Pathway Analysis of Fatty liver disease. Aherns et al.
Tina Cole
2026
R Markdown
================================================================
1. Load Libraries Needed for Analysis
================================================================
## [1] "Assumed this study would be challenging due to the overwhelming amout of sugar in the current US diet. The challenge was to try to get data here or abroad. The studies outside the states are a lot more difficult to procure. The studies in the states for sugar are limited to specific organ targets or animal, non-human models in most cases; or even a drastically reduced sample size."##
## The downloaded binary packages are in
## /var/folders/rg/x_7b05fn3sj3v_jq8q367xzm0000gn/T//RtmpAzHlVD/downloaded_packages================================================================
2. Acquire Dataset & Prepare for DE Analysis
================================================================
##
## Control Healthy.Obese Steatosis NASH
## 14 27 14 18## [1] TRUE## [1] 33298 21## ID
## 1 7896736
## 2 7896738
## 3 7896740
## 4 7896742
## 5 7896744
## 6 7896746
## Gene title
## 1
## 2
## 3 olfactory receptor family 4 subfamily F member 17///olfactory receptor family 4 subfamily F member 5///olfactory receptor family 4 subfamily F member 4
## 4 uncharacterized LOC100134822///long intergenic non-protein coding RNA 266-1
## 5 olfactory receptor family 4 subfamily F member 29///olfactory receptor family 4 subfamily F member 21///olfactory receptor family 4 subfamily F member 16///olfactory receptor family 4 subfamily F member 3
## 6
## Gene symbol Gene ID
## 1
## 2
## 3 OR4F17///OR4F5///OR4F4 81099///79501///26682
## 4 LOC100134822///LINC00266-1 100134822///140849
## 5 OR4F29///OR4F21///OR4F16///OR4F3 729759///441308///81399///26683
## 6
## UniGene title UniGene symbol
## 1
## 2
## 3
## 4
## 5
## 6## ProbeID Symbol EntrezID
## 3 7896740 OR4F17///OR4F5///OR4F4 <NA>
## 4 7896742 LOC100134822///LINC00266-1 <NA>
## 5 7896744 OR4F29///OR4F21///OR4F16///OR4F3 <NA>
## 10 7896754 LOC100287934///LOC100287497 <NA>
## 11 7896756 FAM87A 157693
## 12 7896759 LINC01128 643837
## GeneTitle
## 3 olfactory receptor family 4 subfamily F member 17///olfactory receptor family 4 subfamily F member 5///olfactory receptor family 4 subfamily F member 4
## 4 uncharacterized LOC100134822///long intergenic non-protein coding RNA 266-1
## 5 olfactory receptor family 4 subfamily F member 29///olfactory receptor family 4 subfamily F member 21///olfactory receptor family 4 subfamily F member 16///olfactory receptor family 4 subfamily F member 3
## 10 uncharacterized LOC100287934///septin 7 pseudogene
## 11 family with sequence similarity 87 member A
## 12 long intergenic non-protein coding RNA 1128================================================================
3. Differential Expression (DE Analysis)
================================================================
## Control Healthy.Obese Steatosis NASH
## 1 1 0 0 0
## 2 1 0 0 0
## 3 1 0 0 0
## 4 1 0 0 0
## 5 1 0 0 0
## 6 0 0 0 1
## 7 0 1 0 0
## 8 1 0 0 0
## 9 1 0 0 0
## 10 1 0 0 0
## 11 0 0 0 1
## 12 0 1 0 0
## 13 0 1 0 0
## 14 0 1 0 0
## 15 0 1 0 0
## 16 0 1 0 0
## 17 0 0 1 0
## 18 0 0 0 1
## 19 0 0 1 0
## 20 0 0 1 0
## 21 0 1 0 0
## 22 0 1 0 0
## 23 0 1 0 0
## 24 0 0 1 0
## 25 0 1 0 0
## 26 0 0 0 1
## 27 0 0 0 1
## 28 0 1 0 0
## 29 1 0 0 0
## 30 0 0 1 0
## 31 0 1 0 0
## 32 0 0 0 1
## 33 0 0 0 1
## 34 0 0 0 1
## 35 0 0 0 1
## 36 0 1 0 0
## 37 0 0 0 1
## 38 0 1 0 0
## 39 0 0 0 1
## 40 1 0 0 0
## 41 1 0 0 0
## 42 0 0 1 0
## 43 0 1 0 0
## 44 0 1 0 0
## 45 0 1 0 0
## 46 0 0 0 1
## 47 0 1 0 0
## 48 0 0 0 1
## 49 1 0 0 0
## 50 0 1 0 0
## 51 0 1 0 0
## 52 0 0 1 0
## 53 0 1 0 0
## 54 0 0 1 0
## 55 1 0 0 0
## 56 0 0 1 0
## 57 0 0 0 1
## 58 0 0 1 0
## 59 0 1 0 0
## 60 0 0 1 0
## 61 0 1 0 0
## 62 1 0 0 0
## 63 0 1 0 0
## 64 0 0 0 1
## 65 0 0 1 0
## 66 0 0 0 1
## 67 0 0 0 1
## 68 0 0 1 0
## 69 0 1 0 0
## 70 0 0 0 1
## 71 0 0 1 0
## 72 0 1 0 0
## 73 0 1 0 0
## attr(,"assign")
## [1] 1 1 1 1
## attr(,"contrasts")
## attr(,"contrasts")$`metadata$Group`
## [1] "contr.treatment"## [1] "EDIT MEE.............It's important to note however that this risk can be mitigated/amplified by other factors. Thus, later we will review some system or pathway analysis."================================================================
4. Visualization and Results Interpretation
