GSE74548

Load packages

knitr::opts_chunk$set(dev = "png")

suppressPackageStartupMessages({
    library("missMethyl")
    library("GEOquery")
    library("limma")
    library("topconfects")
    library("minfi")
    library("IlluminaHumanMethylation450kmanifest")
    library("IlluminaHumanMethylation450kanno.ilmn12.hg19")
    library("DMRcate")
    library("mitch")

    library("kableExtra")
    library("forestplot")
    library("RColorBrewer")
    library("plyr")
    library("R.utils")
    library("eulerr")
    library("gplots")
    library("reshape2")
    library("beeswarm")
  })

## Warning: package 'locfit' was built under R version 4.1.0

## Warning: replacing previous import 'minfi::getMeth' by 'bsseq::getMeth' when
## loading 'DMRcate'

# Annotation
ann450k <- getAnnotation(IlluminaHumanMethylation450kanno.ilmn12.hg19)
myann <- data.frame(ann450k[,c("UCSC_RefGene_Name","Regulatory_Feature_Group")])
promoters <- grep("Prom",myann$Regulatory_Feature_Group)

Loading functions

These functions provide shortcuts to help with charts and other analysis. They will eventually be shoved into another Rscript or package but can stay here for now.

source("https://raw.githubusercontent.com/markziemann/ART_methylation/master/meth_functions.R")
myranks<-function(x) {
  x$score <- sign(x$logFC)/log10(x$adj.P.Val)
  y <- x[,"score",drop=FALSE]
  y$rn <- x$Row.names
  return(y)
}

# heatmap for continuous data
heatmap_c<-function(dm,name,mx,n, groups) {
  my_palette <- colorRampPalette(c("blue", "white", "red"))(25)
  topgenes <-  rownames(head(dm[order(dm$P.Value),],n))
  ss <- mx[which(rownames(mx) %in% topgenes),]
  mycols <- colorRampPalette(c("white","yellow", "orange", "red","darkred"))(n = length(groups))
  colCols <- mycols[order(groups)]
  heatmap.2(ss,scale="row",margin=c(10, 10),cexRow=0.6,trace="none",cexCol=0.4,
  ColSideColors=colCols ,  col=my_palette, main=name)
}

# heatmap for continuous data - topconfects
make_heatmap2_c <- function(confects,name,mx,n, groups) {
  topgenes <-  head(confects$table$name,n)
  my_palette <- colorRampPalette(c("blue", "white", "red"))(25)
  ss <- mx[which(rownames(mx) %in% topgenes),]
  mycols <- colorRampPalette(c("white","yellow", "orange", "red","darkred"))(n = length(groups))
  colCols <- mycols[order(groups)]
  heatmap.2(ss,scale="row",margin=c(10, 10),cexRow=0.6,trace="none",cexCol=0.4,
  ColSideColors=colCols ,  col=my_palette, main=name)  
}

Data import

load("AccesionnumberGSE74548.Rdata")

Enrichment analysis

We will be using the recently published package mitch to perform enrichment analysis, using average promoter methylation change as an indicator of gene activity. Enrichment will be tested with the mitch package.

dma1a (placebo_vs_supplement_followup(sex,age))

dma2ba (placebo_vs_supplement_Baseline(sex,age))

dma3a (hcy_levels(sex,age))

dma4a (folate_levels(sex,age))

dma5a (vitb12 levels(sex,age))

library("mitch")
# gene sets
download.file("https://reactome.org/download/current/ReactomePathways.gmt.zip", 
    destfile="ReactomePathways.gmt.zip")
unzip("ReactomePathways.gmt.zip",overwrite = TRUE)
genesets <- gmt_import("ReactomePathways.gmt")

One dimensional enrichment analysis with REACTOME gene sets using average promoter methylation t-statistic.

placebo_vs_supplement_followup_sex_age_mitch <- run_mitch_1d(dma= dma1a, name="placebo_vs_supplement_followup(sex,age)_mitch")

## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.

head(placebo_vs_supplement_followup_sex_age_mitch,50) %>% kbl() %>% kable_paper("hover", full_width = F)

