Pathway Analysis of Glucose in pacreatic tissue (utilizing A1C). Zhou et al.

R Markdown

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1. Load Libraries Needed for Analysis

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## [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//RtmprNDOTs/downloaded_packages

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2. Acquire Dataset & Prepare for DE Analysis

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##            age_range  bmi   a1c_status
## GSM1216753       65> 24.7 Non_Diabetic
## GSM1216755     31-50 23.9 Non_Diabetic

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3. Differential Expression (DE Analysis)

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## [1] "Since we are utlizing RNA-seq data for this analyis, we use the appropriate tool: DESeq2."

##      GeneID   baseMean log2FoldChange     lfcSE     stat       pvalue
## 1     50486  387.87464       3.641426 0.5224640 6.969717 3.175799e-12
## 2     51129  821.53255       3.445426 0.5043655 6.831209 8.420211e-12
## 3 101928745   18.24317       4.379092 0.6606858 6.628101 3.400333e-11
## 4 105371893   11.44189       3.310608 0.5198118 6.368859 1.904391e-10
## 5     54541 2168.44139       2.629321 0.4187858 6.278439 3.419886e-10
## 6      9123  221.19176       3.463307 0.5587802 6.197977 5.719332e-10
##           padj          Symbol                       Description
## 1 7.335779e-08            G0S2                    G0/G1 switch 2
## 2 9.724923e-08         ANGPTL4               angiopoietin like 4
## 3 2.618143e-07 LL21NC02-21A1.1   uncharacterized LL21NC02-21A1.1
## 4 1.099738e-06    SLC9A3R1-AS1          SLC9A3R1 antisense RNA 1
## 5 1.579919e-06           DDIT4 DNA damage inducible transcript 4
## 6 2.201847e-06         SLC16A3 solute carrier family 16 member 3

##       GeneID    baseMean log2FoldChange     lfcSE      stat       pvalue
## 1      50486  387.874644       3.641426 0.5224640  6.969717 3.175799e-12
## 2      51129  821.532546       3.445426 0.5043655  6.831209 8.420211e-12
## 3  101928745   18.243174       4.379092 0.6606858  6.628101 3.400333e-11
## 4  105371893   11.441887       3.310608 0.5198118  6.368859 1.904391e-10
## 5      54541 2168.441393       2.629321 0.4187858  6.278439 3.419886e-10
## 6       9123  221.191761       3.463307 0.5587802  6.197977 5.719332e-10
## 7  101928841    4.533614       4.210083 0.6930791  6.074463 1.244031e-09
## 8       4985  256.614621      -1.761315 0.2956129 -5.958181 2.550611e-09
## 9       7058 2971.480528       1.941216 0.3256012  5.961942 2.492573e-09
## 10      1052  218.234995       2.680591 0.4515502  5.936419 2.913150e-09
## 11 101928399   17.585426       2.410073 0.4057354  5.940011 2.850024e-09
## 12    284889  171.898422       3.362225 0.5710738  5.887549 3.919639e-09
## 13      4828  124.774795       2.597629 0.4430634  5.862883 4.548991e-09
## 14      3726  513.346646       2.805663 0.4919300  5.703379 1.174555e-08
## 15      1675   11.622504       2.560852 0.4508246  5.680374 1.344008e-08
## 16     25907  172.126633       1.933956 0.3418051  5.658066 1.530883e-08
## 17 101929918    9.623633       2.828023 0.4995563  5.661070 1.504318e-08
## 18 102725238    5.968278       3.817776 0.6864203  5.561863 2.669101e-08
## 19    112703   28.767842       3.009840 0.5402534  5.571165 2.530423e-08
## 20     29923  872.366754       3.942818 0.7104753  5.549550 2.864060e-08
##            padj          Symbol
## 1  7.335779e-08            G0S2
## 2  9.724923e-08         ANGPTL4
## 3  2.618143e-07 LL21NC02-21A1.1
## 4  1.099738e-06    SLC9A3R1-AS1
## 5  1.579919e-06           DDIT4
## 6  2.201847e-06         SLC16A3
## 7  4.105123e-06    LOC101928841
## 8  6.117351e-06           OPRD1
## 9  6.117351e-06           THBS2
## 10 6.117351e-06           CEBPD
## 11 6.117351e-06       LINC01679
## 12 7.544978e-06         MIF-AS1
## 13 8.082857e-06             NMB
## 14 1.937931e-05            JUNB
## 15 2.069682e-05             CFD
## 16 2.080110e-05         TMEM158
## 17 2.080110e-05    LOC101929918
## 18 3.244924e-05    LOC102725238
## 19 3.244924e-05         GARIN5A
## 20 3.307847e-05          HILPDA
##                                               Description
## 1                                          G0/G1 switch 2
## 2                                     angiopoietin like 4
## 3                         uncharacterized LL21NC02-21A1.1
## 4                                SLC9A3R1 antisense RNA 1
## 5                       DNA damage inducible transcript 4
## 6                       solute carrier family 16 member 3
## 7                                   replaced by ID 113622
## 8                                 opioid receptor delta 1
## 9                                        thrombospondin 2
## 10                   CCAAT enhancer binding protein delta
## 11            long intergenic non-protein coding RNA 1679
## 12                                    MIF antisense RNA 1
## 13                                           neuromedin B
## 14 JunB proto-oncogene, AP-1 transcription factor subunit
## 15                                    complement factor D
## 16                              transmembrane protein 158
## 17                           uncharacterized LOC101929918
## 18                           uncharacterized LOC102725238
## 19                    golgi associated RAB2 interactor 5A
## 20             hypoxia inducible lipid droplet associated

