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Knit directory: paed-inflammation-CITEseq/
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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)
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
# 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 |
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$Proportions %>%
data.frame %>%
left_join(info,
by = c("sample" = "sample.id")) %>%
ggplot(aes(x = sample, y = Freq, fill = clusters)) +
geom_bar(stat = "identity", color = "black", size = 0.1) +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1),
legend.text = element_text(size = 8),
legend.position = "bottom") +
labs(y = "Proportion", fill = "Cell Label") +
scale_fill_paletteer_d("miscpalettes::pastel", direction = 1) +
facet_grid(~Group, scales = "free_x", space = "free_x")
Version | Author | Date |
---|---|---|
7ae0444 | Jovana Maksimovic | 2024-12-24 |
info %>%
ggplot(aes(x = sample.id, y = ncells, fill = Disease)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1),
legend.text = element_text(size = 8),
legend.position = "bottom") +
labs(y = "No. cells", fill = "Disease") +
facet_grid(~Group, scales = "free_x", space = "free_x") +
geom_hline(yintercept = 1000, linetype = "dashed")
Version | Author | Date |
---|---|---|
7ae0444 | Jovana Maksimovic | 2024-12-24 |
props$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 |
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 |
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 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 |
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$consensus)
fit2 <- contrasts.fit(fit, contr)
fit2 <- eBayes(fit2, robust=TRUE, trend=FALSE)
pvalue <- 0.05
summary(decideTests(fit2, p.value = pvalue))
CF.NO_MODvNON_CF.CTRL CF.IVAvCF.NO_MOD CF.LUMA_IVAvCF.NO_MOD
Down 1 0 1
NotSig 15 16 15
Up 0 0 0
CF.NO_MOD.SvCF.NO_MOD.M
Down 0
NotSig 16
Up 0
p <- vector("list", ncol(contr))
for(i in 1:ncol(contr)){
print(knitr::kable(topTable(fit2, coef = i, number = Inf),
caption = colnames(contr)[i]))
props$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]])
}
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 |
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 |
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 |
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 |
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)
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[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