	set	setSize	pANOVA	s.dist	p.adjustANOVA
62	Assembly of collagen fibrils and other multimeric structures	10	0.0003459	0.6534943	0.3947064
582	Negative regulation of NMDA receptor-mediated neuronal transmission	11	0.0009702	0.5745218	0.4802622
1119	mRNA 3’-end processing	28	0.0016493	-0.3438167	0.4802622
686	Processing of Capped Intron-Containing Pre-mRNA	175	0.0017206	-0.1380200	0.4802622
689	Processing of Intronless Pre-mRNAs	14	0.0024286	-0.4681452	0.4802622
831	Removal of the Flap Intermediate from the C-strand	15	0.0027331	-0.4469522	0.4802622
153	Condensation of Prometaphase Chromosomes	10	0.0039265	-0.5267674	0.4802622
61	Assembly and cell surface presentation of NMDA receptors	12	0.0039475	0.4806398	0.4802622
832	Reproduction	43	0.0054452	-0.2452345	0.4802622
84	Butyrate Response Factor 1 (BRF1) binds and destabilizes mRNA	14	0.0054848	-0.4287889	0.4802622
962	Signaling by WNT in cancer	23	0.0055028	0.3345609	0.4802622
824	Regulation of mRNA stability by proteins that bind AU-rich elements	73	0.0055057	-0.1882597	0.4802622
1091	Tristetraprolin (TTP, ZFP36) binds and destabilizes mRNA	14	0.0056113	-0.4276437	0.4802622
690	Processive synthesis on the C-strand of the telomere	17	0.0070472	-0.3776241	0.4802622
497	Meiosis	39	0.0072387	-0.2487675	0.4802622
254	ER to Golgi Anterograde Transport	101	0.0078083	-0.1536073	0.4802622
552	Myogenesis	13	0.0084440	-0.4219999	0.4802622
504	Metabolism of RNA	498	0.0085719	-0.0697885	0.4802622
1121	mRNA Splicing	137	0.0091133	-0.1295283	0.4802622
400	IKK complex recruitment mediated by RIP1	17	0.0093644	-0.3641542	0.4802622
114	Cell Cycle	482	0.0095721	-0.0698591	0.4802622
561	NOD1/2 Signaling Pathway	22	0.0100526	-0.3171801	0.4802622
864	SMAD2/SMAD3:SMAD4 heterotrimer regulates transcription	23	0.0102618	-0.3093642	0.4802622
756	RNA Polymerase II Transcription Termination	35	0.0106228	-0.2497632	0.4802622
97	COPII-mediated vesicle transport	47	0.0112911	-0.2138664	0.4802622
1088	Transport to the Golgi and subsequent modification	124	0.0113651	-0.1320677	0.4802622
498	Meiotic recombination	19	0.0118861	-0.3334972	0.4802622
184	DNA Double-Strand Break Repair	106	0.0121241	-0.1414273	0.4802622
1124	mRNA decay by 3’ to 5’ exoribonuclease	13	0.0122065	-0.4015516	0.4802622
463	KSRP (KHSRP) binds and destabilizes mRNA	14	0.0131439	-0.3829093	0.4903408
981	Synaptic adhesion-like molecules	10	0.0133221	0.4520945	0.4903408
20	Aberrant regulation of mitotic cell cycle due to RB1 defects	29	0.0137798	0.2644379	0.4913365
21	Aberrant regulation of mitotic exit in cancer due to RB1 defects	15	0.0158453	0.3598945	0.5380612
1027	Telomere Maintenance	53	0.0170665	-0.1896711	0.5380612
1003	TICAM1, RIP1-mediated IKK complex recruitment	17	0.0170858	-0.3342289	0.5380612
1122	mRNA Splicing - Major Pathway	129	0.0171699	-0.1219346	0.5380612
633	PI3K/AKT Signaling in Cancer	50	0.0174481	-0.1945819	0.5380612
275	Extension of Telomeres	41	0.0183704	-0.2130597	0.5515947
407	Inactivation of APC/C via direct inhibition of the APC/C complex	16	0.0198398	0.3364838	0.5654945
416	Inhibition of the proteolytic activity of APC/C required for the onset of anaphase by mitotic spindle checkpoint components	16	0.0198398	0.3364838	0.5654945
530	Mitochondrial calcium ion transport	13	0.0203201	0.3718001	0.5654945
155	Constitutive Signaling by AKT1 E17K in Cancer	23	0.0211349	-0.2778793	0.5741641
396	Homology Directed Repair	84	0.0225500	-0.1443025	0.5983622
1025	Telomere C-strand (Lagging Strand) Synthesis	27	0.0249657	-0.2494449	0.6040367
795	Regulation of HSF1-mediated heat shock response	62	0.0250091	-0.1648559	0.6040367
616	Nucleotide-binding domain, leucine rich repeat containing receptor (NLR) signaling pathways	37	0.0253776	-0.2125932	0.6040367
910	Signaling by Erythropoietin	19	0.0254756	0.2962009	0.6040367
427	Integrin signaling	16	0.0261639	-0.3212346	0.6040367
662	Platelet Aggregation (Plug Formation)	16	0.0261639	-0.3212346	0.6040367
998	TBC/RABGAPs	39	0.0264696	-0.2055769	0.6040367

placebo_vs_supplement_Baseline_sex_age_mitch <- run_mitch_1d(dma= dma2a, name="placebo_vs_supplement_Baseline_(sex,age)_mitch")

## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.

head(placebo_vs_supplement_Baseline_sex_age_mitch,50) %>% kbl() %>% kable_paper("hover", full_width = F)

	set	setSize	pANOVA	s.dist	p.adjustANOVA
769	RORA activates gene expression	10	0.0011338	0.5945025	0.4356347
336	GPVI-mediated activation cascade	23	0.0011549	0.3916311	0.4356347
73	BMAL1:CLOCK,NPAS2 activates circadian gene expression	14	0.0012542	0.4981083	0.4356347
390	Heme signaling	25	0.0015272	0.3663958	0.4356347
140	Circadian Clock	50	0.0022265	0.2503106	0.4555077
910	Signaling by Erythropoietin	19	0.0046361	0.3753460	0.4555077
710	RAF activation	27	0.0059071	0.3062762	0.4555077
1135	rRNA processing in the nucleus and cytosol	145	0.0073218	-0.1295323	0.4555077
1134	rRNA processing	149	0.0077501	-0.1268995	0.4555077
592	Neurotransmitter receptors and postsynaptic signal transmission	73	0.0080317	0.1797770	0.4555077
114	Cell Cycle	482	0.0081778	-0.0713072	0.4555077
812	Regulation of TP53 Activity through Phosphorylation	74	0.0088900	-0.1762455	0.4555077
1103	Vasopressin regulates renal water homeostasis via Aquaporins	18	0.0093115	0.3541772	0.4555077
504	Metabolism of RNA	498	0.0096577	-0.0687051	0.4555077
98	CREB1 phosphorylation through NMDA receptor-mediated activation of RAS signaling	15	0.0097217	0.3857114	0.4555077
32	Activation of NMDA receptors and postsynaptic events	43	0.0103306	0.2262815	0.4555077
639	PKA-mediated phosphorylation of CREB	11	0.0105203	0.4455634	0.4555077
324	G1/S DNA Damage Checkpoints	57	0.0114586	-0.1939051	0.4555077
381	HIV Transcription Initiation	37	0.0114733	-0.2403844	0.4555077
748	RNA Polymerase II HIV Promoter Escape	37	0.0114733	-0.2403844	0.4555077
750	RNA Polymerase II Promoter Escape	37	0.0114733	-0.2403844	0.4555077
753	RNA Polymerase II Transcription Initiation	37	0.0114733	-0.2403844	0.4555077
754	RNA Polymerase II Transcription Initiation And Promoter Clearance	37	0.0114733	-0.2403844	0.4555077
755	RNA Polymerase II Transcription Pre-Initiation And Promoter Opening	37	0.0114733	-0.2403844	0.4555077
811	Regulation of TP53 Activity through Methylation	16	0.0114831	-0.3651172	0.4555077
483	M Phase	272	0.0117768	-0.0894121	0.4555077
1127	p53-Dependent G1 DNA Damage Response	55	0.0128204	-0.1942828	0.4555077
1128	p53-Dependent G1/S DNA damage checkpoint	55	0.0128204	-0.1942828	0.4555077
57	Aquaporin-mediated transport	19	0.0128780	0.3297355	0.4555077
499	Meiotic synapsis	22	0.0130023	-0.3060554	0.4555077
768	RND3 GTPase cycle	28	0.0134530	-0.2700437	0.4555077
1030	The NLRP3 inflammasome	12	0.0137038	-0.4110615	0.4555077
97	COPII-mediated vesicle transport	47	0.0142987	-0.2067872	0.4555077
329	G2/M Transition	143	0.0145713	-0.1188035	0.4555077
1074	Translesion synthesis by Y family DNA polymerases bypasses lesions on DNA template	34	0.0148485	-0.2416161	0.4555077
766	RND1 GTPase cycle	29	0.0151270	-0.2608284	0.4555077
79	Bile acid and bile salt metabolism	14	0.0156561	0.3731826	0.4555077
349	Glucagon signaling in metabolic regulation	16	0.0162503	0.3471532	0.4555077
894	Signal Transduction	1263	0.0163476	0.0417704	0.4555077
303	Formation of TC-NER Pre-Incision Complex	45	0.0164619	-0.2068992	0.4555077
497	Meiosis	39	0.0166915	-0.2217075	0.4555077
116	Cell Cycle, Mitotic	390	0.0169410	-0.0712338	0.4555077
495	Major pathway of rRNA processing in the nucleolus and cytosol	140	0.0176070	-0.1166571	0.4555077
1111	Vpu mediated degradation of CD4	43	0.0178434	-0.2090183	0.4555077
723	RHO GTPases activate PKNs	26	0.0180000	0.2682031	0.4555077
951	Signaling by ROBO receptors	146	0.0186913	-0.1132043	0.4555077
992	Synthesis of bile acids and bile salts	13	0.0187632	0.3765765	0.4555077
541	Mitotic G2-G2/M phases	145	0.0203578	-0.1120456	0.4810867
672	Post NMDA receptor activation events	37	0.0206602	0.2200592	0.4810867
638	PKA activation	10	0.0216398	0.4195152	0.4844200