##      GeneID   baseMean log2FoldChange     lfcSE     stat       pvalue
## 1     50486  387.87464       3.641426 0.5224640 6.969717 3.175799e-12
## 2     51129  821.53255       3.445426 0.5043655 6.831209 8.420211e-12
## 3 101928745   18.24317       4.379092 0.6606858 6.628101 3.400333e-11
## 4 105371893   11.44189       3.310608 0.5198118 6.368859 1.904391e-10
## 5     54541 2168.44139       2.629321 0.4187858 6.278439 3.419886e-10
## 6      9123  221.19176       3.463307 0.5587802 6.197977 5.719332e-10
##           padj          Symbol                       Description
## 1 7.335779e-08            G0S2                    G0/G1 switch 2
## 2 9.724923e-08         ANGPTL4               angiopoietin like 4
## 3 2.618143e-07 LL21NC02-21A1.1   uncharacterized LL21NC02-21A1.1
## 4 1.099738e-06    SLC9A3R1-AS1          SLC9A3R1 antisense RNA 1
## 5 1.579919e-06           DDIT4 DNA damage inducible transcript 4
## 6 2.201847e-06         SLC16A3 solute carrier family 16 member 3

## Here we see that the top 10 DE genes are: G0S2, ANGPTL4, LL21NC02-21A1.1, SLC9A3R1-AS1, DDIT4, SLC16A3, LOC101928841, OPRD1, THBS2, CEBPD

## [1] "For context, G0S2 (G0/G1 switch gene 2) is a protein-coding gene that produces a small (103 amino acids) mitochondrial protein primarily known for inhibiting adipose triglyceride lipase (ATGL). 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."

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4. Visualization and Results Interpretation

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## [1] "For clarity, the participants were grouped by A1c status as diabetic if the result was 6.5 or higher and is not indicative of true clincial diagnosis.  The heatmap however does show a continuous correlation between A1c and gene expression and skewed for higher aged brackets: 51 or older however some younger groups cluster with diabetics. There is a REAL correlation, however moderate. To view this more closely, we can look at A1c without categorically naming it as diabetic vs non-diabetic."

