2025_exp_4_alginate_DEG
Nour El Husseini
2025
1 Overview
| batch | condition | media | treatment | induced | exp_4 | |
|---|---|---|---|---|---|---|
| SK064 | 1 | LB-IPTG | LB | DMSO | yes | control |
| SK065 | 2 | LB-IPTG | LB | DMSO | yes | control |
| SK066 | 3 | LB-IPTG | LB | DMSO | yes | control |
| SK070 | 1 | LB-EbO-IPTG | LB | EbO | yes | treatment |
| SK071 | 2 | LB-EbO-IPTG | LB | EbO | yes | treatment |
| SK072 | 3 | LB-EbO-IPTG | LB | EbO | yes | treatment |
2 Project variables
## batch condition media treatment induced exp_4
## SK064 1 LB-IPTG LB DMSO yes control
## SK065 2 LB-IPTG LB DMSO yes control
## SK066 3 LB-IPTG LB DMSO yes control
## SK070 1 LB-EbO-IPTG LB EbO yes treatment
## SK071 2 LB-EbO-IPTG LB EbO yes treatment
## SK072 3 LB-EbO-IPTG LB EbO yes treatment## [1] "The control is: LB-IPTG"## [1] "The treatment is: LB-EbO-IPTG"# Load raw count data
rawcounts <- read.csv("../../raw-data/htseq_SK_unfiltered_reverse_strand_count.csv",
row.names = 1, header = TRUE)
# Append a prefix to the batch and run columns to turn to character class
metadata[["batch"]] <- paste0("b", metadata[["batch"]])
metadata[["run"]] <- paste0("r", metadata[["run"]])
# Subset the rawcounts dataframe to only include samples that are present in experiment
samples <- rownames(metadata)
rawcounts <- select(rawcounts, all_of(samples))
# Ensure that the order of samples in metadata matches the order of columns in rawcounts
all(rownames(metadata) == colnames(rawcounts)) # Should return TRUE if order is correct## [1] TRUE# Define the design formula for DESeq2, including main condition and batch effect
design_formula <- ~ exp_4 + batch
# Create DESeq2 dataset object from the filtered count matrix and metadata
dds <- DESeqDataSetFromMatrix(
countData = rawcounts,
colData = metadata,
design = design_formula
)## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors3 Library size per sample
# Sum up the counts for each sample
rawcounts_sums <- colSums(rawcounts)
# Turn it into a data frame
data <- data.frame(
Category = names(rawcounts_sums),
Count = as.numeric(rawcounts_sums)
)
# Make sure sample order is consistent
data <- data %>%
mutate(Category = factor(Category, levels = sort(unique(Category))))
# Plot bar chart of library sizes
p <- ggplot(data, aes(x = Category, y = Count, fill = Category)) +
geom_bar(stat = "identity") +
# coord_flip() # flip if too many samples to read labels easily
labs(y = "Library size (M)", x = NULL) + # remove x label, rename y axis
# scale_fill_brewer(palette = "Dark2") + # optional color palette
scale_y_continuous(
labels = scales::label_number(scale = 1e-6), # show counts in millions
limits = c(0, (max(data$Count) + 1e6)) # add a bit of space on top
) +
theme(
panel.grid = element_blank(),
panel.background = element_blank(),
axis.text = element_text(size = text.size, angle = 45, hjust = 1), # slant labels
axis.title = element_text(size = axis.text.size),
legend.text = element_text(size = text.size),
legend.title = element_text(size = axis.text.size),
panel.border = element_rect(fill = NA),
legend.position = "none", # no legend needed since each bar is a different sample
axis.ticks = element_line(linewidth = 0.2),
legend.key = element_blank()
)
# Show the plot
p
# Save the plot as a PDF with today’s date and project name
ggsave(paste0(Sys.Date(), "_library_size_", exp, "_", treatment,".vs.", control, ".pdf"), device = "pdf")## Saving 7 x 5 in image4 Expression distribution
# Stack rawcounts into single vector
dat <- stack(rawcounts)
# Format expression distribution plot with the expression level and density
expression_dist <- ggplot(dat, aes(x = log2(values+1), fill=ind, label = ind )) +
geom_density(alpha = 0.5, position = "identity") +
ylab("Density") +
geom_textdensity(hjust = "ymax", vjust = 0,
text_only = TRUE, text_smoothing = 20) +
theme(panel.grid = element_blank(),
panel.background = element_blank(),
axis.text = element_text(size = text.size),
axis.title = element_text(size = axis.text.size),
legend.text = element_text(size = text.size),
legend.title = element_text(size = axis.text.size),
panel.border = element_rect(fill = NA),
legend.position = "none",
axis.ticks = element_line(linewidth = 0.2),
legend.key = element_blank()) +
coord_cartesian(xlim = c(0, 15))
expression_dist
#save figure
cairo_pdf(paste0(Sys.Date(), "_expression_dist_", exp, "_", treatment, ".vs.", control, ".pdf"))
expression_dist
dev.off() ## quartz_off_screen
## 25 Non-zero genes observed
# Prepare data by getting library sizes and non-zero counts
x <- colSums(rawcounts)
y <- colSums(rawcounts != 0)
df <- data.frame(x,y)
df$ind <- rownames(df)
# Calculate axis limits
x_min <- min(df$x)
x_max <- max(df$x)
y_min <- min(df$y)
y_max <- max(df$y)
# Non-zero plot
nonzero <- ggplot(data = df, aes(x = x, y = y, color = ind, label = ind)) +
geom_point(size = 3) +
geom_text(hjust = -0.1, vjust = -0.1, color = "black", size = 5) +
scale_y_continuous(limits=c(y_min, y_max)) +
scale_x_continuous(
limits = c(x_min-1e3, x_max+9e5),
labels = scales::comma_format(scale = 1e-6, accuracy = 1) # Format labels without decimals
) +
xlab("Millions of reads") + ylab("Number of non-zero genes observed") +
labs(color = "Sample") +
theme(panel.grid = element_blank(),
panel.background = element_blank(),
axis.text = element_text(size = text.size),
axis.title = element_text(size = axis.text.size),
legend.text = element_text(size = text.size),
legend.title = element_text(size = axis.text.size),
panel.border = element_rect(fill = NA),
legend.position = "none",
axis.ticks = element_line(linewidth = 0.2),
legend.key = element_blank()) +
coord_cartesian()
# Show nonzero plot
nonzero
# Save plot
cairo_pdf(paste0(Sys.Date(), "_non-zero_", exp, "_", treatment, ".vs.", control, ".pdf"))
nonzero
dev.off() ## quartz_off_screen
## 26 RPKM and CPM transformations
# Load GFF annotation
gff_annot <- load_gff_annotations("../../raw-data/paeruginosa_pa14.gff",
id_col = "gene_id", type = "gene")## Returning a df with 16 columns and 5979 rows.## seqnames start end width strand source type score phase
## gene1650835 chromosome 483 2027 1545 + PseudoCAP gene NA 0
## gene1650837 chromosome 2056 3159 1104 + PseudoCAP gene NA 0
## gene1650839 chromosome 3169 4278 1110 + PseudoCAP gene NA 0
## gene1650841 chromosome 4275 6695 2421 + PseudoCAP gene NA 0
## gene1650843 chromosome 7018 7791 774 - PseudoCAP gene NA 0
## gene1650845 chromosome 7803 8339 537 - PseudoCAP gene NA 0
## gene_id Name Dbxref Alias name Parent locus
## gene1650835 gene1650835 GeneID:4384099 PA14_00010 dnaA <NA>
## gene1650837 gene1650837 GeneID:4384100 PA14_00020 dnaN <NA>
## gene1650839 gene1650839 GeneID:4384101 PA14_00030 recF <NA>
## gene1650841 gene1650841 GeneID:4384102 PA14_00050 gyrB <NA>
## gene1650843 gene1650843 GeneID:4385186 PA14_00060 PA14_00060 <NA>
## gene1650845 gene1650845 GeneID:4385187 PA14_00070 PA14_00070 <NA>## SK064 SK065 SK066 SK070 SK071 SK072
## gene1650835 2234 2528 1719 857 1136 856
## gene1650837 1266 1754 1368 678 757 719
## gene1650839 722 770 657 272 312 208
## gene1650841 3671 4307 3347 2806 2782 2278
## gene1650843 436 435 313 144 154 121
## gene1650845 257 299 177 76 97 91## [1] 5979 16## [1] 5979 6## Mode FALSE TRUE
## logical 932 5047## Mode TRUE
## logical 5979# Create an expression object with counts, metadata, and gene info
expt <- create_expt(count_dataframe = rawcounts,
metadata = metadata,
gene_info = gff_annot)## Reading the sample metadata.## Did not find the column: sampleid.## The rownames do not appear numeric, using them.## The sample definitions comprises: 6 rows(samples) and 8 columns(metadata fields).## Matched 5979 annotations and counts.## Bringing together the count matrix and gene information.## Some annotations were lost in merging, setting them to 'undefined'.## Saving the expressionset to 'expt.rda'.## The final expressionset has 5979 features and 6 samples.# Normalize counts log2 CPM
cpm_normalized_exp <- normalize_expt(expt,
transform = "log2",
norm = "raw",
convert = "cpm",
row_min = "10")## transform_counts: Found 288 values equal to 0, adding 1 to the matrix.####### Calculate RPKM ########
# Grab gene lengths from GFF (should be in base pairs)
gene_lengths <- gff_annot$width
names(gene_lengths) <- rownames(gff_annot) # set gene names
# Make sure gene_lengths are in the same order as rawcounts rows
gene_lengths <- gene_lengths[rownames(rawcounts)]
# Make DGEList object for edgeR
dge <- DGEList(counts = rawcounts)
# Calculate RPKM (reads per kilobase per million)
rpkm <- rpkm(dge, gene.length = gene_lengths)
# Wrap RPKM in an experiment object too
expt_rpkm <- create_expt(count_dataframe = rpkm,
metadata = metadata,
gene_info = gff_annot)## Reading the sample metadata.## Did not find the column: sampleid.## The rownames do not appear numeric, using them.## The sample definitions comprises: 6 rows(samples) and 8 columns(metadata fields).## Matched 5979 annotations and counts.## Bringing together the count matrix and gene information.## Some annotations were lost in merging, setting them to 'undefined'.## Saving the expressionset to 'expt.rda'.## The final expressionset has 5979 features and 6 samples.# log2 transform the RPKM (again, no normalization beyond log)
rpkm_normalized_exp <- normalize_expt(expt_rpkm,
