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Introduction

Our goals here are to run Logistic SuSiE on differential expression results from TCGA. We want to assess:

  1. If the resulting enrichment results look good/interpretable across multiple/concatenated gene sets
  2. Assess sensitivity to a range of p-value thresholds
  3. Evaluate the potential of the summary stat latent model
library(GSEABenchmarkeR)
library(EnrichmentBrowser)
library(tidyverse)
library(susieR)
library(DT)
source('code/load_gene_sets.R')
source('code/utils.R')
source('code/logistic_susie_vb.R')
source('code/logistic_susie_veb_boost.R')
source('code/latent_logistic_susie.R')

Setup

Load Gene Sets

loadGeneSetX uniformly formats gene sets and generates the \(X\) matrix We can source any gene set from WebGestaltR::listGeneSet()

gs_list <- WebGestaltR::listGeneSet()
gobp <- loadGeneSetX('geneontology_Biological_Process', min.size=50)  # just huge number of gene sets
gobp_nr <- loadGeneSetX('geneontology_Biological_Process_noRedundant', min.size=1)
gomf <- loadGeneSetX('geneontology_Molecular_Function', min.size=1)
kegg <- loadGeneSetX('pathway_KEGG', min.size=1)
reactome <- loadGeneSetX('pathway_Reactome', min.size=1)
wikipathway_cancer <- loadGeneSetX('pathway_Wikipathway_cancer', min.size=1)
wikipathway <- loadGeneSetX('pathway_Wikipathway', min.size=1)

genesets <- list(
  gobp=gobp,
  gobp_nr=gobp_nr,
  gomf=gomf,
  kegg=kegg,
  reactome=reactome,
  wikipathway_cancer=wikipathway_cancer,
  wikipathway=wikipathway
)
load('data/pbmc-purified/deseq2-pbmc-purified.RData')

convert_labels <- function(y, from='SYMBOL', to='ENTREZID'){
  hs <- org.Hs.eg.db::org.Hs.eg.db
  gene_symbols <- names(y)
  symbol2entrez <- AnnotationDbi::select(hs, keys=gene_symbols, columns=c(to, from), keytype = from)
  symbol2entrez <- symbol2entrez[!duplicated(symbol2entrez[[from]]),]
  symbol2entrez <- symbol2entrez[!is.na(symbol2entrez[[to]]),]
  symbol2entrez <- symbol2entrez[!is.na(symbol2entrez[[from]]),]
  rownames(symbol2entrez) <- symbol2entrez[[from]]
  ysub <- y[names(y) %in% symbol2entrez[[from]]]
  names(ysub) <- symbol2entrez[names(ysub),][[to]]
  return(ysub)
}


par(mfrow=c(1,1))
deseq$`CD19+ B` %>% .$padj %>% hist(main='CD19+B p-values')
Loading required package: DESeq2

Version Author Date
a2bdb56 karltayeb 2022-03-29

Fit logistic SuSiE

logistic_susie_driver = function(db, celltype, thresh){
  gs <- genesets[[db]]
  data <- deseq[[celltype]]
  
  # set up binary y
  y <- data %>%
    as.data.frame %>%
    rownames_to_column('gene') %>%
    dplyr::select(gene, padj) %>%
    filter(!is.na(padj)) %>%
    mutate(y = as.integer(padj < thresh)) %>%
    select(gene, y) %>%
    tibble2namedlist %>%
    convert_labels('ENSEMBL')
  
  u <- process_input(gs$X, y)  # subset to common genes
  vb.fit <- logistic.susie(  # fit model
    u$X, u$y, L=10, init.intercept = 0, verbose=1, maxit=100)

  # summarise results
  set.summary <- vb.fit$pip %>% 
    as_tibble(rownames='geneSet') %>%
    rename(pip=value) %>%
    mutate(
      top_component = apply(vb.fit$alpha, 2, which.max),
      active_set = top_component %in% vb.fit$sets$cs_index,
      top_component = paste0('L', top_component),
      cs = purrr::map(top_component, ~tryCatch(
        colnames(gs$X)[get(.x, vb.fit$sets$cs)], error = function(e) list())),
      in_cs = geneSet %in% cs,
      beta = colSums(vb.fit$mu * vb.fit$alpha),
      geneListSize = sum(u$y),
      geneSetSize = colSums(u$X),
      overlap = (u$y %*% u$X)[1,],
      nGenes = length(u$y),
      propSetInList = overlap / geneSetSize,
      oddsRatio = (overlap / (geneListSize - overlap)) / (
        (geneSetSize - overlap) / (nGenes - geneSetSize + overlap)),
    pValueHypergeometric = phyper(
      overlap-1, geneListSize, nGenes, geneSetSize, lower.tail= FALSE),
    db = db,
    celltype = celltype,
    thresh = thresh
    ) %>% left_join(gs$geneSet$geneSetDes)
  return(list(fit = vb.fit, set.summary=set.summary))
}