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5. Additional DE Analysis, Unlocking Categorical A1c

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## log2 fold change (MLE): a1c 
## Wald test p-value: a1c 
## DataFrame with 6 rows and 6 columns
##            baseMean log2FoldChange     lfcSE      stat      pvalue        padj
##           <numeric>      <numeric> <numeric> <numeric>   <numeric>   <numeric>
## 4985      256.61462      -0.828793  0.116137  -7.13636 9.58323e-13 2.08339e-08
## 29923     872.36675       2.058622  0.292694   7.03335 2.01630e-12 2.19172e-08
## 102724660  71.36380       1.818599  0.279740   6.50104 7.97678e-11 5.78051e-07
## 8497       17.65388       2.284252  0.363598   6.28236 3.33467e-10 1.81239e-06
## 105376557   9.67117      -0.904528  0.148274  -6.10037 1.05825e-09 4.60127e-06
## 6514      361.56285      -0.824423  0.138558  -5.95001 2.68131e-09 8.62406e-06

##     11185      1510     79805      1902      4907      8795    284611    391059 
## 0.4789188 0.4495404 0.4436639 0.4402909 0.4397770 0.4385780 0.4337292 0.4302244 
## 101928646 107986742    503637 107984345      5176     83998      4622       928 
## 0.4285115 0.4261376 0.4246772 0.4235836 0.4189061 0.4186426 0.4181268 0.4157966 
##      5156      3939      3554      9781 
## 0.4125026 0.4104339 0.4100386 0.4092349

##     117154       5208       7049      10021       4985      29842  101929550 
## -0.4947606 -0.4968688 -0.4999388 -0.5071593 -0.5099526 -0.5115206 -0.5129304 
##       3651     732253     340533     137970      10752       9882      57467 
## -0.5232209 -0.5288866 -0.5325891 -0.5354615 -0.5386237 -0.5451459 -0.5457915 
##  101927193       6514     128178       2555       5502  105374577 
## -0.5474121 -0.5487561 -0.5565300 -0.5666360 -0.5674002 -0.5709801

##     ENTREZID       SYMBOL                                      GENENAME
## 1      11185         INMT           indolethylamine N-methyltransferase
## 2       1510         CTSE                                   cathepsin E
## 3      79805        VASH2                                   vasohibin 2
## 4       1902        LPAR1              lysophosphatidic acid receptor 1
## 5       4907         NT5E                          5'-nucleotidase ecto
## 6       8795    TNFRSF10B           TNF receptor superfamily member 10b
## 7     284611        EEIG2                          EEIG family member 2
## 8     391059        FRRS1                    ferric chelate reductase 1
## 9  101928646    BMAL2-AS1                         BMAL2 antisense RNA 1
## 10 107986742 LOC107986742                  uncharacterized LOC107986742
## 11    503637       DUXAP8                double homeobox A pseudogene 8
## 12 107984345       SMIM38            small integral membrane protein 38
## 13      5176     SERPINF1                      serpin family F member 1
## 14     83998         REG4                  regenerating family member 4
## 15      4622         MYH4                          myosin heavy chain 4
## 16       928          CD9                                  CD9 molecule
## 17      5156       PDGFRA platelet derived growth factor receptor alpha
## 18      3939         LDHA                       lactate dehydrogenase A
## 19      3554        IL1R1                 interleukin 1 receptor type 1
## 20      9781      RNF144A                      ring finger protein 144A
##          rho
## 1  0.4789188
## 2  0.4495404
## 3  0.4436639
## 4  0.4402909
## 5  0.4397770
## 6  0.4385780
## 7  0.4337292
## 8  0.4302244
## 9  0.4285115
## 10 0.4261376
## 11 0.4246772
## 12 0.4235836
## 13 0.4189061
## 14 0.4186426
## 15 0.4181268
## 16 0.4157966
## 17 0.4125026
## 18 0.4104339
## 19 0.4100386
## 20 0.4092349

