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4_Statistics.Rmd
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4_Statistics.Rmd
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---
title: "4 - Statistics"
author: "Nathalia Graf Grachet"
date: "2/2/2021"
output: html_document
---
# 1 - Libraries
```{r libraries}
library(tidyverse)
library(vegan)
library(factoextra)
library(ggrepel)
library(ggnewscale)
library(patchwork)
source('functions_statistics.R')
```
# 2 - Import necessary data
```{r pathVariables}
# set path variables
project_name = 'MetaboDirect'
preprocess_dir = '1_preprocessing_output'
stats_dir = '4_statistics'
metadata_filename = 'metadata.csv'
```
```{r data}
# matrix of features/sample
matrix_file = file.path(project_name, preprocess_dir, 'matrix_features.csv')
df <- read.csv(matrix_file, row.names = 1)
df <- as.data.frame(t(df))
df[is.na(df)] <- 0
# metadata
metadata <- read.csv(metadata_filename)
```
# 3 - NMDS - Root, 7 and 14 dpi
```{r nmdsRoot}
matrix <- get_target_samples(df, metadata, SampleID, Tissue, "in", "root")
matrix <- get_target_samples(matrix, metadata, SampleID, Time, "out", "10 dpi")
## Converting absolute abundance to either relative abundance or presence/absence
## Relative abundance use method "total"
## For presence/absence transformation use method "pa"
trans = "pa"
t.matrix <- decostand(matrix, trans)
## Calculate distance matrix
## Use "bray" for relative abundance
## Use "jaccard for presence/absence
dm.method = "euclidean"
dist.matrix <- vegdist(t.matrix , method = dm.method) # dist matrix
set.seed(123)
nmds <- make_nmds(dist.matrix, dm.method)
# **A good rule of thumb:
# < 0.05 provides an excellent representation in reduced dimensions,
# < 0.1 is great,
# < 0.2 is good/ok,
# < 0.3 provides a poor representation.**
stressplot(nmds)
nmds.scores <- as.data.frame(scores(nmds))
nmds.scores$SampleID <- rownames(nmds.scores)
nmds.scores <- merge(nmds.scores, metadata, by = 'SampleID')
list_colors <- c("healthy" = "#228B22", "inoculated" = "#C71585")
p.size = 2; size = 10; h=4; w=8; res=300
nmds_root <- nmds_plot(nmds.scores, NMDS1, NMDS2, Treatment, Treatment, Time, size, p.size) +
facet_wrap(~Cultivar) +
ggtitle("Root") +
scale_color_manual(values=list_colors) +
theme(
strip.text=element_text(face="bold"),
strip.background = element_rect(fill="white", colour="white"),
)
filename <- file.path(project_name, stats_dir, 'nmds_root.png')
ggsave(filename, width=w, height=h, dpi=res, nmds_root)
```
# 4 - PERMANOVA - Root, 7 and 14 dpi
```{r permanovaRoot}
set.seed(456)
# using the matrix from the ccell below above!!!!!
subset_metadata <- metadata %>%
filter(SampleID %in% rownames(matrix))
# t.matrix <- as.data.frame(t(matrix))
dm.method = "bray"
dist.matrix <- vegdist(matrix , method = dm.method) # dist matrix
permanova <- adonis(dist.matrix ~ Treatment + Time + Cultivar,
data=subset_metadata,
permutations=999,
method=dm.method)
permanova
filename <- file.path(project_name, stats_dir, 'permanova_root.csv')
write.csv(as.data.frame(permanova$aov.tab), filename)
```
# 5 - NMDS - Leaf, 7 and 14 dpi
```{r nmdsLeaf}
matrix <- get_target_samples(df, metadata, SampleID, Tissue, "in", "leaf")
matrix <- get_target_samples(matrix, metadata, SampleID, Time, "out", "10 dpi")
## Converting absolute abundance to either relative abundance or presence/absence
## Relative abundance use method "total"
## For presence/absence transformation use method "pa"
trans = "pa"
t.matrix <- decostand(matrix, trans)
## Calculate distance matrix
## Use "bray" for relative abundance
## Use "jaccard for presence/absence
dm.method = "euclidean"
dist.matrix <- vegdist(t.matrix , method = dm.method) # dist matrix
set.seed(123)
nmds <- make_nmds(dist.matrix, dm.method)
# **A good rule of thumb:
# < 0.05 provides an excellent representation in reduced dimensions,
# < 0.1 is great,
# < 0.2 is good/ok,
# < 0.3 provides a poor representation.**
stressplot(nmds)
nmds.scores <- as.data.frame(scores(nmds))
nmds.scores$SampleID <- rownames(nmds.scores)
nmds.scores <- merge(nmds.scores, metadata, by = 'SampleID')
list_colors <- c("healthy" = "#228B22", "inoculated" = "#C71585")
p.size = 2; size = 10; h=4; w=8; res=300
nmds_leaf <- nmds_plot(nmds.scores, NMDS1, NMDS2, Treatment, Treatment, Time, size, p.size) +
facet_wrap(~Cultivar) +
ggtitle("Leaf") +
scale_color_manual(values=list_colors) +
theme(
strip.text=element_text(face="bold"),
strip.background = element_rect(fill="white", colour="white"),
)
filename <- file.path(project_name, stats_dir, 'nmds_leaf.png')
ggsave(filename, width=w, height=h, dpi=res, nmds_leaf)
```
# 6 - PERMANOVA - Leaf, 7 and 14 dpi
```{r}
set.seed(456)
# using the matrix from the ccell below above!!!!!
subset_metadata <- metadata %>%
filter(SampleID %in% rownames(matrix))
# t.matrix <- as.data.frame(t(matrix))
dm.method = "bray"
dist.matrix <- vegdist(matrix , method = dm.method) # dist matrix
permanova <- adonis(dist.matrix ~ Treatment + Time + Cultivar,
data=subset_metadata,
permutations=999,
method=dm.method)
permanova
filename <- file.path(project_name, stats_dir, 'permanova_leaf.csv')
write.csv(as.data.frame(permanova$aov.tab), filename)
```