For each celltype, we fit logistic SuSiE using multiple gene set sources at various threshold of padj.

celltypes <- names(deseq)
pthresh <- c(0.1, 0.01, 0.001, 0.0001, 0.00001, 0.000001)
db_name <- names(genesets)
crossed <- cross3(db_name, celltypes, pthresh)

pbmc_res <- xfun::cache_rds({
  res <- purrr::map(crossed, purrr::lift_dl(logistic_susie_driver))
  for (i in 1:length(res)){  # save some space
    res[[i]]$fit$dat <- NULL
  }
  res
  }, file = 'logistic_susie_pbmc_genesets_pthresh.rds'
)

pbmc_res_set_summary <- dplyr::bind_rows(purrr::map(pbmc_res, ~ pluck(.x, 'set.summary')))

Summary functions

Just a few functions to help streamline looking at output

pval_focussed_table = function(thresh=1e-3, filter_db=NULL, filter_celltype=NULL, top.n=50){
  pbmc_res_set_summary %>%
  filter(
    case_when(
      is.null(filter_db) ~ TRUE,
      !is.null(filter_db) ~ db %in% filter_db
    ) &
    thresh == thresh &
    case_when(
      is.null(filter_celltype) ~ TRUE,
      !is.null(filter_celltype) ~ celltype %in% filter_celltype
    )
  )  %>%
  dplyr::arrange(celltype, db, pValueHypergeometric) %>%
  group_by(celltype, db) %>% slice(1:top.n) %>%
  select(celltype, db, geneSet, description, pip, top_component, oddsRatio, propSetInList, pValueHypergeometric) %>%
  mutate_at(vars(celltype, db), factor) %>%
  datatable(filter = 'top')
}

set_focussed_table = function(thresh=1e-3, filter_db=NULL, filter_celltype=NULL){
  pbmc_res_set_summary %>%
  filter(
    case_when(
      is.null(filter_db) ~ TRUE,
      !is.null(filter_db) ~ db %in% filter_db
    ) &
    thresh == 1e-3 &
    in_cs & active_set &
    case_when(
      is.null(filter_celltype) ~ TRUE,
      !is.null(filter_celltype) ~ celltype %in% filter_celltype
    )
  )  %>%
  dplyr::arrange(celltype, db, desc(pip)) %>%
  select(celltype, db, geneSet, description, pip, top_component, oddsRatio, propSetInList, pValueHypergeometric) %>%
  mutate_at(vars(celltype, geneSet, db), factor) %>%
  datatable(filter = 'top')
}

Results/Explore enrichments

Our goal is to assess 1. The quality of the gene set enrichments we get from each celltype - do reported gene set enrichments seem celltype specific/celltype relevant? - how much “interesting” marginal enrichment do we fail to capture in the multivariate model - how sensitive are we to the choice of pvalue threshold

Results

Lets take a look at what enrichment we’re getting across cell-types.