##        ENTREZID       SYMBOL
## 35375 105374577 LOC105374577
## 35374      5502      PPP1R1A
## 35373      2555       GABRA2
## 35372    128178      EDARADD
## 35371      6514       SLC2A2
## 35370 101927193     CHL1-AS1
## 35369     57467        HHATL
## 35368      9882       TBC1D4
## 35367     10752         CHL1
## 35366    137970        UNC5D
## 35365    340533       NEXMIF
## 35364    732253        TDRG1
## 35363      3651         PDX1
## 35362 101929550 LOC101929550
## 35361     29842      TFCP2L1
## 35360      4985        OPRD1
## 35359     10021         HCN4
## 35358      7049       TGFBR3
## 35357      5208       PFKFB2
## 35356    117154        DACH2
##                                                                      GENENAME
## 35375                                            uncharacterized LOC105374577
## 35374                   protein phosphatase 1 regulatory inhibitor subunit 1A
## 35373                  gamma-aminobutyric acid type A receptor subunit alpha2
## 35372                                        EDAR associated via death domain
## 35371                                        solute carrier family 2 member 2
## 35370                                                    CHL1 antisense RNA 1
## 35369                                           hedgehog acyltransferase like
## 35368                                             TBC1 domain family member 4
## 35367                                          cell adhesion molecule L1 like
## 35366                                                 unc-5 netrin receptor D
## 35365                                  neurite extension and migration factor
## 35364                                            testis development related 1
## 35363                                      pancreatic and duodenal homeobox 1
## 35362                                            uncharacterized LOC101929550
## 35361                                         transcription factor CP2 like 1
## 35360                                                 opioid receptor delta 1
## 35359 hyperpolarization activated cyclic nucleotide gated potassium channel 4
## 35358                              transforming growth factor beta receptor 3
## 35357                   6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2
## 35356                                 dachshund family transcription factor 2
##              rho
## 35375 -0.5709801
## 35374 -0.5674002
## 35373 -0.5666360
## 35372 -0.5565300
## 35371 -0.5487561
## 35370 -0.5474121
## 35369 -0.5457915
## 35368 -0.5451459
## 35367 -0.5386237
## 35366 -0.5354615
## 35365 -0.5325891
## 35364 -0.5288866
## 35363 -0.5232209
## 35362 -0.5129304
## 35361 -0.5115206
## 35360 -0.5099526
## 35359 -0.5071593
## 35358 -0.4999388
## 35357 -0.4968688
## 35356 -0.4947606

## [1] 100

###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.

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6. Downstream Analysis: Continuous A1c

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### Repeat plots from before with filtered data #####

# Convert DESeq2 results to a dataframe for ggplot
volcano_data <- as.data.frame(res_a1c)
volcano_data$gene_symbol <- mapIds(org.Hs.eg.db, 
                                   keys=rownames(volcano_data), 
                                   column="SYMBOL", 
                                   keytype="ENTREZID")

# Calculate -log10 P-value
volcano_data$negLogPval <- -log10(volcano_data$padj)

# ggplot logic
ggplot(volcano_data, aes(x = log2FoldChange, y = negLogPval)) +
  geom_point(alpha = 0.4, color = "grey") +
  # Highlight genes with padj < 0.05 (you can adjust the log2FoldChange threshold)
  geom_point(data = subset(volcano_data, padj < 0.05 & abs(log2FoldChange) > 0.1), 
             aes(x = log2FoldChange, y = negLogPval), color = "steelblue", alpha = 0.8) +
  geom_hline(yintercept = -log10(0.05), linetype = "dashed", color = "red") +
  labs(title = "Volcano Plot: Effect of HbA1c (Continuous)", 
       x = "Log2 Fold Change (per 1% A1c)", 
       y = "-Log10 Adjusted P-value") +
  theme_minimal()

# Identify top significant genes from DESeq2 results to drive the MDS
top_de_genes <- rownames(res_a1c[order(res_a1c$padj), ])[1:500]

# Extract transformed counts for these genes
mds_matrix <- assay(vsd)[top_de_genes, ]

# Plot MDS
# We color by 'a1c_status' (Diabetic vs Non-Diabetic) to see if they cluster
plotMDS(mds_matrix, 
        col = as.numeric(colData(vsd)$a1c_status), 
        pch = 19,
        main = "MDS Plot: Clustering by HbA1c Status")
legend("topright", 
       legend = levels(colData(vsd)$a1c_status), 
       col = 1:2, pch = 19, cex = 0.8)

# 1. Identify Top 10 Up and Down genes based on log2FoldChange
# Use which() to safely handle NAs in the padj column
res_sorted <- res_a1c[order(res_a1c$log2FoldChange, decreasing = TRUE), ]
top_up_genes <- rownames(head(res_sorted[which(res_sorted$padj < 0.05), ], 10))

res_sorted_down <- res_a1c[order(res_a1c$log2FoldChange, decreasing = FALSE), ]
top_down_genes <- rownames(head(res_sorted_down[which(res_sorted_down$padj < 0.05), ], 10))

par(mfrow = c(1, 2)) 

