Last updated: 2026-02-20

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File Version Author Date Message
html 5017caa John D. Hurley 2026-02-18 Build site.
Rmd 8e92475 John D. Hurley 2026-02-18 Adding functions from DGE
Rmd ad16973 John D. Hurley 2026-02-18 error in include statment
Rmd d593740 John D. Hurley 2026-02-18 Typo
Rmd 1d6a7ee John D. Hurley 2026-02-18 Moving Clust FC to CorMotif
Rmd b8dfde3 John D. Hurley 2026-02-18 MovedClustersToCormotif
Rmd c85837b John D. Hurley 2026-02-18 SiteUpDate_CorrelationHeatMap
html 3956def John D. Hurley 2026-01-28 Build site.
Rmd 07967c8 John D. Hurley 2026-01-28 Needed to run Cormotif code
Rmd e6624d7 John D. Hurley 2026-01-28 Duplicate Block names
Rmd 6b3f4af John D. Hurley 2026-01-28 website edit
Rmd 6c3cce3 John D. Hurley 2026-01-28 Cormotif Species 
knitr::opts_chunk$set(echo = TRUE,warning=FALSE,message=FALSE, dev=c("png","pdf"))
####Library Loading####
library("edgeR")
library("ggplot2")
library("tibble")
library("dplyr")
library("ggrepel")
library("readr")
library("org.Hs.eg.db")
library("AnnotationDbi")
library("pheatmap")
library("tidyverse")
library("workflowr")
library("RUVSeq")
library("SummarizedExperiment")
library("readxl")
library("ggfortify")
library("ComplexHeatmap")

# BiocManager::install("SummarizedExperiment")

RNA_fc_df <- readRDS("data/Raw_Data/RNA_fc_df.RDS")
RNA_Metadata <- readRDS("data/Raw_Data/RNA_Metadata.RDS")
ann_colors <- readRDS("data/QC/ann_colors.RDS")

RNA_Metadata_No4 <- readRDS("data/QC/RNA_Metatdata_No4.RDS")
Filt_RMG0_RNA_fc_NoD4 <- readRDS("data/QC/Filt_RMG0_RNA_fc_NoD4.RDS")
Filt_RMG0_RNA_log2cpm_NoD4 <- readRDS("data/QC/Filt_RMG0_RNA_log2cpm_NoD4.RDS")
ann_colors_No4 <- readRDS("data/QC/ann_colors_no4.RDS")

Filt_RMG0_RNA_fc_NoD4_NoRep <- readRDS("data/Cormotif/Filt_RMG0_RNA_fc_NoD4_NoRep.RDS")
Filt_RMG0_RNA_log2cpm_NoD4_NoRep <- readRDS("data/Cormotif/Filt_RMG0_RNA_log2cpm_NoD4_NoRep.RDS")

TopTable_RNA_All <- readRDS("data/TopTable_RNA_all.RDS")

clust1_RNA <- readRDS("data/Cormotif/Species/clust1_RNA_Species.RDS")
clust2_RNA <- readRDS("data/Cormotif/Species/clust2_RNA_Species.RDS")
clust3_RNA <- readRDS("data/Cormotif/Species/clust3_RNA_Species.RDS")
clust4_RNA <- readRDS("data/Cormotif/Species/clust4_RNA_Species.RDS")
check_overlap <- function(list_of_lists) {
  # create empty list to store results
  overlap_results <- list()
  
  # loop over each cluster
  for (i in seq_along(list_of_lists)) {
    cluster_name <- names(list_of_lists)[i]
    genes_i <- list_of_lists[[i]]
    
    # compare to all other clusters
    overlaps <- sapply(seq_along(list_of_lists), function(j) {
      if (i == j) return(NA)  # skip self
      genes_j <- list_of_lists[[j]]
      length(intersect(genes_i, genes_j))  # count of overlapping genes
    })
    names(overlaps) <- names(list_of_lists)
    
    overlap_results[[cluster_name]] <- overlaps
  }
  
  return(overlap_results)
}
Filt_RMG0_RNA_log2cpm_NoD4_NoRep <- Filt_RMG0_RNA_log2cpm_NoD4 %>%
  as.data.frame() %>%             # convert to data frame
  dplyr::select(-contains("R")) %>%  # remove columns containing "R"
  as.matrix()                     # convert back to matrix

