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 the propeller and limma approach

Results

Session info

Last updated: 2024-08-09

Checks: 7 0

Knit directory: paed-inflammation-CITEseq/

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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

file <- here("data",
            "C133_Neeland_merged",
            glue("C133_Neeland_full_clean_macrophages_annotated_diet.SEU.rds"))

seu <- readRDS(file)
seu

An object of class Seurat 
21568 features across 165209 samples within 1 assay 
Active assay: RNA (21568 features, 0 variable features)

Analyse Cell type proportions

# Differences in cell type proportions
props <- getTransformedProps(clusters = seu$Annotation,
                             sample = seu$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
macro-alveolar	0.4750594	0.3657272	0.2234348	0.2772310	0.3252813	0.5249291	0.2832045	0.2779856	0.3101203	0.2744355	0.0458716	0.3651142	0.2920383	0.4566441	0.3492996	0.3700948	0.2007401	0.3678344	0.0897690	0.1948441	0.3414193	0.1957774	0.1299001	0.2399031	0.2225579	0.4291600	0.3967425	0.5215483	0.3704100	0.2633531	0.4786416	0.3373068	0.4354883	0.3365323	0.3201247	0.2666667	0.2914300	0.0884395	0.3238696	0.3059867	0.3034502	0.2702652	0.4068059	0.2751848	0.3496872
macro-APOC2+	0.0109264	0.0651118	0.1585296	0.1371391	0.0198544	0.1236319	0.0955727	0.1273043	0.0854212	0.0868313	0.0000000	0.0002580	0.1261976	0.0467342	0.0329869	0.1250182	0.0888067	0.1756142	0.0429043	0.1804556	0.1162201	0.1199616	0.1345119	0.2617124	0.2832326	0.0205997	0.0200459	0.0670391	0.0745098	0.0318991	0.0685604	0.0463576	0.1048143	0.1081254	0.1050757	0.0848200	0.0903459	0.0362209	0.0835962	0.0082357	0.0108078	0.0045455	0.1941222	0.0767440	0.0922360
macro-CCL	0.0361045	0.0285066	0.0134980	0.0469160	0.0488087	0.0137819	0.0597330	0.0212396	0.0658907	0.0289438	0.0550459	0.0273513	0.0181698	0.0230856	0.0162675	0.0207148	0.1091582	0.0639217	0.1141914	0.0449640	0.0380159	0.0527831	0.0860876	0.0129241	0.0312185	0.0183108	0.0478179	0.0179569	0.0278075	0.1331602	0.0388723	0.1249448	0.0093535	0.0287908	0.0258237	0.0860720	0.0753743	0.4633263	0.0988433	0.2169781	0.0267424	0.1350379	0.0201083	0.0344234	0.0344658
macro-CCL18	0.1453682	0.0664075	0.0709362	0.0318241	0.1358372	0.0506688	0.0548138	0.1180871	0.0307753	0.0162146	0.0091743	0.0704425	0.0716881	0.0185811	0.0329869	0.0229030	0.0471785	0.0363967	0.1881188	0.0281775	0.1028240	0.0556622	0.1614143	0.1090468	0.0757805	0.0444038	0.0281896	0.0458899	0.1547237	0.1283383	0.1114908	0.1086093	0.0503439	0.0153551	0.0347284	0.0694836	0.0433660	0.0167522	0.0525762	0.1042129	0.0529306	0.0543561	0.0487239	0.0798826	0.0497976
macro-IFI27	0.0370546	0.1127308	0.1748995	0.1466535	0.0415288	0.0664775	0.0660576	0.1297088	0.1110673	0.0971359	0.0000000	0.1559799	0.0835811	0.1216216	0.2227745	0.0850474	0.0638298	0.1037307	0.0554455	0.1366906	0.0615496	0.0556622	0.0253651	0.0678514	0.0518630	0.1210803	0.1229902	0.0243416	0.0377897	0.0541543	0.0337463	0.0189845	0.1155433	0.1666667	0.0756901	0.0475743	0.1040268	0.0937217	0.0541535	0.0538486	0.1806845	0.2045455	0.0193349	0.1701934	0.2012756