================================================================
================================================================
5. Additional DE Analysis, Unlocking Categorical
================================================================
## disease_group
## Control Disease
## 14 59## logFC AveExpr t P.Value adj.P.Val B
## 8067985 1.6581257 6.775381 5.702269 2.242235e-07 0.007465971 6.272205
## 7949995 -0.4404043 7.309512 -5.487978 5.348513e-07 0.008904472 5.537474
## 7946641 0.4541986 5.527712 5.352440 9.210477e-07 0.010222709 5.077742
## 7957221 1.0836217 4.805792 5.200089 1.686022e-06 0.012352448 4.566093
## 7926223 0.4601744 6.958234 5.175874 1.854889e-06 0.012352448 4.485300
## 8020827 1.2492322 6.003273 5.071523 2.792822e-06 0.014886257 4.138885## Symbol logFC t P.Value adj.P.Val
## 21264 NCAM2 1.6581257 5.702269 2.242235e-07 0.007465971
## 9575 MRPL21 -0.4404043 -5.487978 5.348513e-07 0.008904472
## 9213 GALNT18 0.4541986 5.352440 9.210477e-07 0.010222709
## 7128 CAMK1D 0.4601744 5.175874 1.854889e-06 0.012352448
## 10318 TRHDE-AS1///TRHDE 1.0836217 5.200089 1.686022e-06 0.012352448
## 7319 H2AFY2 0.5971328 5.042337 3.129525e-06 0.014886257###Need addititional explanative HERE###
This is transcriptomics being converted to systems biology. The way to measure this goes beyond basic DE analysis, although DE analysis can be foundation to downstream analysis .
Let’s first do some additional analysis and visualization. We will first filter for low occurrence to improve signal/noise, thereby improving analysis and statistical significance. This improves confidence, reduces false-discovery, increases confidence, and concentrations active transcripts.
================================================================
6. Downstream Analysis: Plots including
================================================================
###Histogram
library(pheatmap)
# ---- 1) Make sure disease_group exists + is ordered ----
disease_group <- factor(disease_group, levels = c("Control", "Disease"))
# Important: name it so it can align to sample IDs
names(disease_group) <- rownames(metadata)
# ---- 2) If you already created expr_by_gene earlier, skip this block ----
# (expr_by_gene = gene symbols x samples)
if (!exists("expr_by_gene")) {
# Build probe -> symbol map from anno
probe2symbol <- anno[, c("ProbeID", "Symbol")]
probe2symbol <- probe2symbol[!is.na(probe2symbol$Symbol) & probe2symbol$Symbol != "", ]
# Expression matrix (probe x sample)
expr <- exprs(gse)
# Keep only probes with symbols
common_probes <- intersect(rownames(expr), probe2symbol$ProbeID)
expr <- expr[common_probes, , drop = FALSE]
# Merge expression with symbols
expr_df <- as.data.frame(expr)
expr_df$ProbeID <- rownames(expr_df)
expr_df <- merge(expr_df, probe2symbol, by = "ProbeID")
# Collapse probes -> gene symbol (mean expression)
library(dplyr)
expr_by_gene <- expr_df %>%
dplyr::select(-ProbeID) %>%
dplyr::group_by(Symbol) %>%
dplyr::summarise(across(where(is.numeric), mean, na.rm = TRUE)) %>%
as.data.frame()
rownames(expr_by_gene) <- expr_by_gene$Symbol
expr_by_gene$Symbol <- NULL
}
# ---- 3) Select top genes from res_all_sym ----
top_n <- 50
genes <- head(res_all_sym$Symbol, top_n)
genes <- genes[genes %in% rownames(expr_by_gene)]
# ---- 4) Build heatmap matrix and Z-score rows ----
mat <- expr_by_gene[genes, , drop = FALSE]
mat_z <- t(scale(t(mat)))
mat_z <- mat_z[complete.cases(mat_z), , drop = FALSE]
# ---- 5) Align and order samples as Control -> Disease ----
grp <- disease_group[colnames(mat_z)] # align to columns
col_order <- order(grp) # Control first, then Disease
mat_z <- mat_z[, col_order, drop = FALSE]
grp <- grp[col_order]
# ---- 6) Annotation + gap between groups ----
anno_col <- data.frame(Group = grp)
rownames(anno_col) <- colnames(mat_z)
gap_after <- sum(grp == "Control")