RNA_Metadata_NoD4_NoRep <- RNA_Metadata_No4 %>%
  dplyr::filter(!grepl("R", Individual))

# saveRDS(Filt_RMG0_RNA_log2cpm_NoD4_NoRep,"data/Cormotif/Filt_RMG0_RNA_log2cpm_NoD4_NoRep.RDS")
# saveRDS(RNA_Metadata_NoD4_NoRep,"data/Cormotif/RNA_Metadata_NoD4_NoRep.RDS")
# groupid <- c(rep(1:5,7),rep(6:10,7))
# compid_Cond1 <- c(1,2,3,4,5)
# compid_Cond2 <- c(6,7,8,9,10)
# compid <- cbind(compid_Cond1,compid_Cond2)
# 
# #Model Fitting
# 
# set.seed(8)
# motif.fitted<-cormotiffit(exprs = Filt_RMG0_RNA_log2cpm_NoD4_NoRep,
#                           groupid = groupid,
#                           compid = compid,
#                           K=1:10,
#                           max.iter = 3000,
#                           runtype = "logCPM")
# 
# 
# saveRDS(motif.fitted,"data/Cormotif/Species/cormotif_RNA_seed8_k1_10_HC_Day_emmaCode.RDS")

motif.fitted <- readRDS("data/Cormotif/Species/cormotif_RNA_seed8_k1_10_HC_Day_emmaCode.RDS")
plotIC(motif.fitted)

Version Author Date
3956def John D. Hurley 2026-01-28 
motif.fitted$bic
       K      bic
 [1,]  1 455689.5
 [2,]  2 445999.6
 [3,]  3 445588.4
 [4,]  4 445371.9
 [5,]  5 445371.9
 [6,]  6 445427.1
 [7,]  7 445485.9
 [8,]  8 445542.1
 [9,]  9 445601.4
[10,] 10 445659.0
plotMotif(motif.fitted)

Version Author Date
3956def John D. Hurley 2026-01-28 
#  Studies Human Day0 - 1. HDay2, 2. HDay5, 3. HDay15, 4. HDay30
#          Chimp Day0 - 5. CDay2, 6. CDay5, 7. CDay15, 8. CDay30
# Set up Variables
groupid <- c(rep(1:5,7),rep(6:10,7))
compid_Cond1 <- c(1,2,3,4,5)
compid_Cond2 <- c(6,7,8,9,10)
compid <- cbind(compid_Cond1,compid_Cond2)

# Extract motif matrix
motif_matrix <- as.matrix(motif.fitted$bestmotif$motif.q)

# Label rows and columns
rownames(motif_matrix) <- paste0("Motif_", seq_len(nrow(motif_matrix)))
colnames(motif_matrix) <- paste0("C", compid[,1], "_vs_C", compid[,2])

# Convert to tidy format
motif_df <- as.data.frame(motif_matrix) %>%
  mutate(Motif = rownames(.)) %>%
  pivot_longer(-Motif, names_to = "Comparison", values_to = "Probability")

# Reorder motifs 1–11
motif_df$Motif <- factor(motif_df$Motif, levels = paste0("Motif_", 1:4))

motif_df$Comparison <- factor(motif_df$Comparison, levels = c("C1_vs_C6","C2_vs_C7","C3_vs_C8","C4_vs_C9","C5_vs_C10"))

# Plot
ggplot(motif_df, aes(x = Comparison, y = Motif, fill = Probability)) +
  geom_tile(color = "white") +
  scale_fill_gradient(low = "white", high = "black") +
  labs(
    title = "CorMotif Motif Activity Across Comparisons \n  (no day4) (no replicates) (RowMeans>0) Days Across Species" ,
    x = "Comparison (Condition 1 vs Condition 2)",
    y = "Motif (1–4)",
    fill = "Posterior Probability"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid = element_blank()
  )

