18 Functional Profiling
This chapter has not been updated since the 2022 edition of this book. The quality of the content may not match the same quality or consistency as the main chapters.
For this chapter’s exercises, if not already performed, you will need to download the chapter’s dataset, decompress the archive, and create and activate the conda environment.
Do this, use wget or right click and save to download this Zenodo archive: 10.5281/zenodo.6983188, and unpack
tar xvf 5c-functional-genomics.tar.gz
cd 5c-functional-genomics/You can then create the subsequently activate environment with
conda env create -f day5.yml
conda activate phylogenomics-functionalThe above conda environment does not include HUMAnN3 due to conflicts with the R packages in the environment.
This chapter does not require the execute of any HUMAnN3 commands due to very large database requirements.
However if you wanted to try the example commands out, you could install HUMANn3 with conda in a separate environment with the following command.
conda create -n humann3 -c bioconda humann18.1 Preparation
Open R Studio from within the conda environment
rstudioOpen a new script file we can load the required libraries for this walkthrough.
library(mixOmics) ## For PCA generation
## Utility packages (pretty stuff)
library(knitr)
library(data.table)
library(tidyverse)
library(gplots)
library(ggrepel)
library(viridis)
library(patchwork)18.2 HUMAnN3 Pathways
First, we would run HUMAnN3 to align reads against gene databases and convert to gene family names counts.
Running HUMAnN3 module requires about 72 GB of memory because it has to load a larger reference database containing the lineage-specific marker genes of checkM.
If you have sufficient computational memory resources, you can run the following steps to run the bin refinement yourself.
We will not run HUMANn3 here as it requires very large databases and takes a long time to run, we have already prepared output for you.
## DO NOT RUN!
# run humann3
humann3 --input file.fastq --output output --threads <threads>
# join all output tables (can do for both gene and pathways)
humann_join_tables -i output/ -o genefamilies_joined.tsv --file_name unmapped_genefamilies
# normalize the output (here by tss - total sum scaling, can do for both gene and pathways)
humann_renorm_table --input genefamilies_joined.tsv --output genefamilies_joined_cpm.tsv --units tss
# regroup the table to combine gene families (standardise gene family IDs across taxa)
humann_regroup_table --input genefamilies_joined_cpm.tsv --output genefamilies_joined_cpm_ur90rxn.tsv --groups uniref90_rxn
# give the gene families names
humann_rename_table --input genefamilies_joined_cpm_ur90rxn.tsv --output genefamilies_joined_cpm_ur90rxn_names.tsv -n metacyc-rxn18.3 humann3 tables
First lets load a pre-made pathway abundance file
## load the species and genus tables generated with humann3
humann3_path_full <- fread("./pathabundance_joined_cpm.tsv")
humann3_path_full <- as_tibble(humann3_path_full)
# clean the file names
humann3_path_full <- rename(humann3_path_full, Pathway = `# Pathway`)
colnames(humann3_path_full) <- gsub(".unmapped_Abundance","", colnames(humann3_path_full))
colnames(humann3_path_full) <- gsub(".SG1","", colnames(humann3_path_full))
# remove unmapped and ungrouped reads
humann3_path <- humann3_path_full %>% filter(!str_detect(Pathway, "UNMAPPED|UNINTEGRATED"))Then lets load associated sample metadata to help make it easier for comparative analysis and make actual informative inferences.
The data being used in this session, is from Velsko et al. 2022 (PNAS Nexus), where we tried to find associations between dental pathologies and taxonomic and genome content. We had a large skeletal collection from a single site in the Netherlands, with a lot of osteological metadata. The study aimed to see if there were any links between the oral microbiome and groups of dental pathologies.
# load the metadata file
full_metadata <- fread("full_combined_metadata.tsv")
## Example of metadata
tibble(full_metadata %>%
filter(Site_code == "MID") %>%
select(Site, Time_period, Library_ID, Sequencing_instrument, Pipenotch, Max_Perio_Score, `%teeth_with_caries`))First step: we can pre-define various functions for generate PCAs we will use downstream - you don’t have to worry about these too much they are just custom functions to quickly plot PCAs from a mixOmics PCA output object with ggplot, but we leave the code here for if you’re curious.