CD19+ B

pbmc_res_set_summary %>%
  filter(celltype == 'CD19+ B') %>%
  filter(active_set, thresh==1e-4) %>%
  group_by(db, celltype, top_component) %>%
  arrange(db, celltype, top_component, desc(pip)) %>%
  select(geneSet, description, pip) %>% chop(c(geneSet, description, pip)) %>%
  knitr::kable()
Adding missing grouping variables: `db`, `celltype`, `top_component`
db celltype top_component geneSet description pip
gobp CD19+ B L1 GO:0002376 immune system process 0.9999575
gobp CD19+ B L2 GO:0045047, GO:0006613, GO:0072599, GO:0006614 protein targeting to ER , cotranslational protein targeting to membrane , establishment of protein localization to endoplasmic reticulum, SRP-dependent cotranslational protein targeting to membrane 0.96205208, 0.02165616, 0.01453688, 0.00410197
gobp CD19+ B L3 GO:0042773, GO:0042775, GO:0006119 ATP synthesis coupled electron transport , mitochondrial ATP synthesis coupled electron transport, oxidative phosphorylation 0.62449631, 0.36002221, 0.01752387
gobp CD19+ B L5 GO:0001775, GO:0045321, GO:0002366, GO:0002263 cell activation , leukocyte activation , leukocyte activation involved in immune response, cell activation involved in immune response 0.9780541751, 0.0218972721, 0.0005957131, 0.0005770067
gobp CD19+ B L6 GO:0008380, GO:0000377, GO:0000398, GO:0000375, GO:0006397, GO:0050684, GO:0043484, GO:0000380, GO:0048024, GO:0016071 RNA splicing , RNA splicing, via transesterification reactions with bulged adenosine as nucleophile, mRNA splicing, via spliceosome , RNA splicing, via transesterification reactions , mRNA processing , regulation of mRNA processing , regulation of RNA splicing , alternative mRNA splicing, via spliceosome , regulation of mRNA splicing, via spliceosome , mRNA metabolic process 0.822736133, 0.049644134, 0.049644134, 0.033005117, 0.017234053, 0.009417204, 0.007220671, 0.002939483, 0.001834918, 0.001051637
gobp_nr CD19+ B L1 GO:0002446, GO:0036230 neutrophil mediated immunity, granulocyte activation 0.5151440, 0.4857358
gobp_nr CD19+ B L2 GO:0070972 protein localization to endoplasmic reticulum 0.999958
gobp_nr CD19+ B L4 GO:0009123, GO:0009141 nucleoside monophosphate metabolic process, nucleoside triphosphate metabolic process 0.98449756, 0.01648675
gobp_nr CD19+ B L5 GO:0002764 immune response-regulating signaling pathway 0.9961853
gomf CD19+ B L1 GO:0003723 RNA binding 0.999744
gomf CD19+ B L2 GO:0000981, GO:0003700 DNA-binding transcription factor activity, RNA polymerase II-specific, DNA-binding transcription factor activity 0.995003783, 0.005058241
gomf CD19+ B L3 GO:0000977, GO:0001012, GO:0000987, GO:0000978, GO:0000976, GO:0003690, GO:1990837, GO:0001067, GO:0044212 RNA polymerase II regulatory region sequence-specific DNA binding, RNA polymerase II regulatory region DNA binding , proximal promoter sequence-specific DNA binding , RNA polymerase II proximal promoter sequence-specific DNA binding, transcription regulatory region sequence-specific DNA binding , double-stranded DNA binding , sequence-specific double-stranded DNA binding , regulatory region nucleic acid binding , transcription regulatory region DNA binding 0.4872513913, 0.4313993168, 0.0365953412, 0.0243679373, 0.0132413813, 0.0059278956, 0.0013679037, 0.0004652721, 0.0003557059
gomf CD19+ B L6 GO:0003735 structural constituent of ribosome 0.9953441
gomf CD19+ B L7 GO:0003954, GO:0008137, GO:0050136, GO:0016655, GO:0016651 NADH dehydrogenase activity , NADH dehydrogenase (ubiquinone) activity , NADH dehydrogenase (quinone) activity , oxidoreductase activity, acting on NAD(P)H, quinone or similar compound as acceptor, oxidoreductase activity, acting on NAD(P)H 0.32595362, 0.21858318, 0.21858318, 0.17726552, 0.03879761
kegg CD19+ B L1 hsa00190, hsa05012 Oxidative phosphorylation, Parkinson disease 0.9979902, 0.0125303
kegg CD19+ B L2 hsa04640 Hematopoietic cell lineage 0.9997951
kegg CD19+ B L3 hsa03010 Ribosome 1
reactome CD19+ B L1 R-HSA-168256 Immune System 1
reactome CD19+ B L2 R-HSA-156842 , R-HSA-156902 , R-HSA-192823 , R-HSA-2408557, R-HSA-72764 , R-HSA-72689 , R-HSA-1799339, R-HSA-72706 , R-HSA-156827 Eukaryotic Translation Elongation , Peptide chain elongation , Viral mRNA Translation , Selenocysteine synthesis , Eukaryotic Translation Termination , Formation of a pool of free 40S subunits , SRP-dependent cotranslational protein targeting to membrane , GTP hydrolysis and joining of the 60S ribosomal subunit , L13a-mediated translational silencing of Ceruloplasmin expression 0.760651464, 0.124389740, 0.067132923, 0.026006681, 0.017267678, 0.003305376, 0.003131980, 0.001226762, 0.001000830