hcy_levels_sex_age_mitch <- run_mitch_1d(dma= dma3a, name="hcy_levels(sex,age)_mitch")

## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.

head(hcy_levels_sex_age_mitch,50) %>% kbl() %>% kable_paper("hover", full_width = F)

	set	setSize	pANOVA	s.dist	p.adjustANOVA
114	Cell Cycle	482	0.0000307	0.1123523	0.0350588
1076	Transmission across Chemical Synapses	93	0.0001346	-0.2295945	0.0533443
591	Neuronal System	127	0.0001622	-0.1944369	0.0533443
593	Neurotransmitter release cycle	18	0.0001870	-0.5087470	0.0533443
116	Cell Cycle, Mitotic	390	0.0002692	0.1086585	0.0547750
504	Metabolism of RNA	498	0.0003756	0.0944120	0.0547750
171	Cytokine Signaling in Immune system	411	0.0004086	0.1028010	0.0547750
411	Infectious disease	507	0.0004106	0.0929967	0.0547750
405	Immune System	1091	0.0004321	0.0652212	0.0547750
483	M Phase	272	0.0011051	0.1157945	0.1260869
855	SARS-CoV Infections	109	0.0012608	0.1792976	0.1307817
427	Integrin signaling	16	0.0016644	0.4541637	0.1320768
662	Platelet Aggregation (Plug Formation)	16	0.0016644	0.4541637	0.1320768
58	Arachidonic acid metabolism	21	0.0016686	-0.3964353	0.1320768
32	Activation of NMDA receptors and postsynaptic events	43	0.0017363	-0.2763243	0.1320768
496	Maturation of nucleoprotein	10	0.0025905	0.5502384	0.1648188
792	Regulation of FZD by ubiquitination	12	0.0026060	-0.5020459	0.1648188
136	Chromatin modifying enzymes	137	0.0027446	0.1487530	0.1648188
137	Chromatin organization	137	0.0027446	0.1487530	0.1648188
329	G2/M Transition	143	0.0030462	0.1440907	0.1737864
369	HATs acetylate histones	53	0.0040229	0.2287063	0.2185770
354	Glutamate Neurotransmitter Release Cycle	11	0.0042456	-0.4979804	0.2185886
541	Mitotic G2-G2/M phases	145	0.0044063	0.1375310	0.2185886
129	Cellular responses to stress	425	0.0051997	0.0799945	0.2472028
756	RNA Polymerase II Transcription Termination	35	0.0057747	0.2698393	0.2635583
128	Cellular responses to external stimuli	430	0.0061600	0.0779768	0.2703291
592	Neurotransmitter receptors and postsynaptic signal transmission	73	0.0073119	-0.1819166	0.3036982
1063	Transcriptional regulation of granulopoiesis	24	0.0074844	-0.3155578	0.3036982
636	PIP3 activates AKT signaling	172	0.0077558	0.1182396	0.3036982
385	HSP90 chaperone cycle for steroid hormone receptors (SHR)	32	0.0081954	0.2702679	0.3036982
543	Mitotic Prometaphase	146	0.0083241	0.1270156	0.3036982
392	Heparan sulfate/heparin (HS-GAG) metabolism	19	0.0087367	-0.3476356	0.3036982
672	Post NMDA receptor activation events	37	0.0087836	-0.2491698	0.3036982
373	HDACs deacetylate histones	26	0.0102370	0.2911089	0.3280580
231	Dopamine Neurotransmitter Release Cycle	10	0.0104297	-0.4678365	0.3280580
1119	mRNA 3’-end processing	28	0.0106448	0.2790657	0.3280580
639	PKA-mediated phosphorylation of CREB	11	0.0107763	-0.4441063	0.3280580
819	Regulation of expression of SLITs and ROBOs	117	0.0114286	0.1358073	0.3280580
638	PKA activation	10	0.0117196	-0.4603915	0.3280580
687	Processing of Capped Intronless Pre-mRNA	21	0.0118759	0.3172895	0.3280580
686	Processing of Capped Intron-Containing Pre-mRNA	175	0.0119129	0.1107336	0.3280580
985	Synthesis of Leukotrienes (LT) and Eoxins (EX)	10	0.0120758	-0.4584643	0.3280580
976	Stimuli-sensing channels	35	0.0131532	-0.2424068	0.3455051
856	SARS-CoV-1 Infection	41	0.0133236	0.2236219	0.3455051
644	PTEN Regulation	105	0.0137762	0.1395174	0.3493033
371	HCMV Infection	67	0.0146450	0.1727610	0.3632600
927	Signaling by Interleukins	256	0.0170422	0.0872273	0.3923026
214	Disease	900	0.0172435	0.0480752	0.3923026
447	Interleukin-4 and Interleukin-13 signaling	47	0.0172547	0.2010121	0.3923026
222	Diseases of carbohydrate metabolism	22	0.0175578	-0.2926410	0.3923026

folate_levels_sex_age_mitch <- run_mitch_1d(dma= dma4a, name="folate_levels(sex,age)_mitch")

## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.