macro-IFI27+APOC2+	0.0009501	0.0119857	0.0953475	0.0626640	0.0043018	0.0218889	0.0281096	0.0454181	0.0327481	0.0259130	0.0000000	0.0003870	0.0419557	0.0118243	0.0140081	0.0288840	0.0379278	0.0586897	0.0455446	0.0977218	0.0177408	0.0374280	0.0299769	0.0379645	0.0362538	0.0059510	0.0060555	0.0027933	0.0057041	0.0126113	0.0044853	0.0035320	0.0231087	0.0489443	0.0276046	0.0169014	0.0294269	0.0437670	0.0089380	0.0003168	0.0018013	0.0022727	0.0108275	0.0459654	0.0482031
macro-IFI27+CCL18+	0.0114014	0.0077745	0.0376221	0.0104987	0.0095963	0.0020268	0.0063247	0.0253807	0.0023673	0.0010608	0.0000000	0.0145788	0.0095804	0.0011261	0.0112969	0.0029176	0.0037003	0.0050045	0.1425743	0.0065947	0.0126720	0.0076775	0.0215219	0.0105008	0.0060423	0.0080110	0.0043850	0.0019952	0.0092692	0.0181751	0.0057668	0.0039735	0.0137552	0.0047985	0.0035619	0.0059468	0.0121322	0.0086025	0.0026288	0.0142540	0.0213385	0.0227273	0.0015468	0.0346259	0.0202379
macro-IFN	0.0114014	0.0145773	0.0134980	0.0065617	0.0090999	0.0060803	0.0063247	0.0040075	0.0130203	0.0122746	0.0000000	0.0167720	0.0105715	0.0191441	0.0108450	0.0086069	0.0129510	0.0113740	0.0099010	0.0239808	0.0170167	0.0038388	0.0146042	0.0121163	0.0123364	0.0144198	0.0110670	0.0063847	0.0049911	0.0096439	0.0102520	0.0154525	0.0178817	0.0339091	0.0258237	0.0172144	0.0172948	0.0033203	0.0157729	0.0326259	0.0230012	0.0195076	0.0038670	0.0054672	0.0042929
macro-IGF1	0.0047506	0.0874636	0.0290063	0.0272310	0.0799140	0.0141873	0.0238932	0.0545017	0.0420201	0.1160782	0.0000000	0.0458005	0.0697060	0.0996622	0.0488025	0.0414296	0.0277521	0.0195632	0.0085809	0.0629496	0.0467053	0.0095969	0.0084550	0.0306947	0.0760322	0.0771344	0.1160994	0.0327215	0.0185383	0.0103858	0.0226399	0.0088300	0.0486933	0.0665387	0.0814782	0.0278560	0.0281363	0.0090552	0.1035752	0.0402281	0.1292781	0.0797348	0.0232019	0.0173129	0.0099350
macro-interstitial	0.0118765	0.0330418	0.0031591	0.0249344	0.0072799	0.0077017	0.0182713	0.0044082	0.0339317	0.0045461	0.7706422	0.0130306	0.0082590	0.0242117	0.0162675	0.0051058	0.1110083	0.0045496	0.0217822	0.0191847	0.0021723	0.1986564	0.1813989	0.0767367	0.0324773	0.0082399	0.0077260	0.0554669	0.0313725	0.0459941	0.0175139	0.1094923	0.0027510	0.0063980	0.0160285	0.0691706	0.0627259	0.0107154	0.0315457	0.0310421	0.0052653	0.0068182	0.0092807	0.0045560	0.0034343
macro-lipid	0.0418052	0.0382248	0.0163699	0.0423228	0.1767042	0.0194568	0.1742797	0.0081485	0.0658907	0.1786634	0.0091743	0.0708296	0.0723489	0.0506757	0.0750113	0.0762947	0.1119334	0.0113740	0.0118812	0.0263789	0.0557567	0.0527831	0.0322829	0.0185784	0.0317221	0.0391394	0.1016914	0.0311253	0.0245989	0.1275964	0.0258437	0.1125828	0.0211829	0.0409469	0.0943900	0.1276995	0.0862158	0.0123755	0.0289169	0.0627178	0.0780103	0.0257576	0.0177881	0.0750228	0.0073593