Version Author Date
3956def John D. Hurley 2026-01-28 
#  Comparison C1 = Human Day0
#  Comparison C2 = Human Day2
#  Comparison C3 = Human Day5
#  Comparison C4 = Human Day15
#  Comparison C5 = Human Day30
#  Comparison C6 = Chimp Day0
#  Comparison C7 = Chimp Day2
#  Comparison C8 = Chimp Day5
#  Comparison C9 = Chimp Day15
#  Comparison C10 = Chimp Day30
motif.fit <- motif.fitted
log2cpm <- Filt_RMG0_RNA_log2cpm_NoD4

#extract the posterior probability that these genes belong to motifs
gene_prob_ppost <- motif.fit$bestmotif$p.post
rownames(gene_prob_ppost) <- rownames(log2cpm)
# saveRDS(gene_prob_ppost,"data/Cormotif/Species/RNA_gene_prob_ppost_Species.RDS")

#assign each gene to a motif with max post prob
assigned_motifs <- apply(gene_prob_ppost, 1, which.max)
max_probs <- apply(gene_prob_ppost, 1, max)

#combine these into a dataframe - motif assigned genes (p.post)
motif_assignment_df <- gene_prob_ppost %>%
  as.data.frame() %>%
  rownames_to_column("Gene") %>%
  mutate(
    Assigned_Motif = assigned_motifs[Gene],
    Max_Probability = max_probs[Gene]
  )

#make some histograms of the unfiltered data from Cormotif p.post

gene_prob_ppost %>%
  as.data.frame() %>%
  ggplot(., aes(x = V1))+
  geom_histogram(bins = 50)+
  xlim(0,1)+
  ggtitle("Cormtif p.post RNA V1")

Version Author Date
3956def John D. Hurley 2026-01-28 
gene_prob_ppost %>%
  as.data.frame() %>%
  ggplot(., aes(x = V2))+
  geom_histogram(bins = 50)+
  xlim(0,1)+
  ggtitle("Cormtif p.post RNA V2")

Version Author Date
3956def John D. Hurley 2026-01-28 
gene_prob_ppost %>%
  as.data.frame() %>%
  ggplot(., aes(x = V3))+
  geom_histogram(bins = 50)+
  xlim(0,1)+
  ggtitle("Cormtif p.post RNA V3")

Version Author Date
3956def John D. Hurley 2026-01-28 
gene_prob_ppost %>%
  as.data.frame() %>%
  ggplot(., aes(x = V4))+
  geom_histogram(bins = 50)+
  xlim(0,1)+
  ggtitle("Cormtif p.post RNA V4")

Version Author Date
3956def John D. Hurley 2026-01-28 
gene_prob_ppost %>%
  as.data.frame() %>%
  ggplot(., aes(x = V5))+
  geom_histogram(bins = 50)+
  xlim(0,1)+
  ggtitle("Cormtif p.post RNA V5")

Version Author Date
3956def John D. Hurley 2026-01-28 
motif.fit <- motif.fitted
log2cpm <- Filt_RMG0_RNA_log2cpm_NoD4
#extract the cluster likelihood - which DEGs are most likely to be in this cluster
motif_prob <- motif.fit$bestmotif$clustlike
rownames(motif_prob) <- rownames(gene_prob_ppost)
# saveRDS(motif_prob,"data/RNA_motif_prob_Trajectory.RDS")

# motif.fitted$bestmotif$clustlike

clust1_RNA <-
  motif_prob %>%
  as.data.frame() %>%
  filter(V1 > 0.5 & V2 <  0.5 & V3 < 0.5   & V4 < 0.5)  %>%
  rownames()
print("clust1_RNA")
[1] "clust1_RNA"
length(clust1_RNA)
[1] 10473
clust2_RNA <-
  motif_prob %>%
  as.data.frame() %>%
  filter(V1 < 0.5 & V2 > 0.5 & V3 < 0.5 & V4 < 0.5)  %>%
  rownames()
print("clust2_RNA")
[1] "clust2_RNA"
length(clust2_RNA)
[1] 525
clust3_RNA <-
  motif_prob %>%
  as.data.frame() %>%
  filter(V1 < 0.5 & V2 < 0.5 & V3 > 0.5 & V4)  %>%
  rownames()
print("clust3_RNA")
[1] "clust3_RNA"
length(clust3_RNA)
[1] 2129
clust4_RNA <-
  motif_prob %>%
  as.data.frame() %>%
  filter(V1 < 0.5 & V2 < 0.5 & V3 < 0.5 & V4 > 0.5) %>%
  rownames()
print("clust4_RNA")
[1] "clust4_RNA"
length(clust4_RNA)
[1] 933
clust_names_list <- list(
  clust1_RNA,
  clust2_RNA,
  clust3_RNA,
  clust4_RNA
)