head(folate_levels_sex_age_mitch,50) %>% kbl() %>% kable_paper("hover", full_width = F)

	set	setSize	pANOVA	s.dist	p.adjustANOVA
504	Metabolism of RNA	498	0.0000014	0.1282046	0.0015500
542	Mitotic Metaphase and Anaphase	186	0.0000803	0.1684906	0.0361717
539	Mitotic Anaphase	185	0.0001080	0.1658729	0.0361717
1066	Translation	225	0.0001268	0.1491903	0.0361717
564	NOTCH3 Activation and Transmission of Signal to the Nucleus	15	0.0002541	0.5456194	0.0438017
641	PLC beta mediated events	29	0.0002961	-0.3884567	0.0438017
882	Selenocysteine synthesis	65	0.0003846	0.2550419	0.0438017
174	DAG and IP3 signaling	26	0.0003847	-0.4024492	0.0438017
819	Regulation of expression of SLITs and ROBOs	117	0.0004269	0.1891245	0.0438017
922	Signaling by GPCR	195	0.0005234	-0.1448175	0.0438017
649	Peptide chain elongation	64	0.0006067	0.2482024	0.0438017
1134	rRNA processing	149	0.0006227	0.1630403	0.0438017
426	Integrin cell surface interactions	23	0.0006487	-0.4109725	0.0438017
467	L13a-mediated translational silencing of Ceruloplasmin expression	82	0.0006873	0.2172992	0.0438017
535	Mitochondrial translation elongation	81	0.0007401	0.2173183	0.0438017
601	Nonsense Mediated Decay (NMD) independent of the Exon Junction Complex (EJC)	69	0.0007444	0.2352026	0.0438017
1135	rRNA processing in the nucleus and cytosol	145	0.0007746	0.1623527	0.0438017
337	GTP hydrolysis and joining of the 60S ribosomal subunit	83	0.0007900	0.2135606	0.0438017
951	Signaling by ROBO receptors	146	0.0009514	0.1590537	0.0438017
865	SRP-dependent cotranslational protein targeting to membrane	81	0.0009549	0.2127624	0.0438017
334	GPCR downstream signalling	178	0.0009574	-0.1442089	0.0438017
892	Separation of Sister Chromatids	141	0.0009657	0.1616027	0.0438017
23	Activated NOTCH1 Transmits Signal to the Nucleus	19	0.0009712	0.4372856	0.0438017
495	Major pathway of rRNA processing in the nucleolus and cytosol	140	0.0009727	0.1620707	0.0438017
563	NOTCH2 Activation and Transmission of Signal to the Nucleus	12	0.0010013	0.5486118	0.0438017
881	Selenoamino acid metabolism	73	0.0011005	0.2213148	0.0438017
185	DNA Repair	232	0.0011390	0.1247902	0.0438017
534	Mitochondrial translation	86	0.0011442	0.2033475	0.0438017
1070	Translesion Synthesis by POLH	16	0.0011840	0.4683599	0.0438017
420	Inositol phosphate metabolism	29	0.0011871	-0.3480376	0.0438017
271	Eukaryotic Translation Elongation	67	0.0011901	0.2293684	0.0438017
537	Mitochondrial translation termination	80	0.0012921	0.2085223	0.0438223
894	Signal Transduction	1263	0.0012983	-0.0559473	0.0438223
600	Nonsense Mediated Decay (NMD) enhanced by the Exon Junction Complex (EJC)	81	0.0013489	0.2064458	0.0438223
602	Nonsense-Mediated Decay (NMD)	81	0.0013489	0.2064458	0.0438223
633	PI3K/AKT Signaling in Cancer	50	0.0013963	-0.2615338	0.0438223
273	Eukaryotic Translation Termination	66	0.0014296	0.2273361	0.0438223
102	CaM pathway	20	0.0015756	-0.4083663	0.0438223
103	Calmodulin induced events	20	0.0015756	-0.4083663	0.0438223
277	Extracellular matrix organization	75	0.0016116	-0.2110299	0.0438223
9	APC/C-mediated degradation of cell cycle proteins	73	0.0016229	0.2137411	0.0438223
825	Regulation of mitotic cell cycle	73	0.0016229	0.2137411	0.0438223
536	Mitochondrial translation initiation	81	0.0017065	0.2020489	0.0438223
516	Metabolism of proteins	1217	0.0017562	0.0552935	0.0438223
47	Anti-inflammatory response favouring Leishmania parasite infection	51	0.0017892	-0.2531165	0.0438223
475	Leishmania parasite growth and survival	51	0.0017892	-0.2531165	0.0438223
547	Muscle contraction	64	0.0018661	-0.2252040	0.0438223
100	Ca-dependent events	21	0.0018735	-0.3921415	0.0438223
8	APC-Cdc20 mediated degradation of Nek2A	21	0.0018901	0.3918127	0.0438223
304	Formation of a pool of free 40S subunits	73	0.0019203	0.2103878	0.0438223

vitb12_levels_sex_age_mitch <- run_mitch_1d(dma= dma5a, name="vitb12 levels(sex,age)_mitch")

## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.

head(vitb12_levels_sex_age_mitch,50) %>% kbl() %>% kable_paper("hover", full_width = F)