# Plot Top Up
plotMDS(assay(vsd)[top_up_genes, ], 
        col = as.numeric(colData(vsd)$a1c_status), 
        pch = 19, main = "Top 10 A1c-Up Genes")

# Plot Top Down
plotMDS(assay(vsd)[top_down_genes, ], 
        col = as.numeric(colData(vsd)$a1c_status), 
        pch = 19, main = "Top 10 A1c-Down Genes")

par(mfrow = c(1, 1))

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7. Pathway Analysis, Reactome

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##                        ID
## R-HSA-72706   R-HSA-72706
## R-HSA-156827 R-HSA-156827
## R-HSA-72689   R-HSA-72689
## R-HSA-72613   R-HSA-72613
## R-HSA-72737   R-HSA-72737
## R-HSA-156902 R-HSA-156902
##                                                                    Description
## R-HSA-72706            GTP hydrolysis and joining of the 60S ribosomal subunit
## R-HSA-156827 L13a-mediated translational silencing of Ceruloplasmin expression
## R-HSA-72689                           Formation of a pool of free 40S subunits
## R-HSA-72613                                  Eukaryotic Translation Initiation
## R-HSA-72737                               Cap-dependent Translation Initiation
## R-HSA-156902                                          Peptide chain elongation
##              setSize enrichmentScore      NES pvalue  p.adjust       qvalue
## R-HSA-72706      112       0.6127036 2.825327  1e-10 6.768e-09 5.056842e-09
## R-HSA-156827     111       0.6141312 2.824067  1e-10 6.768e-09 5.056842e-09
## R-HSA-72689      101       0.6219505 2.816896  1e-10 6.768e-09 5.056842e-09
## R-HSA-72613      119       0.6003628 2.802221  1e-10 6.768e-09 5.056842e-09
## R-HSA-72737      119       0.6003628 2.802221  1e-10 6.768e-09 5.056842e-09
## R-HSA-156902      89       0.6256187 2.790760  1e-10 6.768e-09 5.056842e-09
##               rank                   leading_edge
## R-HSA-72706  11453 tags=81%, list=32%, signal=55%
## R-HSA-156827 11453 tags=82%, list=32%, signal=56%
## R-HSA-72689  11453 tags=84%, list=32%, signal=57%
## R-HSA-72613  11453 tags=81%, list=32%, signal=55%
## R-HSA-72737  11453 tags=81%, list=32%, signal=55%
## R-HSA-156902 11453 tags=85%, list=32%, signal=58%
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        core_enrichment
