This chapter covers the integration layer between molecular measurements and enrichment backends. It separates feature-level aggregation, identifier harmonization, directional conflict handling, pathway-level late fusion, and contribution tracing so that the provenance of a combined result remains inspectable.
Multi-omics Early Integration
With the rapid development of sequencing technologies, it is very common to perform functional analysis on multi-omics data (e.g., Transcriptomics + Proteomics). A naive approach is to perform enrichment analysis on each omics separately and then intersect the enriched pathways. However, this late-integration strategy completely ignores the fact that different omics might provide weak but consistent signals for the same pathway, which would be filtered out by strict thresholds in single-omics analysis.
To solve this, enrichit introduces an Early Integration pipeline. The core philosophy is simple: Integrate first, enrich later. Decouple the integration layer from the enrichment layer.
1. Statistical Aggregation (aggregate_omics)
Instead of finding the intersection of pathways, we aggregate the evidence at the feature level. aggregate_omics() takes multi-source statistics (e.g., p-values or signed scores) and merges them into a unified evidence score. For p-values, it supports classic methods like fisher or stouffer, and internally converts them to a monotonically increasing score suitable for ranking. It also supports Brown’s method for correlation-aware p-value aggregation and weighted_mean for signed statistics with explicit layer weights.
library (enrichit)# Create mock p-values for RNA and Protein data across 1000 genes set.seed (123 )<- paste0 ("Gene" , 1 : 1000 )<- runif (1000 , 0 , 1 )names (rna_pvals) <- omics_gene_ids<- runif (1000 , 0 , 1 )names (prot_pvals) <- omics_gene_ids# Inject consistent but weak signals into the first 20 genes (pathway of interest) 1 : 20 ] <- runif (20 , 0.01 , 0.08 )1 : 20 ] <- runif (20 , 0.01 , 0.08 )# Aggregate p-values from RNA and Protein data using Fisher's method <- aggregate_omics (x = list (RNA = rna_pvals, PROT = prot_pvals),method = "fisher" ,input = "pvalue" ,feature_type = "gene" # Look at the top aggregated scores head (sort (agg_fisher_res$ score, decreasing = TRUE ))
Gene911 Gene254 Gene9 Gene478 Gene74 Gene20
2.712320 2.551594 2.446322 2.394249 2.366397 2.273862
1a. Correlation-aware Aggregation with Brown’s Method
Fisher’s method is simple and powerful, but it silently assumes that the evidences are independent. Real multi-omics layers are often correlated: RNA and protein do not live on separate planets. If you pretend they are independent, you are inviting double counting.
Brown’s method fixes this by adjusting the degrees of freedom of Fisher’s statistic using an empirical or user-supplied covariance structure. In enrichit, this is exposed directly through aggregate_omics(method = "brown").
set.seed (123 )<- cbind (RNA = rna_pvals[1 : 80 ], PROT = prot_pvals[1 : 80 ])rownames (omics_pmat) <- omics_gene_ids[1 : 80 ]<- matrix (c (4 , 1.2 ,1.2 , 4 ),nrow = 2 <- aggregate_omics (x = omics_pmat,method = "brown" ,input = "pvalue" ,cov_matrix = cov_mathead (sort (agg_brown_res$ score, decreasing = TRUE ))
Gene9 Gene74 Gene20 Gene8 Gene5 Gene2
2.102805 2.038788 1.964581 1.935310 1.897638 1.887833
2. ID Harmonization (harmonize_ids)
Proteomics data often use UniProt IDs or protein groups, while your pathway annotations are usually gene-based. Directly feeding protein-level data to gene set enrichment will cause a huge ID mismatch.
harmonize_ids() acts as a dedicated ID mapping layer. It maps the protein-level aggregated results to the gene-level, and handles many-to-one mappings gracefully using folding strategies like min_p or max_abs.
