This vignette demonstrates the visualization functions available in
Hotgenes. All examples use fit_Hotgenes, the pre-built limma-based
example object that ships with the package.
For details on creating Hotgenes objects, see 01 Creating Hotgenes Objects. For details on the API, see 02 API and Methods.
library(Hotgenes)
fit_Hotgenes <- readRDS(
system.file("extdata", "fit_Hotgenes.RDS",
package = "Hotgenes",
mustWork = TRUE)
)DEPlot() gives a bird’s-eye view of DE results across all contrasts in
the object. Each bar shows the number of significant features for one
contrast, split by direction (up / down).
DEPlot(fit_Hotgenes, .log2FoldChange = 0, padj_cut = 0.1)
Pass a hotList to highlight specific features across contrasts:
DEPlot(fit_Hotgenes,
hotList = c("CSF1", "IL6"),
.log2FoldChange = 0,
padj_cut = 0.1)
VPlot() renders a standard volcano plot for a single contrast. Points
are coloured by significance and fold-change. Labels are added for the
top hits (or for features in hotList).
VPlot(fit_Hotgenes,
contrasts = "sh_EWS_vs_Ctrl",
.log2FoldChange = 1,
padj_cut = 0.1)
Highlight a gene of interest with hotList:
VPlot(fit_Hotgenes,
contrasts = "sh_EWS_vs_Ctrl",
.log2FoldChange = 1,
padj_cut = 0.1,
point_label_size = 4,
hotList = "CXCL6",
Hide_labels = FALSE)
Venn_Report() identifies features that are significant in more than
one contrast (or contrast direction). It returns both a Venn diagram and
the underlying intersection tables.
Use Report = "Features" to overlap by feature name (ignoring
direction), or Report = "contrast_dir" to treat up- and down-regulated
hits as separate sets (maximum two contrasts).
# Overlapping features (ignoring direction) across three contrasts
venn_out <- fit_Hotgenes |>
DE(
Report = "Features",
contrasts = c("sh_EWS_vs_Ctrl", "Hrs_2_vs_0", "Hrs_6_vs_0"),
padj_cut = 0.1
) |>
Venn_Report(set_name_size = 4, stroke_size = 0.5, text_size = 4)
## Coordinate system already present.
## ℹ Adding new coordinate system, which will replace the existing one.
venn_out$vennD
# Including directionality (up/down) for two contrasts
venn_dir_out <- fit_Hotgenes |>
DE(
Report = "contrast_dir",
contrasts = c("sh_EWS_vs_Ctrl", "Hrs_6_vs_0"),
padj_cut = 0.1
) |>
Venn_Report(set_name_size = 4)
## Coordinate system already present.
## ℹ Adding new coordinate system, which will replace the existing one.
venn_dir_out$vennD
Retrieve the names and gene lists from the intersections:
# Names of features found in all intersecting sets
venn_out$Names
## [1] "Hrs_2_vs_0:Hrs_6_vs_0" "sh_EWS_vs_Ctrl:Hrs_2_vs_0"
## [3] "sh_EWS_vs_Ctrl:Hrs_6_vs_0" "sh_EWS_vs_Ctrl:Hrs_2_vs_0:Hrs_6_vs_0"
# All intersection sets as a named list
venn_out$Intsect |> head()
## $Hrs_2_vs_0
## [1] "NFE2L2" "KEAP1" "PDGFA" "HDAC4" "OXER1" "GAPDH" "MEF2C"
##
## $Hrs_6_vs_0
## [1] "CXCL5" "STAT2" "NR3C1" "MAP3K1" "HSPB2" "MAPK8" "DAXX" "MKNK1" "MAP2K6"
## [10] "IL1B" "BCL6" "TLR3" "GRB2" "IL6R" "IL15" "CREB1" "IL1RN" "RELA"
## [19] "IFIT3" "MAP3K5" "TGFB3" "TGFB2" "IL1A" "CCL20" "PGK1" "MAPK3"
##
## $sh_EWS_vs_Ctrl
## [1] "HIF1A" "C3" "RAC1" "GNB1" "TUBB" "BCL2L1" "CSF1" "PTGER3" "ROCK2"
## [10] "MX2" "HMGN1" "CLTC" "GNAQ" "LY96" "CD40" "CFD" "HRAS" "RHOA"
## [19] "HPRT1" "TCF4" "MX1" "OAS2" "LTB4R2"
##
## $`Hrs_2_vs_0:Hrs_6_vs_0`
## [1] "CXCL8" "TNFAIP3" "CXCL1" "IL11" "PTGS2" "DDIT3" "IFIT2" "TGFBR1"
## [9] "MAFF" "CXCR4" "MAFK" "PTGFR" "FOS" "MYC" "RIPK2" "IL2"
## [17] "MAFG" "CSF2" "TWIST2" "IFIT1" "FLT1"
##
## $`sh_EWS_vs_Ctrl:Hrs_2_vs_0`
## [1] "HMGB2" "MAP3K9" "CEBPB" "IRF1"
##
## $`sh_EWS_vs_Ctrl:Hrs_6_vs_0`
## [1] "C1R" "C1S" "MMP3" "CXCL6" "STAT1" "PTGS1" "HMGB1" "MASP1" "TRAF2" "IFI44"
## [11] "CCL7"DEphe() generates a pheatmap of the top Topn features for a selected
contrast, annotated with sample metadata.