names(clust_names_list) <- paste0("clust", 1:4)

print(overlap_matirx <- check_overlap(clust_names_list))
$clust1
clust1 clust2 clust3 clust4 
    NA      0      0      0 

$clust2
clust1 clust2 clust3 clust4 
     0     NA      0      0 

$clust3
clust1 clust2 clust3 clust4 
     0      0     NA      0 

$clust4
clust1 clust2 clust3 clust4 
     0      0      0     NA 
TopTable_RNA_All$AbsFC <- abs(TopTable_RNA_All$logFC)
cluster_names <- c("clust1_RNA","clust2_RNA","clust3_RNA","clust4_RNA")
cluster_lists <- list(
  clust1_RNA  = clust1_RNA,
  clust2_RNA  = clust2_RNA,
  clust3_RNA  = clust3_RNA,
  clust4_RNA  = clust4_RNA
)

cluster_plots <- lapply(names(cluster_lists), function(clust_name) {
  plot_cluster_fc(cluster_lists[[clust_name]], clust_name, TopTable_RNA_All)
})
clust1_RNA : 52365 rows, 10473 unique genes
clust2_RNA : 2625 rows, 525 unique genes
clust3_RNA : 10645 rows, 2129 unique genes
clust4_RNA : 4665 rows, 933 unique genes
names(cluster_plots) <- names(cluster_lists)

for (p in cluster_plots) print(p)

Version Author Date
5017caa John D. Hurley 2026-02-18

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5017caa John D. Hurley 2026-02-18

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5017caa John D. Hurley 2026-02-18

Version Author Date
5017caa John D. Hurley 2026-02-18 
cluster_lists <- list(
  clust1_RNA  = clust1_RNA,
  clust2_RNA  = clust2_RNA,
  clust3_RNA  = clust3_RNA,
  clust4_RNA  = clust4_RNA
)

cluster_plots <- lapply(names(cluster_lists), function(clust_name) {
  plot_cluster_absfc(cluster_lists[[clust_name]], clust_name, TopTable_RNA_All)
})
clust1_RNA : 52365 rows, 10473 unique genes
clust2_RNA : 2625 rows, 525 unique genes
clust3_RNA : 10645 rows, 2129 unique genes
clust4_RNA : 4665 rows, 933 unique genes
names(cluster_plots) <- names(cluster_lists)

for (p in cluster_plots) print(p)

Version Author Date
5017caa John D. Hurley 2026-02-18

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5017caa John D. Hurley 2026-02-18

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5017caa John D. Hurley 2026-02-18

Version Author Date
5017caa John D. Hurley 2026-02-18 
# git -> commit all changes
# git -> push
# wflow_publish("analysis/RNA_Cormotif_Comp_Species_Ensembl.Rmd")

sessionInfo()
R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/Chicago
tzcode source: internal

attached base packages:
[1] grid      stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] ComplexHeatmap_2.24.1       ggfortify_0.4.19           
 [3] readxl_1.4.5                RUVSeq_1.42.0              
 [5] EDASeq_2.42.0               ShortRead_1.66.0           
 [7] GenomicAlignments_1.44.0    SummarizedExperiment_1.38.1
 [9] MatrixGenerics_1.20.0       matrixStats_1.5.0          
[11] Rsamtools_2.24.0            GenomicRanges_1.60.0       
[13] Biostrings_2.76.0           GenomeInfoDb_1.44.3        
[15] XVector_0.48.0              BiocParallel_1.42.1        
[17] lubridate_1.9.5             forcats_1.0.1              
[19] stringr_1.6.0               purrr_1.2.1                
[21] tidyr_1.3.2                 tidyverse_2.0.0            
[23] pheatmap_1.0.13             org.Hs.eg.db_3.21.0        
[25] AnnotationDbi_1.70.0        IRanges_2.42.0             
[27] S4Vectors_0.46.0            Biobase_2.68.0             
[29] BiocGenerics_0.54.1         generics_0.1.4             
[31] readr_2.1.6                 ggrepel_0.9.6              
[33] dplyr_1.1.4                 tibble_3.3.1               
[35] ggplot2_4.0.2               edgeR_4.6.3                
[37] limma_3.64.3                workflowr_1.7.2            

