Last updated: 2024-09-10

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Rmd	9a02edf	Jovana Maksimovic	2024-09-10	wflow_publish("analysis/14.0_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))

# seuLst[1:2] <- lapply(seuLst[1:2], function(s){
#   s@meta.data %>%
#     mutate(ann_level_2 = Annotation) %>%
#     dplyr::rename("ann_level_1" = "Broad",
#                   "ann_level_3" = "Annotation") -> s@meta.data
#   s
# })

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   12079525   645.2   20478437  1093.7   13853513   739.9
Vcells 1354150807 10331.4 3693733684 28181.0 3551484622 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

props$Proportions %>%
  data.frame %>%
  inner_join(seu@meta.data %>%
               dplyr::select(sample.id, 
                             Disease,
                             Treatment,
                             Severity,
                             Group,
                             Batch, 
                             Age, 
                             Sex), 
             by = c("sample" = "sample.id")) %>%
  distinct()-> dat

ggplot(dat, 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")

No. cells per sample

props$Counts %>%
  data.frame %>%
  inner_join(seu@meta.data %>%
               dplyr::select(sample.id, 
                             Disease,
                             Treatment,
                             Severity, 
                             Group,
                             Batch, 
                             Age, 
                             Sex), 
             by = c("sample" = "sample.id")) %>%
  distinct() -> dat

ggplot(dat, aes(x = sample, y = Freq, 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")

Remove sample with too few cells.

seu <- subset(seu, cells = which(!seu$sample.id %in% c("sample_23.1")))

# 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_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	0.0057254	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	0.0262504	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	0.0310036	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	0.0398617	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	0.0012963	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	0.0034568	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	0.0003241	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	0.0046451	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	0.7980987	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	0.0001080	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	0.0468834	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	0.0006482	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	0.0016204	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	0.0000000	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	0.0392136	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	0.0008642	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 proportions by sample

props$Proportions %>%
  data.frame %>%
  inner_join(seu@meta.data %>%
               dplyr::select(sample.id,
                             Disease,
                             Treatment,
                             Severity,
                             Group,
                             Batch,
                             Age,
                             Sex),
             by = c("sample" = "sample.id")) %>%
  distinct()-> dat

ggplot(dat, 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")

Cell proportions by cell type

props$Proportions %>%
  data.frame %>%
  inner_join(seu@meta.data %>%
               dplyr::select(sample.id,
                             ann_level_1,
                             Disease,
                             Treatment,
                             Severity, 
                             Batch, 
                             Age, 
                             Sex), 
             by = c("sample" = "sample.id", "clusters" = "ann_level_1")) %>%
  distinct()-> dat

ggplot(dat, 
       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()

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(seu@meta.data %>%
               dplyr::select(sample.id, 
                             Disease,
                             Treatment,
                             Severity, 
                             Batch, 
                             Age, 
                             Sex), 
             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")

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(seu@meta.data %>%
               dplyr::select(sample.id, 
                             Disease,
                             Treatment,
                             Severity,
                             Participant,
                             Batch, 
                             Age, 
                             Sex), 
             by = c("sample" = "sample.id")) %>%
    group_by(sample) %>%
    mutate(ncells = n()) %>%
    ungroup() %>%
    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")

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(seu@meta.data %>%
               dplyr::select(sample.id, 
                             Disease,
                             Treatment,
                             Severity, 
                             Batch, 
                             Age, 
                             Sex), 
             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")

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(seu@meta.data %>%
               dplyr::select(sample.id, 
                             Disease,
                             Treatment,
                             Severity, 
                             Batch, 
                             Age, 
                             Sex), 
             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")

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(seu@meta.data %>%
               dplyr::select(sample.id, 
                             Disease,
                             Treatment,
                             Severity,
                             Participant,
                             Batch, 
                             Age, 
                             Sex), 
             by = c("sample" = "sample.id")) %>%
    group_by(sample) %>%
    mutate(ncells = n()) %>%
    ungroup() %>%
    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")

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(seu@meta.data %>%
               dplyr::select(sample.id, 
                             Disease,
                             Treatment,
                             Severity,
                             Participant,
                             Batch, 
                             Age, 
                             Sex), 
             by = c("sample" = "sample.id")) %>%
    group_by(sample) %>%
    mutate(ncells = n()) %>%
    ungroup() %>%
    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")

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.

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

Collect all of the known sample traits.