macro-lipid-APOC2+	0.0004751	0.0058309	0.0097645	0.0252625	0.0105890	0.0052696	0.0393535	0.0050761	0.0213060	0.0371268	0.0000000	0.0001290	0.0363396	0.0033784	0.0031631	0.0268417	0.0518039	0.0054595	0.0079208	0.0221823	0.0202752	0.0374280	0.0837817	0.0193861	0.0324773	0.0020600	0.0045939	0.0043895	0.0046346	0.0252226	0.0057668	0.0229581	0.0038514	0.0057582	0.0213713	0.0519562	0.0309757	0.0123755	0.0094637	0.0031676	0.0013856	0.0003788	0.0100541	0.0231852	0.0023304
macro-monocyte-derived	0.1292162	0.0631681	0.0789776	0.0492126	0.0342488	0.0328334	0.0288124	0.0435480	0.0952851	0.0403091	0.1100917	0.1385628	0.0630988	0.0534910	0.0402169	0.1296864	0.0832562	0.0618744	0.2158416	0.0557554	0.0774801	0.1007678	0.0284397	0.0387722	0.0347432	0.1215381	0.0703696	0.0913807	0.1433155	0.0778932	0.0931226	0.0569536	0.0679505	0.0697377	0.0828139	0.0666667	0.0562726	0.1696348	0.1493165	0.0424454	0.0644312	0.0825758	0.1337974	0.0725929	0.0983687
macro-MT	0.0190024	0.0168448	0.0103389	0.0252625	0.0190271	0.0372923	0.0421644	0.0617152	0.0341290	0.0315199	0.0000000	0.0130306	0.0247770	0.0185811	0.0533213	0.0138585	0.0083256	0.0191083	0.0105611	0.0263789	0.0231716	0.0239923	0.0138355	0.0040388	0.0239174	0.0189975	0.0187931	0.0319234	0.0281640	0.0181751	0.0237078	0.0123620	0.0181568	0.0127959	0.0133571	0.0178404	0.0260712	0.0075460	0.0215563	0.0202724	0.0597201	0.0361742	0.0409899	0.0188316	0.0134920
macro-proliferating-G2M	0.0109264	0.0181406	0.0068926	0.0219816	0.0198544	0.0226996	0.0168658	0.0189687	0.0126258	0.0081831	0.0000000	0.0247710	0.0181698	0.0112613	0.0293719	0.0103574	0.0101758	0.0150136	0.0112211	0.0191847	0.0184649	0.0239923	0.0184473	0.0153473	0.0156093	0.0274662	0.0127375	0.0207502	0.0210339	0.0129822	0.0196497	0.0048565	0.0242091	0.0153551	0.0227070	0.0115806	0.0105834	0.0049804	0.0057834	0.0177384	0.0076209	0.0140152	0.0193349	0.0139719	0.0116522
macro-proliferating-S	0.0304038	0.0398445	0.0272832	0.0465879	0.0309398	0.0356709	0.0323261	0.0292546	0.0232788	0.0174269	0.0000000	0.0220617	0.0393129	0.0236486	0.0253050	0.0128373	0.0185014	0.0227480	0.0165017	0.0383693	0.0293266	0.0143954	0.0284397	0.0371567	0.0284491	0.0357061	0.0223429	0.0331205	0.0338681	0.0211424	0.0288338	0.0057395	0.0360385	0.0271913	0.0365093	0.0203443	0.0209086	0.0090552	0.0063091	0.0389610	0.0250797	0.0295455	0.0185615	0.0263238	0.0268613
macro-T	0.0232779	0.0246194	0.0304423	0.0177165	0.0271343	0.0154033	0.0238932	0.0252471	0.0201223	0.0233369	0.0000000	0.0209005	0.0142055	0.0163288	0.0180750	0.0194019	0.0129510	0.0177434	0.0072607	0.0161871	0.0191890	0.0095969	0.0015373	0.0072698	0.0052870	0.0077821	0.0083525	0.0111732	0.0092692	0.0092730	0.0111064	0.0070640	0.0068776	0.0121561	0.0129118	0.0122066	0.0147135	0.0101117	0.0031546	0.0069686	0.0084523	0.0117424	0.0216551	0.0257163	0.0263707

