0766777 0.2898866 0.9866568 -5.649573
monocytes 0.0204331 0.1046134 0.9115003 0.3690271 0.9866568 -5.807150
CD8 T cells 0.0365180 0.1671905 0.7953837 0.4324648 0.9866568 -5.903003
neutrophils 0.0180237 0.0365714 0.7879191 0.4366948 0.9866568 -5.908806
gamma delta T cells -0.0131149 0.0242845 -0.6772627 0.5032350 0.9866568 -5.987763
B cells -0.0232246 0.1209003 -0.4604248 0.6484389 0.9866568 -6.108684
macrophages -0.0324599 1.1311489 -0.4301181 0.6700981 0.9866568 -6.121979
DC cells 0.0105310 0.1376617 0.2937740 0.7708971 0.9866568 -6.170703
proliferating T/NK 0.0033930 0.0310578 0.2711860 0.7880358 0.9866568 -6.177025
dividing innate cells 0.0034334 0.0232467 0.2233408 0.8247274 0.9866568 -6.188731
proliferating macrophages 0.0057278 0.1876178 0.2118509 0.8336040 0.9866568 -6.191205
NK cells -0.0036478 0.0505933 -0.1918965 0.8490679 0.9866568 -6.195195
epithelial cells -0.0034896 0.0868234 -0.0803995 0.9364385 0.9866568 -6.210234
innate lymphocyte -0.0015606 0.0733282 -0.0668821 0.9471027 0.9866568 -6.211222
CD4 T cells 0.0007665 0.1587382 0.0168600 0.9866568 0.9866568 -6.213299

Version Author Date
7ae0444 Jovana Maksimovic 2024-12-24
CF.LUMA_IVAvCF.NO_MOD
logFC AveExpr t P.Value adj.P.Val B
neutrophils -0.0623699 0.0365714 -3.4098325 0.0018145 0.0290321 -1.482959
innate lymphocyte 0.0449772 0.0733282 2.4107062 0.0220055 0.1760439 -3.756250
mast cells -0.0165537 0.0158193 -1.7798855 0.0848459 0.4089010 -4.914571
CD8 T cells 0.0618318 0.1671905 1.6842303 0.1022253 0.4089010 -5.066366
NK cells 0.0236804 0.0505933 1.5579412 0.1293431 0.4138978 -5.255670
CD4 T cells 0.0514344 0.1587382 1.4148505 0.1671225 0.4456600 -5.455042
monocytes 0.0223527 0.1046134 1.2470181 0.2216793 0.4552617 -5.667274
DC cells 0.0342966 0.1376617 1.1964989 0.2406122 0.4552617 -5.726384
macrophages -0.0697972 1.1311489 -1.1566397 0.2562936 0.4552617 -5.771489
gamma delta T cells -0.0168610 0.0242845 -1.0889147 0.2845386 0.4552617 -5.844982
NK-T cells 0.0075934 0.0158486 0.8946787 0.3778153 0.5495495 -6.032605
B cells 0.0332548 0.1209003 0.8244896 0.4159878 0.5546504 -6.091776
proliferating T/NK 0.0053150 0.0310578 0.5312473 0.5990128 0.7372465 -6.288659
proliferating macrophages 0.0039370 0.1876178 0.1821064 0.8566796 0.9363171 -6.413262
epithelial cells 0.0053809 0.0868234 0.1550425 0.8777973 0.9363171 -6.417843
dividing innate cells -0.0001671 0.0232467 -0.0135948 0.9892399 0.9892399 -6.429838

Version Author Date
7ae0444 Jovana Maksimovic 2024-12-24
CF.NO_MOD.SvCF.NO_MOD.M
logFC AveExpr t P.Value adj.P.Val B
neutrophils 0.0399716 0.0365714 2.1596541 0.0386053 0.3092130 -4.238117
B cells 0.0881572 0.1209003 2.1600493 0.0386516 0.3092130 -4.238184
macrophages -0.0954724 1.1311489 -1.5635542 0.1281186 0.4022276 -5.236842
NK cells 0.0222587 0.0505933 1.4472245 0.1578166 0.4022276 -5.400533
CD4 T cells 0.0486446 0.1587382 1.3224106 0.1957475 0.4022276 -5.563891
gamma delta T cells 0.0204081 0.0242845 1.3025331 0.2022756 0.4022276 -5.588735
mast cells 0.0113367 0.0158193 1.2046501 0.2374051 0.4022276 -5.705992