reactome CD19+ B L3 R-HSA-74160 , R-HSA-212436, R-HSA-73857 Gene expression (Transcription), Generic Transcription Pathway , RNA Polymerase II Transcription 0.88567935, 0.07266257, 0.04183476
reactome CD19+ B L4 R-HSA-163200 , R-HSA-1428517, R-HSA-611105 Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins., The citric acid (TCA) cycle and respiratory electron transport , Respiratory electron transport 0.845323000, 0.149383366, 0.005965744
reactome CD19+ B L5 R-HSA-983168, R-HSA-983169 Antigen processing: Ubiquitination & Proteasome degradation, Class I MHC mediated antigen processing & presentation 0.997049696, 0.003118792
reactome CD19+ B L6 R-HSA-180585 , R-HSA-1234176, R-HSA-211733 , R-HSA-69601 , R-HSA-69610 , R-HSA-69613 , R-HSA-69541 , R-HSA-69229 , R-HSA-75815 , R-HSA-180534 , R-HSA-169911 , R-HSA-349425 , R-HSA-9604323, R-HSA-8854050, R-HSA-1236978, R-HSA-174154 , R-HSA-1169091, R-HSA-174084 , R-HSA-1234174, R-HSA-2262749, R-HSA-983705 , R-HSA-174113 , R-HSA-450408 , R-HSA-69563 , R-HSA-69580 , R-HSA-174184 , R-HSA-351202 , R-HSA-68867 , R-HSA-176409 , R-HSA-350562 , R-HSA-69615 , R-HSA-5362768, R-HSA-174178 , R-HSA-179419 , R-HSA-5610780 Vif-mediated degradation of APOBEC3G , Oxygen-dependent proline hydroxylation of Hypoxia-inducible Factor Alpha , Regulation of activated PAK-2p34 by proteasome mediated degradation , Ubiquitin Mediated Degradation of Phosphorylated Cdc25A , p53-Independent DNA Damage Response , p53-Independent G1/S DNA damage checkpoint , Stabilization of p53 , Ubiquitin-dependent degradation of Cyclin D1 , Ubiquitin-dependent degradation of Cyclin D , Vpu mediated degradation of CD4 , Regulation of Apoptosis , Autodegradation of the E3 ubiquitin ligase COP1 , Negative regulation of NOTCH4 signaling , FBXL7 down-regulates AURKA during mitotic entry and in early mitosis , Cross-presentation of soluble exogenous antigens (endosomes) , APC/C:Cdc20 mediated degradation of Securin , Activation of NF-kappaB in B cells , Autodegradation of Cdh1 by Cdh1:APC/C , Regulation of Hypoxia-inducible Factor (HIF) by oxygen , Cellular response to hypoxia , Signaling by the B Cell Receptor (BCR) , SCF-beta-TrCP mediated degradation of Emi1 , AUF1 (hnRNP D0) binds and destabilizes mRNA , p53-Dependent G1 DNA Damage Response , p53-Dependent G1/S DNA damage checkpoint , Cdc20:Phospho-APC/C mediated degradation of Cyclin A , Metabolism of polyamines , Assembly of the pre-replicative complex , APC/C:Cdc20 mediated degradation of mitotic proteins , Regulation of ornithine decarboxylase (ODC) , G1/S DNA Damage Checkpoints , Hh mutants that don’t undergo autocatalytic processing are degraded by ERAD , APC/C:Cdh1 mediated degradation of Cdc20 and other APC/C:Cdh1 targeted proteins in late mitosis/early G1, APC:Cdc20 mediated degradation of cell cycle proteins prior to satisfation of the cell cycle checkpoint , Degradation of GLI1 by the proteasome 0.7490393108, 0.0319137209, 0.0256472437, 0.0242873384, 0.0242873384, 0.0242873384, 0.0225422403, 0.0120133659, 0.0120133659, 0.0119846233, 0.0108046763, 0.0101911528, 0.0071579856, 0.0070377887, 0.0040655484, 0.0038589122, 0.0037679238, 0.0036141487, 0.0030579708, 0.0030579708, 0.0028683640, 0.0024109404, 0.0017126798, 0.0015009738, 0.0015009738, 0.0013319088, 0.0013173159, 0.0012105047, 0.0011649631, 0.0010246799, 0.0009578296, 0.0009514080, 0.0008622334, 0.0007207196, 0.0006610228
reactome CD19+ B L7 R-HSA-72163 , R-HSA-72172 , R-HSA-109688, R-HSA-73856 , R-HSA-72203 mRNA Splicing - Major Pathway , mRNA Splicing , Cleavage of Growing Transcript in the Termination Region, RNA Polymerase II Transcription Termination , Processing of Capped Intron-Containing Pre-mRNA 0.5457887505, 0.4439546364, 0.0021482288, 0.0021482288, 0.0006461411
reactome CD19+ B L8 R-HSA-76005 , R-HSA-114608, R-HSA-76002 , R-HSA-109582 Response to elevated platelet cytosolic Ca2+ , Platelet degranulation , Platelet activation, signaling and aggregation, Hemostasis 0.7063848487, 0.2314322026, 0.0252623002, 0.0004075344
wikipathway CD19+ B L1 WP477 Cytoplasmic Ribosomal Proteins 1
wikipathway CD19+ B L2 WP111 Electron Transport Chain (OXPHOS system in mitochondria) 0.9999189
wikipathway_cancer CD19+ B L1 WP619 Type II interferon signaling (IFNG) 0.9998636