Cell type proportions by sample

props$Proportions %>%
  data.frame %>%
  inner_join(seu@meta.data %>%
               dplyr::select(sample.id, 
                             Disease,
                             Treatment,
                             Status, 
                             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,
                             Status, 
                             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$Annotation,
                             sample = seu$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
macro-alveolar	0.4750594	0.3657272	0.2234348	0.2772310	0.3252813	0.5249291	0.2832045	0.2779856	0.3101203	0.2744355	0.3651142	0.2920383	0.4566441	0.3492996	0.3700948	0.2007401	0.3678344	0.0897690	0.1948441	0.3414193	0.1957774	0.1299001	0.2399031	0.2225579	0.4291600	0.3967425	0.5215483	0.3704100	0.2633531	0.4786416	0.3373068	0.4354883	0.3365323	0.3201247	0.2666667	0.2914300	0.0884395	0.3238696	0.3059867	0.3034502	0.2702652	0.4068059	0.2751848	0.3496872
macro-APOC2+	0.0109264	0.0651118	0.1585296	0.1371391	0.0198544	0.1236319	0.0955727	0.1273043	0.0854212	0.0868313	0.0002580	0.1261976	0.0467342	0.0329869	0.1250182	0.0888067	0.1756142	0.0429043	0.1804556	0.1162201	0.1199616	0.1345119	0.2617124	0.2832326	0.0205997	0.0200459	0.0670391	0.0745098	0.0318991	0.0685604	0.0463576	0.1048143	0.1081254	0.1050757	0.0848200	0.0903459	0.0362209	0.0835962	0.0082357	0.0108078	0.0045455	0.1941222	0.0767440	0.0922360
macro-CCL	0.0361045	0.0285066	0.0134980	0.0469160	0.0488087	0.0137819	0.0597330	0.0212396	0.0658907	0.0289438	0.0273513	0.0181698	0.0230856	0.0162675	0.0207148	0.1091582	0.0639217	0.1141914	0.0449640	0.0380159	0.0527831	0.0860876	0.0129241	0.0312185	0.0183108	0.0478179	0.0179569	0.0278075	0.1331602	0.0388723	0.1249448	0.0093535	0.0287908	0.0258237	0.0860720	0.0753743	0.4633263	0.0988433	0.2169781	0.0267424	0.1350379	0.0201083	0.0344234	0.0344658
macro-CCL18	0.1453682	0.0664075	0.0709362	0.0318241	0.1358372	0.0506688	0.0548138	0.1180871	0.0307753	0.0162146	0.0704425	0.0716881	0.0185811	0.0329869	0.0229030	0.0471785	0.0363967	0.1881188	0.0281775	0.1028240	0.0556622	0.1614143	0.1090468	0.0757805	0.0444038	0.0281896	0.0458899	0.1547237	0.1283383	0.1114908	0.1086093	0.0503439	0.0153551	0.0347284	0.0694836	0.0433660	0.0167522	0.0525762	0.1042129	0.0529306	0.0543561	0.0487239	0.0798826	0.0497976
macro-IFI27	0.0370546	0.1127308	0.1748995	0.1466535	0.0415288	0.0664775	0.0660576	0.1297088	0.1110673	0.0971359	0.1559799	0.0835811	0.1216216	0.2227745	0.0850474	0.0638298	0.1037307	0.0554455	0.1366906	0.0615496	0.0556622	0.0253651	0.0678514	0.0518630	0.1210803	0.1229902	0.0243416	0.0377897	0.0541543	0.0337463	0.0189845	0.1155433	0.1666667	0.0756901	0.0475743	0.1040268	0.0937217	0.0541535	0.0538486	0.1806845	0.2045455	0.0193349	0.1701934	0.2012756
macro-IFI27+APOC2+	0.0009501	0.0119857	0.0953475	0.0626640	0.0043018	0.0218889	0.0281096	0.0454181	0.0327481	0.0259130	0.0003870	0.0419557	0.0118243	0.0140081	0.0288840	0.0379278	0.0586897	0.0455446	0.0977218	0.0177408	0.0374280	0.0299769	0.0379645	0.0362538	0.0059510	0.0060555	0.0027933	0.0057041	0.0126113	0.0044853	0.0035320	0.0231087	0.0489443	0.0276046	0.0169014	0.0294269	0.0437670	0.0089380	0.0003168	0.0018013	0.0022727	0.0108275	0.0459654	0.0482031
macro-IFI27+CCL18+	0.0114014	0.0077745	0.0376221	0.0104987	0.0095963	0.0020268	0.0063247	0.0253807	0.0023673	0.0010608	0.0145788	0.0095804	0.0011261	0.0112969	0.0029176	0.0037003	0.0050045	0.1425743	0.0065947	0.0126720	0.0076775	0.0215219	0.0105008	0.0060423	0.0080110	0.0043850	0.0019952	0.0092692	0.0181751	0.0057668	0.0039735	0.0137552	0.0047985	0.0035619	0.0059468	0.0121322	0.0086025	0.0026288	0.0142540	0.0213385	0.0227273	0.0015468	0.0346259	0.0202379
macro-IFN	0.0114014	0.0145773	0.0134980	0.0065617	0.0090999	0.0060803	0.0063247	0.0040075	0.0130203	0.0122746	0.0167720	0.0105715	0.0191441	0.0108450	0.0086069	0.0129510	0.0113740	0.0099010	0.0239808	0.0170167	0.0038388	0.0146042	0.0121163	0.0123364	0.0144198	0.0110670	0.0063847	0.0049911	0.0096439	0.0102520	0.0154525	0.0178817	0.0339091	0.0258237	0.0172144	0.0172948	0.0033203	0.0157729	0.0326259	0.0230012	0.0195076	0.0038670	0.0054672	0.0042929