	set	setSize	pANOVA	s.dist	p.adjustANOVA
1134	rRNA processing	149	0.0000233	0.2015577	0.0266050
1135	rRNA processing in the nucleus and cytosol	145	0.0000546	0.1948467	0.0311454
495	Major pathway of rRNA processing in the nucleolus and cytosol	140	0.0001389	0.1872003	0.0528380
504	Metabolism of RNA	498	0.0002441	0.0973755	0.0696268
194	Defective CFTR causes cystic fibrosis	51	0.0012008	0.2624751	0.2187878
774	RUNX1 regulates transcription of genes involved in differentiation of HSCs	55	0.0012204	0.2524409	0.2187878
1066	Translation	225	0.0014064	0.1243209	0.2187878
171	Cytokine Signaling in Immune system	411	0.0015340	-0.0921528	0.2187878
811	Regulation of TP53 Activity through Methylation	16	0.0022985	0.4403481	0.2335804
522	Metalloprotease DUBs	16	0.0024063	0.4383563	0.2335804
193	Dectin-1 mediated noncanonical NF-kB signaling	50	0.0024961	0.2474957	0.2335804
951	Signaling by ROBO receptors	146	0.0027676	0.1440392	0.2335804
560	NIK–>noncanonical NF-kB signaling	49	0.0028198	0.2469305	0.2335804
189	Deactivation of the beta-catenin transactivating complex	27	0.0032455	0.3274703	0.2335804
204	Degradation of the extracellular matrix	26	0.0033008	-0.3330989	0.2335804
337	GTP hydrolysis and joining of the 60S ribosomal subunit	83	0.0035154	0.1857288	0.2335804
819	Regulation of expression of SLITs and ROBOs	117	0.0035735	0.1564299	0.2335804
881	Selenoamino acid metabolism	73	0.0042566	0.1938616	0.2335804
91	CDT1 association with the CDC6:ORC:origin complex	44	0.0043894	0.2484848	0.2335804
63	Assembly of the pre-replicative complex	53	0.0045714	0.2254809	0.2335804
882	Selenocysteine synthesis	65	0.0046133	0.2035213	0.2335804
394	Hh mutants are degraded by ERAD	46	0.0046841	0.2412793	0.2335804
641	PLC beta mediated events	29	0.0048176	-0.3026493	0.2335804
595	Nicotinamide salvaging	10	0.0049132	-0.5137438	0.2335804
304	Formation of a pool of free 40S subunits	73	0.0055667	0.1880164	0.2377549
131	Chaperone Mediated Autophagy	12	0.0058416	0.4596733	0.2377549
405	Immune System	1091	0.0059458	-0.0509788	0.2377549
649	Peptide chain elongation	64	0.0061192	0.1984702	0.2377549
1058	Transcriptional regulation by RUNX1	122	0.0065743	0.1429326	0.2377549
1070	Translesion Synthesis by POLH	16	0.0067669	0.3911753	0.2377549
467	L13a-mediated translational silencing of Ceruloplasmin expression	82	0.0069576	0.1727827	0.2377549
1093	UCH proteinases	69	0.0073759	0.1868737	0.2377549
976	Stimuli-sensing channels	35	0.0074324	0.2616810	0.2377549
472	Late endosomal microautophagy	21	0.0077462	0.3358381	0.2377549
187	DNA Replication Pre-Initiation	66	0.0077959	0.1897193	0.2377549
271	Eukaryotic Translation Elongation	67	0.0079325	0.1878934	0.2377549
199	Degradation of AXIN	46	0.0082584	0.2253584	0.2377549
1095	Ubiquitin Mediated Degradation of Phosphorylated Cdc25A	43	0.0083350	0.2327759	0.2377549
1129	p53-Independent DNA Damage Response	43	0.0083350	0.2327759	0.2377549
1130	p53-Independent G1/S DNA damage checkpoint	43	0.0083350	0.2327759	0.2377549
1033	The role of GTSE1 in G2/M progression after G2 checkpoint	51	0.0087507	0.2124869	0.2435265
894	Signal Transduction	1263	0.0092629	-0.0452683	0.2476525
690	Processive synthesis on the C-strand of the telomere	17	0.0094250	-0.3638437	0.2476525
859	SCF-beta-TrCP mediated degradation of Emi1	46	0.0095501	0.2211270	0.2476525
203	Degradation of beta-catenin by the destruction complex	67	0.0100529	0.1821681	0.2548977
564	NOTCH3 Activation and Transmission of Signal to the Nucleus	15	0.0103480	0.3824910	0.2551152
927	Signaling by Interleukins	256	0.0107838	-0.0932152	0.2551152
539	Mitotic Anaphase	185	0.0112013	0.1086819	0.2551152
47	Anti-inflammatory response favouring Leishmania parasite infection	51	0.0113647	-0.2051654	0.2551152
475	Leishmania parasite growth and survival	51	0.0113647	-0.2051654	0.2551152

Run multi-dimensional enrichment analysis

xl <- list("psf"=dma1a,"psb"=dma2a,"hcy"=dma3a,"folate"=dma4a,"vitb12"= dma5a)
xxl <- lapply(X = xl,run_mitch_rank)  
xxxl <- lapply(xxl,function(xxl) { xxl$genenames <- rownames(xxl) ; xxl} )
xxll <- join_all(xxxl,by="genenames")
rownames(xxll) <- xxll$genenames
xxll$genenames=NULL
colnames(xxll) <- names(xl)
head(xxll) %>% kbl() %>% kable_paper("hover", full_width = F)

	psf	psb	hcy	folate	vitb12
A1BG	-0.5657309	-1.4174791	-0.7558603	0.0645071	0.0892108
AAAS	-0.6107363	-0.8349572	0.2709769	0.2348791	0.3931456
AACS	-0.3554806	-0.6653036	-0.0617422	0.4633213	0.7147652
AAGAB	-1.2687914	-1.5150322	1.3762765	-0.8286218	0.1746070
AAK1	-0.4346004	-1.0208703	-0.2475015	1.5236908	0.8921750
AAMP	-1.5657801	-0.8688060	1.7589146	-0.6785549	-0.0254100

capture.output(
        res <- mitch_calc(xxll,genesets = genesets,priority = "significance"),
        file = "/dev/null", append = FALSE,
        type = c("output", "message"), split = FALSE)

## Note: When prioritising by significance (ie: small 
##             p-values), large effect sizes might be missed.

head(res$enrichment_result,50) %>% kbl() %>% kable_paper("hover", full_width = F) 