DEphe(fit_Hotgenes,
contrasts = "sh_EWS_vs_Ctrl",
Topn = 5,
cellheight = 10,
cellwidth = 8,
annotation_colors = coldata_palettes(fit_Hotgenes),
annotations = c("Hrs", "sh"))Use label_by to replace the default Feature IDs with any alias column
in the mapper:
DEphe(fit_Hotgenes,
contrasts = "sh_EWS_vs_Ctrl",
label_by = "ensembl_id",
Topn = 5,
cellheight = 10,
cellwidth = 8,
annotation_colors = coldata_palettes(fit_Hotgenes),
annotations = c("Hrs", "sh"))Subset samples on the fly with SampleIDs:
selected_samples <- SampleIDs_(fit_Hotgenes)[1:8]
DEphe(fit_Hotgenes,
contrasts = "sh_EWS_vs_Ctrl",
Topn = 5,
SampleIDs = selected_samples,
cellheight = 10,
cellwidth = 8,
annotation_colors = coldata_palettes(fit_Hotgenes),
arrangeby = c("Hrs", "sh"),
annotations = c("Hrs", "sh"))ExpsPlot() plots the expression trajectory for one or more features
across samples, coloured and faceted by metadata variables. Expression
data and coldata are joined automatically.
ExpsPlot(fit_Hotgenes,
xVar = "Hrs",
yVar = c("CXCL6", "IL6"),
fill = "Hrs",
boxplot = TRUE)
Filter samples on the fly with filter_eval:
ExpsPlot(fit_Hotgenes,
xVar = "Hrs",
yVar = c("CXCL6", "IL6"),
fill = "Hrs",
boxplot = TRUE,
filter_eval = Hrs != 2)
Reorder factor levels using named_levels:
ExpsPlot(fit_Hotgenes,
xVar = "Hrs",
yVar = c("CXCL6", "IL6"),
boxplot = TRUE,
fill = "Hrs",
named_levels = list(Feature = "IL6",
Hrs = c("6", "2", "0")))
BoxPlot() renders a boxplot of expression values for each sample. It
is most useful for QC: checking normalization and identifying outlier
samples.
BoxPlot(fit_Hotgenes)
Restrict to a subset of samples:
BoxPlot(fit_Hotgenes,
SampleIDs = SampleIDs_(fit_Hotgenes)[1:6])
FactoWrapper() runs a full PCA via FactoMineR on the top features for
a given contrast, then clusters samples using HCPC (Hierarchical
Clustering on Principal Components).
