Draft omics 02

omics/crib/cont-fgf-s30.R

@@ -8,7 +8,7 @@ library(tidyverse)
 # Import ------------------------------------------------------------------
 
 # 🐸 import the s30 data
-s30 <- read_csv("data-raw/xlaevis_counts_S30.csv")
+s30 <- read_csv("data-raw/xlaevis_s30_filtered.csv")
 
 
 # Explore -----------------------------------------------------------------
@@ -192,10 +192,263 @@ write_csv(s30_filtered,
 
 
 # OMICS 2 STATISTICAL ANALYSIS --------------------------------------------
+library(tidyverse)
+library(conflicted)
+library(DESeq2)
+
+# import the filtered s30
+s30_filtered <- read_csv("data-processed/s30_filtered.csv")
+
+# number of genes and samples. remember we filtered out any genes 
+# with 4, 5 or 6 zeros and those where the total count was less than 20
+
+
+# first find genes that are expressed only in one group. that is those
+# only zeros in one group but values in all of the others.
+
+# Find the genes that are expressed only in the FGF-treated group:
+
+s30_fgf_only <- s30_filtered |> 
+  filter(S30_C_5 == 0, 
+         S30_C_6 == 0, 
+         S30_C_A == 0, 
+         S30_F_5 > 0, 
+         S30_F_6 > 0, 
+    S30_F_A > 0)
+# there are 26 genes expressed in the FGF-treated group but not in the control group
+
+# genes that are expressed only in the control group.
+
+s30_con_only <- s30_filtered |> 
+  filter(S30_C_5 > 0, 
+         S30_C_6 > 0, 
+         S30_C_A > 0, 
+         S30_F_5 == 0, 
+         S30_F_6 == 0, 
+         S30_F_A == 0)
+# there are no genes expressed in the control group but not in the FGF-treated group
+
+# Write to file 
+write_csv(s30_fgf_only, "results/s30_fgf_only.csv")
+
+
+## Import metadata that maps the sample names to treatments
+meta <- read_table("meta/frog_meta_data.txt")
+row.names(meta) <- meta$sample_id
+
+# We only need the s30
+meta_S30 <- meta |>
+  dplyr::filter(stage == "stage_30")
+
+
+
+# 1. check the names match: rownames of meta and column names of counts
+names(s30_filtered)
+row.names(meta_S30)
+
+# verfiy with equality
+names(s30_filtered[2:7]) == row.names(meta_S30)
+
+
+
+# 2. Create a DESeq2 object
+# bioconductor has custom data types
+# to make a DESeqDataSet  object we need 
+#    a) count matrix, 
+#    b) metadata and
+#    c) a design matrix
+
+# The count matrix must contain only the counts
+# the genes are not inside the matrix, but are the row names
+s30_count_mat <- s30_filtered |>
+  select(-xenbase_gene_id) |>
+  as.matrix()
+# add the genes as row names
+row.names(s30_count_mat) <- s30_filtered$xenbase_gene_id
+
+View(s30_count_mat)
+knitr::kable(head(s30_count_mat))
+
+
+# creates the DESeqDataSet (dds) object
+dds <- DESeqDataSetFromMatrix(s30_count_mat,
+                              colData = meta_S30,
+                              design = ~ treatment + sibling_rep)
+# design matrix is a formula that describes the experimental design using the 
+# column names of the metadata dataframe
+
+
+# This is fine: 
+# converting counts to integer mode
+# Warning message:
+
+#   some variables in design formula are characters, converting to factors
+
+
+
+# To see the counts in the DESeqDataSet object, we can use the counts() function
+counts(dds) |> View()
+# This should be what is in s30_count_mat
+
+# The normalisation is done automatically with the DE
+# However, we need the normalised counts for data viz.
+
+
+#  The normalised counts can be generated using:
+#  estimateSizeFactors() for estimating the factors for normalisation.
+dds <- estimateSizeFactors(dds)
+# By assigning the results back to the dds object, we are filling in the
+# slots of the DESeqDataSet object with the appropriate information.
+
+# We can take a look at the normalization factors of each sample using:
+sizeFactors(dds)
+# S30_C_5   S30_C_6   S30_C_A   S30_F_5   S30_F_6   S30_F_A 
+# 0.8812200 0.9454600 1.2989886 1.0881870 1.0518961 0.8322894 
+
+# Notice there is a relationship between the total number of 
+# counts and the size factors which we can see with a quick scatter plot
+plot(colSums(s30_count_mat), sizeFactors(dds))
+
+# Then to get the normalized counts as a matrix, we can use:
+normalised_counts <- counts(dds, normalized = TRUE)
+
+
+# Save this normalized data matrix to file for later use:
+data.frame(normalised_counts,
+           xenbase_gene_id = row.names(normalised_counts)) |>
+  write_csv(file = "results/S30_normalised_counts.csv")
+
+# These normalized counts will be useful for downstream visualization
+# of results, but cannot be used as input to DESeq2 or any other tools that
+# perform differential expression analysis that use the negative binomial model.
+
+
+# DE WITH DESeq2 ----------------------------------------------------------
+
+# run the actual differential expression analysis,
+# we use a single call to the function DESeq().
+# Run analysis
+dds <- DESeq(dds)
+
+# check dispersion estimates, checks the assumption of the DESeq2 model
+plotDispEsts(dds)
+# look fine
 