	set	setSize	pMANOVA	s.psf	s.psb	s.hcy	s.folate	s.vitb12	p.psf	p.psb	p.hcy	p.folate	p.vitb12	s.dist	SD	p.adjustMANOVA
504	Metabolism of RNA	498	0.0000000	-0.0697885	-0.0687051	0.0944120	0.1282046	0.0973755	0.0085719	0.0096577	0.0003756	0.0000014	0.0002441	0.2107673	0.0972555	0.0000000
114	Cell Cycle	482	0.0000014	-0.0698591	-0.0713072	0.1123523	0.0557405	0.0101540	0.0095721	0.0081778	0.0000307	0.0387190	0.7065268	0.1606181	0.0798799	0.0007757
116	Cell Cycle, Mitotic	390	0.0000020	-0.0490737	-0.0712338	0.1086585	0.0877162	0.0373771	0.0999738	0.0169410	0.0002692	0.0032742	0.2102537	0.1684646	0.0803219	0.0007757
819	Regulation of expression of SLITs and ROBOs	117	0.0000029	0.0316131	-0.1219789	0.1358073	0.1891245	0.1564299	0.5560760	0.0231048	0.0114286	0.0004269	0.0035735	0.3075065	0.1264760	0.0008228
1134	rRNA processing	149	0.0000043	0.0123926	-0.1268995	0.0626039	0.1630403	0.2015577	0.7948701	0.0077501	0.1890291	0.0006227	0.0000233	0.2956078	0.1302194	0.0008753
483	M Phase	272	0.0000051	-0.0600325	-0.0894121	0.1157945	0.1074240	0.0437637	0.0908399	0.0117768	0.0011051	0.0024750	0.2176937	0.1961173	0.0944708	0.0008753
1135	rRNA processing in the nucleus and cytosol	145	0.0000054	0.0219109	-0.1295323	0.0696364	0.1623527	0.1948467	0.6501424	0.0073218	0.1494244	0.0007746	0.0000546	0.2939925	0.1285029	0.0008753
686	Processing of Capped Intron-Containing Pre-mRNA	175	0.0000072	-0.1380200	-0.0158566	0.1107336	0.1234802	0.0463992	0.0017206	0.7188163	0.0119129	0.0050427	0.2920806	0.2212761	0.1069471	0.0010266
1066	Translation	225	0.0000083	-0.0171916	-0.0613936	0.0858546	0.1491903	0.1243209	0.6588687	0.1148726	0.0274549	0.0001268	0.0014064	0.2216962	0.0913532	0.0010522
32	Activation of NMDA receptors and postsynaptic events	43	0.0000107	0.1916652	0.2262815	-0.2763243	-0.2296518	-0.1577732	0.0298455	0.0103306	0.0017363	0.0092474	0.0737810	0.4918600	0.2397095	0.0012218
542	Mitotic Metaphase and Anaphase	186	0.0000185	-0.0286868	-0.0546685	0.0949508	0.1684906	0.1069017	0.5021271	0.2008816	0.0263043	0.0000803	0.0123702	0.2294434	0.0950945	0.0019208
495	Major pathway of rRNA processing in the nucleolus and cytosol	140	0.0000221	0.0188465	-0.1166571	0.0718544	0.1620707	0.1872003	0.7013929	0.0176070	0.1437443	0.0009727	0.0001389	0.2836159	0.1219954	0.0020450
539	Mitotic Anaphase	185	0.0000233	-0.0294864	-0.0535774	0.0951617	0.1658729	0.1086819	0.4914325	0.2112250	0.0263682	0.0001080	0.0112013	0.2283009	0.0944555	0.0020450
591	Neuronal System	127	0.0000254	0.0918258	0.1029264	-0.1944369	-0.1131847	-0.0491564	0.0749589	0.0459315	0.0001622	0.0281631	0.3404874	0.2684375	0.1292364	0.0020684
1076	Transmission across Chemical Synapses	93	0.0000475	0.0764105	0.1252860	-0.2295945	-0.1158070	-0.0533191	0.2040239	0.0372722	0.0001346	0.0542095	0.3754541	0.3008371	0.1438222	0.0036098
882	Selenocysteine synthesis	65	0.0000722	0.1117126	-0.1438314	0.0982550	0.2550419	0.2035213	0.1200093	0.0453040	0.1714929	0.0003846	0.0046133	0.3863787	0.1534818	0.0049671
649	Peptide chain elongation	64	0.0000776	0.1142242	-0.1075982	0.1252295	0.2482024	0.1984702	0.1146812	0.1372853	0.0837184	0.0006067	0.0061192	0.3759014	0.1363478	0.0049671
951	Signaling by ROBO receptors	146	0.0000784	0.0126730	-0.1132043	0.0852251	0.1590537	0.1440392	0.7923784	0.0186913	0.0766766	0.0009514	0.0027676	0.2574578	0.1114902	0.0049671
592	Neurotransmitter receptors and postsynaptic signal transmission	73	0.0000870	0.0835091	0.1797770	-0.1819166	-0.1807279	-0.1231870	0.2182729	0.0080317	0.0073119	0.0077043	0.0693446	0.3467345	0.1660719	0.0050750
600	Nonsense Mediated Decay (NMD) enhanced by the Exon Junction Complex (EJC)	81	0.0000957	0.0980566	-0.0574147	0.1481809	0.2064458	0.1299576	0.1279686	0.3728050	0.0214238	0.0013489	0.0436475	0.3072100	0.0990018	0.0050750
602	Nonsense-Mediated Decay (NMD)	81	0.0000957	0.0980566	-0.0574147	0.1481809	0.2064458	0.1299576	0.1279686	0.3728050	0.0214238	0.0013489	0.0436475	0.3072100	0.0990018	0.0050750
329	G2/M Transition	143	0.0001096	-0.0525791	-0.1188035	0.1440907	0.1139516	0.0882287	0.2796919	0.0145713	0.0030462	0.0191258	0.0696695	0.2416821	0.1143384	0.0050750
271	Eukaryotic Translation Elongation	67	0.0001102	0.1183645	-0.0909257	0.1282772	0.2293684	0.1878934	0.0944637	0.1989314	0.0699274	0.0011901	0.0079325	0.3558742	0.1234765	0.0050750
601	Nonsense Mediated Decay (NMD) independent of the Exon Junction Complex (EJC)	69	0.0001129	0.1156610	-0.0747442	0.1337445	0.2352026	0.1642828	0.0972871	0.2839399	0.0551815	0.0007444	0.0185041	0.3451968	0.1153602	0.0050750
1121	mRNA Splicing	137	0.0001133	-0.1295283	-0.0045752	0.1098446	0.1325899	0.0406440	0.0091133	0.9266223	0.0270098	0.0075979	0.4132702	0.2193088	0.1044721	0.0050750