proliferating macrophages -0.0258037 0.1876178 -1.1795495 0.2471137 0.4022276 -5.734717
proliferating T/NK 0.0114745 0.0310578 1.1334637 0.2656620 0.4022276 -5.786035
dividing innate cells 0.0139205 0.0232467 1.1191517 0.2716253 0.4022276 -5.801588
epithelial cells 0.0389018 0.0868234 1.1077417 0.2765315 0.4022276 -5.813788
NK-T cells 0.0086840 0.0158486 1.0111665 0.3197215 0.4262954 -5.913028
monocytes -0.0115897 0.1046134 -0.6389809 0.5275030 0.6492345 -6.214598
innate lymphocyte 0.0043002 0.0733282 0.2277804 0.8213044 0.8980556 -6.392380
DC cells 0.0058333 0.1376617 0.2011164 0.8419271 0.8980556 -6.398113
CD8 T cells 0.0012509 0.1671905 0.0336741 0.9733538 0.9733538 -6.417864

Version Author Date
7ae0444 Jovana Maksimovic 2024-12-24

Session info

The analysis and this document were prepared using the following software (click triangle to expand)
sessioninfo::session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.3 (2024-02-29)
 os       Ubuntu 22.04.4 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_AU.UTF-8
 ctype    en_AU.UTF-8
 tz       Etc/UTC
 date     2024-12-31
 pandoc   3.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 ! package              * version    date (UTC) lib source
 P abind                  1.4-5      2016-07-21 [?] RSPM (R 4.3.0)
 P AnnotationDbi        * 1.64.1     2023-11-03 [?] Bioconductor
 P backports              1.4.1      2021-12-13 [?] RSPM (R 4.3.0)
 P base64enc              0.1-3      2015-07-28 [?] RSPM (R 4.3.0)
 P Biobase              * 2.62.0     2023-10-24 [?] Bioconductor
 P BiocGenerics         * 0.48.1     2023-11-01 [?] Bioconductor
 P BiocManager            1.30.22    2023-08-08 [?] RSPM (R 4.3.0)
 P Biostrings             2.70.2     2024-01-28 [?] Bioconductor 3.18 (R 4.3.3)
 P bit                    4.0.5      2022-11-15 [?] RSPM (R 4.3.0)
 P bit64                  4.0.5      2020-08-30 [?] RSPM (R 4.3.0)
 P bitops                 1.0-7      2021-04-24 [?] RSPM (R 4.3.0)
 P blob                   1.2.4      2023-03-17 [?] RSPM (R 4.3.0)
 P bslib                  0.6.1      2023-11-28 [?] RSPM (R 4.3.0)
 P cachem                 1.0.8      2023-05-01 [?] RSPM (R 4.3.0)
 P callr                  3.7.3      2022-11-02 [?] RSPM (R 4.3.0)
 P checkmate              2.3.1      2023-12-04 [?] RSPM (R 4.3.0)
 P circlize               0.4.15     2022-05-10 [?] RSPM (R 4.3.0)
 P cli                    3.6.2      2023-12-11 [?] RSPM (R 4.3.0)
 P clue                   0.3-65     2023-09-23 [?] RSPM (R 4.3.0)
 P cluster                2.1.6      2023-12-01 [?] CRAN (R 4.3.2)
 P clustree             * 0.5.1      2023-11-05 [?] RSPM (R 4.3.0)
 P codetools              0.2-19     2023-02-01 [?] CRAN (R 4.2.2)
 P colorspace             2.1-0      2023-01-23 [?] RSPM (R 4.3.0)
 P ComplexHeatmap         2.18.0     2023-10-24 [?] Bioconductor
 P cowplot                1.1.3      2024-01-22 [?] RSPM (R 4.3.0)
 P crayon                 1.5.2      2022-09-29 [?] RSPM (R 4.3.0)
 P data.table             1.15.0     2024-01-30 [?] RSPM (R 4.3.0)