## R-HSA-72706                            6134/8664/9045/1968/6173/6231/9349/1973/6175/6159/3921/6191/6124/6129/6206/8668/6188/6205/6155/7311/6218/6208/6222/8894/4736/1983/6204/6130/6141/6194/8667/6207/6170/6158/6210/6154/6189/6230/10480/6142/6147/6171/6223/1965/6202/8666/6125/6229/6201/6135/6139/6143/6203/6136/6152/6128/1975/6232/6167/23521/1964/6193/6164/3646/51065/11224/6160/6224/6217/200916/6169/6165/8665/6123/6133/6227/51386/27335/6187/8662/6132/6166/6146/25873/6156/6161/6181/6144/6138/6228/6137
## R-HSA-156827                          26986/6134/8664/9045/1968/6173/6231/9349/1973/6175/6159/3921/6191/6124/6129/6206/8668/6188/6205/6155/7311/6218/6208/6222/8894/4736/6204/6130/6141/6194/8667/6207/6170/6158/6210/6154/6189/6230/10480/6142/6147/6171/6223/1965/6202/8666/6125/6229/6201/6135/6139/6143/6203/6136/6152/6128/1975/6232/6167/23521/1964/6193/6164/3646/51065/11224/6160/6224/6217/200916/6169/6165/8665/6123/6133/6227/51386/27335/6187/8662/6132/6166/6146/25873/6156/6161/6181/6144/6138/6228/6137
## R-HSA-72689                                                          6134/8664/9045/6173/6231/9349/6175/6159/3921/6191/6124/6129/6206/8668/6188/6205/6155/7311/6218/6208/6222/4736/6204/6130/6141/6194/8667/6207/6170/6158/6210/6154/6189/6230/10480/6142/6147/6171/6223/6202/8666/6125/6229/6201/6135/6139/6143/6203/6136/6152/6128/6232/6167/23521/1964/6193/6164/3646/51065/11224/6160/6224/6217/200916/6169/6165/8665/6123/6133/6227/51386/27335/6187/8662/6132/6166/6146/25873/6156/6161/6181/6144/6138/6228/6137
## R-HSA-72613  26986/6134/8664/9045/1968/6173/6231/9349/1973/6175/6159/3921/6191/6124/6129/6206/8668/6188/6205/6155/7311/6218/6208/6222/8894/4736/1983/6204/6130/6141/6194/8667/6207/6170/6158/6210/6154/6189/6230/10480/6142/6147/6171/6223/1965/6202/8666/6125/6229/6201/6135/1967/6139/6143/6203/6136/6152/6128/1975/6232/6167/23521/1964/6193/6164/3646/51065/11224/8892/6160/6224/6217/200916/6169/6165/8665/6123/6133/6227/51386/1978/27335/6187/8662/8890/6132/6166/6146/25873/6156/6161/6181/6144/6138/6228/6137
## R-HSA-72737  26986/6134/8664/9045/1968/6173/6231/9349/1973/6175/6159/3921/6191/6124/6129/6206/8668/6188/6205/6155/7311/6218/6208/6222/8894/4736/1983/6204/6130/6141/6194/8667/6207/6170/6158/6210/6154/6189/6230/10480/6142/6147/6171/6223/1965/6202/8666/6125/6229/6201/6135/1967/6139/6143/6203/6136/6152/6128/1975/6232/6167/23521/1964/6193/6164/3646/51065/11224/8892/6160/6224/6217/200916/6169/6165/8665/6123/6133/6227/51386/1978/27335/6187/8662/8890/6132/6166/6146/25873/6156/6161/6181/6144/6138/6228/6137
## R-HSA-156902                                                                                                         6134/9045/6173/6231/9349/6175/6159/3921/6191/6124/6129/1915/6206/6188/6205/6155/7311/6218/6208/6222/4736/6204/6130/6141/6194/6207/6170/6158/6210/6154/6189/6230/6142/6147/6171/6223/6202/6125/6229/6201/6135/6139/6143/6203/6136/6152/6128/6232/6167/23521/6193/6164/51065/11224/6160/6224/6217/200916/6169/6165/6123/6133/6227/1938/6187/6132/6166/6146/25873/6156/6161/6181/6144/6138/6228/6137