# plot PCA with colored dots and the title including the # of species or genera
plot_pca <- function(df, pc1, pc2, color_group, shape_group, ncomps) {
metadata_group_colors <- get(paste(color_group, "_colors", sep = ""))
metadata_group_shapes <- get(paste(shape_group, "_shapes", sep = ""))
pca.list <- mixOmics::pca(df, ncomp = ncomps, logratio = 'CLR')
## Pull out loadings
exp_var <- paste0(round(pca.list$explained_variance * 100, 2), "%")
df_X <- pca.list$variates$X %>%
as.data.frame() %>%
rownames_to_column("Library_ID") %>%
inner_join(full_metadata, by = "Library_ID")
color_group = df_X[[color_group]]
shape_group = df_X[[shape_group]]
## Selecting which PCs to plot
if (pc1 == 'PC1') {
pc1 <- df_X$PC1
exp_var_pc1 <- exp_var[1]
xaxis <- c("PC1")
} else if (pc1 == 'PC2') {
pc1 <- df_X$PC2
exp_var_pc1 <- exp_var[2]
xaxis <- c("PC2")
} else if (pc1 == 'PC3') {
pc1 <- df_X$PC3
exp_var_pc1 <- exp_var[3]
xaxis <- c("PC3")
}
if (pc2 == 'PC1') {
pc2 <- df_X$PC1
exp_var_pc2 <- exp_var[1]
yaxis <- c("PC1")
} else if (pc2 == 'PC2') {
pc2 <- df_X$PC2
exp_var_pc2 <- exp_var[2]
yaxis <- c("PC2")
} else if (pc2 == 'PC3') {
pc2 <- df_X$PC3
exp_var_pc2 <- exp_var[3]
yaxis <- c("PC3")
}
## Generate figure
pca_plot <- ggplot(df_X, aes(pc1, pc2)) +
geom_point(aes(fill = color_group, shape = shape_group), size = 4.5, stroke = 0.3) +
scale_fill_manual(values = metadata_group_colors) +
scale_shape_manual(values = metadata_group_shapes) +
# stat_ellipse() +
xlab(paste(xaxis, " - ", exp_var_pc1)) +
ylab(paste(yaxis, " - ", exp_var_pc2)) +
theme_minimal(base_size = 16) +
theme(text = element_text(size=16)) +
theme(legend.title = element_blank(),
legend.key.size = unit(2,"mm"),
legend.text = element_text(size = 6)) +
theme(legend.position = "top")
return(pca_plot)
}
# for continuous data
plot_pca_cont <- function(df, pc1, pc2, color_group, shape_group, ncomps, title_text) {
pca.list <- mixOmics::pca(df, ncomp = ncomps, logratio = 'CLR')
exp_var <- paste0(round(pca.list$explained_variance * 100, 2), "%")
df_X <- pca.list$variates$X %>%
as.data.frame() %>%
rownames_to_column("Library_ID") %>%
inner_join(full_metadata, by = "Library_ID")
color_group = df_X[[color_group]]
shape_group = df_X[[shape_group]]
if (pc1 == 'PC1') {
pc1 <- df_X$PC1
exp_var_pc1 <- exp_var[1]
xaxis <- c("PC1")
} else if (pc1 == 'PC2') {
pc1 <- df_X$PC2
exp_var_pc1 <- exp_var[2]
xaxis <- c("PC2")
} else if (pc1 == 'PC3') {
pc1 <- df_X$PC3
exp_var_pc1 <- exp_var[3]
xaxis <- c("PC3")
}
if (pc2 == 'PC1') {
pc2 <- df_X$PC1
exp_var_pc2 <- exp_var[1]
yaxis <- c("PC1")
} else if (pc2 == 'PC2') {
pc2 <- df_X$PC2
exp_var_pc2 <- exp_var[2]
yaxis <- c("PC2")
} else if (pc2 == 'PC3') {
pc2 <- df_X$PC3
exp_var_pc2 <- exp_var[3]
yaxis <- c("PC3")
}
pca_plot <- ggplot(df_X, aes(pc1, pc2, fill = color_group, shape = shape_group)) +
geom_point(size = 5, color = "black") +
scale_fill_viridis_c(option = "C") +
scale_shape_manual(values = c(24,21)) +
# stat_ellipse() +
xlab(paste(xaxis, " - ", exp_var_pc1)) +
ylab(paste(yaxis, " - ", exp_var_pc2)) +
theme_minimal(base_size = 16) +
theme(text = element_text(size=16)) +
theme(legend.title = element_blank(),
legend.key.size = unit(2,"mm"),
legend.text = element_text(size = 6)) +
theme(legend.position = "top") +