CD56+ NK

pbmc_res_set_summary %>%
  filter(celltype == 'CD56+ NK') %>%
  filter(active_set, thresh==1e-4) %>%
  group_by(db, celltype, top_component) %>%
  arrange(db, celltype, top_component, desc(pip)) %>%
  select(geneSet, description, pip) %>% chop(c(geneSet, description, pip)) %>%
  knitr::kable()
Adding missing grouping variables: `db`, `celltype`, `top_component`
db celltype top_component geneSet description pip
gobp CD56+ NK L1 GO:0002376 immune system process 0.9999984
gobp CD56+ NK L2 GO:0045047, GO:0072599, GO:0006614, GO:0006613 protein targeting to ER , establishment of protein localization to endoplasmic reticulum, SRP-dependent cotranslational protein targeting to membrane , cotranslational protein targeting to membrane 0.68278614, 0.20978513, 0.07123053, 0.03822676
gobp CD56+ NK L4 GO:0006119, GO:0009123, GO:0042773, GO:0042775, GO:0009161, GO:0009126, GO:0009167, GO:0022900, GO:0022904, GO:0046034 oxidative phosphorylation , nucleoside monophosphate metabolic process , ATP synthesis coupled electron transport , mitochondrial ATP synthesis coupled electron transport, ribonucleoside monophosphate metabolic process , purine nucleoside monophosphate metabolic process , purine ribonucleoside monophosphate metabolic process , electron transport chain , respiratory electron transport chain , ATP metabolic process 0.9815754083, 0.0075172568, 0.0050461669, 0.0035658370, 0.0031462719, 0.0018470112, 0.0018470112, 0.0017166414, 0.0015391764, 0.0009472366
gobp CD56+ NK L5 GO:0008380, GO:0006397, GO:0000375, GO:0000377, GO:0000398, GO:0016071 RNA splicing , mRNA processing , RNA splicing, via transesterification reactions , RNA splicing, via transesterification reactions with bulged adenosine as nucleophile, mRNA splicing, via spliceosome , mRNA metabolic process 0.59074452, 0.22252471, 0.05825611, 0.05441422, 0.05441422, 0.01452235
gobp_nr CD56+ NK L1 GO:0036230, GO:0002446 granulocyte activation , neutrophil mediated immunity 0.6655074, 0.3354429
gobp_nr CD56+ NK L2 GO:0006413 translational initiation 0.9954877
gobp_nr CD56+ NK L3 GO:0042110, GO:0007159 T cell activation , leukocyte cell-cell adhesion 0.985660130, 0.008171918
gobp_nr CD56+ NK L4 GO:0009123, GO:0009141, GO:0009259 nucleoside monophosphate metabolic process, nucleoside triphosphate metabolic process , ribonucleotide metabolic process 0.981592710, 0.015904774, 0.001372449
gobp_nr CD56+ NK L5 GO:0042113 B cell activation 0.9972725
gomf CD56+ NK L1 GO:0003735 structural constituent of ribosome 1
gomf CD56+ NK L2 GO:0000981, GO:0003700 DNA-binding transcription factor activity, RNA polymerase II-specific, DNA-binding transcription factor activity 0.996770584, 0.003356549
gomf CD56+ NK L3 GO:0000977, GO:0000976, GO:0001012, GO:1990837, GO:0003690, GO:0043565, GO:0000987, GO:0001067, GO:0044212 RNA polymerase II regulatory region sequence-specific DNA binding, transcription regulatory region sequence-specific DNA binding , RNA polymerase II regulatory region DNA binding , sequence-specific double-stranded DNA binding , double-stranded DNA binding , sequence-specific DNA binding , proximal promoter sequence-specific DNA binding , regulatory region nucleic acid binding , transcription regulatory region DNA binding 0.5246638008, 0.2816905881, 0.1760785081, 0.0110809288, 0.0067171732, 0.0006260858, 0.0003679068, 0.0002517074, 0.0002268130
kegg CD56+ NK L1 hsa03010 Ribosome 1
kegg CD56+ NK L2 hsa05012 Parkinson disease 0.9999978
kegg CD56+ NK L3 hsa04640 Hematopoietic cell lineage 0.9978541
reactome CD56+ NK L1 R-HSA-168256 Immune System 1
reactome CD56+ NK L2 R-HSA-156842, R-HSA-156902, R-HSA-192823, R-HSA-72764 , R-HSA-72689 Eukaryotic Translation Elongation , Peptide chain elongation , Viral mRNA Translation , Eukaryotic Translation Termination , Formation of a pool of free 40S subunits 0.864742537, 0.106897566, 0.027158260, 0.002413175, 0.001418708
reactome CD56+ NK L3 R-HSA-212436, R-HSA-74160 , R-HSA-73857 Generic Transcription Pathway , Gene expression (Transcription), RNA Polymerase II Transcription 0.68127740, 0.29679295, 0.02222635
reactome CD56+ NK L4 R-HSA-163200 , R-HSA-1428517, R-HSA-611105 Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins., The citric acid (TCA) cycle and respiratory electron transport , Respiratory electron transport 0.993324533, 0.006034017, 0.001575567
reactome CD56+ NK L5 R-HSA-8878171, R-HSA-157118 , R-HSA-8939236 Transcriptional regulation by RUNX1 , Signaling by NOTCH , RUNX1 regulates transcription of genes involved in differentiation of HSCs 0.97128181, 0.04393861, 0.01998012
reactome CD56+ NK L6 R-HSA-983168 , R-HSA-983169 , R-HSA-8951664 Antigen processing: Ubiquitination & Proteasome degradation, Class I MHC mediated antigen processing & presentation , Neddylation 0.8808248324, 0.1189949639, 0.0009720837
wikipathway CD56+ NK L1 WP477 Cytoplasmic Ribosomal Proteins 1
wikipathway CD56+ NK L2 WP111, WP623 Electron Transport Chain (OXPHOS system in mitochondria), Oxidative phosphorylation 0.99086202, 0.01897664