macro-IGF1	0.0047506	0.0874636	0.0290063	0.0272310	0.0799140	0.0141873	0.0238932	0.0545017	0.0420201	0.1160782	0.0458005	0.0697060	0.0996622	0.0488025	0.0414296	0.0277521	0.0195632	0.0085809	0.0629496	0.0467053	0.0095969	0.0084550	0.0306947	0.0760322	0.0771344	0.1160994	0.0327215	0.0185383	0.0103858	0.0226399	0.0088300	0.0486933	0.0665387	0.0814782	0.0278560	0.0281363	0.0090552	0.1035752	0.0402281	0.1292781	0.0797348	0.0232019	0.0173129	0.0099350
macro-interstitial	0.0118765	0.0330418	0.0031591	0.0249344	0.0072799	0.0077017	0.0182713	0.0044082	0.0339317	0.0045461	0.0130306	0.0082590	0.0242117	0.0162675	0.0051058	0.1110083	0.0045496	0.0217822	0.0191847	0.0021723	0.1986564	0.1813989	0.0767367	0.0324773	0.0082399	0.0077260	0.0554669	0.0313725	0.0459941	0.0175139	0.1094923	0.0027510	0.0063980	0.0160285	0.0691706	0.0627259	0.0107154	0.0315457	0.0310421	0.0052653	0.0068182	0.0092807	0.0045560	0.0034343
macro-lipid	0.0418052	0.0382248	0.0163699	0.0423228	0.1767042	0.0194568	0.1742797	0.0081485	0.0658907	0.1786634	0.0708296	0.0723489	0.0506757	0.0750113	0.0762947	0.1119334	0.0113740	0.0118812	0.0263789	0.0557567	0.0527831	0.0322829	0.0185784	0.0317221	0.0391394	0.1016914	0.0311253	0.0245989	0.1275964	0.0258437	0.1125828	0.0211829	0.0409469	0.0943900	0.1276995	0.0862158	0.0123755	0.0289169	0.0627178	0.0780103	0.0257576	0.0177881	0.0750228	0.0073593
macro-lipid-APOC2+	0.0004751	0.0058309	0.0097645	0.0252625	0.0105890	0.0052696	0.0393535	0.0050761	0.0213060	0.0371268	0.0001290	0.0363396	0.0033784	0.0031631	0.0268417	0.0518039	0.0054595	0.0079208	0.0221823	0.0202752	0.0374280	0.0837817	0.0193861	0.0324773	0.0020600	0.0045939	0.0043895	0.0046346	0.0252226	0.0057668	0.0229581	0.0038514	0.0057582	0.0213713	0.0519562	0.0309757	0.0123755	0.0094637	0.0031676	0.0013856	0.0003788	0.0100541	0.0231852	0.0023304
macro-monocyte-derived	0.1292162	0.0631681	0.0789776	0.0492126	0.0342488	0.0328334	0.0288124	0.0435480	0.0952851	0.0403091	0.1385628	0.0630988	0.0534910	0.0402169	0.1296864	0.0832562	0.0618744	0.2158416	0.0557554	0.0774801	0.1007678	0.0284397	0.0387722	0.0347432	0.1215381	0.0703696	0.0913807	0.1433155	0.0778932	0.0931226	0.0569536	0.0679505	0.0697377	0.0828139	0.0666667	0.0562726	0.1696348	0.1493165	0.0424454	0.0644312	0.0825758	0.1337974	0.0725929	0.0983687
macro-MT	0.0190024	0.0168448	0.0103389	0.0252625	0.0190271	0.0372923	0.0421644	0.0617152	0.0341290	0.0315199	0.0130306	0.0247770	0.0185811	0.0533213	0.0138585	0.0083256	0.0191083	0.0105611	0.0263789	0.0231716	0.0239923	0.0138355	0.0040388	0.0239174	0.0189975	0.0187931	0.0319234	0.0281640	0.0181751	0.0237078	0.0123620	0.0181568	0.0127959	0.0133571	0.0178404	0.0260712	0.0075460	0.0215563	0.0202724	0.0597201	0.0361742	0.0409899	0.0188316	0.0134920
macro-proliferating-G2M	0.0109264	0.0181406	0.0068926	0.0219816	0.0198544	0.0226996	0.0168658	0.0189687	0.0126258	0.0081831	0.0247710	0.0181698	0.0112613	0.0293719	0.0103574	0.0101758	0.0150136	0.0112211	0.0191847	0.0184649	0.0239923	0.0184473	0.0153473	0.0156093	0.0274662	0.0127375	0.0207502	0.0210339	0.0129822	0.0196497	0.0048565	0.0242091	0.0153551	0.0227070	0.0115806	0.0105834	0.0049804	0.0057834	0.0177384	0.0076209	0.0140152	0.0193349	0.0139719	0.0116522
macro-proliferating-S	0.0304038	0.0398445	0.0272832	0.0465879	0.0309398	0.0356709	0.0323261	0.0292546	0.0232788	0.0174269	0.0220617	0.0393129	0.0236486	0.0253050	0.0128373	0.0185014	0.0227480	0.0165017	0.0383693	0.0293266	0.0143954	0.0284397	0.0371567	0.0284491	0.0357061	0.0223429	0.0331205	0.0338681	0.0211424	0.0288338	0.0057395	0.0360385	0.0271913	0.0365093	0.0203443	0.0209086	0.0090552	0.0063091	0.0389610	0.0250797	0.0295455	0.0185615	0.0263238	0.0268613
macro-T	0.0232779	0.0246194	0.0304423	0.0177165	0.0271343	0.0154033	0.0238932	0.0252471	0.0201223	0.0233369	0.0209005	0.0142055	0.0163288	0.0180750	0.0194019	0.0129510	0.0177434	0.0072607	0.0161871	0.0191890	0.0095969	0.0015373	0.0072698	0.0052870	0.0077821	0.0083525	0.0111732	0.0092692	0.0092730	0.0111064	0.0070640	0.0068776	0.0121561	0.0129118	0.0122066	0.0147135	0.0101117	0.0031546	0.0069686	0.0084523	0.0117424	0.0216551	0.0257163	0.0263707