FactoOutput <- FactoWrapper(
fit_Hotgenes,
contrasts = "sh_EWS_vs_Ctrl",
coldata_ids = c("Hrs", "sh"),
biplot = FALSE
)
## Appending TopTibble with available aliases: ensembl_idFactoOutput$res_PPI_pa_1
Inspect cluster assignments and top contributing features:
FactoOutput$TopTibble # top features per cluster
## # A tibble: 66 × 10
## Cluster Interpretation Feature v.test `Mean in category` `Overall mean`
## <fct> <fct> <chr> <dbl> <dbl> <dbl>
## 1 1 Above average in cluster IL1R1 2.35 12.1 11.6
## 2 1 Above average in cluster IFI44 1.98 8.57 8.22
## 3 1 Below average in cluster TRAF2 -2.09 9.20 9.59
## 4 1 Below average in cluster JUN -2.10 11.1 11.7
## 5 1 Below average in cluster CD40 -2.15 8.47 8.67
## 6 1 Below average in cluster CXCL2 -2.34 6.92 7.93
## 7 1 Below average in cluster MEF2D -2.43 10.5 10.9
## 8 1 Below average in cluster MEF2D -2.43 10.5 10.9
## 9 1 Below average in cluster MAP3K9 -2.55 4.52 5.68
## 10 1 Below average in cluster MAPKAPK2 -2.62 12.4 12.6
## # ℹ 56 more rows
## # ℹ 4 more variables: `sd in category` <dbl>, `Overall sd` <dbl>, p.value <dbl>,
## # ensembl_id <chr>
FactoOutput$TopGroups # cluster membership per sample
## # A tibble: 2 × 8
## Cluster Interpretation Category `Cla/Mod` `Mod/Cla` Global p.value v.test
## <fct> <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 5 Above average in cluster sh=EWS 66.7 100 50 0.0303 2.17
## 2 5 Below average in cluster sh=Ctrl 0 0 50 0.0303 -2.17coldata_palettes() generates a named list of colour vectors for each
factor in the coldata. This can be passed directly to DEphe() or used
in custom ggplot2 themes.
coldata_palettes(fit_Hotgenes)
## $sh
## Ctrl EWS
## "lightgrey" "black"
##
## $Bio_Rep
## 1 2
## "lightgrey" "black"
##
## $Hrs
## 0 2 6
## "#1B9E77" "#D95F02" "#7570B3"msigdbr_wrapper() returns a named list of gene sets sourced from
MSigDB via the msigdbr package.
H_paths <- msigdbr_wrapper(
species = "human",
set = c("H"),
gene_col = "gene_symbol"
)
length(H_paths)
## [1] 50
H_paths |> names() |> head(5)
## [1] "hallmark_adipogenesis" "hallmark_allograft_rejection"
## [3] "hallmark_androgen_response" "hallmark_angiogenesis"
## [5] "hallmark_apical_junction"fgsea_() accepts ranked vectors returned by
DE(..., Report = "Ranks").
InputRanks <- fit_Hotgenes |>
DE(
Report = "Ranks",
contrasts = "sh_EWS_vs_Ctrl",
Rank_name = "Feature",
padj_cut = 1
)
head(InputRanks)
## $sh_EWS_vs_Ctrl
## MMP3 HMGB2 MEF2D PTGS1 JUN HMGB1
## 9.23196931 8.63826832 6.91859713 6.55635291 6.05519763 5.63392842
## RAC1 MAP3K9 GNB1 TUBB BCL2L1 PTGER3
## 5.34665347 5.22983101 4.93340815 4.75746470 4.69675031 4.44199537
## BIRC2 ROCK2 CXCL3 HMGN1 MEF2A CLTC
## 4.05478250 4.03982126 3.99791138 3.81714901 3.53935236 3.28231690
## GNAQ TRAF2 CD40 CXCL2 HRAS HPRT1
## 3.25811676 3.22697764 3.15857799 2.99626024 2.92880736 2.88052300
## SMAD7 RHOA TCF4 MAPKAPK2 LTB4R2 IL18
## 2.84888019 2.84571370 2.73163608 2.72155543 2.54990152 2.43811463
## RIPK2 PLCB1 RAF1 MAP3K5 HSPB2 C3AR1
## 2.34153355 2.29249232 2.21308496 2.10088683 2.06498990 2.04691182
## SHC1 FLT1 MAPK1 PRKCA