881	Selenoamino acid metabolism	73	0.0001201	0.1113734	-0.1332414	0.0921539	0.2213148	0.1938616	0.1005956	0.0494802	0.1742760	0.0011005	0.0042566	0.3538534	0.1397118	0.0050750
337	GTP hydrolysis and joining of the 60S ribosomal subunit	83	0.0001201	0.0861890	-0.1002613	0.1044792	0.2135606	0.1857288	0.1756726	0.1151806	0.1006706	0.0007900	0.0035154	0.3293935	0.1230240	0.0050750
541	Mitotic G2-G2/M phases	145	0.0001412	-0.0505255	-0.1120456	0.1375310	0.1147165	0.0970070	0.2955979	0.0203578	0.0044063	0.0175490	0.0446141	0.2378909	0.1113799	0.0055117
467	L13a-mediated translational silencing of Ceruloplasmin expression	82	0.0001432	0.0855665	-0.0929431	0.1111155	0.2172992	0.1727827	0.1814311	0.1466183	0.0826635	0.0006873	0.0069576	0.3246221	0.1189619	0.0055117
1107	Viral mRNA Translation	65	0.0001449	0.1103411	-0.1081976	0.1425101	0.2192386	0.1654113	0.1246255	0.1321162	0.0473197	0.0022752	0.0213216	0.3458576	0.1260804	0.0055117
672	Post NMDA receptor activation events	37	0.0001830	0.1546247	0.2200592	-0.2491698	-0.2340015	-0.1864568	0.1039669	0.0206602	0.0087836	0.0138628	0.0499139	0.4732265	0.2272360	0.0067346
1122	mRNA Splicing - Major Pathway	129	0.0001899	-0.1219346	-0.0130350	0.1209207	0.1369307	0.0405473	0.0171699	0.7989424	0.0181166	0.0074456	0.4281623	0.2237273	0.1057262	0.0067694
865	SRP-dependent cotranslational protein targeting to membrane	81	0.0001985	0.0317143	-0.1410293	0.1220391	0.2127624	0.1384361	0.6225179	0.0285694	0.0581573	0.0009549	0.0316257	0.3165770	0.1357692	0.0068629
304	Formation of a pool of free 40S subunits	73	0.0002125	0.0769394	-0.1186926	0.1178843	0.2103878	0.1880164	0.2566796	0.0801364	0.0822149	0.0019203	0.0055667	0.3369236	0.1308441	0.0071312
639	PKA-mediated phosphorylation of CREB	11	0.0003012	0.0031539	0.4455634	-0.4441063	-0.4237629	-0.2177834	0.9855549	0.0105203	0.0107763	0.0149730	0.2112030	0.7891588	0.3679791	0.0098176
105	Cap-dependent Translation Initiation	90	0.0003689	0.0643874	-0.0735431	0.1325721	0.1773119	0.1414869	0.2923162	0.2290518	0.0301276	0.0037261	0.0206533	0.2803350	0.0993439	0.0113771
272	Eukaryotic Translation Initiation	90	0.0003689	0.0643874	-0.0735431	0.1325721	0.1773119	0.1414869	0.2923162	0.2290518	0.0301276	0.0037261	0.0206533	0.2803350	0.0993439	0.0113771
273	Eukaryotic Translation Termination	66	0.0004031	0.0977808	-0.0869678	0.1244672	0.2273361	0.1747084	0.1703184	0.2226400	0.0809056	0.0014296	0.0142779	0.3388529	0.1194547	0.0121040
842	Response of EIF2AK4 (GCN2) to amino acid deficiency	72	0.0004943	0.1034869	-0.1205556	0.1128237	0.1945692	0.1511999	0.1297019	0.0775236	0.0985330	0.0043809	0.0268254	0.3141512	0.1221702	0.0144602
811	Regulation of TP53 Activity through Methylation	16	0.0005365	-0.2614305	-0.3651172	-0.0382878	0.3997767	0.4403481	0.0703430	0.0114831	0.7909959	0.0056458	0.0022985	0.7462241	0.3710475	0.0153047
129	Cellular responses to stress	425	0.0005735	0.0110043	0.0042310	0.0799945	0.0781623	0.0398834	0.7007328	0.8825244	0.0051997	0.0063264	0.1636160	0.1193237	0.0358499	0.0158342
516	Metabolism of proteins	1217	0.0005829	-0.0185399	-0.0151510	0.0377194	0.0552935	0.0352059	0.2942698	0.3914006	0.0328455	0.0017562	0.0464001	0.0793276	0.0335625	0.0158342
641	PLC beta mediated events	29	0.0006067	-0.0895010	0.0573311	-0.1511775	-0.3884567	-0.3026493	0.4045677	0.5934116	0.1591582	0.0002961	0.0048176	0.5259725	0.1758646	0.0160984
892	Separation of Sister Chromatids	141	0.0006322	-0.0593520	-0.0727709	0.0795204	0.1616027	0.1047995	0.2255775	0.1373213	0.1044461	0.0009657	0.0323603	0.2285609	0.1038003	0.0163952
582	Negative regulation of NMDA receptor-mediated neuronal transmission	11	0.0007500	0.5745218	0.1932348	-0.4029953	-0.1861340	-0.2347148	0.0009702	0.2672900	0.0206808	0.2852689	0.1778278	0.7871195	0.3933599	0.0190173
100	Ca-dependent events	21	0.0009395	0.0022533	0.2836061	-0.1912015	-0.3921415	-0.2983734	0.9857476	0.0245341	0.1295458	0.0018735	0.0179921	0.5998309	0.2686950	0.0233031
638	PKA activation	10	0.0010733	0.0035906	0.4195152	-0.4603915	-0.3905873	-0.2346688	0.9843192	0.0216398	0.0117196	0.0324983	0.1989413	0.7717474	0.3563011	0.0258752
392	Heparan sulfate/heparin (HS-GAG) metabolism	19	0.0010885	0.0622388	-0.1690195	-0.3476356	-0.3031258	-0.1695859	0.6387955	0.2024044	0.0087367	0.0222333	0.2008959	0.5233894	0.1597053	0.0258752
922	Signaling by GPCR	195	0.0011797	0.0171414	0.0709382	-0.0533887	-0.1448175	-0.0975409	0.6814921	0.0893869	0.2011190	0.0005234	0.0195016	0.1966284	0.0866568	0.0274705
194	Defective CFTR causes cystic fibrosis	51	0.0012240	-0.0864399	-0.1616559	0.0427227	0.2374151	0.2624751	0.2862738	0.0461141	0.5981799	0.0033969	0.0012008	0.4008601	0.1893018	0.0278252