Cell proportions by sample

props$Proportions %>%
  data.frame %>%
  inner_join(seu@meta.data %>%
               dplyr::select(sample.id, 
                             Disease,
                             Treatment,
                             Status, 
                             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,
                             Annotation,
                             Annotation,
                             Disease,
                             Treatment,
                             Status, 
                             Severity, 
                             Batch, 
                             Age, 
                             Sex), 
             by = c("sample" = "sample.id", "clusters" = "Annotation")) %>%
  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,
                             Status, 
                             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,
                             Status, 
                             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,
                             Status, 
                             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,
                             Status, 
                             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,
                             Status, 
                             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,
                             Status, 
                             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,
                Status, 
                Severity,
                Participant,
                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, Status,
                                    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(Status, 
                            labels = 1:length(unique(Status))),
         Sex = factor(Sex, labels = length(unique(Sex))),
         Severity = factor(Severity, labels = length(unique(Severity)))) %>%
  mutate(across(everything(), as.numeric)) %>%
  dplyr::select(-Participant, -Status)

datTraits %>% 
  knitr::kable()

Batch	Disease	Group	Severity	Age	Sex	ncells	Donor	Treatment
4	2	7	1	-0.2590872	2	11.03960	1	4
4	1	5	2	-0.0939001	2	11.59199	2	3
4	1	5	2	-0.1151479	1	11.76570	3	3
5	1	5	2	-0.0441471	1	11.57365	4	3
5	1	5	2	0.1428834	2	12.56129	5	3
6	1	5	2	-0.0729608	1	11.26854	6	3
4	2	7	1	0.1464588	2	10.47472	7	4
6	1	6	3	0.5597097	2	12.86998	8	3
4	1	6	3	1.5743836	1	12.30749	9	3
4	1	1	2	1.5993830	2	12.68803	10	1
6	1	1	2	2.3883594	2	12.92017	11	1
2	1	6	3	2.2957230	1	11.56367	12	3
2	1	5	2	2.3360877	2	10.79442	13	3
2	1	1	2	2.2980155	2	11.11179	14	1
6	1	5	2	2.5790214	1	12.74294	15	3
2	1	6	3	2.5823250	1	10.07815	16	3
6	2	7	1	0.1321035	2	12.10198	17	4
2	1	6	3	2.5889097	1	10.56510	18	3
2	1	5	2	2.5583683	1	10.70390	19	3
2	1	5	2	2.5670653	1	11.43150	20	3
2	1	2	3	2.5730557	2	10.02514	21	1
7	1	5	2	-0.9343238	1	10.34541	22	3
7	1	5	2	0.0918737	1	10.27380	22	3
7	1	5	2	1.0409164	1	11.95565	22	3
7	1	5	2	0.0807044	2	12.09309	23	3
7	1	5	2	0.9940589	2	12.22551	23	3
7	1	6	3	-0.0564254	1	11.29117	24	3
7	1	4	3	1.1764977	1	11.45379	24	2
3	1	5	2	1.5597097	1	11.39660	25	3
3	1	3	2	2.1930156	1	12.19291	25	2
3	1	3	2	2.2980155	1	11.14530	25	2
3	1	1	2	1.5703964	2	11.82774	26	1
3	1	1	2	2.0206033	2	11.61010	26	1
3	1	1	2	2.3485584	2	11.13314	26	1
5	1	5	2	1.9730702	1	11.64160	27	3
5	1	3	2	2.6297159	1	11.91961	27	2
7	2	7	1	0.2923784	2	12.69392	28	4
1	1	6	3	1.5801455	2	10.89330	29	3
1	1	5	2	1.5801455	2	11.62434	30	3
1	1	2	3	1.5993178	2	12.81718	31	1
1	2	7	1	1.5849625	2	12.36632	32	4
3	2	7	1	3.0699187	1	10.33651	33	4
4	2	7	1	2.4204621	2	13.26986	34	4
4	2	7	1	2.2356012	1	12.99312	35	4