## [1] "Reactome GSEA showing pathways positively (activated) and negatively (suppressed) associated with increasing A1c. Activation and suppression denote direction of association (NES sign), not causal effects. \n      \n      Genes belonging to this pathway are disproportionately represented among those most positively correlated with increasing A1c, indicating coordinated upregulation of the pathway as glycemic control worsens."

================================================================

8. Pathway Analysis, GO and KEGG - Smokers, up-regulated ONLY

================================================================

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 ################

## [1] "1. Zhang X, Heckmann BL, Campbell LE, Liu J. G0S2: A small giant controller of lipolysis and adipose-liver fatty acid flux. Biochim Biophys Acta Mol Cell Biol Lipids. 2017 Oct;1862(10 Pt B):1146-1154. doi: 10.1016/j.bbalip.2017.06.007. Epub 2017 Jun 21. PMID: 28645852; PMCID: PMC5890940."

## [1] "2. Zhou Y, Park SY, Su J, Bailey K et al. TCF7L2 is a master regulator of insulin production and processing. Hum Mol Genet 2014 Dec 15;23(24):6419-31. PMID: 25015099\nFadista J, Vikman P, Laakso EO, Mollet IG et al. Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism. Proc Natl Acad Sci U S A 2014 Sep 23;111(38):13924-9. PMID: 25201977\nTaneera J, Fadista J, Ahlqvist E, Atac D et al. Identification of novel genes for glucose metabolism based upon expression pattern in human islets and effect on insulin secretion and glycemia. Hum Mol Genet 2015 Apr 1;24(7):1945-55. PMID: 25489054\nMalenczyk K, Girach F, Szodorai E, Storm P et al. A TRPV1-to-secretagogin regulatory axis controls pancreatic β-cell survival by modulating protein turnover. EMBO J 2017 Jul 14;36(14):2107-2125. PMID: 28637794\nTaneera J, Mohammed I, Mohammed AK, Hachim M et al. Orphan G-protein coupled receptor 183 (GPR183) potentiates insulin secretion and prevents glucotoxicity-induced β-cell dysfunction. Mol Cell Endocrinol 2020 Jan 1;499:110592. PMID: 31550518\nLagou V, Mägi R, Hottenga JJ, Grallert H et al. Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability. Nat Commun 2021 Jan 5;12(1):24. PMID: 33402679"

## [1] "3. Software:"

## Please cite the following if utilizing the GEOquery software:
## 
##   Davis S, Meltzer P (2007). "GEOquery: a bridge between the Gene
##   Expression Omnibus (GEO) and BioConductor." _Bioinformatics_, *14*,
##   1846-1847. doi:10.1093/bioinformatics/btm254
##   <https://doi.org/10.1093/bioinformatics/btm254>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     author = {Sean Davis and Paul Meltzer},
##     title = {GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor},
##     journal = {Bioinformatics},
##     year = {2007},
##     volume = {14},
##     pages = {1846--1847},
##     doi = {10.1093/bioinformatics/btm254},
##   }

## To cite package 'DESeq2' in publications use:
## 
##   Love, M.I., Huber, W., Anders, S. Moderated estimation of fold change
##   and dispersion for RNA-seq data with DESeq2 Genome Biology 15(12):550
##   (2014)
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     title = {Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2},
##     author = {Michael I. Love and Wolfgang Huber and Simon Anders},
##     year = {2014},
##     journal = {Genome Biology},
##     doi = {10.1186/s13059-014-0550-8},
##     volume = {15},
##     issue = {12},
##     pages = {550},
##   }

## To cite package 'pheatmap' in publications use:
## 
##   Kolde R (2025). _pheatmap: Pretty Heatmaps_.
##   doi:10.32614/CRAN.package.pheatmap
##   <https://doi.org/10.32614/CRAN.package.pheatmap>, R package version
##   1.0.13, <https://CRAN.R-project.org/package=pheatmap>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {pheatmap: Pretty Heatmaps},
##     author = {Raivo Kolde},
##     year = {2025},
##     note = {R package version 1.0.13},
##     url = {https://CRAN.R-project.org/package=pheatmap},
##     doi = {10.32614/CRAN.package.pheatmap},
##   }

## Please cite G. Yu (2015) for using ReactomePA. In addition, please cite
## G. Yu (2012) when using compareCluster in clusterProfiler package, G.
## Yu (2015) when applying enrichment analysis to NGS data by using
## ChIPseeker
## 
##   Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for
##   reactome pathway analysis and visualization. Molecular BioSystems
##   2016, 12(2):477-479
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     title = {ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization},
##     author = {Guangchuang Yu and Qing-Yu He},
##     journal = {Molecular BioSystems},
##     year = {2016},
##     volume = {12},
##     number = {12},
##     pages = {477-479},
##     pmid = {26661513},
##     url = {http://pubs.rsc.org/en/Content/ArticleLanding/2015/MB/C5MB00663E},
##     doi = {10.1039/C5MB00663E},
##   }

## Please cite S. Xu (2024) for using clusterProfiler. In addition, please
## cite G. Yu (2010) when using GOSemSim, G. Yu (2015) when using DOSE and
## G. Yu (2015) when using ChIPseeker.
## 
##   G Yu. Thirteen years of clusterProfiler. The Innovation. 2024,
##   5(6):100722
## 
##   S Xu, E Hu, Y Cai, Z Xie, X Luo, L Zhan, W Tang, Q Wang, B Liu, R
##   Wang, W Xie, T Wu, L Xie, G Yu. Using clusterProfiler to characterize
##   multiomics data. Nature Protocols. 2024, 19(11):3292-3320
## 
##   T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L
##   Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal
##   enrichment tool for interpreting omics data. The Innovation. 2021,
##   2(3):100141
## 
##   Guangchuang Yu, Li-Gen Wang, Yanyan Han and Qing-Yu He.
##   clusterProfiler: an R package for comparing biological themes among
##   gene clusters. OMICS: A Journal of Integrative Biology 2012,
##   16(5):284-287
## 
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.