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

info <- seu@meta.data %>%
  dplyr::select(sample.id, 
                Disease,
                Treatment,
                Severity,
                Participant,
                Group_severity,
                Group,
                Batch, 
                Age, 
                Sex) %>%
  group_by(sample.id) %>%
  mutate(ncells = n()) %>%
  ungroup() %>%
  distinct()

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

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_MODvCF.IVA = 0.5*(CF.NO_MOD.M + CF.NO_MOD.S) - 0.5*(CF.IVA.M + CF.IVA.S),
                       CF.NO_MODvCF.LUMA_IVA = 0.5*(CF.NO_MOD.M + CF.NO_MOD.S) - 0.5*(CF.LUMA_IVA.M + CF.LUMA_IVA.S),
                       CF.NO_MOD.MvCF.NO_MOD.S = CF.NO_MOD.M - CF.NO_MOD.S,
                       CF.NO_MODvNON_CF.CTRL = 0.5*(CF.NO_MOD.M + CF.NO_MOD.S) - NON_CF.CTRL,
                       CF.IVAvNON_CF.CTRL = 0.5*(CF.IVA.M + CF.IVA.S) - NON_CF.CTRL,
                       CF.LUMA_IVAvNON_CF.CTRL = 0.5*(CF.LUMA_IVA.M + CF.LUMA_IVA.S) - NON_CF.CTRL,
                       levels = design)
contr

               Contrasts
Levels          CF.NO_MODvCF.IVA CF.NO_MODvCF.LUMA_IVA CF.NO_MOD.MvCF.NO_MOD.S
  CF.IVA.M                  -0.5                   0.0                       0
  CF.IVA.S                  -0.5                   0.0                       0
  CF.LUMA_IVA.M              0.0                  -0.5                       0
  CF.LUMA_IVA.S              0.0                  -0.5                       0
  CF.NO_MOD.M                0.5                   0.5                       1
  CF.NO_MOD.S                0.5                   0.5                      -1
  NON_CF.CTRL                0.0                   0.0                       0
  batch1                     0.0                   0.0                       0
  batch2                     0.0                   0.0                       0
  batch3                     0.0                   0.0                       0
  batch4                     0.0                   0.0                       0
  batch5                     0.0                   0.0                       0
  batch6                     0.0                   0.0                       0
  age                        0.0                   0.0                       0
  sexM                       0.0                   0.0                       0
               Contrasts
Levels          CF.NO_MODvNON_CF.CTRL CF.IVAvNON_CF.CTRL
  CF.IVA.M                        0.0                0.5
  CF.IVA.S                        0.0                0.5
  CF.LUMA_IVA.M                   0.0                0.0
  CF.LUMA_IVA.S                   0.0                0.0
  CF.NO_MOD.M                     0.5                0.0
  CF.NO_MOD.S                     0.5                0.0
  NON_CF.CTRL                    -1.0               -1.0
  batch1                          0.0                0.0
  batch2                          0.0                0.0
  batch3                          0.0                0.0
  batch4                          0.0                0.0
  batch5                          0.0                0.0
  batch6                          0.0                0.0
  age                             0.0                0.0
  sexM                            0.0                0.0
               Contrasts
Levels          CF.LUMA_IVAvNON_CF.CTRL
  CF.IVA.M                          0.0
  CF.IVA.S                          0.0
  CF.LUMA_IVA.M                     0.5
  CF.LUMA_IVA.S                     0.5
  CF.NO_MOD.M                       0.0
  CF.NO_MOD.S                       0.0
  NON_CF.CTRL                      -1.0
  batch1                            0.0
  batch2                            0.0
  batch3                            0.0
  batch4                            0.0
  batch5                            0.0
  batch6                            0.0
  age                               0.0
  sexM                              0.0

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_MODvCF.IVA CF.NO_MODvCF.LUMA_IVA CF.NO_MOD.MvCF.NO_MOD.S
Down                  0                     0                       0
NotSig               16                    15                      16
Up                    0                     1                       0
       CF.NO_MODvNON_CF.CTRL CF.IVAvNON_CF.CTRL CF.LUMA_IVAvNON_CF.CTRL
Down                       1                  0                       0
NotSig                    15                 16                      16
Up                         0                  0                       0

Results

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

for(i in 1:ncol(contr)){
  print(knitr::kable(topTable(fit2, coef = i),
                     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_boxplot(outlier.shape = NA, colour = "grey") +
    geom_jitter(stat = "identity",
                width = 0.15,
                size = 2) +
    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") +
    ggtitle(colnames(contr)[i]) -> p[[i]]
  
  print(p[[i]])
}

CF.NO_MODvCF.IVA
	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

CF.NO_MODvCF.LUMA_IVA
	logFC	AveExpr	t	P.Value	adj.P.Val	B
neutrophils	0.0623699	0.0365714	3.409832	0.0018145	0.0290321	-1.482959
innate lymphocyte	-0.0449772	0.0733282	-2.410706	0.0220055	0.1760439	-3.756250
mast cells	0.0165537	0.0158193	1.779886	0.0848459	0.4089010	-4.914571
CD8 T cells	-0.0618318	0.1671905	-1.684230	0.1022253	0.4089010	-5.066366
NK cells	-0.0236804	0.0505933	-1.557941	0.1293431	0.4138978	-5.255670
CD4 T cells	-0.0514344	0.1587382	-1.414851	0.1671225	0.4456600	-5.455042
monocytes	-0.0223527	0.1046134	-1.247018	0.2216793	0.4552617	-5.667274
DC cells	-0.0342966	0.1376617	-1.196499	0.2406122	0.4552617	-5.726384
macrophages	0.0697972	1.1311489	1.156640	0.2562936	0.4552617	-5.771489
gamma delta T cells	0.0168610	0.0242845	1.088915	0.2845386	0.4552617	-5.844982