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 the `propeller` and `limma` approach

Create the design matrix.

group <- factor(info$Group)
participant <- factor(info$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] 0.6239257

$cor
[1] 0.6239257

$atanh.correlations
 [1]  0.80080074  1.27682941  0.70836292  1.46926384  0.63499798  1.30012069
 [7]  1.42121492  0.64466135  1.01553796  0.29787332  0.45725389  0.35069044
[13]  1.03135931  0.60024395 -0.53606034  0.04722491  0.38956524

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               17                    17                      17
Up                    0                     0                       0
       CF.NO_MODvNON_CF.CTRL CF.IVAvNON_CF.CTRL CF.LUMA_IVAvNON_CF.CTRL
Down                       0                  0                       0
NotSig                    16                 17                      17
Up                         1                  0                       0

Results

ANOVA

topTable(fit2)

                       CF.NO_MODvCF.IVA CF.NO_MODvCF.LUMA_IVA
macro-IGF1                  0.050563981           0.061744172
macro-IFN                   0.005061011          -0.000092287
macro-CCL18                 0.049904213          -0.106668775
macro-CCL                   0.079740988           0.018977756
macro-monocyte-derived      0.015417080          -0.008099482
macro-lipid                -0.006315674           0.066955026
macro-interstitial         -0.055066945          -0.010743380
macro-IFI27                -0.077260200           0.005317983
macro-MT                   -0.028504541          -0.009934977
macro-T                     0.006184831           0.006298974
                       CF.NO_MOD.MvCF.NO_MOD.S CF.NO_MODvNON_CF.CTRL
macro-IGF1                         0.011182588           0.123893641
macro-IFN                          0.027162711           0.023610543
macro-CCL18                       -0.024433628           0.023684366
macro-CCL                         -0.021956841          -0.083967600
macro-monocyte-derived            -0.064934650          -0.022834204
macro-lipid                        0.016993962           0.075332072
macro-interstitial                -0.038926740           0.042750434
macro-IFI27                        0.048956879          -0.039742334
macro-MT                          -0.021845981          -0.002905788
macro-T                            0.006796749          -0.010467995
                       CF.IVAvNON_CF.CTRL CF.LUMA_IVAvNON_CF.CTRL   AveExpr
macro-IGF1                     0.07332966             0.062149469 0.2016823
macro-IFN                      0.01854953             0.023702830 0.1102832
macro-CCL18                   -0.02621985             0.130353141 0.2540647
macro-CCL                     -0.16370859            -0.102945356 0.2263490
macro-monocyte-derived        -0.03825128            -0.014734723 0.2791111
macro-lipid                    0.08164775             0.008377046 0.2289080
macro-interstitial             0.09781738             0.053493814 0.1522175
macro-IFI27                    0.03751787            -0.045060316 0.3007553
macro-MT                       0.02559875             0.007029189 0.1487592
macro-T                       -0.01665283            -0.016766968 0.1182567
                               F    P.Value adj.P.Val
macro-IGF1             3.8240902 0.01217645 0.2069997
macro-IFN              2.1062387 0.10346587 0.6220110
macro-CCL18            2.0623237 0.10976664 0.6220110
macro-CCL              1.3669524 0.26792271 0.9797920
macro-monocyte-derived 1.2004227 0.33027728 0.9797920
macro-lipid            1.1634347 0.34580894 0.9797920
macro-interstitial     0.7448426 0.56883505 0.9845472
macro-IFI27            0.7072369 0.59309489 0.9845472
macro-MT               0.5862388 0.67494545 0.9845472
macro-T                0.4560253 0.76728082 0.9845472

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

for(i in 1:ncol(contr)){
props$Proportions %>% data.frame %>%
  left_join(info,
            by = c("sample" = "sample.id")) %>%
  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", ncol = 4) -> p[[i]]
}

p

[[1]]


[[2]]


[[3]]


[[4]]


[[5]]


[[6]]