transform = "log2",
norm = "raw")## transform_counts: Found 288 values equal to 0, adding 1 to the matrix.# Get the actual expression matrices from the experiment objects
rpkm <- exprs(rpkm_normalized_exp)
cpm <- exprs(cpm_normalized_exp)
# Merge CPM and RPKM matrices with gene annotation info
cpm_annotated <- merge(gff_annot, cpm, by = 0)
rpkm_annotated <- merge(gff_annot, rpkm, by = 0)6.1 Heatmap of alginate genes
This generates a small heatmap of the alginate genes across samples to visualize expression.
# Loop through CPM and RPKM to plot both heatmaps
for (type in c("cpm", "rpkm")) {
# Define gene list
alg_operon_genes <- c(
"algA", "algF", "algJ", "algI", "algL", "algX", "algG", "algE",
"algK", "alg44", "alg8", "algD", "mucC", "mucB", "algU", "algW",
"algP", "algQ", "algR", "algZ", "algC", "algB"
)
# Get the right data per type of transformation
data_annot <- get(paste0(type, "_annotated"))
# Fix rownames using gene name (make unique)
rownames(data_annot) <- make.names(data_annot$name, unique = TRUE)
# Drop undesired metadata columns
cols_to_exclude <- c("Row.names","seqnames","start","end","width",
"strand","source","type","score","phase",
"gene_id","Name","Dbxref","Alias","name",
"Parent","locus")
keep <- !names(data_annot) %in% cols_to_exclude
expr_data <- data_annot[, keep]
# Extract matrix for alg genes
alg_genes <- expr_data[alg_operon_genes, , drop = FALSE]
mat <- alg_genes - rowMeans(alg_genes)
# Pull sample annotations
anno <- as.data.frame(metadata[, c("condition", "induced")])
colnames(anno) <- c("Condition", "Induced")
# Color settings for annotation
# ann_colors <- list(
# Condition = c("LB-DMSO" = "#D95F02", "LB-IPTG" = "#66A61E"),
# Induced = c("no" = "lightgrey", "yes" = "gray27")
# )
# Scale data by row (Z-score per gene)
scaled_mat <- t(scale(t(mat)))
# Build heatmap
ht <- Heatmap(
scaled_mat,
name = "Z-score",
top_annotation = HeatmapAnnotation(df = anno#,
#col = ann_colors
),
cluster_rows = TRUE,
cluster_columns = TRUE,
row_names_side = "left",
column_names_side = "bottom",
column_names_rot = 45,
row_names_gp = gpar(fontsize = 10),
column_names_gp = gpar(fontsize = 10),
show_row_dend = TRUE,
show_column_dend = TRUE
)
# Draw and save
draw(ht)
cairo_pdf(paste0(Sys.Date(), "_alg_heatmap_", type, "_", exp, "_", treatment, ".vs.", control, ".pdf"))
draw(ht)
dev.off()
}
7 DESeq
# Create DESeqDataSet
dds <- DESeqDataSetFromMatrix(
countData = rawcounts,
colData = metadata,
design = design_formula
)## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## [1] "Number of genes before filtering:"## [1] 5979# Filter: keep genes with counts >=10 in at least 2 samples
print("Filtering genes with counts >=10 in at least 2 samples")## [1] "Filtering genes with counts >=10 in at least 2 samples"keep <- rowSums(counts(dds) >= 10) >= 2
dds_filtered <- dds[keep, ]
# Check number of genes after filtering
print("Number of genes after filtering:")## [1] "Number of genes after filtering:"## [1] 5725## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## [1] "Intercept" "exp_4_treatment_vs_control"
## [3] "batch_b2_vs_b1" "batch_b3_vs_b1"
8 Save DE tables
res <- results(dds_final, contrast = c("exp_4", "treatment", "control"))
resdf <- as.data.frame(res)
resdf$gene_id <- rownames(resdf)
cpm <- as.data.frame(cpm)
rpkm <- as.data.frame(rpkm)
# append type of value to the dataframe
colnames(rpkm) <- paste0(colnames(rpkm), "_rpkm")
colnames(cpm) <- paste0(colnames(cpm), "_cpm")
rawcounts2 <- rawcounts
colnames(rawcounts2) <- paste0(colnames(rawcounts2), "_rawcounts")
# ensure column gene_id is there
rpkm$gene_id <- rownames(rpkm)
cpm$gene_id <- rownames(cpm)
rawcounts2$gene_id <- rownames(rawcounts2)
gff_annot <- gff_annot[rownames(resdf),]
dim(resdf)## [1] 5725 7# merge with siggenes
resdf <- full_join(resdf, gff_annot, by = "gene_id", copy = FALSE)
head(resdf)## baseMean log2FoldChange lfcSE stat pvalue padj
## 1 1433.5096 -0.57542360 0.2027957 -2.8374549 0.0045474775 0.015614397
## 2 1022.8812 -0.40904226 0.2094370 -1.9530566 0.0508128832 0.111344077
## 3 447.8995 -0.84892945 0.2368050 -3.5849300 0.0003371686 0.001787407
## 4 3108.6397 0.08073561 0.2049896 0.3938522 0.6936901383 0.790701174
## 5 242.0401 -0.88329828 0.2425974 -3.6410049 0.0002715760 0.001493973
## 6 149.8484 -0.84289423 0.2750258 -3.0647824 0.0021782841 0.008387440
## gene_id seqnames start end width strand source type score phase
## 1 gene1650835 chromosome 483 2027 1545 + PseudoCAP gene NA 0
## 2 gene1650837 chromosome 2056 3159 1104 + PseudoCAP gene NA 0
## 3 gene1650839 chromosome 3169 4278 1110 + PseudoCAP gene NA 0
## 4 gene1650841 chromosome 4275 6695 2421 + PseudoCAP gene NA 0
## 5 gene1650843 chromosome 7018 7791 774 - PseudoCAP gene NA 0
## 6 gene1650845 chromosome 7803 8339 537 - PseudoCAP gene NA 0
## Name Dbxref Alias name Parent locus
## 1 GeneID:4384099 PA14_00010 dnaA <NA>
## 2 GeneID:4384100 PA14_00020 dnaN <NA>
## 3 GeneID:4384101 PA14_00030 recF <NA>
## 4 GeneID:4384102 PA14_00050 gyrB <NA>
## 5 GeneID:4385186 PA14_00060 PA14_00060 <NA>
## 6 GeneID:4385187 PA14_00070 PA14_00070 <NA>## locusId accession GI scaffoldId start stop strand Alias
## 1 2194572 YP_788156.1 116053721 4582 483 2027 + PA14_00010
## 2 2194573 YP_788157.1 116053722 4582 2056 3159 + PA14_00020
## 3 2194574 YP_788158.1 116053723 4582 3169 4278 + PA14_00030
## 4 2194575 YP_788159.1 116053724 4582 4275 6695 + PA14_00050
## 5 2194576 YP_788160.1 116053725 4582 7791 7018 - PA14_00060
## 6 2194577 YP_788161.1 116053726 4582 8339 7803 - PA14_00070
## name desc COG
## 1 dnaA chromosomal replication initiator protein DnaA (NCBI) COG593
## 2 dnaN DNA polymerase III, beta chain (NCBI) COG592
## 3 recF DNA replication and repair protein RecF (NCBI) COG1195
## 4 gyrB DNA gyrase subunit B (NCBI) COG187
## 5 PA14_00060 putative acyltransferase (NCBI) COG204
## 6 PA14_00070 putative histidinol-phosphatase (NCBI) COG241
## COGFun
## 1 L
## 2 L
## 3 L
## 4 L
## 5 I
## 6 E
## COGDesc
## 1 ATPase involved in DNA replication initiation
## 2 DNA polymerase sliding clamp subunit (PCNA homolog)
## 3 Recombinational DNA repair ATPase (RecF pathway)
## 4 Type IIA topoisomerase (DNA gyrase/topo II, topoisomerase IV), B subunit
## 5 1-acyl-sn-glycerol-3-phosphate acyltransferase
## 6 Histidinol phosphatase and related phosphatases
## TIGRFam
## 1 TIGR00362 chromosomal replication initiator protein DnaA [dnaA]
## 2 TIGR00663 DNA polymerase III, beta subunit [dnaN]
## 3 TIGR00611 DNA replication and repair protein RecF [recF]
## 4 TIGR01059 DNA gyrase, B subunit [gyrB]
## 5
## 6 TIGR01656 histidinol-phosphate phosphatase domain,TIGR01662 HAD hydrolase, family IIIA
## TIGRRoles
## 1 DNA metabolism:DNA replication, recombination, and repair
## 2 DNA metabolism:DNA replication, recombination, and repair
## 3 DNA metabolism:DNA replication, recombination, and repair
## 4 DNA metabolism:DNA replication, recombination, and repair
## 5
## 6 Unknown function:Enzymes of unknown specificity
## GO
## 1 GO:0006270,GO:0006275,GO:0003688,GO:0017111,GO:0005524
## 2 GO:0006260,GO:0003677,GO:0003893,GO:0008408,GO:0016449,GO:0019984,GO:0003889,GO:0003894,GO:0015999,GO:0016450,GO:0003890,GO:0003895,GO:0016000,GO:0016451,GO:0003891,GO:0016448,GO:0016452
## 3 GO:0006281,GO:0005694,GO:0005524,GO:0017111,GO:0003697
## 4 GO:0006304,GO:0006265,GO:0005694,GO:0003918,GO:0005524
## 5 GO:0008152,GO:0003841
## 6 GO:0000105,GO:0004401
## EC ECDesc
## 1
## 2 2.7.7.7 DNA-directed DNA polymerase.
## 3
## 4 5.99.1.3 DNA topoisomerase (ATP-hydrolyzing).
## 5 2.3.1.51 1-acylglycerol-3-phosphate O-acyltransferase.
## 6 3.1.3.-col_to_remove <- intersect(colnames(pa_go),colnames(resdf))
col_to_remove <- setdiff(col_to_remove, "Alias") # returns all except the second vector
pa_go <- pa_go %>% select(-all_of(col_to_remove))
resdf <- full_join(resdf, rpkm, by = "gene_id", copy = FALSE)
resdf <- full_join(resdf, cpm, by = "gene_id", copy = FALSE)
resdf <- full_join(resdf, rawcounts2, by = "gene_id", copy = FALSE)
#resdf <- full_join(resdf, pa_go, by = "Alias", copy = FALSE)
resdf <- merge(resdf, pa_go, by = "Alias", copy = FALSE)
head(resdf)## Alias baseMean log2FoldChange lfcSE stat pvalue