AGER BCL6
## 1.99753727 1.99178517 1.97835698 1.97268600 1.91980175 1.88135779
## MAPK14 NR3C1 ALOX12 CCR3 MAP2K1 RELA
## 1.87734699 1.87308583 1.81846355 1.76542547 1.75116207 1.74003567
## MAPK8 CSF2 MEF2C PTGS2 RPS6KA5 NOD1
## 1.70248065 1.69619991 1.66302886 1.65918469 1.57702114 1.57475870
## CCL16 TNFAIP3 KNG1 IL7 HDAC4 CFL1
## 1.52998464 1.52016828 1.45713906 1.37151983 1.36197518 1.35910547
## IL1RN GNAS MAP2K6 C9 TGFB1 CXCR4
## 1.28807643 1.13923758 1.13682039 1.11379029 1.10907917 1.08688136
## TLR9 NFATC3 PTGER4 CREB1 PRKCB PDGFA
## 1.04211822 0.99403714 0.96579934 0.92827009 0.90364739 0.88111963
## IL5 CCL11 IL13 CCL23 TBXA2R CXCR1
## 0.85638689 0.83872011 0.83608986 0.78379499 0.78371335 0.73907642
## IL9 MYC CDC42 CD4 IFNA1 MAFF
## 0.68440559 0.68283007 0.64149979 0.63458716 0.63349190 0.63345455
## CSF3 C5 MAP3K7 C1QA IL21 TNF
## 0.61981183 0.58409989 0.57212744 0.57136316 0.56323090 0.56185131
## IFNB1 TOLLIP IL10 IRF5 CCR4 CD86
## 0.55960333 0.53718110 0.52533802 0.50062435 0.39346853 0.36857620
## PGK1 ITGB2 PTGER2 CYSLTR1 PTGER1 TSLP
## 0.35051302 0.34742610 0.32246173 0.29637546 0.27127102 0.26369319
## CCR2 FXYD2 LIMK1 IL1B GAPDH TLR5
## 0.25680206 0.24915511 0.22882858 0.21907742 0.20411311 0.18423028
## IL3 C7 GUSB KEAP1 MAFG LTB4R
## 0.17969763 0.14711371 0.14167607 0.12396588 0.11430101 0.10602917
## AREG GNGT1 C8A PTK2 IL12A OASL
## 0.08918235 0.07272003 0.06829105 0.06355101 0.03876954 0.03663648
## OXER1 CCR7 CXCL10 MRC1 ATF2 TREM2
## 0.01481433 -0.04508789 -0.05451975 -0.08015497 -0.09419450 -0.11124414
## TGFB3 CXCR2 CXCL8 NOS2 IL22RA2 ELK1
## -0.11444802 -0.17076714 -0.18936019 -0.20298377 -0.21307741 -0.22752012
## HSPB1 TGFB2 C4A MMP9 MAP2K4 C2
## -0.24608467 -0.25794332 -0.31284137 -0.37912828 -0.46437909 -0.48184256
## IRF3 CRP TYROBP ALOX5 PPP1R12B CYSLTR2
## -0.48844459 -0.49206283 -0.52135089 -0.56020537 -0.57878816 -0.63191174
## CCL8 IL12B MYD88 MYL2 FASLG CXCL5
## -0.63357891 -0.64198240 -0.65038405 -0.66616429 -0.66761332 -0.66884649
## ALOX15 CCL20 NLRP3 IL15 DAXX IL22
## -0.67378904 -0.70258198 -0.70696634 -0.71178944 -0.71290303 -0.73049341
## MAFK IL23R GRB2 FOS IL1RAP CCL4
## -0.73646359 -0.73752846 -0.73811230 -0.74235993 -0.80163491 -0.80245589
## C1QB ARG1 IFIT3 TWIST2 TGFBR1 HLA-DRA
## -0.80999697 -0.84137555 -0.84535559 -0.84602905 -0.85655367 -0.85924057
## CCL3 C8B IL23A NOX1 MBL2 RIPK1
## -0.87792208 -0.90576344 -0.90860054 -0.91454048 -0.91689183 -0.94899158
## IL11 CCL21 PTGDR2 PIK3C2G C6 CCL24
## -0.95395457 -0.98968590 -0.99050188 -1.00519745 -1.01649649 -1.03806427
## CD40LG CD163 CCL17 PTGFR DDIT3 MAPKAPK5
## -1.07576140 -1.08307756 -1.08502447 -1.09841469 -1.10025860 -1.10643401
## PLA2G4A LTB MASP2 CCL13 TLR3 IFIT1
## -1.10773788 -1.11457576 -1.12837981 -1.12849322 -1.16468731 -1.17471821
## NFE2L2 CCL22 HLA-DRB1 MAP3K1 IL17A TRADD
## -1.18422620 -1.19042278 -1.19395048 -1.23714807 -1.29525695 -1.34336734
## LTA IL18RAP RAPGEF2 STAT3 DEFA1 CXCL9
## -1.36731548 -1.46906485 -1.49059634 -1.56494743 -1.61268606 -1.61341813
## TLR4 BORCS8-MEF2B TNFSF14 NOD2 