# Suppose our proteomics data used UniProt IDs (e.g., Q9Y6N7) # and we need to map them back to standard Gene Symbols <- data.frame (source_id = c ("P12345" , "P12346" , "Q9Y6N7" ),target_id = c ("Gene1" , "Gene1" , "Gene2" ) # P12345 and P12346 both map to Gene1 # For demonstration, we create a mock protein-level aggregated result <- aggregate_omics (x = list (PROT = prot_pvals[1 : 3 ]),method = "fisher" ,input = "pvalue" ,feature_type = "protein" names (protein_agg_res$ score) <- c ("P12345" , "P12346" , "Q9Y6N7" )$ feature_id <- c ("P12345" , "P12346" , "Q9Y6N7" )# Map and collapse protein-level features to gene-level <- harmonize_ids (x = protein_agg_res, # Assuming this is an aggregated result from proteomics mapping = id_mapping_df,from = "protein" ,to = "gene" ,collapse = "min_p" # If multiple proteins map to one gene, take the most significant one
$input_type
[1] "pvalue"
$feature_type
[1] "gene"
$feature_id
[1] "Gene1" "Gene2"
$score
Gene1 Gene2
1.390788 1.097789
$pvalue
Gene1 Gene2
0.04066417 0.07983821
attr(,"class")
[1] "omics_aggregated"
3. Handling Directional Conflicts (conflict_policy)
When integrating signed statistics across omics (e.g., transcriptomics and proteomics), you often encounter conflicting directions—a gene might be significantly up-regulated in RNA but down-regulated in Protein.
Instead of naively averaging them out to zero, aggregate_omics() introduces a conflict_policy parameter when input = "signed_score". You can choose "strict" to completely drop conflicting features, or "penalty" to halve their aggregated scores. This actively filters out biological noise and reduces false positives before enrichment.
# Example with conflicting directions between RNA and PROT <- - log10 (rna_pvals) * rep (c (1 , - 1 ), length.out = length (rna_pvals))<- - log10 (prot_pvals) * rep (c (1 , 1 , - 1 , - 1 ), length.out = length (prot_pvals))names (rna_signed) <- omics_gene_idsnames (prot_signed) <- omics_gene_ids<- aggregate_omics (x = list (RNA = rna_signed, PROT = prot_signed),input = "signed_score" ,method = "mean" ,conflict_policy = "strict" # Drops conflicting genes automatically
$input_type
[1] "signed_score"
$feature_type
[1] "gene"
$feature_id
[1] "Gene1" "Gene4" "Gene5" "Gene8" "Gene9" "Gene12"
[7] "Gene13" "Gene16" "Gene17" "Gene20" "Gene21" "Gene24"
[13] "Gene25" "Gene28" "Gene29" "Gene32" "Gene33" "Gene36"
[19] "Gene37" "Gene40" "Gene41" "Gene44" "Gene45" "Gene48"
[25] "Gene49" "Gene52" "Gene53" "Gene56" "Gene57" "Gene60"
[31] "Gene61" "Gene64" "Gene65" "Gene68" "Gene69" "Gene72"
[37] "Gene73" "Gene76" "Gene77" "Gene80" "Gene81" "Gene84"
[43] "Gene85" "Gene88" "Gene89" "Gene92" "Gene93" "Gene96"
[49] "Gene97" "Gene100" "Gene101" "Gene104" "Gene105" "Gene108"
[55] "Gene109" "Gene112" "Gene113" "Gene116" "Gene117" "Gene120"
[61] "Gene121" "Gene124" "Gene125" "Gene128" "Gene129" "Gene132"
[67] "Gene133" "Gene136" "Gene137" "Gene140" "Gene141" "Gene144"
[73] "Gene145" "Gene148" "Gene149" "Gene152" "Gene153" "Gene156"
[79] "Gene157" "Gene160" "Gene161" "Gene164" "Gene165" "Gene168"
[85] "Gene169" "Gene172" "Gene173" "Gene176" "Gene177" "Gene180"
[91] "Gene181" "Gene184" "Gene185" "Gene188" "Gene189" "Gene192"