## To cite package 'limma' in publications use:
## 
##   Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and
##   Smyth, G.K. (2015). limma powers differential expression analyses for
##   RNA-sequencing and microarray studies. Nucleic Acids Research 43(7),
##   e47.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     author = {Matthew E Ritchie and Belinda Phipson and Di Wu and Yifang Hu and Charity W Law and Wei Shi and Gordon K Smyth},
##     title = {{limma} powers differential expression analyses for {RNA}-sequencing and microarray studies},
##     journal = {Nucleic Acids Research},
##     year = {2015},
##     volume = {43},
##     number = {7},
##     pages = {e47},
##     doi = {10.1093/nar/gkv007},
##   }

## To cite package 'enrichplot' in publications use:
## 
##   Yu G (2025). _enrichplot: Visualization of Functional Enrichment
##   Result_. doi:10.18129/B9.bioc.enrichplot
##   <https://doi.org/10.18129/B9.bioc.enrichplot>, R package version
##   1.30.4, <https://bioconductor.org/packages/enrichplot>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {enrichplot: Visualization of Functional Enrichment Result},
##     author = {Guangchuang Yu},
##     year = {2025},
##     note = {R package version 1.30.4},
##     url = {https://bioconductor.org/packages/enrichplot},
##     doi = {10.18129/B9.bioc.enrichplot},
##   }

## To cite package 'org.Hs.eg.db' in publications use:
## 
##   Carlson M (2025). _org.Hs.eg.db: Genome wide annotation for Human_. R
##   package version 3.22.0.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {org.Hs.eg.db: Genome wide annotation for Human},
##     author = {Marc Carlson},
##     year = {2025},
##     note = {R package version 3.22.0},
##   }
## 
## ATTENTION: This citation information has been auto-generated from the
## package DESCRIPTION file and may need manual editing, see
## 'help("citation")'.

## To cite ggplot2 in publications, please use
## 
##   H. Wickham. ggplot2: Elegant Graphics for Data Analysis.
##   Springer-Verlag New York, 2016.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Book{,
##     author = {Hadley Wickham},
##     title = {ggplot2: Elegant Graphics for Data Analysis},
##     publisher = {Springer-Verlag New York},
##     year = {2016},
##     isbn = {978-3-319-24277-4},
##     url = {https://ggplot2.tidyverse.org},
##   }

## To cite package 'stringr' in publications use:
## 
##   Wickham H (2025). _stringr: Simple, Consistent Wrappers for Common
##   String Operations_. doi:10.32614/CRAN.package.stringr
##   <https://doi.org/10.32614/CRAN.package.stringr>, R package version
##   1.6.0, <https://CRAN.R-project.org/package=stringr>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {stringr: Simple, Consistent Wrappers for Common String Operations},
##     author = {Hadley Wickham},
##     year = {2025},
##     note = {R package version 1.6.0},
##     url = {https://CRAN.R-project.org/package=stringr},
##     doi = {10.32614/CRAN.package.stringr},
##   }

## To cite package 'tidyverse' in publications use:
## 
##   Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R,
##   Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller
##   E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V,
##   Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). "Welcome to
##   the tidyverse." _Journal of Open Source Software_, *4*(43), 1686.
##   doi:10.21105/joss.01686 <https://doi.org/10.21105/joss.01686>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     title = {Welcome to the {tidyverse}},
##     author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani},
##     year = {2019},
##     journal = {Journal of Open Source Software},
##     volume = {4},
##     number = {43},
##     pages = {1686},
##     doi = {10.21105/joss.01686},
##   }