 P DBI                    1.2.1      2024-01-12 [?] RSPM (R 4.3.0)
 P DelayedArray           0.28.0     2023-10-24 [?] Bioconductor
 P deldir                 2.0-2      2023-11-23 [?] RSPM (R 4.3.0)
 P dendextend             1.17.1     2023-03-25 [?] RSPM (R 4.3.0)
 P digest                 0.6.34     2024-01-11 [?] RSPM (R 4.3.0)
 P dittoSeq             * 1.14.2     2024-02-09 [?] Bioconductor 3.18 (R 4.3.3)
 P doParallel             1.0.17     2022-02-07 [?] RSPM (R 4.3.0)
 P dplyr                * 1.1.4      2023-11-17 [?] RSPM (R 4.3.0)
 P dynamicTreeCut         1.63-1     2016-03-11 [?] RSPM (R 4.3.0)
 P edgeR                * 4.0.15     2024-02-11 [?] Bioconductor 3.18 (R 4.3.3)
 P ellipsis               0.3.2      2021-04-29 [?] RSPM (R 4.3.0)
 P evaluate               0.23       2023-11-01 [?] RSPM (R 4.3.0)
 P fansi                  1.0.6      2023-12-08 [?] RSPM (R 4.3.0)
 P farver                 2.1.1      2022-07-06 [?] RSPM (R 4.3.0)
 P fastcluster            1.2.6      2024-01-12 [?] RSPM (R 4.3.0)
 P fastmap                1.1.1      2023-02-24 [?] RSPM (R 4.3.0)
 P fitdistrplus           1.1-11     2023-04-25 [?] RSPM (R 4.3.0)
 P forcats              * 1.0.0      2023-01-29 [?] RSPM (R 4.3.0)
 P foreach                1.5.2      2022-02-02 [?] RSPM (R 4.3.0)
 P foreign                0.8-86     2023-11-28 [?] CRAN (R 4.3.2)
 P Formula                1.2-5      2023-02-24 [?] RSPM (R 4.3.0)
 P fs                     1.6.3      2023-07-20 [?] RSPM (R 4.3.0)
 P future                 1.33.1     2023-12-22 [?] RSPM (R 4.3.0)
 P future.apply           1.11.1     2023-12-21 [?] RSPM (R 4.3.0)
 P generics               0.1.3      2022-07-05 [?] RSPM (R 4.3.0)
 P GenomeInfoDb         * 1.38.6     2024-02-08 [?] Bioconductor 3.18 (R 4.3.3)
 P GenomeInfoDbData       1.2.11     2024-04-23 [?] Bioconductor
 P GenomicRanges        * 1.54.1     2023-10-29 [?] Bioconductor
 P GetoptLong             1.0.5      2020-12-15 [?] RSPM (R 4.3.0)
 P getPass                0.2-4      2023-12-10 [?] RSPM (R 4.3.0)
 P ggforce                0.4.2      2024-02-19 [?] RSPM (R 4.3.0)
 P ggplot2              * 3.5.0      2024-02-23 [?] RSPM (R 4.3.0)
 P ggraph               * 2.2.0      2024-02-27 [?] RSPM (R 4.3.0)
 P ggrepel                0.9.5      2024-01-10 [?] RSPM (R 4.3.0)
 P ggridges               0.5.6      2024-01-23 [?] RSPM (R 4.3.0)
 P git2r                  0.33.0     2023-11-26 [?] RSPM (R 4.3.0)
 P glmGamPoi            * 1.14.3     2024-02-11 [?] Bioconductor 3.18 (R 4.3.3)
 P GlobalOptions          0.1.2      2020-06-10 [?] RSPM (R 4.3.0)
 P globals                0.16.2     2022-11-21 [?] RSPM (R 4.3.0)
 P glue                 * 1.7.0      2024-01-09 [?] RSPM (R 4.3.0)
 P GO.db                  3.18.0     2024-04-23 [?] Bioconductor
 P goftest                1.2-3      2021-10-07 [?] RSPM (R 4.3.0)
 P graphlayouts           1.1.0      2024-01-19 [?] RSPM (R 4.3.0)
 P gridExtra              2.3        2017-09-09 [?] RSPM (R 4.3.0)