ggtitle(title_text) + theme(plot.title = element_text(size = 10))
return(pca_plot)
}
plot_pca_bi <- function(df, pc1, pc2, metadata_group, columntitle) {
metadata_group_colors <- get(paste(metadata_group, "_colors", sep = ""))
metadata_group_shapes <- get(paste(metadata_group, "_shapes", sep = ""))
arrow_pc <- enquo(columntitle)
exp_var <- paste0(round(df$explained_variance * 100, 2), "%") # explained variance for x- and y-labels
# select only the PCs from the PCA and add metadata
df_X <- df$variates$X %>%
as.data.frame() %>%
rownames_to_column("Library_ID") %>%
inner_join(full_metadata, by = "Library_ID")
metadata_group = df_X[[metadata_group]]
corr_lam <- df$sdev[c("PC1", "PC2", "PC3")] * sqrt(nrow(df_X))
df_X <- df_X %>%
mutate(PC1 = PC1 / corr_lam[1],
PC2 = PC2 / corr_lam[2],
PC3 = PC3 / corr_lam[3])
# select the correct PC column and explained variance for PC1
if (pc1 == 'PC1') {
Pc1 <- df_X$PC1
exp_var_pc1 <- exp_var[1]
xaxis <- c("PC1")
} else if (pc1 == 'PC2') {
Pc1 <- df_X$PC2
exp_var_pc1 <- exp_var[2]
xaxis <- c("PC2")
} else if (pc1 == 'PC3') {
Pc1 <- df_X$PC3
exp_var_pc1 <- exp_var[3]
xaxis <- c("PC3")
}
# select the correct PC column and explained variance for PC2
if (pc2 == 'PC1') {
Pc2 <- df_X$PC1
exp_var_pc2 <- exp_var[1]
yaxis <- c("PC1")
} else if (pc2 == 'PC2') {
Pc2 <- df_X$PC2
exp_var_pc2 <- exp_var[2]
yaxis <- c("PC2")
} else if (pc2 == 'PC3') {
Pc2 <- df_X$PC3
exp_var_pc2 <- exp_var[3]
yaxis <- c("PC3")
}
# Identify the 10 pathways that have highest positive and negative loadings in the selected PC
pws_10 <- df$loadings$X %>%
as.data.frame(.) %>%
rownames_to_column(var = "Pathway") %>%
separate(Pathway, into = "Pathway", sep = ":", extra = "drop") %>%
top_n(10, !!arrow_pc)
neg_10 <- df$loadings$X %>%
as.data.frame(.) %>%
rownames_to_column(var = "Pathway") %>%
separate(Pathway, into = "Pathway", sep = ":", extra = "drop") %>%
top_n(-10, !!arrow_pc)
pca_plot_bi <- ggplot(df_X, aes(x = Pc1, y = Pc2)) +
geom_point(size = 3.5, aes(shape = metadata_group, fill = metadata_group))+
geom_segment(data = pws_10,
aes(xend = get(paste(pc1)), yend = get(paste(pc2))),
x = 0, y = 0, colour = "black",
size = 0.5,
arrow = arrow(length = unit(0.03, "npc"))) +
geom_label_repel(data = pws_10,
aes(x = get(paste(pc1)), y = get(paste(pc2)), label = Pathway),
size = 2.5, colour = "grey20", label.padding = 0.2, force = 5, max.overlaps = 20) +
geom_segment(data = neg_10,
aes(xend = get(paste(pc1)), yend = get(paste(pc2))),
x = 0, y = 0, colour = "grey50",
size = 0.5,
arrow = arrow(length = unit(0.03, "npc"))) +
geom_label_repel(data = neg_10,
aes(x = get(paste(pc1)), y = get(paste(pc2)), label = Pathway),
size = 2.5, colour = "grey20", label.padding = 0.2, max.overlaps = 12) +
labs(x = paste(xaxis, " - ", exp_var_pc1),
y = paste(yaxis, " - ", exp_var_pc2)) +
scale_fill_manual(values = metadata_group_colors) +
scale_shape_manual(values = metadata_group_shapes) +
theme_minimal() + theme(text = element_text(size = 16)) +
theme(text = element_text(size=16)) +
theme(legend.position = "top")
return(pca_plot_bi)
}As we are dealing with aDNA, and we often have bad samples, its sometimes interesting to see differences between well/badly preserved samples at all stages of analysis.