CF.NO_MOD.MvCF.NO_MOD.S
	logFC	AveExpr	t	P.Value	adj.P.Val	B
neutrophils	-0.0399716	0.0365714	-2.159654	0.0386053	0.3092130	-4.238117
B cells	-0.0881572	0.1209003	-2.160049	0.0386516	0.3092130	-4.238184
macrophages	0.0954724	1.1311489	1.563554	0.1281186	0.4022276	-5.236842
NK cells	-0.0222587	0.0505933	-1.447224	0.1578166	0.4022276	-5.400533
CD4 T cells	-0.0486446	0.1587382	-1.322411	0.1957475	0.4022276	-5.563891
gamma delta T cells	-0.0204081	0.0242845	-1.302533	0.2022756	0.4022276	-5.588735
mast cells	-0.0113367	0.0158193	-1.204650	0.2374051	0.4022276	-5.705992
proliferating macrophages	0.0258037	0.1876178	1.179550	0.2471137	0.4022276	-5.734717
proliferating T/NK	-0.0114745	0.0310578	-1.133464	0.2656620	0.4022276	-5.786035
dividing innate cells	-0.0139205	0.0232467	-1.119152	0.2716253	0.4022276	-5.801588

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

CF.IVAvNON_CF.CTRL
	logFC	AveExpr	t	P.Value	adj.P.Val	B
monocytes	-0.0662473	0.1046134	-2.9328342	0.0062469	0.0999506	-2.462301
neutrophils	0.0375806	0.0365714	1.6304171	0.1130779	0.9046228	-4.944336
B cells	-0.0518727	0.1209003	-1.0205810	0.3153863	0.9177239	-5.698767
CD8 T cells	-0.0375963	0.1671905	-0.8126659	0.4226339	0.9177239	-5.882408
macrophages	0.0562524	1.1311489	0.7397390	0.4650480	0.9177239	-5.937399
mast cells	0.0078039	0.0158193	0.6658675	0.5103935	0.9177239	-5.988067
NK cells	-0.0102128	0.0505933	-0.5331910	0.5976827	0.9177239	-6.065943
NK-T cells	-0.0056516	0.0158486	-0.5284187	0.6009510	0.9177239	-6.068430
proliferating macrophages	0.0134737	0.1876178	0.4945648	0.6243790	0.9177239	-6.085437
epithelial cells	-0.0174496	0.0868234	-0.3989841	0.6926521	0.9177239	-6.127450

CF.LUMA_IVAvNON_CF.CTRL
	logFC	AveExpr	t	P.Value	adj.P.Val	B
monocytes	-0.0643277	0.1046134	-2.6369129	0.0129307	0.2068904	-3.058370
innate lymphocyte	0.0545310	0.0733282	2.1475906	0.0396307	0.3170456	-4.022453
neutrophils	-0.0428130	0.0365714	-1.7198463	0.0953758	0.3290446	-4.744614
NK-T cells	0.0197436	0.0158486	1.7092746	0.0973393	0.3290446	-4.760848
mast cells	-0.0212727	0.0158193	-1.6806490	0.1028264	0.3290446	-4.804395
CD4 T cells	0.0505941	0.1587382	1.0226167	0.3144378	0.8385007	-5.626538
NK cells	0.0171154	0.0505933	0.8273789	0.4143115	0.9454605	-5.799364
dividing innate cells	-0.0074978	0.0232467	-0.4481766	0.6571254	0.9454605	-6.033954
proliferating macrophages	0.0116829	0.1876178	0.3970673	0.6940266	0.9454605	-6.055087
DC cells	0.0146423	0.1376617	0.3753407	0.7099742	0.9454605	-6.063289

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     2024-09-10
 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

Inflammation of Paediatric Pulmonary Diseases

Cell type proportions analysis - broad labels

Jovana Maksimovic

September 10, 2024

Load libraries

Load Data

Analyse Cell type proportions

Cell type proportions by sample

No. cells per sample

Cell proportions by sample

Cell proportions by cell type

Explore sources of variation

Cell count data

Cell proportion data

Principal components versus traits

Statistical analysis using `propeller` and `limma`

Results

Session info

Inflammation of Paediatric Pulmonary Diseases

Cell type proportions analysis - broad labels

Jovana Maksimovic

September 10, 2024

Load libraries

Load Data

Analyse Cell type proportions

Cell type proportions by sample

No. cells per sample

Cell proportions by sample

Cell proportions by cell type

Explore sources of variation

Cell count data

Cell proportion data

Principal components versus traits

Statistical analysis using propeller and limma

Results

Session info

Statistical analysis using `propeller` and `limma`