 P gtable                 0.3.4      2023-08-21 [?] RSPM (R 4.3.0)
 P here                 * 1.0.1      2020-12-13 [?] RSPM (R 4.3.0)
 P highr                  0.10       2022-12-22 [?] RSPM (R 4.3.0)
 P Hmisc                  5.1-1      2023-09-12 [?] RSPM (R 4.3.0)
 P hms                    1.1.3      2023-03-21 [?] RSPM (R 4.3.0)
 P htmlTable              2.4.2      2023-10-29 [?] RSPM (R 4.3.0)
 P htmltools              0.5.7      2023-11-03 [?] RSPM (R 4.3.0)
 P htmlwidgets            1.6.4      2023-12-06 [?] RSPM (R 4.3.0)
 P httpuv                 1.6.14     2024-01-26 [?] RSPM (R 4.3.0)
 P httr                   1.4.7      2023-08-15 [?] RSPM (R 4.3.0)
 P ica                    1.0-3      2022-07-08 [?] RSPM (R 4.3.0)
 P igraph                 2.0.1.1    2024-01-30 [?] RSPM (R 4.3.0)
 P impute                 1.76.0     2023-10-24 [?] Bioconductor
 P IRanges              * 2.36.0     2023-10-24 [?] Bioconductor
 P irlba                  2.3.5.1    2022-10-03 [?] RSPM (R 4.3.0)
 P iterators              1.0.14     2022-02-05 [?] RSPM (R 4.3.0)
 P jquerylib              0.1.4      2021-04-26 [?] RSPM (R 4.3.0)
 P jsonlite               1.8.8      2023-12-04 [?] RSPM (R 4.3.0)
 P KEGGREST               1.42.0     2023-10-24 [?] Bioconductor
 P KernSmooth             2.23-24    2024-05-17 [?] RSPM (R 4.3.0)
 P knitr                  1.45       2023-10-30 [?] RSPM (R 4.3.0)
 P labeling               0.4.3      2023-08-29 [?] RSPM (R 4.3.0)
 P later                  1.3.2      2023-12-06 [?] RSPM (R 4.3.0)
 P lattice                0.22-5     2023-10-24 [?] CRAN (R 4.3.1)
 P lazyeval               0.2.2      2019-03-15 [?] RSPM (R 4.3.0)
 P leiden                 0.4.3.1    2023-11-17 [?] RSPM (R 4.3.0)
 P lifecycle              1.0.4      2023-11-07 [?] RSPM (R 4.3.0)
 P limma                * 3.58.1     2023-10-31 [?] Bioconductor
 P listenv                0.9.1      2024-01-29 [?] RSPM (R 4.3.0)
 P lmtest                 0.9-40     2022-03-21 [?] RSPM (R 4.3.0)
 P locfit                 1.5-9.8    2023-06-11 [?] RSPM (R 4.3.0)
 P lubridate            * 1.9.3      2023-09-27 [?] RSPM (R 4.3.0)
 P magrittr               2.0.3      2022-03-30 [?] RSPM (R 4.3.0)
 P MASS                   7.3-60.0.1 2024-01-13 [?] RSPM (R 4.3.0)
 P Matrix                 1.6-5      2024-01-11 [?] CRAN (R 4.3.3)
 P MatrixGenerics       * 1.14.0     2023-10-24 [?] Bioconductor
 P matrixStats          * 1.2.0      2023-12-11 [?] RSPM (R 4.3.0)
 P memoise                2.0.1      2021-11-26 [?] RSPM (R 4.3.0)
 P mime                   0.12       2021-09-28 [?] RSPM (R 4.3.0)
 P miniUI                 0.1.1.1    2018-05-18 [?] RSPM (R 4.3.0)
 P munsell                0.5.0      2018-06-12 [?] RSPM (R 4.3.0)
 P nlme                   3.1-164    2023-11-27 [?] RSPM (R 4.3.0)
 P nnet                   7.3-19     2023-05-03 [?] CRAN (R 4.3.1)
 P org.Hs.eg.db         * 3.18.0     2024-04-23 [?] Bioconductor
 P paletteer            * 1.6.0      2024-01-21 [?] RSPM (R 4.3.0)
 P parallelly             1.37.0     2024-02-14 [?] RSPM (R 4.3.0)