[97] "Gene193" "Gene196" "Gene197" "Gene200" "Gene201" "Gene204"
[103] "Gene205" "Gene208" "Gene209" "Gene212" "Gene213" "Gene216"
[109] "Gene217" "Gene220" "Gene221" "Gene224" "Gene225" "Gene228"
[115] "Gene229" "Gene232" "Gene233" "Gene236" "Gene237" "Gene240"
[121] "Gene241" "Gene244" "Gene245" "Gene248" "Gene249" "Gene252"
[127] "Gene253" "Gene256" "Gene257" "Gene260" "Gene261" "Gene264"
[133] "Gene265" "Gene268" "Gene269" "Gene272" "Gene273" "Gene276"
[139] "Gene277" "Gene280" "Gene281" "Gene284" "Gene285" "Gene288"
[145] "Gene289" "Gene292" "Gene293" "Gene296" "Gene297" "Gene300"
[151] "Gene301" "Gene304" "Gene305" "Gene308" "Gene309" "Gene312"
[157] "Gene313" "Gene316" "Gene317" "Gene320" "Gene321" "Gene324"
[163] "Gene325" "Gene328" "Gene329" "Gene332" "Gene333" "Gene336"
[169] "Gene337" "Gene340" "Gene341" "Gene344" "Gene345" "Gene348"
[175] "Gene349" "Gene352" "Gene353" "Gene356" "Gene357" "Gene360"
[181] "Gene361" "Gene364" "Gene365" "Gene368" "Gene369" "Gene372"
[187] "Gene373" "Gene376" "Gene377" "Gene380" "Gene381" "Gene384"
[193] "Gene385" "Gene388" "Gene389" "Gene392" "Gene393" "Gene396"
[199] "Gene397" "Gene400" "Gene401" "Gene404" "Gene405" "Gene408"
[205] "Gene409" "Gene412" "Gene413" "Gene416" "Gene417" "Gene420"
[211] "Gene421" "Gene424" "Gene425" "Gene428" "Gene429" "Gene432"
[217] "Gene433" "Gene436" "Gene437" "Gene440" "Gene441" "Gene444"
[223] "Gene445" "Gene448" "Gene449" "Gene452" "Gene453" "Gene456"
[229] "Gene457" "Gene460" "Gene461" "Gene464" "Gene465" "Gene468"
[235] "Gene469" "Gene472" "Gene473" "Gene476" "Gene477" "Gene480"
[241] "Gene481" "Gene484" "Gene485" "Gene488" "Gene489" "Gene492"
[247] "Gene493" "Gene496" "Gene497" "Gene500" "Gene501" "Gene504"
[253] "Gene505" "Gene508" "Gene509" "Gene512" "Gene513" "Gene516"
[259] "Gene517" "Gene520" "Gene521" "Gene524" "Gene525" "Gene528"
[265] "Gene529" "Gene532" "Gene533" "Gene536" "Gene537" "Gene540"
[271] "Gene541" "Gene544" "Gene545" "Gene548" "Gene549" "Gene552"
[277] "Gene553" "Gene556" "Gene557" "Gene560" "Gene561" "Gene564"
[283] "Gene565" "Gene568" "Gene569" "Gene572" "Gene573" "Gene576"
[289] "Gene577" "Gene580" "Gene581" "Gene584" "Gene585" "Gene588"
[295] "Gene589" "Gene592" "Gene593" "Gene596" "Gene597" "Gene600"
[301] "Gene601" "Gene604" "Gene605" "Gene608" "Gene609" "Gene612"
[307] "Gene613" "Gene616" "Gene617" "Gene620" "Gene621" "Gene624"
[313] "Gene625" "Gene628" "Gene629" "Gene632" "Gene633" "Gene636"
[319] "Gene637" "Gene640" "Gene641" "Gene644" "Gene645" "Gene648"
[325] "Gene649" "Gene652" "Gene653" "Gene656" "Gene657" "Gene660"
[331] "Gene661" "Gene664" "Gene665" "Gene668" "Gene669" "Gene672"
[337] "Gene673" "Gene676" "Gene677" "Gene680" "Gene681" "Gene684"
[343] "Gene685" "Gene688" "Gene689" "Gene692" "Gene693" "Gene696"
[349] "Gene697" "Gene700" "Gene701" "Gene704" "Gene705" "Gene708"
[355] "Gene709" "Gene712" "Gene713" "Gene716" "Gene717" "Gene720"
[361] "Gene721" "Gene724" "Gene725" "Gene728" "Gene729" "Gene732"
[367] "Gene733" "Gene736" "Gene737" "Gene740" "Gene741" "Gene744"
[373] "Gene745" "Gene748" "Gene749" "Gene752" "Gene753" "Gene756"
[379] "Gene757" "Gene760" "Gene761" "Gene764" "Gene765" "Gene768"
[385] "Gene769" "Gene772" "Gene773" "Gene776" "Gene777" "Gene780"