# ---- 7) Plot ----
pheatmap(
mat_z,
annotation_col = anno_col,
show_colnames = FALSE,
fontsize_row = 7,
cluster_cols = FALSE, # key: keep grouped order
cluster_rows = TRUE,
gaps_col = gap_after, # key: visual separator
clustering_distance_rows = "correlation",
main = paste0("Top ", top_n, " Genes (Symbol): Disease vs Control (Grouped)")
)================================================================
7. Pathway Analysis, Reactome
================================================================
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -5.48798 -0.86757 -0.04836 -0.05441 0.76012 5.70227## [1] 17416## ID
## R-HSA-8868773 R-HSA-8868773
## R-HSA-72312 R-HSA-72312
## R-HSA-72613 R-HSA-72613
## R-HSA-72737 R-HSA-72737
## R-HSA-6791226 R-HSA-6791226
## R-HSA-72706 R-HSA-72706
## Description
## R-HSA-8868773 rRNA processing in the nucleus and cytosol
## R-HSA-72312 rRNA processing
## R-HSA-72613 Eukaryotic Translation Initiation
## R-HSA-72737 Cap-dependent Translation Initiation
## R-HSA-6791226 Major pathway of rRNA processing in the nucleolus and cytosol
## R-HSA-72706 GTP hydrolysis and joining of the 60S ribosomal subunit
## NES pvalue p.adjust qvalue
## R-HSA-8868773 -3.265960 1e-10 4.55e-09 3.236842e-09
## R-HSA-72312 -3.246689 1e-10 4.55e-09 3.236842e-09
## R-HSA-72613 -3.237650 1e-10 4.55e-09 3.236842e-09
## R-HSA-72737 -3.237650 1e-10 4.55e-09 3.236842e-09
## R-HSA-6791226 -3.229929 1e-10 4.55e-09 3.236842e-09
## R-HSA-72706 -3.214599 1e-10 4.55e-09 3.236842e-09
================================================================
8. Pathway Analysis, GO and KEGG - Smokers, up-regulated ONLY
================================================================
## ID Description NES pvalue
## GO:0042254 GO:0042254 ribosome biogenesis -3.073861 1e-10
## GO:0006364 GO:0006364 rRNA processing -2.958005 1e-10
## GO:0002181 GO:0002181 cytoplasmic translation -2.905639 1e-10
## GO:0042274 GO:0042274 ribosomal small subunit biogenesis -2.905614 1e-10
## GO:0016072 GO:0016072 rRNA metabolic process -2.850143 1e-10
## GO:0022613 GO:0022613 ribonucleoprotein complex biogenesis -2.838387 1e-10
## p.adjust qvalue
## GO:0042254 6.807778e-08 6.072515e-08
## GO:0006364 6.807778e-08 6.072515e-08
## GO:0002181 6.807778e-08 6.072515e-08
## GO:0042274 6.807778e-08 6.072515e-08
## GO:0016072 6.807778e-08 6.072515e-08
## GO:0022613 6.807778e-08 6.072515e-08
## ID Description NES pvalue
## hsa03010 hsa03010 Ribosome -3.164054 1.000000e-10
## hsa05171 hsa05171 Coronavirus disease - COVID-19 -2.241228 1.000000e-10
## hsa05014 hsa05014 Amyotrophic lateral sclerosis -1.973762 1.667661e-09
## hsa03040 hsa03040 Spliceosome -2.196415 5.370261e-09
## hsa03008 hsa03008 Ribosome biogenesis in eukaryotes -2.390366 2.638503e-08
## hsa05012 hsa05012 Parkinson disease -1.938957 9.049412e-08
## p.adjust qvalue
## hsa03010 1.750000e-08 1.305263e-08
## hsa05171 1.750000e-08 1.305263e-08
## hsa05014 1.945605e-07 1.451158e-07
## hsa03040 4.698978e-07 3.504802e-07
## hsa03008 1.846952e-06 1.377576e-06
## hsa05012 5.278824e-06 3.937288e-06
House Keeping
Pathway Analysis Section: GO, Kegg, & Reactome Analysis Type,Function,Best For: GO (BP),enrichGO,“Broad biological mechanisms (e.g.,”“Cell Proliferation”“)” KEGG,enrichKEGG,“Well-defined metabolic/signaling maps (e.g.,”“Glycolysis”“)” Reactome,enrichPathway,Detailed molecular reactions and hierarchies
GO DETAIL: Category,Question it Answers,Level of Detail BP (Biological Process),What is the overall goal?,System-wide / Cellular program MF (Molecular Function),What is the chemical task?,Molecular / Biochemical CC (Cellular Component),Where is this happening?,Structural / Spatial
Results Explained: ####### References ################
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## author = {Raivo Kolde},
## year = {2025},
## note = {R package version 1.0.13},
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## doi = {10.32614/CRAN.package.pheatmap},
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## Result_. doi:10.18129/B9.bioc.enrichplot
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