T cell

pbmc_res_set_summary %>%
  filter(celltype == 'T cell') %>%
  filter(active_set, thresh==1e-4) %>%
  group_by(db, celltype, top_component) %>%
  arrange(db, celltype, top_component, desc(pip)) %>%
  select(geneSet, description, pip) %>% chop(c(geneSet, description, pip)) %>%
  knitr::kable()
Adding missing grouping variables: `db`, `celltype`, `top_component`
db celltype top_component geneSet description pip
gobp T cell L1 GO:0001775, GO:0045321 cell activation , leukocyte activation 0.998243491, 0.002864201
gobp T cell L2 GO:0006119, GO:0042773, GO:0042775 oxidative phosphorylation , ATP synthesis coupled electron transport , mitochondrial ATP synthesis coupled electron transport 0.998867321, 0.002306692, 0.002090733
gobp T cell L3 GO:0010941, GO:0008219, GO:0043067, GO:0042981, GO:0012501, GO:0006915, GO:0097190, GO:0048518, GO:0010942, GO:0048522, GO:0048519, GO:0043068, GO:0043065 regulation of cell death , cell death , regulation of programmed cell death , regulation of apoptotic process , programmed cell death , apoptotic process , apoptotic signaling pathway , positive regulation of biological process , positive regulation of cell death , positive regulation of cellular process , negative regulation of biological process , positive regulation of programmed cell death, positive regulation of apoptotic process 0.404035437, 0.367067203, 0.059812581, 0.059590178, 0.042848010, 0.038547811, 0.020693460, 0.009702335, 0.006246010, 0.006037632, 0.005468877, 0.002090144, 0.001933994
gobp T cell L4 GO:0045047, GO:0006614, GO:0072599, GO:0070972, GO:0006613 protein targeting to ER , SRP-dependent cotranslational protein targeting to membrane , establishment of protein localization to endoplasmic reticulum, protein localization to endoplasmic reticulum , cotranslational protein targeting to membrane 0.42251775, 0.37005939, 0.13809458, 0.04385614, 0.03174252
gobp T cell L5 GO:0002376, GO:0006955 immune system process, immune response 0.998468901, 0.002881243
gobp_nr T cell L1 GO:0036230, GO:0002446 granulocyte activation , neutrophil mediated immunity 0.7324297, 0.2703360
gobp_nr T cell L2 GO:0042110 T cell activation 0.9999906
gobp_nr T cell L3 GO:0070972 protein localization to endoplasmic reticulum 0.9998559
gomf T cell L1 GO:0005515 protein binding 1
kegg T cell L1 hsa05010 Alzheimer disease 0.9999993
reactome T cell L1 R-HSA-6798695 Neutrophil degranulation 1
reactome T cell L2 R-HSA-72706 , R-HSA-156827, R-HSA-72689 , R-HSA-72613 , R-HSA-72737 , R-HSA-192823, R-HSA-156902 GTP hydrolysis and joining of the 60S ribosomal subunit , L13a-mediated translational silencing of Ceruloplasmin expression, Formation of a pool of free 40S subunits , Eukaryotic Translation Initiation , Cap-dependent Translation Initiation , Viral mRNA Translation , Peptide chain elongation 0.586005479, 0.400596683, 0.009180040, 0.004668524, 0.004668524, 0.003496445, 0.003174071