Therefore we may generate results for all samples. However for actual analysis where we want to interpret biological differences, should exclude outliers (in this case highly contaminated samples - as identified by the decontam package - see Velsko et al. 2022 _PNAS Nexus for more details).
We can make a list the outliers from the previous authentication analyses.
outliers_mpa3 <- c("EXB059.A2101","EXB059.A2501","EXB015.A3301","EXB034.A2701",
"EXB059.A2201","EXB059.A2301","EXB059.A2401","LIB058.A0103","LIB058.A0106","LIB058.A0104")
poor_samples_mpa3 <- c("CS28","CS38","CSN","ELR003.A0101","ELR010.A0101",
"KT09calc","MID024.A0101","MID063.A0101","MID092.A0101")
outliersF <- str_c(outliers_mpa3, collapse = "|")18.4 Sample Clustering with PCA
18.4.1 Pathway abundance analyses
Once we’ve removed outlier samples, our first simple question is - what is the functional relationships of the groups?
Can we already see distinctive patterns between the different groups in our dataset?
To do this lets clean up the data a bit (cleaning names, removing samples with no metadata etc.), normalise (via a ‘centered-log-ratio’ transform ), and run a PCA.
Once we’ve done this we should always check our PCA’s Scree plot first.
humann3_path_l1 <- humann3_path %>%
filter(!str_detect(Pathway, "\\|")) %>%
# no full_metadata, remove these
select(-c("MID025.A0101","MID033.A0101","MID052.A0101","MID056.A0101",
"MID065.A0101","MID068.A0101","MID076.A0101","MID078.A0101")) %>%
# remove poorly preserved saples
select(-c("MID024.A0101","MID063.A0101","MID092.A0101")) %>%
select(-matches("EXB|LIB")) %>%
# inner_join(., humann3_path.decontam_noblanks_presence_more_30, by = "Pathway") %>%
gather("Library_ID","Counts",2:ncol(.)) %>%
mutate(Counts = Counts + 1) %>%
spread(Pathway,Counts) %>%
column_to_rownames("Library_ID")
# prepare to run a PCA
# check the number of components to retain by tuning the PCA
mixOmics::tune.pca(humann3_path_l1, logratio = 'CLR')
humann3_all_otu.pca <- mixOmics::pca(humann3_path_l1, ncomp = 3, logratio = 'CLR')
humann3_all_pca_values <- humann3_all_otu.pca$variates$X %>%
as.data.frame() %>%
rownames_to_column("Library_ID") %>%
inner_join(., full_metadata, by = "Library_ID")We can see the first couple of PCs in the scree plot account for a good chunk of the variation of our dataset, so lets visualise the PCA itself.
We visualise the PCA with one of our custom functions defined above, and colour by the Pipe notch metadata column.
# pipenotch colors/shapes
Pipenotch_colors = c("#311068","#C83E73")
Pipenotch_shapes = c(16,17)
# by minimum number of pipenotches
pipenotch <- plot_pca_cont(humann3_path_l1, "PC1", "PC2","Min_no_Pipe_notches","Pipenotch", 3,"Min. No. Pipe Notches")
pipenotchWe can see there is a slight separation between the groups, but how do we find out which pathways are maybe driving this pattern?