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 4.3.2 (2023-10-31)
 os       macOS Sonoma 14.5
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Australia/Melbourne
 date     2024-08-09
 pandoc   3.1.11 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (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     2023-11-02 [?] 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     2023-10-26 [?] Bioconductor
 P BiocGenerics         * 0.48.1     2023-11-02 [?] Bioconductor
 P BiocManager            1.30.22    2023-08-08 [?] RSPM (R 4.3.0)
 P Biostrings             2.70.2     2024-01-30 [?] Bioconductor 3.18 (R 4.3.2)
 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 4.3.1)
 P clustree             * 0.5.1      2023-11-05 [?] RSPM (R 4.3.0)
 P codetools              0.2-20     2024-03-31 [?] CRAN (R 4.3.1)
 P colorspace             2.1-0      2023-01-23 [?] RSPM (R 4.3.0)
 P ComplexHeatmap         2.18.0     2023-10-26 [?] 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     2023-11-06 [?] 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     2024-02-10 [?] Bioconductor 3.18 (R 4.3.2)
 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     2024-02-10 [?] Bioconductor 3.18 (R 4.3.2)
 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 4.3.1)
   Formula                1.2-5      2023-02-24 [1] 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     2024-02-10 [?] Bioconductor 3.18 (R 4.3.2)
 P GenomeInfoDbData       1.2.11     2024-02-16 [?] Bioconductor
 P GenomicRanges        * 1.54.1     2023-10-30 [?] 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)
   glmGamPoi            * 1.14.3     2024-02-10 [1] Bioconductor 3.18 (R 4.3.2)
 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     2024-02-21 [?] 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)
   Hmisc                  5.1-1      2023-09-12 [1] RSPM (R 4.3.0)
 P hms                    1.1.3      2023-03-21 [?] RSPM (R 4.3.0)
   htmlTable              2.4.2      2023-10-29 [1] 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     2023-10-26 [?] Bioconductor
 P IRanges              * 2.36.0     2023-10-26 [?] 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     2023-10-26 [?] Bioconductor
 P KernSmooth             2.23-22    2023-07-10 [?] CRAN (R 4.3.2)
 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                0.22-6     2024-03-20 [?] 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     2023-11-02 [?] 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 [?] CRAN (R 4.3.1)
 P Matrix                 1.6-5      2024-01-11 [?] CRAN (R 4.3.1)
 P MatrixGenerics       * 1.14.0     2023-10-26 [?] 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 [?] CRAN (R 4.3.1)
 P nnet                   7.3-19     2023-05-03 [?] CRAN (R 4.3.2)
 P org.Hs.eg.db         * 3.18.0     2024-02-21 [?] 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     2023-10-26 [?] 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 4.3.1)
 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 [?] CRAN (R 4.3.1)
 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      2023-10-26 [?] Bioconductor
 P S4Vectors            * 0.40.2     2023-11-25 [?] Bioconductor 3.18 (R 4.3.2)
 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)
 P sctransform            0.4.1      2023-10-19 [?] RSPM (R 4.3.0)
 P sessioninfo            1.2.2      2021-12-06 [?] RSPM (R 4.3.0)
 P Seurat               * 4.4.0      2023-09-28 [?] RSPM (R 4.3.2)
 P SeuratObject         * 4.1.4      2023-09-26 [?] RSPM (R 4.3.2)
 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     2023-11-06 [?] Bioconductor
 P sp                     2.1-3      2024-01-30 [?] RSPM (R 4.3.0)
 P SparseArray            1.2.4      2024-02-10 [?] Bioconductor 3.18 (R 4.3.2)
 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      2023-10-26 [?] 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     2023-11-06 [?] Bioconductor
 P survival               3.5-8      2024-02-14 [?] CRAN (R 4.3.1)
 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 4.3.2)
 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 4.3.2)
 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 4.3.2)
 P xfun                   0.42       2024-02-08 [?] RSPM (R 4.3.0)
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 P XVector                0.42.0     2023-10-26 [?] Bioconductor
 P yaml                   2.3.8      2023-12-11 [?] RSPM (R 4.3.0)
 P zlibbioc               1.48.0     2023-10-26 [?] Bioconductor
 P zoo                    1.8-12     2023-04-13 [?] RSPM (R 4.3.0)

 [1] /Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/renv/library/R-4.3/aarch64-apple-darwin20
 [2] /Users/maksimovicjovana/Library/Caches/org.R-project.R/R/renv/sandbox/R-4.3/aarch64-apple-darwin20/ac5c2659

 P ── Loaded and on-disk path mismatch.

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

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Australia/Melbourne
tzcode source: internal

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_4.3.2            
 [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_4.3.2              blob_1.2.4             
 [52] promises_1.2.1          crayon_1.5.2            miniUI_0.1.1.1         
 [55] lattice_0.22-6          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_0.2-20       
 [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_3.5-8          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_2.23-22     
 [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_4.3.2          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_4.3.2          listenv_0.9.1          
[139] ggrepel_0.9.5           deldir_2.0-2            Biostrings_2.70.2      
[142] graphlayouts_1.1.0      splines_4.3.2           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 - macrophage labels

Jovana Maksimovic

August 09, 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 the `propeller` and `limma` approach

Results

Session info

Inflammation of Paediatric Pulmonary Diseases

Cell type proportions analysis - macrophage labels

Jovana Maksimovic

August 09, 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 the propeller and limma approach

Results

Session info

Statistical analysis using the `propeller` and `limma` approach