unlink("multi_mitch.pdf")
     capture.output(
        mitch_plots(res,outfile="multi_mitch.pdf")
        , file = "/dev/null", append = FALSE,
        type = c("output", "message"), split = FALSE)

## ```

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```

length(which(res$enrichment_result$p.adjustMANOVA<0.05))

## [1] 67

hm<-as.matrix(res$enrichment_result[1:40,4:8])
rownames(hm)<-res$enrichment_result[1:40,1]
heatmap.2(hm,trace ="none",scale = "none",dendrogram = "none",margins = c(7,25),cexCol = 0.7)

Session information

sessionInfo()

## R version 4.0.5 (2021-03-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 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/liblapack.so.3
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
##  [1] grid      stats4    parallel  stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] ENmix_1.25.1                                       
##  [2] doParallel_1.0.16                                  
##  [3] qqman_0.1.8                                        
##  [4] RCircos_1.2.1                                      
##  [5] DMRcatedata_2.6.0                                  
##  [6] ExperimentHub_1.14.2                               
##  [7] AnnotationHub_2.20.2                               
##  [8] BiocFileCache_1.12.1                               
##  [9] dbplyr_2.1.1                                       
## [10] beeswarm_0.3.1                                     
## [11] reshape2_1.4.4                                     
## [12] gplots_3.1.1                                       
## [13] eulerr_6.1.0                                       
## [14] R.utils_2.10.1                                     
## [15] R.oo_1.24.0                                        
## [16] R.methodsS3_1.8.1                                  
## [17] plyr_1.8.6                                         
## [18] RColorBrewer_1.1-2                                 
## [19] forestplot_1.10.1                                  
## [20] checkmate_2.0.0                                    
## [21] magrittr_2.0.1                                     
## [22] kableExtra_1.3.4                                   
## [23] mitch_1.0.10                                       
## [24] DMRcate_2.2.3                                      
## [25] IlluminaHumanMethylation450kmanifest_0.4.0         
## [26] topconfects_1.4.0                                  
## [27] limma_3.44.3                                       
## [28] GEOquery_2.56.0                                    
## [29] missMethyl_1.22.0                                  
## [30] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [31] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0 
## [32] minfi_1.34.0                                       
## [33] bumphunter_1.30.0                                  
## [34] locfit_1.5-9.4                                     
## [35] iterators_1.0.13                                   
## [36] foreach_1.5.1                                      
## [37] Biostrings_2.56.0                                  
## [38] XVector_0.28.0                                     
## [39] SummarizedExperiment_1.18.2                        
## [40] DelayedArray_0.14.1                                
## [41] matrixStats_0.58.0                                 
## [42] Biobase_2.48.0                                     
## [43] GenomicRanges_1.40.0                               
## [44] GenomeInfoDb_1.24.2                                
## [45] IRanges_2.22.2                                     
## [46] S4Vectors_0.26.1                                   
## [47] BiocGenerics_0.34.0                                
## 
## loaded via a namespace (and not attached):
##   [1] rappdirs_0.3.3                rtracklayer_1.48.0           
##   [3] GGally_2.1.1                  tidyr_1.1.3                  
##   [5] ggplot2_3.3.3                 bit64_4.0.5                  
##   [7] knitr_1.33                    data.table_1.14.0            
##   [9] rpart_4.1-15                  RCurl_1.98-1.3               
##  [11] AnnotationFilter_1.12.0       generics_0.1.0               
##  [13] GenomicFeatures_1.40.1        preprocessCore_1.50.0        
##  [15] RSQLite_2.2.7                 bit_4.0.4                    
##  [17] webshot_0.5.2                 xml2_1.3.2                   
##  [19] httpuv_1.6.1                  assertthat_0.2.1             
##  [21] xfun_0.23                     hms_1.1.0                    
##  [23] jquerylib_0.1.4               evaluate_0.14                
##  [25] promises_1.2.0.1              fansi_0.4.2                  
##  [27] scrime_1.3.5                  progress_1.2.2               
##  [29] caTools_1.18.2                readxl_1.3.1                 
##  [31] DBI_1.1.1                     geneplotter_1.66.0           
##  [33] htmlwidgets_1.5.3             reshape_0.8.8                
##  [35] purrr_0.3.4                   ellipsis_0.3.2               
##  [37] dplyr_1.0.6                   backports_1.2.1              
##  [39] permute_0.9-5                 calibrate_1.7.7              
##  [41] annotate_1.66.0               biomaRt_2.44.4               
##  [43] vctrs_0.3.8                   ensembldb_2.12.1             
##  [45] cachem_1.0.5                  Gviz_1.32.0                  
##  [47] BSgenome_1.56.0               GenomicAlignments_1.24.0     
##  [49] prettyunits_1.1.1             mclust_5.4.7                 
##  [51] svglite_2.0.0                 cluster_2.1.2                
##  [53] RPMM_1.25                     lazyeval_0.2.2               
##  [55] crayon_1.4.1                  genefilter_1.70.0            
##  [57] labeling_0.4.2                edgeR_3.30.3                 
##  [59] pkgconfig_2.0.3               nlme_3.1-152                 
##  [61] ProtGenerics_1.20.0           nnet_7.3-16                  
##  [63] rlang_0.4.11                  lifecycle_1.0.0              
##  [65] dichromat_2.0-0               cellranger_1.1.0             
##  [67] rngtools_1.5                  base64_2.0                   
##  [69] Matrix_1.3-3                  Rhdf5lib_1.10.1              
##  [71] base64enc_0.1-3               png_0.1-7                    
##  [73] viridisLite_0.4.0             bitops_1.0-7                 
##  [75] KernSmooth_2.23-20            blob_1.2.1                   
##  [77] DelayedMatrixStats_1.10.1     doRNG_1.8.2                  
##  [79] stringr_1.4.0                 nor1mix_1.3-0                
##  [81] readr_1.4.0                   jpeg_0.1-8.1                 
##  [83] scales_1.1.1                  lpSolve_5.6.15               
##  [85] memoise_2.0.0                 zlibbioc_1.34.0              
##  [87] compiler_4.0.5                illuminaio_0.30.0            
##  [89] Rsamtools_2.4.0               DSS_2.36.0                   
##  [91] htmlTable_2.2.1               Formula_1.2-4                
##  [93] MASS_7.3-54                   tidyselect_1.1.1             
##  [95] stringi_1.6.2                 highr_0.9                    
##  [97] yaml_2.2.1                    askpass_1.1                  
##  [99] latticeExtra_0.6-29           sass_0.4.0                   
## [101] VariantAnnotation_1.34.0      tools_4.0.5                  
## [103] rstudioapi_0.13               foreign_0.8-81               
## [105] bsseq_1.24.4                  gridExtra_2.3                
## [107] farver_2.1.0                  digest_0.6.27                
## [109] BiocManager_1.30.15           shiny_1.6.0                  
## [111] quadprog_1.5-8                Rcpp_1.0.6                   
## [113] siggenes_1.62.0               BiocVersion_3.11.1           
## [115] later_1.2.0                   org.Hs.eg.db_3.11.4          
## [117] httr_1.4.2                    AnnotationDbi_1.50.3         
## [119] biovizBase_1.36.0             colorspace_2.0-1             
## [121] rvest_1.0.0                   XML_3.99-0.6                 
## [123] splines_4.0.5                 statmod_1.4.36               
## [125] multtest_2.44.0               irr_0.84.1                   
## [127] systemfonts_1.0.2             xtable_1.8-4                 
## [129] jsonlite_1.7.2                dynamicTreeCut_1.63-1        
## [131] R6_2.5.0                      echarts4r_0.4.0              
## [133] Hmisc_4.5-0                   pillar_1.6.1                 
## [135] htmltools_0.5.1.1             mime_0.10                    
## [137] glue_1.4.2                    fastmap_1.1.0                
## [139] BiocParallel_1.22.0           interactiveDisplayBase_1.26.3
## [141] beanplot_1.2                  codetools_0.2-18             
## [143] utf8_1.2.1                    lattice_0.20-44              
## [145] bslib_0.2.5.1                 tibble_3.1.2                 
## [147] curl_4.3.1                    gtools_3.8.2                 
## [149] openssl_1.4.4                 survival_3.2-11              
## [151] rmarkdown_2.8                 munsell_0.5.0                
## [153] rhdf5_2.32.4                  GenomeInfoDbData_1.2.3       
## [155] HDF5Array_1.16.1              impute_1.62.0                
## [157] gtable_0.3.0