For this we can generate a PCA bi-plot which show what loadings are driving the spread of the samples.
Pipenotch_colors = c("#311068","#C83E73")
Pipenotch_shapes = c(24,21)
biplot <- plot_pca_bi(humann3_all_otu.pca, "PC1", "PC2", "Pipenotch", PC1)
biplotFrom the biplot we can see which pathways are differentiating along PC1.
We can pull these IDs out to find out what pathways there are from the biplot object itself.
# make a table of the pathways to save, to use again later in another R notebook
humann3_pathway_biplot_list <- biplot$plot_env$pws_10 %>% arrange(desc(PC1)) %>% select(Pathway, PC1, PC2) %>% mutate(Direction = "PC1+")
humann3_pathway_biplot_list <- humann3_pathway_biplot_list %>%
bind_rows(biplot$plot_env$neg_10 %>% arrange(desc(PC1)) %>% select(Pathway, PC1, PC2)%>% mutate(Direction = "PC1-"))18.4.2 Species contributions to pathways
However, this ID numbers aren’t very informative to us. At this point we have to do a bit of literature review/database scraping to pull the human-readable names/descriptions of the IDs - which we have already done for you.
We can load these back into our environment
# PC biplot loading top 10s
humann3_pathway_biplot_list <- fread("./humann3_pathway_biplot_list.tsv")
humann3_pathway_biplot_list <- humann3_pathway_biplot_list %>%
rename(Pathway = pathway) %>%
mutate(Path = sapply(Pathway, function(f) {
unlist(str_split(f, ":"))[1]
})) %>%
select(Pathway, Path, everything()) %>%
# remove 3 of the 4 ubiquinol pathways w/identical loadings
filter(!str_detect(Pathway, "5856|5857|6708"))
tibble(humann3_pathway_biplot_list)We now have the pathway ID, and a pathway description for each of the loadings of the PCA.
18.4.2.0.1 PC1
While we have the pathways, we don’t who contributed these.
For this, we can join our pathway table back onto the original output from HUMANn3 we loaded at the beginning, which includes the taxa information.
# list the 10 orthologs with strongest loading in PC1 + values
humann3_path_biplot_pc <- humann3_pathway_biplot_list %>%
filter(Direction == "PC1+") %>%
pull(Path) %>%
str_c(., collapse = "|") # need this format for filtering in the next step
# select only those 10 pathways from the list, and split the column with names into 3 (Pathway, Genus, Species)
humann3_path_pc1pws <- humann3_path %>%
filter(str_detect(Pathway, humann3_path_biplot_pc)) %>%
filter(str_detect(Pathway, "\\|")) %>%
gather("SampleID", "CPM", 2:ncol(.)) %>%
mutate(Pathway = str_replace_all(Pathway, "\\.s__", "|s__")) %>%
separate(., Pathway, into = c("Pathway", "Genus", "Species"), sep = "\\|") %>%
mutate(Species = replace_na(Species, "unclassified"),
Genus = str_replace_all(Genus, "g__", ""),
Species = str_replace_all(Species, "s__", "")) %>%
inner_join(., humann3_pathway_biplot_list %>%
select(Pathway, Path) %>%
distinct(.), by = "Pathway") %>%
inner_join(., humann3_pathway_biplot_list %>%
filter(Direction == "PC1+") %>%
select(Path), by = "Path") %>%
# select(-Pathway) %>%
select(Path, everything()) %>%
arrange(Path)
tibble(humann3_path_pc1pws)We can now see who contributed which pathway, and also the abundance information (CPM)!
Given many taxa may contribute to the same pathway, we may want to see which taxa are more ‘dominantly’ contributing to this.
For this we can calculate of all copies of a given pathway what fraction comes from which taxa (you can imagine this like ‘depth’ coverage in genomic analysis), based on the percentage of the total copies per million for that pathway.
# calculate the % for each pathway contributed by each genus
humann3_path_pc1pws_stats <- humann3_path_pc1pws %>%
group_by(Path, Genus) %>%
summarize(Sum = sum(CPM)) %>%
mutate(Percent = Sum/sum(Sum)*100) %>%
ungroup(.)