[391] "Gene781" "Gene784" "Gene785" "Gene788" "Gene789" "Gene792"
[397] "Gene793" "Gene796" "Gene797" "Gene800" "Gene801" "Gene804"
[403] "Gene805" "Gene808" "Gene809" "Gene812" "Gene813" "Gene816"
[409] "Gene817" "Gene820" "Gene821" "Gene824" "Gene825" "Gene828"
[415] "Gene829" "Gene832" "Gene833" "Gene836" "Gene837" "Gene840"
[421] "Gene841" "Gene844" "Gene845" "Gene848" "Gene849" "Gene852"
[427] "Gene853" "Gene856" "Gene857" "Gene860" "Gene861" "Gene864"
[433] "Gene865" "Gene868" "Gene869" "Gene872" "Gene873" "Gene876"
[439] "Gene877" "Gene880" "Gene881" "Gene884" "Gene885" "Gene888"
[445] "Gene889" "Gene892" "Gene893" "Gene896" "Gene897" "Gene900"
[451] "Gene901" "Gene904" "Gene905" "Gene908" "Gene909" "Gene912"
[457] "Gene913" "Gene916" "Gene917" "Gene920" "Gene921" "Gene924"
[463] "Gene925" "Gene928" "Gene929" "Gene932" "Gene933" "Gene936"
[469] "Gene937" "Gene940" "Gene941" "Gene944" "Gene945" "Gene948"
[475] "Gene949" "Gene952" "Gene953" "Gene956" "Gene957" "Gene960"
[481] "Gene961" "Gene964" "Gene965" "Gene968" "Gene969" "Gene972"
[487] "Gene973" "Gene976" "Gene977" "Gene980" "Gene981" "Gene984"
[493] "Gene985" "Gene988" "Gene989" "Gene992" "Gene993" "Gene996"
[499] "Gene997" "Gene1000"
$score
Gene1 Gene4 Gene5 Gene8 Gene9 Gene12
1.38601093 -1.48055429 1.55057901 -1.57728481 1.69561425 -1.41958769
Gene13 Gene16 Gene17 Gene20 Gene21 Gene24
1.28734945 -1.42091397 1.23963943 -1.59801090 0.19346498 -0.20308187
Gene25 Gene28 Gene29 Gene32 Gene33 Gene36
0.13707152 -0.28381594 0.55471367 -0.15483172 0.25492306 -0.29099074
Gene37 Gene40 Gene41 Gene44 Gene45 Gene48
0.47308504 -0.49209840 0.46223653 -0.35804881 0.71612633 -1.10705275
Gene49 Gene52 Gene53 Gene56 Gene57 Gene60
0.31844691 -0.21851256 0.07358986 -0.62170662 0.53768309 -0.28590560
Gene61 Gene64 Gene65 Gene68 Gene69 Gene72
0.15762503 -0.49677445 0.59924639 -0.47856342 0.05568018 -0.13616648
Gene73 Gene76 Gene77 Gene80 Gene81 Gene84
0.10255746 -0.42937097 0.49340094 -0.70169385 0.50981755 -0.24341626
Gene85 Gene88 Gene89 Gene92 Gene93 Gene96
0.55365003 -0.79085684 0.07277038 -0.64368036 0.70104112 -0.42370194
Gene97 Gene100 Gene101 Gene104 Gene105 Gene108
0.05495944 -0.49515568 0.15913438 -0.07426192 0.18914752 -0.12176958
Gene109 Gene112 Gene113 Gene116 Gene117 Gene120
0.20421580 -0.30197451 0.76791781 -0.72943719 0.31754276 -0.31914372
Gene121 Gene124 Gene125 Gene128 Gene129 Gene132
0.44137419 -0.58088697 0.64564677 -0.64307098 0.88848023 -0.06814228
Gene133 Gene136 Gene137 Gene140 Gene141 Gene144
0.16252143 -0.15204404 0.25191228 -0.47136351 0.29312313 -0.37804951
Gene145 Gene148 Gene149 Gene152 Gene153 Gene156
0.36874225 -0.65850758 0.54668163 -0.26283611 0.26821821 -0.41921173
Gene157 Gene160 Gene161 Gene164 Gene165 Gene168
0.71218614 -0.66967341 0.36201511 -0.52168947 0.68740591 -0.59879386
Gene169 Gene172 Gene173 Gene176 Gene177 Gene180
0.60496504 -0.56578114 0.12585873 -0.23510965 0.28590815 -0.21628033
Gene181 Gene184 Gene185 Gene188 Gene189 Gene192
1.34079876 -0.31955611 0.16151859 -0.17574012 0.09938029 -0.37424411
Gene193 Gene196 Gene197 Gene200 Gene201 Gene204
0.17681861 -0.52035083 0.22274637 -0.32813073 0.34415685 -0.21599387
Gene205 Gene208 Gene209 Gene212 Gene213 Gene216
