Species abundances

library(mbtools)
theme_set(theme_minimal())

classes <- c(public_client_id = "character", vendor_observation_id = "character")
cohort <- fread("data/manifest.csv", colClasses = classes)[has_close_microbiome == T]

Let’s rad the data and format it to a phyloseq object.

species <- fread("data/S_counts.csv", colClasses = c(sample = "character"))
map <- c(d = "domain", p = "phylum", o = "order", c = "class", f = "family", g = "genus", s = "species")
names(species)[1:7] <- map[names(species)[1:7]]
abundance <- dcast(species, sample ~ species, value.var = "reads", fun.aggregate = sum)
samples <- abundance[, sample]
abundance <- as.matrix(abundance[, "sample" := NULL])
rownames(abundance) <- samples

taxa <- as.matrix(unique(species[, map, with = F]))
rownames(taxa) <- taxa[, "species"]

sdata <- as.data.frame(cohort)
rownames(sdata) <- sdata$vendor_observation_id
sdata <- sdata[rownames(abundance), ]
sdata$subset <- factor(sdata$subset)
sdata$sex <- factor(sdata$sex)

ps <- phyloseq(
    otu_table(abundance, taxa_are_rows = F),
    tax_table(taxa),
    sample_data(sdata)
)

ps <- ps %>% subset_taxa(species != "" & domain == "Bacteria")
rank <- "species"

tests <- association(
    ps, 
    presence_threshold = 0.5,
    min_abundance = 2,
    in_samples = 0.8,
    confounders = c("age", "bmi", "sex"),
    variables = "subset",
    taxa_rank = rank,
    method = "deseq2",
    shrink=F
)

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bmi <- association(
    ps, 
    presence_threshold = 0.5,
    min_abundance = 2,
    in_samples = 0.8,
    confounders = c("age", "sex"),
    variables = "bmi",
    taxa_rank = rank,
    method = "deseq2",
    shrink=F
)

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patterns are not supportedempty search patterns are not supportedempty search patterns are not supportedempty search patterns are not supportedempty search patterns are not supportedempty search patterns are not supportedempty search patterns are not supportedempty search patterns are not supported

tests <- rbind(tests, bmi)
tests <- merge.data.table(tests, as.data.frame(as(tax_table(ps), "matrix")), on=rank)
tests[order(padj)]

tests[, "t" := log2FoldChange / lfcSE]
wide <- dcast(tests, reformulate("variable", rank), value.var=c("t", "padj"), fill=0)
wide[, "sig" := "none"]
wide[padj_bmi < 0.05, "sig" := "BMI"]
wide[padj_subset < 0.05, "sig" := "weight loss"]
wide[padj_bmi < 0.05 & padj_subset < 0.05, "sig" := "both"]
wide[, "sig" := factor(sig, levels=c("none", "BMI", "weight loss", "both"))]
ggplot(wide, aes(x=t_bmi, y=t_subset, color=sig)) +
    geom_vline(xintercept = 0, color="gray30", lty="dashed") +
    geom_hline(yintercept = 0, color="gray30", lty="dashed") +
    geom_point() + stat_smooth(method="glm", aes(group = 1)) +
    labs(x = "t statistic BMI", y = "t statistic weight loss") +
    scale_color_manual(values=c(none = "black", BMI = "royalblue", `weight loss`="orange", both = "red"))

ggsave("figures/species_t.png", dpi=300, width=5, height=3)
cor.test(~ t_bmi + t_subset, data=wide, method="spearman")

    Spearman's rank correlation rho

data:  t_bmi and t_subset
S = 57413148, p-value = 1.304e-08
alternative hypothesis: true rho is not equal to 0
sample estimates:
       rho 
-0.2202592