 P patchwork            * 1.2.0      2024-01-08 [?] RSPM (R 4.3.0)
 P pbapply                1.7-2      2023-06-27 [?] RSPM (R 4.3.0)
 P pheatmap               1.0.12     2019-01-04 [?] RSPM (R 4.3.0)
 P pillar                 1.9.0      2023-03-22 [?] RSPM (R 4.3.0)
 P pkgconfig              2.0.3      2019-09-22 [?] RSPM (R 4.3.0)
 P plotly                 4.10.4     2024-01-13 [?] RSPM (R 4.3.0)
 P plyr                   1.8.9      2023-10-02 [?] RSPM (R 4.3.0)
 P png                    0.1-8      2022-11-29 [?] RSPM (R 4.3.0)
 P polyclip               1.10-6     2023-09-27 [?] RSPM (R 4.3.0)
 P preprocessCore         1.64.0     2023-10-24 [?] Bioconductor
 P prismatic              1.1.1      2022-08-15 [?] RSPM (R 4.3.0)
 P processx               3.8.3      2023-12-10 [?] RSPM (R 4.3.0)
 P progressr              0.14.0     2023-08-10 [?] RSPM (R 4.3.0)
 P promises               1.2.1      2023-08-10 [?] RSPM (R 4.3.0)
 P ps                     1.7.6      2024-01-18 [?] RSPM (R 4.3.0)
 P purrr                * 1.0.2      2023-08-10 [?] RSPM (R 4.3.0)
 P R6                     2.5.1      2021-08-19 [?] RSPM (R 4.3.0)
 P RANN                   2.6.1      2019-01-08 [?] RSPM (R 4.3.0)
 P RColorBrewer           1.1-3      2022-04-03 [?] RSPM (R 4.3.0)
 P Rcpp                   1.0.12     2024-01-09 [?] RSPM (R 4.3.0)
 P RcppAnnoy              0.0.22     2024-01-23 [?] RSPM (R 4.3.0)
 P RCurl                  1.98-1.14  2024-01-09 [?] RSPM (R 4.3.0)
 P readr                * 2.1.5      2024-01-10 [?] RSPM (R 4.3.0)
 P rematch2               2.1.2      2020-05-01 [?] RSPM (R 4.3.0)
   renv                   1.0.3      2023-09-19 [1] CRAN (R 4.3.3)
 P reshape2               1.4.4      2020-04-09 [?] RSPM (R 4.3.0)
 P reticulate             1.35.0     2024-01-31 [?] RSPM (R 4.3.0)
 P rjson                  0.2.21     2022-01-09 [?] RSPM (R 4.3.0)
 P rlang                  1.1.3      2024-01-10 [?] RSPM (R 4.3.0)
 P rmarkdown              2.25       2023-09-18 [?] RSPM (R 4.3.0)
 P ROCR                   1.0-11     2020-05-02 [?] RSPM (R 4.3.0)
 P rpart                  4.1.23     2023-12-05 [?] RSPM (R 4.3.0)
 P rprojroot              2.0.4      2023-11-05 [?] RSPM (R 4.3.0)
 P RSQLite                2.3.5      2024-01-21 [?] RSPM (R 4.3.0)
 P rstudioapi             0.15.0     2023-07-07 [?] RSPM (R 4.3.0)
 P Rtsne                  0.17       2023-12-07 [?] RSPM (R 4.3.0)
 P S4Arrays               1.2.0      2023-10-24 [?] Bioconductor
 P S4Vectors            * 0.40.2     2023-11-23 [?] Bioconductor 3.18 (R 4.3.3)
 P sass                   0.4.8      2023-12-06 [?] RSPM (R 4.3.0)
 P scales                 1.3.0      2023-11-28 [?] RSPM (R 4.3.0)
 P scattermore            1.2        2023-06-12 [?] RSPM (R 4.3.0)
   sctransform            0.4.1      2023-10-19 [1] RSPM (R 4.3.0)
 P sessioninfo            1.2.2      2021-12-06 [?] RSPM (R 4.3.0)
   Seurat               * 4.4.0      2024-04-25 [1] https://satijalab.r-universe.dev (R 4.3.3)