CCL5 TLR2
## -1.69536667 -1.69979746 -1.76729005 -1.84041264 -1.86370115 -1.86684727
## IL6R IL2 TLR1 IL1A CD55 MAX
## -1.89635505 -1.90345655 -1.90718059 -1.94654165 -1.96167937 -1.99302345
## IL4 CCR1 IFIT2 IFNG MAPK3 IL10RB
## -1.99733859 -2.02165377 -2.02397947 -2.03671124 -2.07009927 -2.08532800
## CCL19 STAT2 PTGIR CFB IRF7 TLR8
## -2.08885030 -2.12890080 -2.13014814 -2.13209391 -2.15587530 -2.17897238
## TLR7 TLR6 HSH2D CXCL1 MKNK1 RELB
## -2.21150790 -2.25866406 -2.28351885 -2.34192970 -2.43354751 -2.51879480
## OAS2 MX1 NFKB1 IRF1 IL6 CCL7
## -2.62802391 -2.68294859 -2.84916512 -2.84978596 -2.92081969 -2.94422937
## IFI44 CFD LY96 MX2 CEBPB MASP1
## -3.06755028 -3.13692189 -3.22665502 -3.93543312 -4.55119889 -4.56206849
## CSF1 C3 HIF1A STAT1 IL1R1 CXCL6
## -4.66393919 -6.29401804 -7.17057201 -7.55684379 -7.99412875 -8.77610451
## C1S CCL2 C1R
## -12.28407654 -15.82342065 -16.73515625Out_GSEA <- fgsea_(
Ranks = InputRanks,
pathways = H_paths,
nproc = 1,
minSize = 5,
maxSize = Inf
)
## | | | 0% | |================ | 20% | |================================ | 40% | |=============================================== | 60% | |=============================================================== | 80% | |===============================================================================| 100%Out_GSEA |>
fgsea_Results(
contrasts = "sh_EWS_vs_Ctrl",
padj_cut = 0.2,
mode = "D"
) |> head()
## $sh_EWS_vs_Ctrl
## # A tibble: 8 × 9
## pathway pval padj log2err ES NES size leadingEdge sign_NES
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <list> <dbl>
## 1 hallmark_apical_juncti… 5.46e-2 1.74e-1 0.266 0.733 1.46 6 <chr [3]> 1
## 2 hallmark_apoptosis 6.04e-2 1.74e-1 0.262 0.548 1.46 17 <chr [5]> 1
## 3 hallmark_il6_jak_stat3… 5.50e-2 1.74e-1 0.266 -0.530 -1.46 24 <chr [13]> -1
## 4 hallmark_allograft_rej… 1.49e-2 6.85e-2 0.381 -0.474 -1.55 54 <chr [22]> -1
## 5 hallmark_interferon_al… 3.75e-3 2.15e-2 0.432 -0.736 -1.75 14 <chr [8]> -1
## 6 hallmark_inflammatory_… 7.71e-4 8.06e-3 0.477 -0.580 -1.81 43 <chr [20]> -1
## 7 hallmark_complement 1.05e-3 8.06e-3 0.455 -0.708 -1.85 20 <chr [9]> -1
## 8 hallmark_interferon_ga… 1.20e-5 2.77e-4 0.593 -0.700 -2.14 37 <chr [16]> -1Out_GSEA |>
fgsea_Results(
contrasts = "sh_EWS_vs_Ctrl",
padj_cut = 0.2,
mode = "leadingEdge"
) |> head()
## $sh_EWS_vs_Ctrl
## $sh_EWS_vs_Ctrl$hallmark_apical_junction
## [1] "HRAS" "SHC1" "MAPK14"
##
## $sh_EWS_vs_Ctrl$hallmark_apoptosis
## [1] "HMGB2" "JUN" "BCL2L1" "SMAD7" "IL18"
##
## $sh_EWS_vs_Ctrl$hallmark_il6_jak_stat3_signaling
## [1] "IL1R1" "STAT1" "CSF1" "CCL7" "IL6" "IRF1" "CXCL1" "STAT2" "IL10RB"
## [10] "CCR1" "TLR2" "CXCL9" "STAT3"
##
## $sh_EWS_vs_Ctrl$hallmark_allograft_rejection
## [1] "CCL2" "STAT1" "HIF1A" "CSF1" "CCL7" "IL6" "TLR6" "IRF7"
## [9] "CCL19" "IFNG" "CCR1" "IL4" "TLR1" "IL2" "TLR2" "CCL5"
## [17] "CXCL9" "IL18RAP" "CCL22" "TLR3" "CCL13" "LTB"
##
## $sh_EWS_vs_Ctrl$hallmark_interferon_alpha_response
## [1] "C1S" "CSF1" "IFI44" "IRF1" "MX1" "IRF7" "STAT2" "IFIT2"
##
## $sh_EWS_vs_Ctrl$hallmark_inflammatory_response