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3      shape_1.4.6.1           rstudioapi_0.18.0      
  [4] jsonlite_2.0.0          magrittr_2.0.4          GenomicFeatures_1.60.0 
  [7] farver_2.1.2            rmarkdown_2.30          GlobalOptions_0.1.3    
 [10] BiocIO_1.18.0           fs_1.6.6                vctrs_0.7.1            
 [13] memoise_2.0.1           RCurl_1.98-1.17         htmltools_0.5.9        
 [16] S4Arrays_1.8.1          progress_1.2.3          curl_7.0.0             
 [19] cellranger_1.1.0        SparseArray_1.8.1       sass_0.4.10            
 [22] bslib_0.10.0            httr2_1.2.2             cachem_1.1.0           
 [25] whisker_0.4.1           iterators_1.0.14        lifecycle_1.0.5        
 [28] pkgconfig_2.0.3         Matrix_1.7-4            R6_2.6.1               
 [31] fastmap_1.2.0           clue_0.3-66             GenomeInfoDbData_1.2.14
 [34] digest_0.6.39           colorspace_2.1-2        ps_1.9.1               
 [37] rprojroot_2.1.1         RSQLite_2.4.5           hwriter_1.3.2.1        
 [40] labeling_0.4.3          filelock_1.0.3          timechange_0.4.0       
 [43] httr_1.4.7              abind_1.4-8             compiler_4.5.1         
 [46] doParallel_1.0.17       bit64_4.6.0-1           withr_3.0.2            
 [49] S7_0.2.1                DBI_1.2.3               R.utils_2.13.0         
 [52] biomaRt_2.64.0          MASS_7.3-65             rappdirs_0.3.4         
 [55] DelayedArray_0.34.1     rjson_0.2.23            tools_4.5.1            
 [58] otel_0.2.0              httpuv_1.6.16           R.oo_1.27.1            
 [61] glue_1.8.0              restfulr_0.0.16         callr_3.7.6            
 [64] promises_1.5.0          getPass_0.2-4           cluster_2.1.8.1        
 [67] gtable_0.3.6            tzdb_0.5.0              R.methodsS3_1.8.2      
 [70] hms_1.1.4               xml2_1.5.2              foreach_1.5.2          
 [73] pillar_1.11.1           later_1.4.5             circlize_0.4.17        
 [76] BiocFileCache_2.16.2    lattice_0.22-7          aroma.light_3.38.0     
 [79] rtracklayer_1.68.0      bit_4.6.0               deldir_2.0-4           
 [82] tidyselect_1.2.1        locfit_1.5-9.12         knitr_1.51             
 [85] git2r_0.36.2            gridExtra_2.3           xfun_0.56              
 [88] statmod_1.5.1           stringi_1.8.7           UCSC.utils_1.4.0       
 [91] yaml_2.3.12             evaluate_1.0.5          codetools_0.2-20       
 [94] interp_1.1-6            cli_3.6.5               processx_3.8.6         
 [97] jquerylib_0.1.4         Rcpp_1.1.1              dbplyr_2.5.1           
[100] png_0.1-8               XML_3.99-0.20           parallel_4.5.1         
[103] blob_1.3.0              prettyunits_1.2.0       latticeExtra_0.6-31    
[106] jpeg_0.1-11             bitops_1.0-9            pwalign_1.4.0          
[109] scales_1.4.0            crayon_1.5.3            GetoptLong_1.1.0       
[112] rlang_1.1.7             KEGGREST_1.48.1