 
+## Define contrasts for FGF overexpression
+contrast_fgf <- c("treatment", "FGF", "control")
+results_fgf <- results(dds,
+                       contrast = contrast_fgf,
+                       alpha = 0.01)
+results_fgf |>
+  data.frame() |> head()
 
 
-# OMICS 3 VISUALISE AND INTERPRET -----------------------------------------
+data.frame(results_fgf,
+           xenbase_gene_id = row.names(results_fgf)) |>
+  write_csv(file = "results/S30_results.csv")
 
 
+
+
+
+
+
+
+
+
+
+
+# OMICS 3 VISUALISE AND INTERPRET and chek the assumptions?? -----------------------------------------
+
+
+
+# PCA AND CLUSTERING -----------------------
+
+# we do this on the log2 transformed normalised counts or the regularized the
+# log transformed counts
+# rlog is a method to bias from low count genes. 
+# https://hbctraining.github.io/DGE_workshop_salmon_online/lessons/03_DGE_QC_analysis.html gives a good explanation of regularized the log transform (rlog)
+
+
+# The rlog transformation of the normalized counts is only necessary
+# for these visualization methods during this quality assessment.
+# They are not used for DE because DESeq2 takes care of that
+
+### Transform counts for data visualization
+rld <- rlog(dds, blind = TRUE)
+# transformed counts are accessed with
+# assay(rld)
+rlogged <- assay(rld) |> t()
+# These techniques want cells (samples in rows) and genes in columns.
+
+
+# perform PCA using standard functions
+pca <- rlogged |>
+  prcomp(scale. = TRUE) 
+
+summary(pca)
+# PC1 proportion 0.3857, PC2 proportion 0.2112
+
+pca_labelled <- data.frame(pca$x,
+                           sample_id = row.names(rlogged))
+
+
+# merge with metadata
+pca_labelled <- pca_labelled |> 
+  left_join(meta_S30, 
+            by = "sample_id")
+
+pca_labelled |> ggplot(aes(x = PC1, y = PC2, 
+                           colour = treatment,
+                           shape = sibling_rep)) +
+  geom_point(size = 3) +
+  scale_colour_viridis_d(end = 0.95) +
+  theme_classic()
+
+# they don't cluster that well, by treatment or sib group
+
+
+# ########## TO COPY TO OTHER COMPARISONS FROM HERE
+# # Hierarchical Clustering
+# #
+# #  pheatmap need a matrix or dataframe so use SummarizedExperiment::assay()
+# #  which is loaded with DESeq2
+# rld_mat <- assay(rld)
+# 
+# # Compute pairwise correlation values
+# rld_cor <- cor(rld_mat)    ## cor() is a base R function
+# 
+# meta_S30 <- as.data.frame(meta_S30)
+# row.names(meta_S30) <-  meta_S30$sample_id
+# # Plot heatmap using the correlation matrix and the metadata object
+# pheatmap(rld_cor,
+#          annotation = meta_S30[3:4])
+
+
+
+## Principle Components Analysis (PCA) 
+
+# explore data further - at the sample and gene level check reps cluster 
+# together do on the normalised counts
+
+
+# venn diagram for frogs, use original dataset
+
+# look up information about the genes
+
+# volcano plots
+
+# MAYBE WEEK 3
+# plotMA(results_fgf)
+# 
+# resultsNames(dds)
+# # "Intercept", "treatment_FGF_vs_control" "sibling_rep_five_vs_A"    "sibling_rep_six_vs_A"
+# 
+# 
+# 
+# # results_fgf_shrunken <- lfcShrink(dds,
+# #                                   coef = "treatment_FGF_vs_control",
+# #                                   type = "apeglm")
+# # 
+# # plotMA(results_fgf_shrunken)