## [1] "CCL2" "CXCL6" "IL1R1" "HIF1A" "CSF1" "CCL7" "IL6" "IRF1"
## [9] "NFKB1" "IRF7" "PTGIR" "CD55" "IL1A" "TLR1" "TLR2" "CCL5"
## [17] "NOD2" "CXCL9" "IL18RAP" "LTA"
##
## $sh_EWS_vs_Ctrl$hallmark_complement
## [1] "C1R" "C1S" "C3" "CEBPB" "IL6" "IRF1" "CXCL1" "IRF7" "CFB"
##
## $sh_EWS_vs_Ctrl$hallmark_interferon_gamma_response
## [1] "C1R" "CCL2" "C1S" "STAT1" "HIF1A" "MX2" "IFI44" "CCL7" "IL6" "IRF1"
## [11] "NFKB1" "MX1" "OAS2" "IRF7" "CFB" "STAT2"Out_GSEA |>
GSEA_Plots(
contrasts = "sh_EWS_vs_Ctrl",
padj_cut = 0.2,
Topn = 3,
width = 20
)
## $sh_EWS_vs_Ctrl
sig_paths <- Out_GSEA |>
fgsea_Results(
contrasts = "sh_EWS_vs_Ctrl",
padj_cut = 0.2,
mode = "D"
)
if (nrow(sig_paths$sh_EWS_vs_Ctrl) > 0) {
first_geneset_name <- sig_paths$sh_EWS_vs_Ctrl$pathway[1]
plotEnrichment_(
fgseaRes = Out_GSEA,
contrast = "sh_EWS_vs_Ctrl",
genesetName = first_geneset_name
)
}
## Leading edge genes for hallmark_apical_junction:
## ℹ HRAS, SHC1, MAPK14
if (nrow(sig_paths$sh_EWS_vs_Ctrl) > 0) {
first_geneset_name <- sig_paths$sh_EWS_vs_Ctrl$pathway[1]
leadingGenes(
fgseaRes = Out_GSEA,
contrast = "sh_EWS_vs_Ctrl",
genesetName = first_geneset_name
)
}
## [1] "HRAS" "SHC1" "MAPK14"OntologyMethods() and OntologyFunctions() can define and retrieve
custom gene-set sources.
Custom_db <- OntologyMethods(
Ontology_Function = list("msigdbr" = msigdbr_wrapper),
InputChoices = list("msigdbr" = c("CP:REACTOME", "CP:KEGG", "H")),
gene_col_choices = list("msigdbr" = c(
"gene_symbol", "entrez_gene", "ensembl_gene"
)),
species_choices = list("msigdbr" = c("human", "mouse", "rat")),
versions = list("msigdbr" = packageVersion("msigdbr"))
)
custom_paths <- OntologyFunctions(
Methods = Custom_db,
db = "msigdbr",
species = "human",
set = c("CP:REACTOME", "CP:KEGG"),
gene_col = "gene_symbol"
)
length(custom_paths)
## [1] 1787
names(custom_paths)[1:5]
## [1] "reactome_2_ltr_circle_formation"
## [2] "reactome_a_tetrasaccharide_linker_sequence_is_required_for_gag_synthesis"
## [3] "reactome_abacavir_adme"
## [4] "reactome_abacavir_transmembrane_transport"
## [5] "reactome_abc_family_proteins_mediated_transport"custom_paths can be passed directly to fgsea_() or HotgeneSets()
in downstream enrichment and pathway activity workflows.
| Function | Purpose |
|---|---|
DEPlot() |
Bar chart of DE counts across all contrasts |
VPlot() |
Volcano plot for a single contrast |
Venn_Report() |
Venn diagram of overlapping features |
DEphe() |
Heatmap of top hits for a contrast |
ExpsPlot() |
Expression trajectory plots |
BoxPlot() |
Sample-level expression boxplots (QC) |
FactoWrapper() |
PCA + HCPC clustering |
coldata_palettes() |
Colour palettes for metadata factors |
msigdbr_wrapper() |
Retrieve MSigDB gene sets for enrichment |
fgsea_() |
Run GSEA from ranked DE vectors |
fgsea_Results() |
Extract enrichment tables or leading-edge genes |
GSEA_Plots() |
Plot top enriched pathways |
plotEnrichment_() |
Plot one pathway enrichment curve |
leadingGenes() |
Return leading-edge genes for one pathway |
OntologyMethods() / OntologyFunctions() |
Configure and retrieve custom gene-set sources |