03.1-Peve-sRNA-summary

Gene expression summary for Porites evermanni sRNA-seq data.

• trimmed reads generated in deep-dive project

• Reads aligned to Porites evermanni transcriptome, details here

0.0.1 Install and load packages

library(tidyverse)
library(ggplot2)
library(reshape2)
library(pheatmap)
library(RColorBrewer)

1 sRNA

1.1 Load count data

Load in the sRNA count matrix generated using ShortStack. Keep in mind this data includes counts of all sRNAs, not just miRNAs. Also note, while we have 5 samples from which RNA was sequenced, only 3 of those were sequenced for sRNA in P. evermanni.

# Read in sRNA counts data
Peve_counts_sRNA_data_OG <- read_delim("../../../deep-dive/E-Peve/output/08.2-Peve-sRNAseq-ShortStack-31bp-fastp-merged/ShortStack_out/Counts.txt", delim="\t") 
head(Peve_counts_sRNA_data_OG)

# A tibble: 6 × 6
  Coords               Name  MIRNA POR-73-S1-TP2-fastp-…¹ POR-79-S1-TP2-fastp-…²
  <chr>                <chr> <chr>                  <dbl>                  <dbl>
1 Porites_evermani_sc… Clus… N                          0                     71
2 Porites_evermani_sc… Clus… N                         58                      0
3 Porites_evermani_sc… Clus… N                         12                     12
4 Porites_evermani_sc… Clus… N                        135                     30
5 Porites_evermani_sc… Clus… N                        113                     35
6 Porites_evermani_sc… Clus… N                          5                     35
# ℹ abbreviated names: ¹​`POR-73-S1-TP2-fastp-adapters-polyG-31bp-merged`,
#   ²​`POR-79-S1-TP2-fastp-adapters-polyG-31bp-merged`
# ℹ 1 more variable: `POR-82-S1-TP2-fastp-adapters-polyG-31bp-merged` <dbl>

1.2 Count data munging

Peve_counts_sRNA <- Peve_counts_sRNA_data_OG

# Remove excess portions of sample column names to just "sample###"
colnames(Peve_counts_sRNA) <- sub("-S1-TP2-fastp-adapters-polyG-31bp-merged", "", colnames(Peve_counts_sRNA))
colnames(Peve_counts_sRNA) <- sub("POR-", "sample", colnames(Peve_counts_sRNA))

# Keep just the counts and cluster names
Peve_counts_sRNA <- Peve_counts_sRNA %>% select("sample73", "sample79", "sample82", "Name")

# I'm not going to be doing any removal of low-count sRNAs for now

# Make the cluster names our new row names
Peve_counts_sRNA <- Peve_counts_sRNA %>% column_to_rownames(var = "Name")

head(Peve_counts_sRNA)

          sample73 sample79 sample82
Cluster_1        0       71       17
Cluster_2       58        0        0
Cluster_3       12       12       26
Cluster_4      135       30       10
Cluster_5      113       35       21
Cluster_6        5       35      129

1.3 Expression levels

Plot histograms of the expression levels in each sample

# Melt the count matrix into long format
Peve_counts_sRNA_melted <- melt(Peve_counts_sRNA, variable.name = "sample", value.name = "counts")

# Plot the expression level histograms for each sample
ggplot(Peve_counts_sRNA_melted, aes(x = counts)) +
  geom_histogram(binwidth = 1, fill = "#1E2761", color = "black") +
  scale_x_log10() +  # Optional: Log-transform the x-axis for better visualization
  facet_wrap(~sample, scales = "free_y") +
  labs(title = "Gene Expression Level Histogram for Each Sample",
       x = "Expression Level (Counts)",
       y = "Frequency") +
  theme_minimal()

1.4 Transcript counts

First let’s check the total number of transcripts in each sample – keep in mind this expression data has not been normalized yet, so there may be different totals for each sample

# Calculate the total number of transcripts for each sample
total_transcripts <- colSums(Peve_counts_sRNA)

# Create a data frame for plotting
total_transcripts_df <- data.frame(sample = names(total_transcripts),
                                   totals = total_transcripts)

# Plot the total number of transcripts for each sample
ggplot(total_transcripts_df, aes(x = sample, y = totals)) +
  geom_bar(stat = "identity", fill = "#1E2761", color = "black") +
  labs(title = "Total Number of Transcripts per Sample",
       x = "Sample",
       y = "Total Transcripts") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

Now let’s check the number of unique transcripts in each sample – that is, how many unique sRNAs are expressed in each sample? This should be pretty much the same across samples, even without normalization.