## 1 PA14_00010 1433.5096 -0.57542360 0.2027957 -2.8374549 0.0045474775
## 2 PA14_00020 1022.8812 -0.40904226 0.2094370 -1.9530566 0.0508128832
## 3 PA14_00030 447.8995 -0.84892945 0.2368050 -3.5849300 0.0003371686
## 4 PA14_00050 3108.6397 0.08073561 0.2049896 0.3938522 0.6936901383
## 5 PA14_00060 242.0401 -0.88329828 0.2425974 -3.6410049 0.0002715760
## 6 PA14_00070 149.8484 -0.84289423 0.2750258 -3.0647824 0.0021782841
## padj gene_id seqnames start end width strand source type
## 1 0.015614397 gene1650835 chromosome 483 2027 1545 + PseudoCAP gene
## 2 0.111344077 gene1650837 chromosome 2056 3159 1104 + PseudoCAP gene
## 3 0.001787407 gene1650839 chromosome 3169 4278 1110 + PseudoCAP gene
## 4 0.790701174 gene1650841 chromosome 4275 6695 2421 + PseudoCAP gene
## 5 0.001493973 gene1650843 chromosome 7018 7791 774 - PseudoCAP gene
## 6 0.008387440 gene1650845 chromosome 7803 8339 537 - PseudoCAP gene
## score phase Name Dbxref name Parent locus SK064_rpkm SK065_rpkm
## 1 NA 0 GeneID:4384099 dnaA <NA> 8.176179 8.165287
## 2 NA 0 GeneID:4384100 dnaN <NA> 7.842995 8.122956
## 3 NA 0 GeneID:4384101 recF <NA> 7.029755 6.934080
## 4 NA 0 GeneID:4384102 gyrB <NA> 8.244496 8.285574
## 5 NA 0 GeneID:4385186 PA14_00060 <NA> 6.823946 6.633153
## 6 NA 0 GeneID:4385187 PA14_00070 <NA> 6.591050 6.619830
## SK066_rpkm SK070_rpkm SK071_rpkm SK072_rpkm SK064_cpm SK065_cpm SK066_cpm
## 1 7.893669 6.967659 7.171523 7.071258 8.802026 8.791120 8.519135
## 2 8.048415 7.113400 7.071516 7.302903 7.985143 8.265208 8.190642
## 3 6.988440 5.803510 5.800306 5.528118 7.179220 7.083470 7.137873
## 4 8.205787 8.024788 7.812078 7.830929 9.517304 9.558461 9.478520
## 5 6.444121 5.414295 5.312490 5.272893 6.458065 6.267796 6.079357
## 6 6.152874 5.030312 5.176708 5.386342 5.706889 5.735417 5.273245
## SK070_cpm SK071_cpm SK072_cpm SK064_rawcounts SK065_rawcounts SK066_rawcounts
## 1 7.591194 7.795595 7.695076 2234 2528 1719
## 2 7.255158 7.213245 7.444783 1266 1754 1368
## 3 5.951508 5.948298 5.675576 722 770 657
## 4 9.297136 9.083908 9.102808 3671 4307 3347
## 5 5.054545 4.953457 4.914153 436 435 313
## 6 4.170875 4.313689 4.518773 257 299 177
## SK070_rawcounts SK071_rawcounts SK072_rawcounts locusId accession GI
## 1 857 1136 856 2194572 YP_788156.1 116053721
## 2 678 757 719 2194573 YP_788157.1 116053722
## 3 272 312 208 2194574 YP_788158.1 116053723
## 4 2806 2782 2278 2194575 YP_788159.1 116053724
## 5 144 154 121 2194576 YP_788160.1 116053725
## 6 76 97 91 2194577 YP_788161.1 116053726
## scaffoldId stop desc COG
## 1 4582 2027 chromosomal replication initiator protein DnaA (NCBI) COG593
## 2 4582 3159 DNA polymerase III, beta chain (NCBI) COG592
## 3 4582 4278 DNA replication and repair protein RecF (NCBI) COG1195
## 4 4582 6695 DNA gyrase subunit B (NCBI) COG187
## 5 4582 7018 putative acyltransferase (NCBI) COG204
## 6 4582 7803 putative histidinol-phosphatase (NCBI) COG241
## COGFun
## 1 L
## 2 L
## 3 L
## 4 L
## 5 I
## 6 E
## COGDesc
## 1 ATPase involved in DNA replication initiation
## 2 DNA polymerase sliding clamp subunit (PCNA homolog)
## 3 Recombinational DNA repair ATPase (RecF pathway)
## 4 Type IIA topoisomerase (DNA gyrase/topo II, topoisomerase IV), B subunit
## 5 1-acyl-sn-glycerol-3-phosphate acyltransferase
## 6 Histidinol phosphatase and related phosphatases
## TIGRFam
## 1 TIGR00362 chromosomal replication initiator protein DnaA [dnaA]
## 2 TIGR00663 DNA polymerase III, beta subunit [dnaN]
## 3 TIGR00611 DNA replication and repair protein RecF [recF]
## 4 TIGR01059 DNA gyrase, B subunit [gyrB]
## 5
## 6 TIGR01656 histidinol-phosphate phosphatase domain,TIGR01662 HAD hydrolase, family IIIA
## TIGRRoles
## 1 DNA metabolism:DNA replication, recombination, and repair
## 2 DNA metabolism:DNA replication, recombination, and repair
## 3 DNA metabolism:DNA replication, recombination, and repair
## 4 DNA metabolism:DNA replication, recombination, and repair
## 5
## 6 Unknown function:Enzymes of unknown specificity
## GO
## 1 GO:0006270,GO:0006275,GO:0003688,GO:0017111,GO:0005524
## 2 GO:0006260,GO:0003677,GO:0003893,GO:0008408,GO:0016449,GO:0019984,GO:0003889,GO:0003894,GO:0015999,GO:0016450,GO:0003890,GO:0003895,GO:0016000,GO:0016451,GO:0003891,GO:0016448,GO:0016452
## 3 GO:0006281,GO:0005694,GO:0005524,GO:0017111,GO:0003697
## 4 GO:0006304,GO:0006265,GO:0005694,GO:0003918,GO:0005524
## 5 GO:0008152,GO:0003841
## 6 GO:0000105,GO:0004401
## EC ECDesc
## 1
## 2 2.7.7.7 DNA-directed DNA polymerase.
## 3
## 4 5.99.1.3 DNA topoisomerase (ATP-hydrolyzing).
## 5 2.3.1.51 1-acylglycerol-3-phosphate O-acyltransferase.
## 6 3.1.3.-9 Save DEG list
## [1] 5718 54## Alias baseMean log2FoldChange lfcSE stat pvalue
## 1 PA14_00010 1433.5096 -0.57542360 0.2027957 -2.8374549 0.0045474775
## 2 PA14_00020 1022.8812 -0.40904226 0.2094370 -1.9530566 0.0508128832
## 3 PA14_00030 447.8995 -0.84892945 0.2368050 -3.5849300 0.0003371686
## 4 PA14_00050 3108.6397 0.08073561 0.2049896 0.3938522 0.6936901383
## 5 PA14_00060 242.0401 -0.88329828 0.2425974 -3.6410049 0.0002715760
## 6 PA14_00070 149.8484 -0.84289423 0.2750258 -3.0647824 0.0021782841
## padj gene_id seqnames start end width strand source type
## 1 0.015614397 gene1650835 chromosome 483 2027 1545 + PseudoCAP gene
## 2 0.111344077 gene1650837 chromosome 2056 3159 1104 + PseudoCAP gene
## 3 0.001787407 gene1650839 chromosome 3169 4278 1110 + PseudoCAP gene
## 4 0.790701174 gene1650841 chromosome 4275 6695 2421 + PseudoCAP gene
## 5 0.001493973 gene1650843 chromosome 7018 7791 774 - PseudoCAP gene
## 6 0.008387440 gene1650845 chromosome 7803 8339 537 - PseudoCAP gene
## score phase Name Dbxref name Parent locus SK064_rpkm SK065_rpkm
## 1 NA 0 GeneID:4384099 dnaA <NA> 8.176179 8.165287
## 2 NA 0 GeneID:4384100 dnaN <NA> 7.842995 8.122956
## 3 NA 0 GeneID:4384101 recF <NA> 7.029755 6.934080
## 4 NA 0 GeneID:4384102 gyrB <NA> 8.244496 8.285574
## 5 NA 0 GeneID:4385186 PA14_00060 <NA> 6.823946 6.633153
## 6 NA 0 GeneID:4385187 PA14_00070 <NA> 6.591050 6.619830
## SK066_rpkm SK070_rpkm SK071_rpkm SK072_rpkm SK064_cpm SK065_cpm SK066_cpm
## 1 7.893669 6.967659 7.171523 7.071258 8.802026 8.791120 8.519135
## 2 8.048415 7.113400 7.071516 7.302903 7.985143 8.265208 8.190642
## 3 6.988440 5.803510 5.800306 5.528118 7.179220 7.083470 7.137873
## 4 8.205787 8.024788 7.812078 7.830929 9.517304 9.558461 9.478520
## 5 6.444121 5.414295 5.312490 5.272893 6.458065 6.267796 6.079357
## 6 6.152874 5.030312 5.176708 5.386342 5.706889 5.735417 5.273245
## SK070_cpm SK071_cpm SK072_cpm SK064_rawcounts SK065_rawcounts SK066_rawcounts
## 1 7.591194 7.795595 7.695076 2234 2528 1719
## 2 7.255158 7.213245 7.444783 1266 1754 1368
## 3 5.951508 5.948298 5.675576 722 770 657
## 4 9.297136 9.083908 9.102808 3671 4307 3347
## 5 5.054545 4.953457 4.914153 436 435 313
## 6 4.170875 4.313689 4.518773 257 299 177
## SK070_rawcounts SK071_rawcounts SK072_rawcounts locusId accession GI
## 1 857 1136 856 2194572 YP_788156.1 116053721
## 2 678 757 719 2194573 YP_788157.1 116053722
## 3 272 312 208 2194574 YP_788158.1 116053723
## 4 2806 2782 2278 2194575 YP_788159.1 116053724
## 5 144 154 121 2194576 YP_788160.1 116053725
## 6 76 97 91 2194577 YP_788161.1 116053726
## scaffoldId stop desc COG
## 1 4582 2027 chromosomal replication initiator protein DnaA (NCBI) COG593
## 2 4582 3159 DNA polymerase III, beta chain (NCBI) COG592
## 3 4582 4278 DNA replication and repair protein RecF (NCBI) COG1195
## 4 4582 6695 DNA gyrase subunit B (NCBI) COG187
## 5 4582 7018 putative acyltransferase (NCBI) COG204
## 6 4582 7803 putative histidinol-phosphatase (NCBI) COG241
## COGFun
## 1 L
## 2 L
## 3 L
## 4 L
## 5 I
## 6 E
## COGDesc
## 1 ATPase involved in DNA replication initiation
## 2 DNA polymerase sliding clamp subunit (PCNA homolog)
## 3 Recombinational DNA repair ATPase (RecF pathway)
## 4 Type IIA topoisomerase (DNA gyrase/topo II, topoisomerase IV), B subunit
## 5 1-acyl-sn-glycerol-3-phosphate acyltransferase
## 6 Histidinol phosphatase and related phosphatases
## TIGRFam
## 1 TIGR00362 chromosomal replication initiator protein DnaA [dnaA]
## 2 TIGR00663 DNA polymerase III, beta subunit [dnaN]
## 3 TIGR00611 DNA replication and repair protein RecF [recF]
## 4 TIGR01059 DNA gyrase, B subunit [gyrB]
## 5
## 6 TIGR01656 histidinol-phosphate phosphatase domain,TIGR01662 HAD hydrolase, family IIIA
## TIGRRoles
## 1 DNA metabolism:DNA replication, recombination, and repair
## 2 DNA metabolism:DNA replication, recombination, and repair
## 3 DNA metabolism:DNA replication, recombination, and repair
## 4 DNA metabolism:DNA replication, recombination, and repair
## 5
## 6 Unknown function:Enzymes of unknown specificity
## GO
## 1 GO:0006270,GO:0006275,GO:0003688,GO:0017111,GO:0005524
## 2 GO:0006260,GO:0003677,GO:0003893,GO:0008408,GO:0016449,GO:0019984,GO:0003889,GO:0003894,GO:0015999,GO:0016450,GO:0003890,GO:0003895,GO:0016000,GO:0016451,GO:0003891,GO:0016448,GO:0016452
## 3 GO:0006281,GO:0005694,GO:0005524,GO:0017111,GO:0003697
## 4 GO:0006304,GO:0006265,GO:0005694,GO:0003918,GO:0005524
## 5 GO:0008152,GO:0003841
## 6 GO:0000105,GO:0004401
## EC ECDesc
## 1
## 2 2.7.7.7 DNA-directed DNA polymerase.
## 3
## 4 5.99.1.3 DNA topoisomerase (ATP-hydrolyzing).
## 5 2.3.1.51 1-acylglycerol-3-phosphate O-acyltransferase.
## 6 3.1.3.-## Mode FALSE
## logical 5718## Mode FALSE TRUE
## logical 5607 111sig_genes <- resdf[abs(resdf$log2FoldChange) >= logfc_cutoff & resdf$padj < pval_cutoff, ] %>% drop_na(log2FoldChange)
sig_genes$direction <- ifelse(sig_genes$log2FoldChange > 0, "up", "down")
summary(is.na(sig_genes$log2FoldChange))## Mode FALSE