0.35394433 -0.67125883 0.43642167 -0.29871261 0.62867460 -0.12196279
Gene217 Gene220 Gene221 Gene224 Gene225 Gene228
0.43566388 -0.03033864 0.50513660 -0.10800855 0.64867018 -0.44869238
Gene229 Gene232 Gene233 Gene236 Gene237 Gene240
0.11104482 -0.46238586 0.37689079 -0.88319597 0.82991534 -0.30103090
Gene241 Gene244 Gene245 Gene248 Gene249 Gene252
0.68366009 -0.27743349 0.30110353 -0.69447277 0.36700210 -0.47293915
Gene253 Gene256 Gene257 Gene260 Gene261 Gene264
0.23638648 -0.08373381 0.92887914 -0.14990686 0.31890209 -0.42829458
Gene265 Gene268 Gene269 Gene272 Gene273 Gene276
0.21998780 -0.24368656 1.37724019 -1.17006544 0.74277409 -0.19482637
Gene277 Gene280 Gene281 Gene284 Gene285 Gene288
0.07226883 -0.29718667 0.40722586 -0.38463385 0.74337345 -0.45315811
Gene289 Gene292 Gene293 Gene296 Gene297 Gene300
0.86167017 -0.63783336 0.91783233 -0.30303952 0.06049955 -0.17119761
Gene301 Gene304 Gene305 Gene308 Gene309 Gene312
0.84286309 -0.10724537 0.16902047 -1.47511249 0.25448947 -0.38470082
Gene313 Gene316 Gene317 Gene320 Gene321 Gene324
0.10963486 -0.53176904 0.09318458 -0.76842903 0.08113423 -0.29981729
Gene325 Gene328 Gene329 Gene332 Gene333 Gene336
0.49730375 -0.88330235 1.19114042 -0.51878664 0.18378154 -0.53807228
Gene337 Gene340 Gene341 Gene344 Gene345 Gene348
0.15108973 -0.14078405 0.45094192 -0.62569743 0.24850109 -0.29888578
Gene349 Gene352 Gene353 Gene356 Gene357 Gene360
0.45512205 -1.16468065 0.58480437 -0.16446979 0.44050667 -0.19452138
Gene361 Gene364 Gene365 Gene368 Gene369 Gene372
0.51501180 -0.43560023 0.53572770 -0.99141913 0.60914279 -0.60018086
Gene373 Gene376 Gene377 Gene380 Gene381 Gene384
0.22710223 -0.05015822 0.26903821 -0.11280510 0.28339973 -0.61713929
Gene385 Gene388 Gene389 Gene392 Gene393 Gene396
0.16168725 -0.30506447 1.11913646 -0.34845441 0.25004927 -0.35821439
Gene397 Gene400 Gene401 Gene404 Gene405 Gene408
0.37313440 -0.31435591 0.31914831 -0.27144499 0.50764863 -0.55334631
Gene409 Gene412 Gene413 Gene416 Gene417 Gene420
0.76701645 -0.05768441 0.43957911 -0.42660699 0.12574488 -1.01311633
Gene421 Gene424 Gene425 Gene428 Gene429 Gene432
0.16466916 -0.26127293 0.19238408 -0.20895357 0.41416127 -0.38568046
Gene433 Gene436 Gene437 Gene440 Gene441 Gene444
0.29004705 -0.31558768 0.57797609 -0.53990158 0.68783766 -0.54180242
Gene445 Gene448 Gene449 Gene452 Gene453 Gene456
0.09665229 -0.59326935 0.37324742 -0.29469799 0.38269407 -0.07424863
Gene457 Gene460 Gene461 Gene464 Gene465 Gene468
0.36268914 -0.91451478 0.01466070 -0.75158335 0.90921987 -0.51084229
Gene469 Gene472 Gene473 Gene476 Gene477 Gene480
0.43466580 -0.63418733 0.25200454 -0.24579209 0.26526546 -0.11687527
Gene481 Gene484 Gene485 Gene488 Gene489 Gene492
0.39273657 -0.12601469 0.01700629 -0.27304404 0.30128220 -1.54517992
Gene493 Gene496 Gene497 Gene500 Gene501 Gene504
0.16949569 -0.04675646 0.30026993 -0.23151847 0.23963218 -0.86708482
Gene505 Gene508 Gene509 Gene512 Gene513 Gene516
0.29743826 -0.33024655 0.19748202 -0.55132381 0.06944188 -0.57094414
Gene517 Gene520 Gene521 Gene524 Gene525 Gene528
1.01217198 -0.40339895 0.33641800 -0.89563311 0.21046796 -0.98569938
Gene529 Gene532 Gene533 Gene536 Gene537 Gene540
0.09799569 -0.16040998 0.19791836 -0.36668545 0.44246139 -0.44173225