# create the list of 10 orthologs again, but don't collapse the list as above
humann3_path_biplot_pc <- humann3_pathway_biplot_list %>%
filter(Direction == "PC1+") %>%
arrange(Path) %>%
pull(Path)
# calculate the total % of all genera that contribute < X% to each ortholog
humann3_path_pc1pws_stats_extra <- lapply(humann3_path_biplot_pc, function(eclass) {
high_percent <- humann3_path_pc1pws_stats %>%
filter(Path == eclass) %>%
filter(Percent < 5) %>%
summarise(Remaining = sum(Percent)) %>%
mutate(Path = eclass,
Genus = "Other")
}) %>%
bind_rows(.)
# add this additional % to the main table
humann3_path_pcbi_bar_df <- humann3_path_pc1pws_stats_extra %>%
rename(Percent = Remaining) %>%
bind_rows(., humann3_path_pc1pws_stats %>%
select(-Sum)) %>%
select(Path, Genus, Percent) %>%
mutate(Direction = "PC1+") %>%
distinct()And we can visualize the contributors to the top 10 pathways) driving the main variation along PC1 (with the assumption these maybe the most biological significant, and to reduce the numbers of pathways we have to research.
For the loadings falling in the positive direction of the PC1:
# plot the values in a bar chart
paths_sp_pc1 <- humann3_path_pcbi_bar_df %>%
# filter(Direction == "PC1+", Genus != "Other") %>% # removing Other plots all species/unassigned - no need to filter the pathways
filter(Percent >= 5 | (Percent <= 5 & Genus == "Other")) %>% # filter out the genera with % < 5, but keep Other < 5
# filter(Percent >= 5) %>% # filter out the genera with % < 5, but keep Other < 5
mutate(
Genus = fct_relevel(Genus, "Other","unclassified","Aggregatibacter","Capnocytophaga","Cardiobacterium",
"Eikenella","Haemophilus","Kingella","Lautropia","Neisseria","Ottowia","Streptococcus"),
Path = fct_relevel(Path, humann3_pathway_biplot_list %>%
filter(Direction == "PC1+") %>%
pull(Path))) %>%
ggplot(., aes(x=Path, y=Percent, fill = Genus)) +
geom_bar(stat = "identity") +
theme_minimal() +
scale_fill_manual(values = c("#0D0887FF","#969696","#5D01A6FF","#7E03A8FF",
"#9C179EFF","#B52F8CFF","#CC4678FF","#DE5F65FF",
"#ED7953FF","#F89441FF","#FDB32FFF","#FBD424FF","#F0F921FF")) +
theme(text = element_text(size=18),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
ylab("Percent") +
ggtitle("Metacyc pathways - PC1 positive") +
theme(title = element_text(size=10))
# viridis_pal(option = "B")(13)
paths_sp_pc1PWY-5345 has no species assignment to that pathway.
And the negative loadings:
# list the 10 orthologs with strongest loading in PC1 + values
humann3_path_biplot_pc <- humann3_pathway_biplot_list %>%
filter(Direction == "PC1-") %>%
arrange(Path) %>%
pull(Path) %>%
str_c(., collapse = "|") # need this format for filtering in the next step
# select only those 10 orthologs from the list, and split the column with names into 3 (Ortholog, Genus, Species)
humann3_path_pc1neg <- humann3_path %>%
filter(str_detect(Pathway, humann3_path_biplot_pc)) %>%
filter(str_detect(Pathway, "\\|")) %>%
gather("SampleID", "CPM", 2:ncol(.)) %>%
mutate(Pathway = str_replace_all(Pathway, "\\.s__", "|s__")) %>%
separate(., Pathway, into = c("Pathway", "Genus", "Species"), sep = "\\|") %>%
mutate(Species = replace_na(Species, "unclassified"),
Genus = str_replace_all(Genus, "g__", ""),
Species = str_replace_all(Species, "s__", "")) %>%
inner_join(., humann3_pathway_biplot_list %>%
select(Pathway, Path) %>%
distinct(.), by = "Pathway") %>%
inner_join(., humann3_pathway_biplot_list %>%
filter(Direction == "PC1-") %>%
select(Path), by = "Path") %>%
select(-Pathway) %>%
select(Path, everything()) %>%
arrange(Path)
# calculate the % for each ortholog contributed by each genus
humann3_path_pc1neg_stats <- humann3_path_pc1neg %>%
group_by(Path, Genus) %>%
summarize(Sum = sum(CPM)) %>%
mutate(Percent = Sum/sum(Sum)*100) %>%
ungroup(.)