## logical 88## Mode FALSE
## logical 88## [1] 88 55## Alias baseMean log2FoldChange lfcSE stat pvalue
## 80 PA14_00960 22.93951 -2.051543 0.5889029 -3.483669 4.945904e-04
## 90 PA14_01080 73.39200 -2.130515 0.4126581 -5.162907 2.431440e-07
## 232 PA14_03020 19.45471 2.077373 0.5814232 3.572910 3.530367e-04
## 334 PA14_04300 19736.03694 2.485906 0.2137388 11.630577 2.881430e-31
## 361 PA14_04650 13864.43974 2.045712 0.2117608 9.660485 4.437570e-22
## 499 PA14_06420 151.13934 -2.133427 0.2899808 -7.357133 1.879016e-13
## padj gene_id seqnames start end width strand source
## 80 2.461552e-03 gene1650995 chromosome 95402 95866 465 - PseudoCAP
## 90 3.527159e-06 gene1651015 chromosome 104835 105881 1047 + PseudoCAP
## 232 1.862733e-03 gene1651313 chromosome 266282 267136 855 - PseudoCAP
## 334 3.235270e-28 gene1651523 chromosome 384875 385207 333 - PseudoCAP
## 361 1.465442e-19 gene1651577 chromosome 413653 414192 540 - PseudoCAP
## 499 1.146608e-11 gene1651855 chromosome 564699 565457 759 + PseudoCAP
## type score phase Name Dbxref name Parent locus SK064_rpkm
## 80 gene NA 0 GeneID:4380425 PA14_00960 <NA> 4.215454
## 90 gene NA 0 GeneID:4383507 PA14_01080 <NA> 5.018643
## 232 gene NA 0 GeneID:4383655 PA14_03020 <NA> 1.925201
## 334 gene NA 0 GeneID:4383734 PA14_04300 <NA> 11.972453
## 361 gene NA 0 GeneID:4384279 pfpI <NA> 11.190724
## 499 gene NA 0 GeneID:4383861 PA14_06420 <NA> 6.436684
## SK065_rpkm SK066_rpkm SK070_rpkm SK071_rpkm SK072_rpkm SK064_cpm SK065_cpm
## 80 4.413025 4.304990 0.9728793 2.253514 2.951479 3.197449 3.384212
## 90 5.068786 4.300292 1.5480067 3.227785 2.485971 5.082906 5.133116
## 232 1.788204 1.166510 2.9155281 2.742662 3.105682 1.762222 1.631355
## 334 12.044165 12.233940 14.0352883 14.262723 14.093989 10.386766 10.458443
## 361 11.031055 11.484658 13.0182620 12.551957 12.957961 10.302281 10.142674
## 499 6.367097 6.140465 3.9768894 3.460016 4.049065 6.044135 5.974808
## SK066_cpm SK070_cpm SK071_cpm SK072_cpm SK064_rawcounts SK065_rawcounts
## 80 3.281914 0.5337433 1.460648 2.046829 41 54
## 90 4.363263 1.5919487 3.287117 2.540626 165 195
## 232 1.045584 2.7215930 2.552759 2.907825 12 12
## 334 10.648134 12.4490545 12.676464 12.507748 6711 8042
## 361 10.596118 12.1294414 11.663193 12.069147 6329 6460
## 499 5.749116 3.6078646 3.103226 3.678648 326 354
## SK066_rawcounts SK070_rawcounts SK071_rawcounts SK072_rawcounts locusId
## 80 41 2 9 13 2194652
## 90 92 9 45 20 2194662
## 232 5 25 25 27 2194811
## 334 7535 24976 33617 24169 2194915
## 361 7268 20012 16652 17832 2194942
## 499 248 50 39 49 2195081
## accession GI scaffoldId stop
## 80 YP_788236.1 116053799 4582 95402
## 90 YP_788246.1 116053809 4582 105881
## 232 YP_788395.1 116053957 4582 266282
## 334 YP_788499.1 116054056 4582 384875
## 361 YP_788526.1 116054083 4582 413653
## 499 YP_788665.1 116054221 4582 565457
## desc COG COGFun
## 80 putative lipoprotein (NCBI) COG3521 S
## 90 hypothetical protein (NCBI) COG3520 S
## 232 putative shikimate 5-dehydrogenase (NCBI) COG169 E
## 334 hypothetical protein (NCBI) COG3422 S
## 361 protease PfpI (NCBI) COG693 R
## 499 putative lactam utilization protein (NCBI) COG1540 R
## COGDesc
## 80 Uncharacterized protein conserved in bacteria
## 90 Uncharacterized protein conserved in bacteria
## 232 Shikimate 5-dehydrogenase
## 334 Uncharacterized conserved protein
## 361 Putative intracellular protease/amidase
## 499 Uncharacterized proteins, homologs of lactam utilization protein B
## TIGRFam
## 80 TIGR03352 type VI secretion lipoprotein, VC_A0113 family
## 90 TIGR03347 type VI secretion protein, VC_A0111 family
## 232
## 334
## 361 TIGR01382 intracellular protease, PfpI family
## 499
## TIGRRoles
## 80 Cellular processes:Pathogenesis
## 90 Cellular processes:Pathogenesis
## 232
## 334
## 361 Protein fate:Degradation of proteins, peptides, and glycopeptides
## 499
## GO EC ECDesc direction
## 80 down
## 90 down
## 232 GO:0005737,GO:0004764 1.1.1.25 Shikimate dehydrogenase. up
## 334 up
## 361 GO:0016798 3.2.-.- up
## 499 GO:0005975,GO:0003824 down10 PCA plot
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors# Variance stabilizing transformations
vsd <- vst(vst, blind = TRUE)
# PCA plot
pcaData <- plotPCA(vsd, intgroup = c("exp_4", "batch", "condition"), returnData = TRUE)## using ntop=500 top features by variancepercentVar <- round(100 * attr(pcaData, "percentVar"))
# Calculate the maximum absolute value among PC1 and PC2
max_abs_val <- max(c(abs(min(pcaData$PC1)), abs(min(pcaData$PC2)), abs(max(pcaData$PC1)), abs(max(pcaData$PC2))))
axis_size <- 10
theme_set(theme_minimal(base_family = "Arial"))
pca <- ggplot(pcaData, aes(PC1, PC2, color = condition, shape = condition)) +
geom_point(size = 3) +
xlab(paste0("PC1: ", percentVar[1], "% variance")) +
ylab(paste0("PC2: ", percentVar[2], "% variance")) +
scale_colour_brewer(palette = "Dark2") +
scale_shape_manual(values = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)) +
coord_fixed(ratio = 1, xlim = c(-max_abs_val, max_abs_val), ylim = c(-max_abs_val, max_abs_val)) +
labs(color = "condition", shape = "condition") +
guides(shape = guide_legend(title = "Condition"), # Adjust size of shape legend
color = guide_legend(title = "Condition")) +
theme(
panel.grid = element_blank(),
panel.background = element_blank(),
axis.text = element_text(size = axis_size),
axis.ticks = element_line(linewidth = 0.2),
axis.line = element_line(linewidth = 0, color = "black"), # Set the axis line linewidth
panel.border = element_rect(fill = NA, linewidth = 0.5), # Set the panel border linewidth
legend.key = element_rect(fill = "white", color = NA) # Set legend key background to white and remove border
)
print(pca)
cairo_pdf(paste0(Sys.Date(),"_pca_",exp, "_", treatment,".vs.", control,".pdf", sep = ""))
pca
dev.off()## quartz_off_screen
## 211 Volcano plot
sequence <- c("treatment")
for (i in sequence) {
set.seed(0)
res <- results(dds_final, contrast = c("exp_4", i, "control"))
gp2 <- wes_palettes$Zissou1
key <- read.csv("../../raw-data/pa14_gene_key.csv", header = TRUE)
rownames(key) <- key$gene_id
res <- merge(key, as.data.frame(res), by=0)
res <- as.data.frame(res)
res$Alias <- rownames(res)
# Define gene list
alg_genes <- c(
"algA", "algF", "algJ", "algI", "algL", "algX", "algG", "algE",
"algK", "alg44", "alg8", "algD", "mucC", "mucB", "algU", "algW",
"algP", "algQ", "algR", "algZ", "algC", "algB"
)
logfc_cutoff
keyvals <- ifelse(
res$log2FoldChange <= -logfc_cutoff & res$padj <= pval_cutoff, gp2[1],
ifelse(res$log2FoldChange >= logfc_cutoff & res$padj <= pval_cutoff, gp2[5],
'lightgrey'))
keyvals[is.na(keyvals)] <- 'lightgrey'
names(keyvals)[keyvals == gp2[5]] <- 'Up-regulated'
names(keyvals)[keyvals == 'lightgrey'] <- 'NS'
names(keyvals)[keyvals == gp2[1]] <- 'Down-regulated'
volcano <- EnhancedVolcano(res,
lab = res[["name"]],
selectLab = alg_genes,
x = 'log2FoldChange',
y = 'padj',
xlab = bquote(~Log[2]~ 'fold change'),
pCutoff = pval_cutoff,
FCcutoff = logfc_cutoff,
cutoffLineType = "dashed",
pointSize = 1,
labSize = 3.0,
axisLabSize = 14,
labCol = 'black',
labFace = 'bold',
#col = c('grey', gp2[2], gp2[3], gp2[1]),
colCustom = keyvals,
colAlpha = 5/5,
legendPosition = 'bottom',
legendLabSize = 12,
legendIconSize = 4.0,
drawConnectors = TRUE,
arrowheads = FALSE,
widthConnectors = 0.2,
gridlines.major = FALSE,
borderWidth = 0.3,
gridlines.minor = FALSE,
title = paste0(treatment," vs ", control, sep = ""),
subtitle = "",
#caption = paste0("total = ", nrow(toptable), " variables"),
caption = paste0("Volcano plot with log2 FC of ", logfc_cutoff," and P value of ",pval_cutoff, "."),
max.overlaps = 30,
#ylim = c(0,10),
colConnectors = 'black') +
theme(axis.ticks=element_line(linewidth =0.2)) # +
# scale_x_continuous(breaks = c(-5, -2.5, 0, 2.5, 5), limits = c(-6,6))
print(volcano)
cairo_pdf(paste0(Sys.Date(),"_volcano_", exp, "_", treatment,".vs.", control,".pdf", sep = ""))
print(volcano)
dev.off()
}
12 MA plot
sequence <- c("treatment")
for (i in sequence) {
set.seed(0)
res <- results(dds_final, contrast = c("exp_4", i, "control"))
gp2 <- wes_palettes$Zissou1
res$gene_id <- rownames(res)
res <- as.data.frame(res)
head(res)
res <- full_join(key, as.data.frame(res), by="gene_id")
rownames(res) <- res$Alias
any(is.na(res)) # Check if there are any NAs
res <- res %>% filter(!is.na(baseMean))
ma <- ggmaplot(res,
fdr = pval_cutoff,
fc = 2^(logfc_cutoff),
size = 1,
palette = c((gp2[5]), (gp2[1])),
select.top.method = "fc",
# ylim = c(-15,15),
legend = "bottom",
title = paste0(treatment," vs ", control, sep = ""),
subtitle = paste0("Volcano plot with log2 FC of ", logfc_cutoff," and P value of ",pval_cutoff, "."),
font.label = c( 12),
label.rectangle = FALSE,
genenames = res$name,
font.legend = c(12),
font.main = c("bold", 14),
alpha = 1.5,
ggtheme = ggplot2::theme(panel.grid =element_blank(),
axis.text = element_text(size = 12),
axis.title = element_text(size = 14),
panel.background = element_blank(),
panel.border = element_rect(fill = NA)))
print(ma)
cairo_pdf(paste0(Sys.Date(),"_ma_", exp, "_", treatment,".vs.", control,".pdf"))
print(ma)
dev.off()
}
---
title: "2025_exp_4_alginate_DEG"
author: "Nour El Husseini"
date: "2025"
output: 
  html_document:
    toc: yes
    toc_float: yes
    toc_collapsed: false  # Ensures TOC is expanded by default
    number_sections: yes
    code_folding: show
    code_download: true
    highlight: kate
    theme:
      bootswatch: zephyr
    css: style.css  # Link to your custom CSS file
editor_options: 
  chunk_output_type: console
  markdown: 
    wrap: 72
---


```{r load_libraries, echo=FALSE, message=FALSE, warning=FALSE}
library("DESeq2")
library("ggplot2")
library("kableExtra")
library("dplyr")
library("ggforce")
library("RColorBrewer")
library("pheatmap")
library("wesanderson")
library("EnhancedVolcano")
library("ggpubr")
library("hpgltools")
library("dplyr")
library("tibble") 
library("edgeR")
library("genefilter")
library("pheatmap")
library("dplyr")
library("tidyverse")
library("DESeq2")
library("ComplexHeatmap")
library("goseq")
library("plyr")
library("geomtextpath")
```