Gene541 Gene544 Gene545 Gene548 Gene549 Gene552
0.70909797 -0.40755970 0.14649127 -0.21959663 0.15514124 -0.87075427
Gene553 Gene556 Gene557 Gene560 Gene561 Gene564
0.27351756 -0.36414187 0.17127405 -0.89791829 0.71133120 -0.67620743
Gene565 Gene568 Gene569 Gene572 Gene573 Gene576
0.24350512 -0.06859665 0.64509277 -0.80347280 0.16741679 -0.13826862
Gene577 Gene580 Gene581 Gene584 Gene585 Gene588
0.95603105 -0.30625253 0.12051142 -0.70765729 0.07437365 -0.29200590
Gene589 Gene592 Gene593 Gene596 Gene597 Gene600
0.18709819 -0.63416553 0.09999652 -0.58805275 0.26097380 -1.01353541
Gene601 Gene604 Gene605 Gene608 Gene609 Gene612
0.40960191 -0.29138991 0.42717687 -0.48875426 0.34783890 -0.57565736
Gene613 Gene616 Gene617 Gene620 Gene621 Gene624
0.99286707 -0.25609225 0.13258873 -0.99750623 1.13038499 -0.27022486
Gene625 Gene628 Gene629 Gene632 Gene633 Gene636
0.85221065 -0.39938013 0.32807449 -0.18535889 0.35641976 -0.35361883
Gene637 Gene640 Gene641 Gene644 Gene645 Gene648
0.38455100 -0.23875522 0.59338245 -0.21974706 0.68746430 -0.56341916
Gene649 Gene652 Gene653 Gene656 Gene657 Gene660
0.46306209 -0.08946643 0.38539930 -0.17479188 0.47915060 -0.28040709
Gene661 Gene664 Gene665 Gene668 Gene669 Gene672
0.07066621 -0.16975955 0.36676127 -0.60599822 0.28086653 -0.26781722
Gene673 Gene676 Gene677 Gene680 Gene681 Gene684
1.23119872 -0.20696206 0.18763779 -0.44694881 0.39228956 -0.26760158
Gene685 Gene688 Gene689 Gene692 Gene693 Gene696
0.25570746 -0.49596854 0.22448186 -0.63839186 0.42655323 -0.15426510
Gene697 Gene700 Gene701 Gene704 Gene705 Gene708
0.40307105 -0.58762276 0.11003389 -0.04652932 0.27424104 -0.32495402
Gene709 Gene712 Gene713 Gene716 Gene717 Gene720
0.36760652 -0.73955381 0.66077367 -0.21971751 1.20923694 -0.96321136
Gene721 Gene724 Gene725 Gene728 Gene729 Gene732
0.23244708 -0.28136633 0.65028235 -0.19803317 0.23098401 -0.20399635
Gene733 Gene736 Gene737 Gene740 Gene741 Gene744
0.84578228 -0.36075894 0.30945595 -1.49669603 0.44927601 -1.24712181
Gene745 Gene748 Gene749 Gene752 Gene753 Gene756
0.68013323 -0.63413888 0.09451083 -0.34364999 0.45059633 -0.30552458
Gene757 Gene760 Gene761 Gene764 Gene765 Gene768
0.17741324 -0.35987112 0.22729498 -0.31069195 0.30733186 -0.42301776
Gene769 Gene772 Gene773 Gene776 Gene777 Gene780
0.15843198 -0.14214001 0.49758962 -0.46999161 0.56630355 -0.20916204
Gene781 Gene784 Gene785 Gene788 Gene789 Gene792
0.69015378 -0.19822262 0.48480947 -1.08020516 0.35856505 -0.85981869
Gene793 Gene796 Gene797 Gene800 Gene801 Gene804
0.44201323 -0.43417426 0.58739533 -0.43924066 0.56810071 -0.75098395
Gene805 Gene808 Gene809 Gene812 Gene813 Gene816
0.39478173 -0.27769956 0.09532909 -0.19085023 0.26954941 -0.59401465
Gene817 Gene820 Gene821 Gene824 Gene825 Gene828
0.36944310 -1.29345508 0.08206796 -0.03231110 0.19817125 -0.66307836
Gene829 Gene832 Gene833 Gene836 Gene837 Gene840
0.30517087 -0.20640976 0.18394753 -0.37157270 0.16646248 -0.56031821
Gene841 Gene844 Gene845 Gene848 Gene849 Gene852
0.24294147 -0.51551383 0.28665444 -1.33320317 0.37666011 -0.10385543
Gene853 Gene856 Gene857 Gene860 Gene861 Gene864
0.16949598 -0.29867657 0.01280617 -0.03931377 0.30630581 -0.19571591
Gene865 Gene868 Gene869 Gene872 Gene873 Gene876