   SeuratObject         * 4.1.4      2024-04-25 [1] https://satijalab.r-universe.dev (R 4.3.3)
 P shape                  1.4.6      2021-05-19 [?] RSPM (R 4.3.0)
 P shiny                  1.8.0      2023-11-17 [?] RSPM (R 4.3.0)
 P SingleCellExperiment * 1.24.0     2023-10-24 [?] Bioconductor
 P sp                     2.1-3      2024-01-30 [?] RSPM (R 4.3.0)
 P SparseArray            1.2.4      2024-02-11 [?] Bioconductor 3.18 (R 4.3.3)
 P spatstat.data          3.0-4      2024-01-15 [?] RSPM (R 4.3.0)
 P spatstat.explore       3.2-6      2024-02-01 [?] RSPM (R 4.3.0)
 P spatstat.geom          3.2-8      2024-01-26 [?] RSPM (R 4.3.0)
 P spatstat.random        3.2-2      2023-11-29 [?] RSPM (R 4.3.0)
 P spatstat.sparse        3.0-3      2023-10-24 [?] RSPM (R 4.3.0)
 P spatstat.utils         3.0-4      2023-10-24 [?] RSPM (R 4.3.0)
 P speckle              * 1.2.0      2023-10-24 [?] Bioconductor
 P statmod                1.5.0      2023-01-06 [?] RSPM (R 4.3.0)
 P stringi                1.8.3      2023-12-11 [?] RSPM (R 4.3.0)
 P stringr              * 1.5.1      2023-11-14 [?] RSPM (R 4.3.0)
 P SummarizedExperiment * 1.32.0     2023-10-24 [?] Bioconductor
 P survival               3.7-0      2024-06-05 [?] RSPM (R 4.3.0)
 P tensor                 1.5        2012-05-05 [?] RSPM (R 4.3.0)
 P tibble               * 3.2.1      2023-03-20 [?] RSPM (R 4.3.0)
 P tidygraph              1.3.1      2024-01-30 [?] RSPM (R 4.3.0)
 P tidyHeatmap          * 1.8.1      2022-05-20 [?] RSPM (R 4.3.3)
 P tidyr                * 1.3.1      2024-01-24 [?] RSPM (R 4.3.0)
 P tidyselect             1.2.0      2022-10-10 [?] RSPM (R 4.3.0)
 P tidyverse            * 2.0.0      2023-02-22 [?] RSPM (R 4.3.0)
 P timechange             0.3.0      2024-01-18 [?] RSPM (R 4.3.0)
 P tweenr                 2.0.3      2024-02-26 [?] RSPM (R 4.3.0)
 P tzdb                   0.4.0      2023-05-12 [?] RSPM (R 4.3.0)
 P utf8                   1.2.4      2023-10-22 [?] RSPM (R 4.3.0)
 P uwot                   0.1.16     2023-06-29 [?] RSPM (R 4.3.0)
 P vctrs                  0.6.5      2023-12-01 [?] RSPM (R 4.3.0)
 P viridis                0.6.5      2024-01-29 [?] RSPM (R 4.3.0)
 P viridisLite            0.4.2      2023-05-02 [?] RSPM (R 4.3.0)
 P WGCNA                  1.72-5     2023-12-07 [?] RSPM (R 4.3.3)
 P whisker                0.4.1      2022-12-05 [?] RSPM (R 4.3.0)
 P withr                  3.0.0      2024-01-16 [?] RSPM (R 4.3.0)
 P workflowr            * 1.7.1      2023-08-23 [?] RSPM (R 4.3.0)
 P xfun                   0.42       2024-02-08 [?] RSPM (R 4.3.0)
 P xtable                 1.8-4      2019-04-21 [?] RSPM (R 4.3.0)
 P XVector                0.42.0     2023-10-24 [?] Bioconductor
 P yaml                   2.3.8      2023-12-11 [?] RSPM (R 4.3.0)
 P zlibbioc               1.48.0     2023-10-24 [?] Bioconductor
 P zoo                    1.8-12     2023-04-13 [?] RSPM (R 4.3.0)

 [1] /mnt/allandata/jovana_data/paed-inflammation-CITEseq/renv/library/R-4.3/x86_64-pc-linux-gnu
 [2] /home/jovana/.cache/R/renv/sandbox/R-4.3/x86_64-pc-linux-gnu/9a444a72