# Overview

```{r sample_table, echo=FALSE}
# Load metadata CSV file into a dataframe, using the first column as row names
metadata <- read.csv("../../raw-data/metadata_alginate_vtl.csv", row.names = 1, header = TRUE)

# Filter the data to include only rows where exp_4 is either "control" or "treatment"
# Then remove other experiments (exp_2 to exp_6)
metadata <- filter(metadata, exp_4 %in% c("control", "treatment")) %>%
    select(batch, condition, media, treatment, induced, exp_4) 

# Display the cleaned metadata table using knitr::kable and kableExtra styling
metadata %>%
  kbl() %>%  # Create a basic table
  kable_classic(full_width = TRUE)  # Apply a classic table style with full width
``` 

# Project variables
```{r project_parameters, echo=FALSE}
# Define the labels for control and treatment groups used in the experiment
head(metadata)
# Extract control and treatment conditions based on `exp_4` labels
control <- metadata[metadata$exp_4 == "control", ]
treatment <- metadata[metadata$exp_4 == "treatment", ]

control <- unique(control$condition)
treatment <- unique(treatment$condition)

print(paste0("The control is: ", control))
print(paste0("The treatment is: ", treatment))

# Set default text sizes for plots
text.size <- 12    
axis.text.size <- 14

logfc_cutoff <- 2
pval_cutoff <- 0.01

# Set variables for the project used in filenames and plot titles
project <- "orn_alginate_rnaseq"
exp <- "exp_4"                    
```

```{r rawdata}
# Load raw count data
rawcounts <- read.csv("../../raw-data/htseq_SK_unfiltered_reverse_strand_count.csv", 
                      row.names = 1, header = TRUE)

# Append a prefix to the batch and run columns to turn to character class
metadata[["batch"]] <- paste0("b", metadata[["batch"]])
metadata[["run"]] <- paste0("r", metadata[["run"]])

# Subset the rawcounts dataframe to only include samples that are present in experiment
samples <- rownames(metadata)
rawcounts <- select(rawcounts, all_of(samples))

# Ensure that the order of samples in metadata matches the order of columns in rawcounts
all(rownames(metadata) == colnames(rawcounts))  # Should return TRUE if order is correct

# Define the design formula for DESeq2, including main condition and batch effect
design_formula <- ~ exp_4 + batch

# Create DESeq2 dataset object from the filtered count matrix and metadata
dds <- DESeqDataSetFromMatrix(
  countData = rawcounts,
  colData = metadata,
  design = design_formula
)
```