# create the list of 10 orthologs again, but don't collapse the list as above
humann3_path_biplot_pc <- humann3_pathway_biplot_list %>%
filter(Direction == "PC1-") %>%
arrange(Path) %>%
pull(Path)
# calculate the total % of all genera that contribute < X% to each ortholog
humann3_path_pc1neg_stats_extra <- lapply(humann3_path_biplot_pc, function(eclass) {
high_percent <- humann3_path_pc1neg_stats %>%
filter(Path == eclass) %>%
filter(Percent < 5) %>%
summarise(Remaining = sum(Percent)) %>%
mutate(Path = eclass,
Genus = "Other")
}) %>%
bind_rows(.)
# add this additional % to the main table
humann3_path_pcbi_bar_df <- humann3_path_pcbi_bar_df %>%
bind_rows(humann3_path_pc1neg_stats_extra %>%
rename(Percent = Remaining) %>%
bind_rows(., humann3_path_pc1neg_stats %>%
select(-Sum)) %>%
select(Path, Genus, Percent) %>%
mutate(Direction = "PC1-")) %>%
distinct()
# plot the values in a bar chart
paths_sp_pc2 <- humann3_path_pcbi_bar_df %>%
filter(Direction == "PC1-") %>% # removing Other plots all species/unassigned - no need to filter the pathways
filter(Percent >= 5 | (Percent <= 5 & Genus == "Other")) %>% # filter out the genera with % < 5, but keep Other < 5
mutate(Genus = fct_relevel(Genus, "Other","unclassified","Desulfobulbus","Desulfomicrobium","Methanobrevibacter"),
Path = fct_relevel(Path, humann3_pathway_biplot_list %>%
filter(Direction == "PC1-") %>%
pull(Path))) %>%
ggplot(., aes(x=Path, y=Percent, fill = Genus)) +
geom_bar(stat = "identity") +
theme_minimal() +
scale_fill_manual(values = c("#0D0887FF","#969696","#B52F8CFF","#ED7953FF","#FCFFA4FF")) +
theme(text = element_text(size=18),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
# facet_wrap(~pathrtholog, nrow=2) +
ylab("Percent") +
ggtitle("Metacyc pathways - PC1 negative") +
theme(title = element_text(size=12))
paths_sp_pc218.4.3 Final Visualisation
Finally, we can stick the biplot and the taxon contribution plots together!
h3biplots <- biplot + paths_sp_pc2 + paths_sp_pc1 +
plot_layout(widths = c(2, 1,1))
ggsave("./h3_paths_biplots.pdf", plot = h3biplots,
device = "pdf", scale = 1, width = 20, height = 9.25, units = c("in"), dpi = 300)
system(paste0('firefox "h3_paths_biplots.pdf"'))This allows us to evaluate all the information together.
From this point onwards, we would have to do manual research/literature reviews into each of the pathways, see if they make ‘sense’ to the sample type and associated groups of samples, and evaluate whether they are interesting or not.
18.5 (Optional) clean-up
Let’s clean up your working directory by removing all the data and output from this chapter.
The command below will remove the /<PATH>/<TO>/functional-profiling directory as well as all of its contents.
Always be VERY careful when using rm -r. Check 3x that the path you are specifying is exactly what you want to delete and nothing more before pressing ENTER!
rm -rf /<PATH>/<TO>/functional-profiling*Once deleted you can move elsewhere (e.g. cd ~).
We can also get out of the conda environment with
conda deactivateTo delete the conda environment
conda remove --name functional-profiling --all -y