CD14+ Monocyte

pbmc_res_set_summary %>%
  filter(celltype == 'CD14+ Monocyte') %>%
  filter(active_set, thresh==1e-4) %>%
  group_by(db, celltype, top_component) %>%
  arrange(db, celltype, top_component, desc(pip)) %>%
  select(geneSet, description, pip) %>% chop(c(geneSet, description, pip)) %>%
  knitr::kable()
Adding missing grouping variables: `db`, `celltype`, `top_component`
db celltype top_component geneSet description pip
gobp CD14+ Monocyte L1 GO:0045321, GO:0001775 leukocyte activation, cell activation 0.9139560, 0.0861039
gobp CD14+ Monocyte L2 GO:0045047, GO:0006614, GO:0006613, GO:0072599, GO:0070972 protein targeting to ER , SRP-dependent cotranslational protein targeting to membrane , cotranslational protein targeting to membrane , establishment of protein localization to endoplasmic reticulum, protein localization to endoplasmic reticulum 0.8615391988, 0.0574691370, 0.0477713854, 0.0336165786, 0.0004015912
gobp CD14+ Monocyte L3 GO:0006119, GO:0042773, GO:0042775 oxidative phosphorylation , ATP synthesis coupled electron transport , mitochondrial ATP synthesis coupled electron transport 0.9990484928, 0.0006152697, 0.0004379516
gobp CD14+ Monocyte L4 GO:0016192, GO:0002376, GO:0006955, GO:0045055, GO:0006810, GO:0042119, GO:0006887, GO:0051234 vesicle-mediated transport , immune system process , immune response , regulated exocytosis , transport , neutrophil activation , exocytosis , establishment of localization 0.9851433899, 0.0210152324, 0.0059841488, 0.0010584611, 0.0009289568, 0.0008104012, 0.0007570762, 0.0007478103
gobp CD14+ Monocyte L5 GO:0006915, GO:0012501, GO:0043067, GO:0042981, GO:0010941, GO:0008219, GO:0097190, GO:0006950, GO:0010942, GO:0050790, GO:0048518, GO:0010033, GO:0070887, GO:2001233, GO:0043065, GO:0043068, GO:0043069, GO:0043066, GO:0051716, GO:0048522, GO:0060548, GO:0050896, GO:0048523, GO:0048519 apoptotic process , programmed cell death , regulation of programmed cell death , regulation of apoptotic process , regulation of cell death , cell death , apoptotic signaling pathway , response to stress , positive regulation of cell death , regulation of catalytic activity , positive regulation of biological process , response to organic substance , cellular response to chemical stimulus , regulation of apoptotic signaling pathway , positive regulation of apoptotic process , positive regulation of programmed cell death, negative regulation of programmed cell death, negative regulation of apoptotic process , cellular response to stimulus , positive regulation of cellular process , negative regulation of cell death , response to stimulus , negative regulation of cellular process , negative regulation of biological process 2.720176e-01, 1.948576e-01, 1.940882e-01, 1.583264e-01, 1.008376e-01, 5.132999e-02, 2.549188e-03, 9.307323e-04, 6.576479e-04, 5.735087e-04, 5.459875e-04, 4.811908e-04, 4.506495e-04, 3.491954e-04, 3.298411e-04, 2.900530e-04, 1.817724e-04, 1.476441e-04, 1.365127e-04, 1.359366e-04, 1.228290e-04, 1.125502e-04, 7.493961e-05, 6.920531e-05
gobp CD14+ Monocyte L6 GO:0006518, GO:0006412, GO:0043043, GO:0043603, GO:0043604 peptide metabolic process , translation , peptide biosynthetic process , cellular amide metabolic process, amide biosynthetic process 0.790995030, 0.119614037, 0.082857945, 0.002543295, 0.001245957
gobp CD14+ Monocyte L7 GO:0008380, GO:0000375, GO:0000377, GO:0000398, GO:0006397, GO:0016071 RNA splicing , RNA splicing, via transesterification reactions , RNA splicing, via transesterification reactions with bulged adenosine as nucleophile, mRNA splicing, via spliceosome , mRNA processing , mRNA metabolic process 0.422448316, 0.196794593, 0.178759471, 0.178759471, 0.021244858, 0.001609996