# Library size per sample
```{r library_size}
# Sum up the counts for each sample
rawcounts_sums <- colSums(rawcounts)

# Turn it into a data frame
data <- data.frame(
  Category = names(rawcounts_sums),
  Count = as.numeric(rawcounts_sums)
)

# Make sure sample order is consistent
data <- data %>%
  mutate(Category = factor(Category, levels = sort(unique(Category))))

# Plot bar chart of library sizes
p <- ggplot(data, aes(x = Category, y = Count, fill = Category)) +
  geom_bar(stat = "identity") +
  # coord_flip() # flip if too many samples to read labels easily
  labs(y = "Library size (M)", x = NULL) +  # remove x label, rename y axis
  # scale_fill_brewer(palette = "Dark2") +  # optional color palette
  scale_y_continuous(
    labels = scales::label_number(scale = 1e-6),  # show counts in millions
    limits = c(0, (max(data$Count) + 1e6))        # add a bit of space on top
  ) +
  theme(
    panel.grid = element_blank(),
    panel.background = element_blank(),
    axis.text = element_text(size = text.size, angle = 45, hjust = 1),  # slant labels
    axis.title = element_text(size = axis.text.size),
    legend.text = element_text(size = text.size),
    legend.title = element_text(size = axis.text.size),
    panel.border = element_rect(fill = NA),
    legend.position = "none",  # no legend needed since each bar is a different sample
    axis.ticks = element_line(linewidth = 0.2),
    legend.key = element_blank()
  )

# Show the plot
p

# Save the plot as a PDF with today’s date and project name
ggsave(paste0(Sys.Date(), "_library_size_", exp, "_", treatment,".vs.", control, ".pdf"), device = "pdf")
```