# Calculate the number of unique transcripts (non-zero counts) for each sample
unique_transcripts <- colSums(Peve_counts_sRNA > 0)

# Create a data frame for plotting
unique_transcripts_df <- data.frame(sample = names(unique_transcripts),
                                    uniques = unique_transcripts)

# Plot the total number of unique transcripts for each sample
ggplot(unique_transcripts_df, aes(x = sample, y = uniques)) +
  geom_bar(stat = "identity", fill = "#1E2761", color = "black") +
  labs(title = "Total Number of Unique Expressed Transcripts per Sample",
       x = "Sample",
       y = "Unique Transcripts") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

2 miRNA

2.1 Load miRNA metadata

The ShortStack output Results.txt includes all clusters of sRNA reads, including those not annotated as valid miRNAs. Now that we’ve looked at all the sRNAs a bit, let’s focus in on those classified as miRNAs.

# Join with full metadata sheet, which only contains valid miRNAs
Peve_metadata_miRNA <- read_csv("../../../deep-dive/DEF-cross-species/output/10-shortRNA-ShortStack-comparison/Peve_results_mature_named.csv") 

Peve_counts_sRNA <- rownames_to_column(Peve_counts_sRNA, var = "Name")

Peve_counts_miRNA <- left_join(Peve_metadata_miRNA, Peve_counts_sRNA, by = c("Name" = "Name"))

# Keep just the counts and given miRNA names (e.g., based on match to previously described miRNA)
Peve_counts_miRNA <- Peve_counts_miRNA %>% select("sample73", "sample79", "sample82", "given_miRNA_name")

# We seem to have a duplicate miRNA name, "peve-mir-novel-4", which would prevent us from making the miRNA names our new row names. We need to first make that column unique
Peve_counts_miRNA$given_miRNA_name <- make.unique(as.character(Peve_counts_miRNA$given_miRNA_name))

# Make the miRNA names our new row names
Peve_counts_miRNA <- Peve_counts_miRNA %>% column_to_rownames(var = "given_miRNA_name")

head(Peve_counts_miRNA)

                  sample73 sample79 sample82
peve-mir-100         86625    64520   116691
peve-mir-novel-31    45912    55736   106369
peve-mir-novel-3     20505    23687    49156
peve-mir-novel-21    51104    16437       61
peve-mir-novel-8     32305     9788    19984
peve-mir-novel-30    14479    19066    21199

2.2 Expression levels

Plot histograms of the miRNA expression levels in each sample

# Melt the count matrix into long format
Peve_counts_miRNA_melted <- melt(Peve_counts_miRNA, variable.name = "sample", value.name = "counts")

# Plot the expression level histograms for each sample
ggplot(Peve_counts_miRNA_melted, aes(x = counts)) +
  geom_histogram(binwidth = 1, fill = "#1E2761", color = "black") +
  scale_x_log10() +  # Optional: Log-transform the x-axis for better visualization
  facet_wrap(~sample, scales = "free_y") +
  labs(title = "Gene Expression Level Histogram for Each Sample",
       x = "Expression Level (Counts)",
       y = "Frequency") +
  theme_minimal()

2.3 miRNA counts

First let’s check the total number of miRNAs in each sample – keep in mind this expression data has not been normalized yet, so there may be different totals for each sample

# Calculate the total number of transcripts for each sample
total_miRNA <- colSums(Peve_counts_miRNA)

# Create a data frame for plotting
total_miRNA_df <- data.frame(sample = names(total_miRNA),
                                   totals = total_miRNA)

# Plot the total number of transcripts for each sample
ggplot(total_miRNA_df, aes(x = sample, y = totals)) +
  geom_bar(stat = "identity", fill = "#1E2761", color = "black") +
  labs(title = "Total Number of miRNAs per Sample",
       x = "Sample",
       y = "Total miRNAs") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

Now let’s check the number of unique miRNAs in each sample – This should be pretty much the same across samples, even without normalization.

# Calculate the number of unique transcripts (non-zero counts) for each sample
unique_miRNA <- colSums(Peve_counts_miRNA > 0)

# Create a data frame for plotting
unique_miRNA_df <- data.frame(sample = names(unique_miRNA),
                                    uniques = unique_miRNA)

# Plot the total number of unique transcripts for each sample
ggplot(unique_miRNA_df, aes(x = sample, y = uniques)) +
  geom_bar(stat = "identity", fill = "#1E2761", color = "black") +
  labs(title = "Total Number of Unique Expressed miRNAs per Sample",
       x = "Sample",
       y = "Unique miRNA") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

2.4 Heatmap

heat_colors <- rev(brewer.pal(12, "RdYlBu"))

pheatmap(Peve_counts_miRNA,
         cluster_rows = TRUE,
         cluster_cols = TRUE,
         show_rownames = TRUE,
         show_colnames = TRUE,
         color = heat_colors,
         fontsize_row = 8,
         fontsize_col = 8)

Well… there’s like 3 miRNAs with much higher expression than the others, which is making visualizing relative differences difficult. Let’s redo the heatmap, normalizing each row to view relative difference in expression between samples (scale='row')

pheatmap(Peve_counts_miRNA,
         cluster_rows = TRUE,
         cluster_cols = TRUE,
         show_rownames = TRUE,
         show_colnames = TRUE,
         scale = 'row',
         color = heat_colors,
         fontsize_row = 8,
         fontsize_col = 8)