gobp_nr CD14+ Monocyte L1 GO:0036230, GO:0002446 granulocyte activation , neutrophil mediated immunity 0.97592803, 0.02469514
gobp_nr CD14+ Monocyte L2 GO:0006413 translational initiation 0.999994
gobp_nr CD14+ Monocyte L3 GO:0009123, GO:0009141 nucleoside monophosphate metabolic process, nucleoside triphosphate metabolic process 0.98835280, 0.01259324
gobp_nr CD14+ Monocyte L4 GO:0002521, GO:0042110, GO:0002694, GO:1903706 leukocyte differentiation , T cell activation , regulation of leukocyte activation, regulation of hemopoiesis 0.619222289, 0.358464783, 0.007935017, 0.004456591
gobp_nr CD14+ Monocyte L5 GO:0008380, GO:0006397, GO:1903311 RNA splicing , mRNA processing , regulation of mRNA metabolic process 0.876773133, 0.089632405, 0.004303895
gobp_nr CD14+ Monocyte L6 GO:0090150, GO:0070972, GO:0006605 establishment of protein localization to membrane, protein localization to endoplasmic reticulum , protein targeting 0.756201036, 0.243465201, 0.004950252
gomf CD14+ Monocyte L1 GO:0003723 RNA binding 0.9999998
gomf CD14+ Monocyte L2 GO:0000981, GO:0003700 DNA-binding transcription factor activity, RNA polymerase II-specific, DNA-binding transcription factor activity 0.9998274825, 0.0002279895
gomf CD14+ Monocyte L3 GO:0001012, GO:0000977, GO:0000987, GO:0000978, GO:0000976, GO:1990837, GO:0043565, GO:0044212 RNA polymerase II regulatory region DNA binding , RNA polymerase II regulatory region sequence-specific DNA binding, proximal promoter sequence-specific DNA binding , RNA polymerase II proximal promoter sequence-specific DNA binding, transcription regulatory region sequence-specific DNA binding , sequence-specific double-stranded DNA binding , sequence-specific DNA binding , transcription regulatory region DNA binding 0.6603205658, 0.2924363216, 0.0360620592, 0.0077244907, 0.0030829257, 0.0007025284, 0.0003868975, 0.0001017222
gomf CD14+ Monocyte L4 GO:0003735 structural constituent of ribosome 1
gomf CD14+ Monocyte L5 GO:0016655, GO:0008137, GO:0050136, GO:0003954, GO:0016651 oxidoreductase activity, acting on NAD(P)H, quinone or similar compound as acceptor, NADH dehydrogenase (ubiquinone) activity , NADH dehydrogenase (quinone) activity , NADH dehydrogenase activity , oxidoreductase activity, acting on NAD(P)H 0.8270835588, 0.0778621792, 0.0778621792, 0.0178702201, 0.0003469471
gomf CD14+ Monocyte L6 GO:0045296, GO:0050839 cadherin binding , cell adhesion molecule binding 0.7890801, 0.1881604
kegg CD14+ Monocyte L1 hsa03010 Ribosome 1
kegg CD14+ Monocyte L2 hsa05012 Parkinson disease 0.9999021
reactome CD14+ Monocyte L1 R-HSA-6798695 Neutrophil degranulation 0.9999954
reactome CD14+ Monocyte L2 R-HSA-72766 Translation 1
reactome CD14+ Monocyte L3 R-HSA-163200, R-HSA-611105 Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins., Respiratory electron transport 0.9996110591, 0.0008392269
reactome CD14+ Monocyte L5 R-HSA-72172, R-HSA-72163, R-HSA-72203 mRNA Splicing , mRNA Splicing - Major Pathway , Processing of Capped Intron-Containing Pre-mRNA 0.67495558, 0.30641144, 0.01826453
reactome CD14+ Monocyte L6 R-HSA-198933 Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell 0.9974877
reactome CD14+ Monocyte L7 R-HSA-379726 Mitochondrial tRNA aminoacylation 0.9972986
wikipathway CD14+ Monocyte L1 WP477 Cytoplasmic Ribosomal Proteins 1
wikipathway CD14+ Monocyte L2 WP111 Electron Transport Chain (OXPHOS system in mitochondria) 0.9998312

CD34+

pbmc_res_set_summary %>%
  filter(celltype == 'CD14+') %>%
  filter(active_set, thresh==1e-4) %>%
  group_by(db, celltype, top_component) %>%
  arrange(db, celltype, top_component, desc(pip)) %>%
  select(geneSet, description, pip) %>% chop(c(geneSet, description, pip)) %>%
  knitr::kable()
Adding missing grouping variables: `db`, `celltype`, `top_component`
db celltype top_component geneSet description pip
knitr::knit_exit()