# Expression distribution

```{r expression_distribution}

# Stack rawcounts into single vector 
dat <- stack(rawcounts)

# Format expression distribution plot with the expression level and density 
expression_dist <- ggplot(dat, aes(x = log2(values+1),  fill=ind, label = ind )) + 
  geom_density(alpha = 0.5, position = "identity") + 
  ylab("Density") + 
  geom_textdensity(hjust = "ymax", vjust = 0,
           text_only = TRUE, text_smoothing = 20) + 
    theme(panel.grid = element_blank(),
        panel.background = element_blank(),
        axis.text = element_text(size = text.size),
        axis.title = element_text(size = axis.text.size),
        legend.text = element_text(size = text.size),
        legend.title = element_text(size = axis.text.size),
        panel.border = element_rect(fill = NA),
        legend.position = "none",
        axis.ticks = element_line(linewidth = 0.2),
        legend.key = element_blank()) +
  coord_cartesian(xlim = c(0, 15))

expression_dist

#save figure
cairo_pdf(paste0(Sys.Date(), "_expression_dist_", exp, "_", treatment, ".vs.", control, ".pdf"))
expression_dist
dev.off() 

```

# Non-zero genes observed

```{r non-zero_genes}
# Prepare data by getting library sizes and non-zero counts
x <- colSums(rawcounts)
y <- colSums(rawcounts != 0) 
df <- data.frame(x,y)
df$ind <- rownames(df)

# Calculate axis limits
x_min <- min(df$x)
x_max <- max(df$x)
y_min <- min(df$y)
y_max <- max(df$y)

# Non-zero plot
nonzero <- ggplot(data = df, aes(x = x, y = y, color = ind, label = ind)) +
  geom_point(size = 3) +
  geom_text(hjust = -0.1, vjust = -0.1, color = "black", size = 5) +
  scale_y_continuous(limits=c(y_min, y_max)) + 
  scale_x_continuous(
    limits = c(x_min-1e3, x_max+9e5),
    labels = scales::comma_format(scale = 1e-6,  accuracy = 1)  # Format labels without decimals
  ) +
  xlab("Millions of reads") + ylab("Number of non-zero genes observed") +
  labs(color = "Sample") + 
    theme(panel.grid = element_blank(),
        panel.background = element_blank(),
        axis.text = element_text(size = text.size),
        axis.title = element_text(size = axis.text.size),
        legend.text = element_text(size = text.size),
        legend.title = element_text(size = axis.text.size),
        panel.border = element_rect(fill = NA),
        legend.position = "none",
        axis.ticks = element_line(linewidth = 0.2),
        legend.key = element_blank()) +
  coord_cartesian()

# Show nonzero plot
nonzero

# Save plot

cairo_pdf(paste0(Sys.Date(), "_non-zero_", exp, "_", treatment, ".vs.", control, ".pdf"))
nonzero
dev.off() 

```

# RPKM and CPM transformations
```{r data_transformations}
# Load GFF annotation 
gff_annot <- load_gff_annotations("../../raw-data/paeruginosa_pa14.gff", 
                                  id_col = "gene_id", type = "gene")
rownames(gff_annot) <- gff_annot$gene_id

head(gff_annot)
head(rawcounts)
dim(gff_annot)
dim(rawcounts)

summary(rownames(gff_annot) == rownames(rawcounts))
gff_annot <- gff_annot[rownames(rawcounts),]
summary(rownames(gff_annot) == rownames(rawcounts))


# Create an expression object with counts, metadata, and gene info
expt <- create_expt(count_dataframe = rawcounts,
                    metadata = metadata, 
                    gene_info = gff_annot)

# Normalize counts log2 CPM
cpm_normalized_exp <- normalize_expt(expt, 
                                     transform = "log2", 
                                     norm = "raw", 
                                     convert = "cpm", 
                                     row_min = "10")

####### Calculate RPKM ########

# Grab gene lengths from GFF (should be in base pairs)
gene_lengths <- gff_annot$width  
names(gene_lengths) <- rownames(gff_annot)  # set gene names

# Make sure gene_lengths are in the same order as rawcounts rows
gene_lengths <- gene_lengths[rownames(rawcounts)]

# Make DGEList object for edgeR
dge <- DGEList(counts = rawcounts)

# Calculate RPKM (reads per kilobase per million)
rpkm <- rpkm(dge, gene.length = gene_lengths)

# Wrap RPKM in an experiment object too
expt_rpkm <- create_expt(count_dataframe = rpkm,
                         metadata = metadata, 
                         gene_info = gff_annot)

# log2 transform the RPKM (again, no normalization beyond log)
rpkm_normalized_exp <- normalize_expt(expt_rpkm, 
                                      transform = "log2", 
                                      norm = "raw")

# Get the actual expression matrices from the experiment objects
rpkm <- exprs(rpkm_normalized_exp)
cpm <- exprs(cpm_normalized_exp)

# Merge CPM and RPKM matrices with gene annotation info
cpm_annotated <- merge(gff_annot, cpm, by = 0)
rpkm_annotated <- merge(gff_annot, rpkm, by = 0)
```

## Heatmap of alginate genes
This generates a small heatmap of the alginate genes across samples to visualize expression.

```{r heatmaps}
# Loop through CPM and RPKM to plot both heatmaps
for (type in c("cpm", "rpkm")) {
  
  # Define gene list
  alg_operon_genes <- c(
    "algA", "algF", "algJ", "algI", "algL", "algX", "algG", "algE",
    "algK", "alg44", "alg8", "algD", "mucC", "mucB", "algU", "algW",
    "algP", "algQ", "algR", "algZ", "algC", "algB"
  )

  # Get the right data per type of transformation
  data_annot <- get(paste0(type, "_annotated"))
  
  # Fix rownames using gene name (make unique)
  rownames(data_annot) <- make.names(data_annot$name, unique = TRUE)
  
  # Drop undesired metadata columns
  cols_to_exclude <- c("Row.names","seqnames","start","end","width",
                       "strand","source","type","score","phase",
                       "gene_id","Name","Dbxref","Alias","name",
                       "Parent","locus")
  keep <- !names(data_annot) %in% cols_to_exclude
  expr_data <- data_annot[, keep]
  
  # Extract matrix for alg genes
  alg_genes <- expr_data[alg_operon_genes, , drop = FALSE]
  mat <- alg_genes - rowMeans(alg_genes)
  
  # Pull sample annotations
  anno <- as.data.frame(metadata[, c("condition", "induced")])
  colnames(anno) <- c("Condition", "Induced")
  
  # Color settings for annotation
 # ann_colors <- list(
 #  Condition = c("LB-DMSO" = "#D95F02", "LB-IPTG" = "#66A61E"),
 #  Induced = c("no" = "lightgrey", "yes" = "gray27")
 # )
  
  # Scale data by row (Z-score per gene)
  scaled_mat <- t(scale(t(mat)))

  # Build heatmap
  ht <- Heatmap(
    scaled_mat,
    name = "Z-score",
    top_annotation = HeatmapAnnotation(df = anno#, 
                                       #col = ann_colors
                                       ),
    cluster_rows = TRUE,
    cluster_columns = TRUE,
    row_names_side = "left",
    column_names_side = "bottom",
    column_names_rot = 45,
    row_names_gp = gpar(fontsize = 10),
    column_names_gp = gpar(fontsize = 10),
    show_row_dend = TRUE,
    show_column_dend = TRUE
  )
  
  # Draw and save
  draw(ht)
  
  cairo_pdf(paste0(Sys.Date(), "_alg_heatmap_", type, "_", exp, "_", treatment, ".vs.", control, ".pdf"))
  draw(ht)
  dev.off()
}

```

# DESeq
```{r DESeq}
# Create DESeqDataSet
dds <- DESeqDataSetFromMatrix(
  countData = rawcounts,
  colData = metadata,
  design = design_formula
)

# Check number of genes before filtering
print("Number of genes before filtering:")
nrow(dds)

# Filter: keep genes with counts >=10 in at least 2 samples
print("Filtering genes with counts >=10 in at least 2 samples")
keep <- rowSums(counts(dds) >= 10) >= 2 
dds_filtered <- dds[keep, ]

# Check number of genes after filtering
print("Number of genes after filtering:")
nrow(dds_filtered)

# Run DESeq on the filtered dataset
dds_final <- DESeq(dds_filtered)

# Show available results
resultsNames(dds_final)

# Plot dispersion estimates
plotDispEsts(dds_final)
```