0.25455245 -0.49727187 0.29964097 -0.42894422 0.36347137 -0.10023195
Gene877 Gene880 Gene881 Gene884 Gene885 Gene888
1.13327981 -0.32912721 0.51925404 -0.71896955 0.73288778 -0.46653369
Gene889 Gene892 Gene893 Gene896 Gene897 Gene900
0.53801377 -0.06255424 0.38485298 -0.18649630 1.05170795 -0.18453257
Gene901 Gene904 Gene905 Gene908 Gene909 Gene912
0.19362561 -0.14673826 0.21262110 -0.09105916 0.14024242 -0.76508566
Gene913 Gene916 Gene917 Gene920 Gene921 Gene924
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Gene925 Gene928 Gene929 Gene932 Gene933 Gene936
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Gene909 0.085934906 0.1945499249
Gene912 -1.282341013 -0.2478303103
Gene913 0.062201272 0.2311094005
Gene916 -0.017970508 -1.0847582808
Gene917 0.228270161 1.1098998619
Gene920 -0.495456789 -0.8359153141
Gene921 0.092381079 0.1059443304
Gene924 -0.067223468 -0.3018081189
Gene925 0.156177817 0.2500623984
Gene928 -0.255956061 -1.1291059337
Gene929 0.862507843 0.2688365971
Gene932 -0.690165755 -0.3593601272
Gene933 0.113158055 1.4912414571
Gene936 -0.799454825 -0.7932846387
Gene937 0.279035497 0.3287369803
Gene940 -1.625459813 -0.1221773231
Gene941 0.010599269 0.2824744400
Gene944 -0.379286353 -0.1749963531
Gene945 1.031863596 1.1740810222
Gene948 -0.466209589 -0.1831768645
Gene949 0.381682564 0.0767830718
Gene952 -0.807225900 -0.1171975489
Gene953 0.019278811 0.6736718554
Gene956 -0.016547221 -0.3241145937
Gene957 0.190152524 0.4277749034
Gene960 -0.370687125 -0.8090058186
Gene961 0.294001160 0.0046199962
Gene964 -0.853940430 -0.6722710299
Gene965 0.041939789 0.4202115481
Gene968 -0.932454461 -1.3526198310
Gene969 0.118028881 0.0292890304
Gene972 -1.336991590 -0.0565246888
Gene973 0.086268424 0.3304146222
Gene976 -0.191639407 -0.1885119472
Gene977 0.023137225 0.8389521547
Gene980 -0.377738586 -0.7253838631
Gene981 0.339251364 0.1247047158
Gene984 -0.202655789 -0.6878372980
Gene985 0.044705949 0.1705074308
Gene988 -0.814585693 -0.3455856418
Gene989 0.718257364 0.2055492013
Gene992 -0.650178728 -0.7811777843
Gene993 0.242491100 0.0666106908
Gene996 -0.081118756 -0.0489454128
Gene997 0.192387983 0.5219813951
Gene1000 -0.963275023 -0.3481812906
attr(,"class")
[1] "omics_aggregated"
4. Distribution to Enrichment Backends
Once the multi-omics data is aggregated and harmonized, you obtain a unified object containing a ranking score.
For GSEA or NSEA , you can directly extract the sorted score vector and feed it to the engine:
<- list (TargetPathway = omics_gene_ids[1 : 20 ])<- sort (agg_fisher_res$ score, decreasing = TRUE )<- gsea (geneList = geneList,gene_sets = omics_pathways,scoreType = "pos" # Fisher/Brown aggregated scores are all positive head (as.data.frame (gsea_res)[, c ("ID" , "setSize" , "enrichmentScore" , "NES" , "pvalue" )])
ID setSize enrichmentScore NES pvalue
1 TargetPathway 20 0.9806122 2.215595 1e-10
For ORA , which requires discrete gene lists, enrichit provides an adapter function select_features_for_ora(). It takes the aggregated object and a cutoff, and returns the selected gene list and background universe, perfectly bridging the gap between continuous multi-omics scores and traditional hypergeometric tests.