 P ── Loaded and on-disk path mismatch.

──────────────────────────────────────────────────────────────────────────────

sessionInfo()
R version 4.3.3 (2024-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.4 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_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

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

other attached packages:
 [1] here_1.0.1                  tidyHeatmap_1.8.1          
 [3] paletteer_1.6.0             patchwork_1.2.0            
 [5] speckle_1.2.0               glue_1.7.0                 
 [7] org.Hs.eg.db_3.18.0         AnnotationDbi_1.64.1       
 [9] clustree_0.5.1              ggraph_2.2.0               
[11] dittoSeq_1.14.2             glmGamPoi_1.14.3           
[13] SeuratObject_4.1.4          Seurat_4.4.0               
[15] lubridate_1.9.3             forcats_1.0.0              
[17] stringr_1.5.1               dplyr_1.1.4                
[19] purrr_1.0.2                 readr_2.1.5                
[21] tidyr_1.3.1                 tibble_3.2.1               
[23] ggplot2_3.5.0               tidyverse_2.0.0            
[25] edgeR_4.0.15                limma_3.58.1               
[27] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
[29] Biobase_2.62.0              GenomicRanges_1.54.1       
[31] GenomeInfoDb_1.38.6         IRanges_2.36.0             
[33] S4Vectors_0.40.2            BiocGenerics_0.48.1        
[35] MatrixGenerics_1.14.0       matrixStats_1.2.0          
[37] workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] fs_1.6.3                spatstat.sparse_3.0-3   bitops_1.0-7           
  [4] httr_1.4.7              RColorBrewer_1.1-3      doParallel_1.0.17      
  [7] dynamicTreeCut_1.63-1   backports_1.4.1         tools_4.3.3            
 [10] sctransform_0.4.1       utf8_1.2.4              R6_2.5.1               
 [13] lazyeval_0.2.2          uwot_0.1.16             GetoptLong_1.0.5       
 [16] withr_3.0.0             sp_2.1-3                gridExtra_2.3          
 [19] preprocessCore_1.64.0   progressr_0.14.0        WGCNA_1.72-5           
 [22] cli_3.6.2               spatstat.explore_3.2-6  labeling_0.4.3         
 [25] sass_0.4.8              prismatic_1.1.1         spatstat.data_3.0-4    
 [28] ggridges_0.5.6          pbapply_1.7-2           foreign_0.8-86         
 [31] sessioninfo_1.2.2       parallelly_1.37.0       impute_1.76.0          
 [34] rstudioapi_0.15.0       RSQLite_2.3.5           generics_0.1.3         
 [37] shape_1.4.6             ica_1.0-3               spatstat.random_3.2-2  
 [40] dendextend_1.17.1       GO.db_3.18.0            Matrix_1.6-5           
 [43] fansi_1.0.6             abind_1.4-5             lifecycle_1.0.4        
 [46] whisker_0.4.1           yaml_2.3.8              SparseArray_1.2.4      
 [49] Rtsne_0.17              grid_4.3.3              blob_1.2.4             
 [52] promises_1.2.1          crayon_1.5.2            miniUI_0.1.1.1         