# Save DE tables 
```{r save_tables}
res <- results(dds_final, contrast = c("exp_4", "treatment", "control"))
resdf <- as.data.frame(res)
resdf$gene_id <- rownames(resdf)

cpm <- as.data.frame(cpm)
rpkm <- as.data.frame(rpkm)

# append type of value to the dataframe   
colnames(rpkm) <- paste0(colnames(rpkm), "_rpkm")
colnames(cpm) <- paste0(colnames(cpm), "_cpm")
rawcounts2 <- rawcounts
colnames(rawcounts2) <- paste0(colnames(rawcounts2), "_rawcounts")

# ensure column gene_id is there
rpkm$gene_id <- rownames(rpkm)
cpm$gene_id <- rownames(cpm)
rawcounts2$gene_id <- rownames(rawcounts2)

gff_annot <- gff_annot[rownames(resdf),]

dim(resdf)
# merge with siggenes
resdf <- full_join(resdf, gff_annot, by = "gene_id", copy = FALSE)
head(resdf)

pa_go <- read.csv("../../raw-data/208963_microbes.online.csv", header = TRUE)
head(pa_go)

col_to_remove <- intersect(colnames(pa_go),colnames(resdf))
col_to_remove <- setdiff(col_to_remove, "Alias") # returns all except the second vector

pa_go <- pa_go %>% select(-all_of(col_to_remove))

resdf <- full_join(resdf, rpkm, by = "gene_id", copy = FALSE)
resdf <- full_join(resdf, cpm, by = "gene_id", copy = FALSE)
resdf <- full_join(resdf, rawcounts2, by = "gene_id", copy = FALSE)
#resdf <- full_join(resdf, pa_go, by = "Alias", copy = FALSE)
resdf <- merge(resdf, pa_go, by = "Alias", copy = FALSE)

head(resdf)
write.csv(resdf, paste0(Sys.Date() , "_results_",exp, "_", treatment,".vs.", control,".csv"), row.names = FALSE)
```

# Save DEG list
```{r save_deg}
# filter for significantly differentially expressed genes (LFC >= 3, padj < pval_cutoff)
dim(resdf)
head(resdf)

summary(is.na(resdf$log2FoldChange))
summary(is.na(resdf$padj))

sig_genes <- resdf[abs(resdf$log2FoldChange) >= logfc_cutoff & resdf$padj < pval_cutoff, ]  %>% drop_na(log2FoldChange)
sig_genes$direction <- ifelse(sig_genes$log2FoldChange > 0, "up", "down")

summary(is.na(sig_genes$log2FoldChange))
summary(is.na(sig_genes$padj))

dim(sig_genes)
head(sig_genes)
write.csv(sig_genes, paste0(Sys.Date(),"_significantgenes_", exp, "_", treatment,".vs.", control,".csv"), row.names = FALSE)
```

# PCA plot 
```{r PCA}
vst <- DESeqDataSetFromMatrix(countData = rawcounts, colData = metadata, design = ~ exp_4 + batch)

# Variance stabilizing transformations
vsd <- vst(vst, blind = TRUE)

# PCA plot
pcaData <- plotPCA(vsd, intgroup = c("exp_4", "batch", "condition"), returnData = TRUE)
percentVar <- round(100 * attr(pcaData, "percentVar"))

# Calculate the maximum absolute value among PC1 and PC2
max_abs_val <- max(c(abs(min(pcaData$PC1)), abs(min(pcaData$PC2)), abs(max(pcaData$PC1)), abs(max(pcaData$PC2))))

axis_size <- 10
theme_set(theme_minimal(base_family = "Arial"))

pca <- ggplot(pcaData, aes(PC1, PC2, color = condition, shape = condition)) +
  geom_point(size = 3) +
  xlab(paste0("PC1: ", percentVar[1], "% variance")) +
  ylab(paste0("PC2: ", percentVar[2], "% variance")) +
  scale_colour_brewer(palette = "Dark2") +
  scale_shape_manual(values = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)) +
  coord_fixed(ratio = 1, xlim = c(-max_abs_val, max_abs_val), ylim = c(-max_abs_val, max_abs_val)) +
  labs(color = "condition", shape = "condition") +
  guides(shape = guide_legend(title = "Condition"),  # Adjust size of shape legend
    color = guide_legend(title = "Condition")) +
  theme(
    panel.grid = element_blank(),
    panel.background = element_blank(),
    axis.text = element_text(size = axis_size),
    axis.ticks = element_line(linewidth = 0.2),
     axis.line = element_line(linewidth = 0, color = "black"),  # Set the axis line linewidth
   panel.border = element_rect(fill = NA, linewidth = 0.5),  # Set the panel border linewidth
  legend.key = element_rect(fill = "white", color = NA)  # Set legend key background to white and remove border
  )

print(pca)

cairo_pdf(paste0(Sys.Date(),"_pca_",exp, "_", treatment,".vs.", control,".pdf", sep = ""))
pca
dev.off()

```

# Volcano plot
```{r volcano}
sequence <- c("treatment")

for (i in sequence) {
    set.seed(0)
    res <- results(dds_final, contrast = c("exp_4", i, "control"))

  gp2 <- wes_palettes$Zissou1
  key <- read.csv("../../raw-data/pa14_gene_key.csv",  header = TRUE)

  rownames(key) <- key$gene_id

  res <- merge(key, as.data.frame(res), by=0)
  res <- as.data.frame(res)
  res$Alias <- rownames(res)

 # Define gene list
  alg_genes <- c(
    "algA", "algF", "algJ", "algI", "algL", "algX", "algG", "algE",
    "algK", "alg44", "alg8", "algD", "mucC", "mucB", "algU", "algW",
    "algP", "algQ", "algR", "algZ", "algC", "algB"
  )

logfc_cutoff
keyvals <- ifelse(
 res$log2FoldChange <= -logfc_cutoff & res$padj <= pval_cutoff, gp2[1],
  ifelse(res$log2FoldChange >= logfc_cutoff & res$padj <= pval_cutoff, gp2[5],
         'lightgrey'))
keyvals[is.na(keyvals)] <- 'lightgrey'
names(keyvals)[keyvals == gp2[5]] <- 'Up-regulated'
names(keyvals)[keyvals == 'lightgrey'] <- 'NS'
names(keyvals)[keyvals == gp2[1]] <- 'Down-regulated'

volcano <- EnhancedVolcano(res,
                     lab = res[["name"]],
                     selectLab = alg_genes,
                     x = 'log2FoldChange',
                     y = 'padj',
                     xlab = bquote(~Log[2]~ 'fold change'),
                     pCutoff = pval_cutoff,
                     FCcutoff = logfc_cutoff,
                     cutoffLineType = "dashed",
                     pointSize = 1,
                     labSize = 3.0,
                     axisLabSize = 14,
                     labCol = 'black',
                     labFace = 'bold',
                     #col = c('grey', gp2[2], gp2[3], gp2[1]),
                     colCustom = keyvals,
                     colAlpha = 5/5,
                     legendPosition = 'bottom',
                     legendLabSize = 12,
                     legendIconSize = 4.0,
                     drawConnectors = TRUE,
                     arrowheads = FALSE,
                     widthConnectors = 0.2,
                     gridlines.major = FALSE,
                     borderWidth = 0.3,
                     gridlines.minor = FALSE,
                     title = paste0(treatment," vs ", control, sep = ""),
                     subtitle = "",
                     #caption = paste0("total = ", nrow(toptable), " variables"),
                     caption = paste0("Volcano plot with log2 FC of ", logfc_cutoff," and P value of ",pval_cutoff, "."),
                     max.overlaps = 30,
                     #ylim = c(0,10),
                     colConnectors = 'black') + 
      theme(axis.ticks=element_line(linewidth =0.2)) # +
 # scale_x_continuous(breaks = c(-5, -2.5, 0, 2.5, 5), limits = c(-6,6))

print(volcano)

cairo_pdf(paste0(Sys.Date(),"_volcano_", exp, "_", treatment,".vs.", control,".pdf", sep = ""))
print(volcano)
dev.off()
}

```

# MA plot
```{r MA_plot}
sequence <- c("treatment")

for (i in sequence) {
    set.seed(0)

res <- results(dds_final, contrast = c("exp_4", i, "control"))

gp2 <- wes_palettes$Zissou1

res$gene_id <- rownames(res)
res <- as.data.frame(res)
head(res)
res <- full_join(key, as.data.frame(res), by="gene_id")
rownames(res) <- res$Alias

any(is.na(res))  # Check if there are any NAs

res <- res %>% filter(!is.na(baseMean))


ma <- ggmaplot(res, 
               fdr = pval_cutoff, 
               fc = 2^(logfc_cutoff), 
               size = 1,
               palette = c((gp2[5]), (gp2[1])),
               select.top.method = "fc",
             #  ylim = c(-15,15),
               legend = "bottom", 
               title = paste0(treatment," vs ", control, sep = ""),
              subtitle = paste0("Volcano plot with log2 FC of ", logfc_cutoff," and P value of ",pval_cutoff, "."),
               font.label = c( 12), 
             label.rectangle = FALSE,
              genenames = res$name,
              font.legend = c(12),
               font.main = c("bold", 14),
               alpha = 1.5,
               ggtheme = ggplot2::theme(panel.grid =element_blank(),
                                        axis.text = element_text(size = 12),
                                        axis.title = element_text(size = 14),
                                        panel.background = element_blank(),
                                        panel.border = element_rect(fill = NA))) 
print(ma)

cairo_pdf(paste0(Sys.Date(),"_ma_", exp, "_", treatment,".vs.", control,".pdf"))
print(ma)
dev.off()
}
```