# Define a mock pathway containing the 20 genes we injected signal into <- select_features_for_ora (x = agg_fisher_res, cutoff = 0.05 , by = "pvalue" # Only genes with significant aggregated evidence will be selected <- ora (gene = selected_ora_inputs$ gene,universe = selected_ora_inputs$ universe,gene_sets = omics_pathwayshead (as.data.frame (early_ora_res)[, c ("ID" , "SetSize" , "Count" , "pvalue" )])
ID SetSize Count pvalue
1 TargetPathway 20 20 1.707415e-25
By decoupling ID mapping, statistical integration, and pathway enrichment, enrichit provides a clean, robust, and highly extensible pipeline for multi-omics functional analysis.
These workflow helpers are also available from clusterProfiler, which means the aggregated result can be sent directly to high-level functions such as enrichGO(), gseGO(), or nseGO() without changing the underlying multi-omics logic.
5. Late Fusion at the Pathway Level
Early fusion is elegant when your features align cleanly. But sometimes that assumption collapses immediately: transcriptomics might have a background of 20,000 genes, proteomics might only detect 4,000 proteins, and region-based omics may not even live in gene space before annotation. In these cases, forcing everything into one feature matrix is a great way to manufacture missing values and mapping noise.
This is where Late Fusion comes in. Each omics is enriched independently using its own background and preferred backend; then aggregate_enrichment() merges the pathway-level p-values across results. The mathematics is the same family as feature-level aggregation, but the biology is much cleaner because each layer is allowed to speak in its native space first.
<- list (SharedPath = omics_gene_ids[1 : 8 ],RNAPath = omics_gene_ids[9 : 16 ],ProtPath = omics_gene_ids[17 : 24 ]<- c (omics_gene_ids[1 : 6 ], omics_gene_ids[9 : 13 ])<- c (omics_gene_ids[c (1 : 4 , 7 : 8 )], omics_gene_ids[17 : 21 ])<- ora (gene = rna_sig,universe = omics_gene_ids,gene_sets = late_fusion_pathways<- ora (gene = prot_sig,universe = omics_gene_ids[c (1 : 60 )],gene_sets = late_fusion_pathways<- aggregate_enrichment (res_list = list (RNA = rna_ora_res, PROT = prot_ora_res),method = "brown" as.data.frame (late_fusion_res)[, c ("ID" , "pvalue" , "p.adjust" , "Count" )]
ID pvalue p.adjust Count
SharedPath SharedPath 0.01646848 0.03719786 8
RNAPath RNAPath 0.02479857 0.03719786 5
ProtPath ProtPath 0.04647113 0.04647113 5
6. Contribution Tracing
After running a multi-omics integration, you may want to know whether a significant pathway was driven by the transcriptome, the proteome, or their combination. enrichit provides tools to trace the contribution back to the original omics data.
First, you can classify the pattern of each pathway by comparing the merged results with single-omics results:
# Run ORA for individual omics <- ora (gene = omics_gene_ids[rna_pvals < 0.05 ],universe = omics_gene_ids,gene_sets = omics_pathways<- ora (gene = omics_gene_ids[prot_pvals < 0.05 ],universe = omics_gene_ids,gene_sets = omics_pathways# Classify contribution patterns <- classify_omics_pattern (merged_res = early_ora_res,single_res = list (RNA = rna_ora_res, PROT = prot_ora_res),p_cutoff = 0.05 ,by = "pvalue" # enrichit::ora() reports pvalue, not p.adjust head (as.data.frame (classified_ora_res)[, c ("ID" , "pvalue" , "Omics_Pattern" )])
ID pvalue Omics_Pattern
1 TargetPathway 1.707415e-25 Shared
The Omics_Pattern column will label the pathway as Shared, RNA-Specific, PROT-Specific, or Enhanced (1+1>2).
Then, you can extract the gene-level contribution for a specific pathway to see the original statistics of the core enrichment genes:
# Get original multi-omics statistics for genes in the pathway <- get_omics_contribution (res = classified_ora_res, agg = agg_fisher_res, pathway_id = "TargetPathway" head (contribution_df, 3 )
Feature RNA PROT Aggregated_Score Aggregated_Pvalue
1 Gene1 0.02117718 0.07981969 1.903800 0.012479586
2 Gene2 0.02011611 0.04066417 2.178296 0.006632908
3 Gene3 0.02044263 0.07983821 1.916972 0.012106753
This data frame is extremely useful for downstream visualization, such as drawing multi-omics heatmaps for the core enriched genes.