 [55] lattice_0.22-5          cowplot_1.1.3           KEGGREST_1.42.0        
 [58] pillar_1.9.0            knitr_1.45              ComplexHeatmap_2.18.0  
 [61] rjson_0.2.21            future.apply_1.11.1     codetools_0.2-19       
 [64] leiden_0.4.3.1          getPass_0.2-4           data.table_1.15.0      
 [67] vctrs_0.6.5             png_0.1-8               gtable_0.3.4           
 [70] rematch2_2.1.2          cachem_1.0.8            xfun_0.42              
 [73] S4Arrays_1.2.0          mime_0.12               tidygraph_1.3.1        
 [76] survival_3.7-0          pheatmap_1.0.12         iterators_1.0.14       
 [79] statmod_1.5.0           ellipsis_0.3.2          fitdistrplus_1.1-11    
 [82] ROCR_1.0-11             nlme_3.1-164            bit64_4.0.5            
 [85] RcppAnnoy_0.0.22        rprojroot_2.0.4         bslib_0.6.1            
 [88] irlba_2.3.5.1           rpart_4.1.23            KernSmooth_2.23-24     
 [91] Hmisc_5.1-1             colorspace_2.1-0        DBI_1.2.1              
 [94] nnet_7.3-19             tidyselect_1.2.0        processx_3.8.3         
 [97] bit_4.0.5               compiler_4.3.3          git2r_0.33.0           
[100] htmlTable_2.4.2         DelayedArray_0.28.0     plotly_4.10.4          
[103] checkmate_2.3.1         scales_1.3.0            lmtest_0.9-40          
[106] callr_3.7.3             digest_0.6.34           goftest_1.2-3          
[109] spatstat.utils_3.0-4    rmarkdown_2.25          XVector_0.42.0         
[112] base64enc_0.1-3         htmltools_0.5.7         pkgconfig_2.0.3        
[115] highr_0.10              fastmap_1.1.1           rlang_1.1.3            
[118] GlobalOptions_0.1.2     htmlwidgets_1.6.4       shiny_1.8.0            
[121] farver_2.1.1            jquerylib_0.1.4         zoo_1.8-12             
[124] jsonlite_1.8.8          RCurl_1.98-1.14         magrittr_2.0.3         
[127] Formula_1.2-5           GenomeInfoDbData_1.2.11 munsell_0.5.0          
[130] Rcpp_1.0.12             viridis_0.6.5           reticulate_1.35.0      
[133] stringi_1.8.3           zlibbioc_1.48.0         MASS_7.3-60.0.1        
[136] plyr_1.8.9              parallel_4.3.3          listenv_0.9.1          
[139] ggrepel_0.9.5           deldir_2.0-2            Biostrings_2.70.2      
[142] graphlayouts_1.1.0      splines_4.3.3           tensor_1.5             
[145] hms_1.1.3               circlize_0.4.15         locfit_1.5-9.8         
[148] ps_1.7.6                fastcluster_1.2.6       igraph_2.0.1.1         
[151] spatstat.geom_3.2-8     reshape2_1.4.4          evaluate_0.23          
[154] renv_1.0.3              BiocManager_1.30.22     tzdb_0.4.0             
[157] foreach_1.5.2           tweenr_2.0.3            httpuv_1.6.14          
[160] RANN_2.6.1              polyclip_1.10-6         future_1.33.1          
[163] clue_0.3-65             scattermore_1.2         ggforce_0.4.2          
[166] xtable_1.8-4            later_1.3.2             viridisLite_0.4.2      
[169] memoise_2.0.1           cluster_2.1.6           timechange_0.3.0       
[172] globals_0.16.2