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Goal is to replicate Fisher’s exact test.

Data mung

First, we need to format the data for the analyses.

# transform to relative abundances
phylo.data.nci.umd <- transform_sample_counts(phylo.data.nci.umd, function(x){x / sum(x)})
phylo.data.tcga.RNAseq <- transform_sample_counts(phylo.data.tcga.RNAseq, function(x){x / sum(x)})
phylo.data.tcga.WGS <- transform_sample_counts(phylo.data.tcga.WGS, function(x){x / sum(x)})

# melt data down for use
dat.16s <- psmelt(phylo.data.nci.umd)
dat.rna <- psmelt(phylo.data.tcga.RNAseq)
dat.wgs <- psmelt(phylo.data.tcga.WGS)

# fix otu formatting
dat.rna$otu2 <- "a"
dat.wgs$otu2 <- "a"
i <- 1
for(i in 1:nrow(dat.rna)){
  dat.rna$otu2[i] <- str_split(dat.rna$OTU[i], ";")[[1]][7]
}
for(i in 1:nrow(dat.wgs)){
  dat.wgs$otu2[i] <- str_split(dat.wgs$OTU[i], ";")[[1]][7]
}


# subset to fuso. nuc. only
# Streptococcus sanguinis 
# Campylobacter concisus
# Prevotella spp.

dat.16s <- filter(
  dat.16s,
  OTU %in% c(
    "Fusobacterium_nucleatum",
    unique(dat.16s$OTU[dat.16s$OTU %like% "Streptococcus_"]),
    unique(dat.16s$OTU[dat.16s$OTU %like% "Campylobacter_"]),
    "Prevotella_melaninogenica")
)
dat.rna <- filter(
  dat.rna,
  otu2 %in% c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
      "Streptococcus oralis",
      "Streptococcus mitis", 
      "Streptococcus pneumoniae", 
      "Streptococcus parasanguinis", 
      "Streptococcus salivarius",
    "Campylobacter concisus",
    "Prevotella melaninogenica")
)
dat.wgs <- filter(
  dat.wgs,
  otu2 %in% c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
      "Streptococcus oralis",
      "Streptococcus mitis", 
      "Streptococcus pneumoniae", 
      "Streptococcus parasanguinis", 
      "Streptococcus salivarius",
    "Campylobacter concisus",
    "Prevotella melaninogenica")
)

# new names
dat.16s$OTU1 <- factor(
  dat.16s$OTU,
  levels = c(
    "Fusobacterium_nucleatum",
    "Streptococcus_dentisani:Streptococcus_infantis:Streptococcus_mitis:Streptococcus_oligofermentans:Streptococcus_oralis:Streptococcus_pneumoniae:Streptococcus_pseudopneumoniae:Streptococcus_sanguinis",
    "Campylobacter_rectus:Campylobacter_showae",
    "Prevotella_melaninogenica"
  ),
  labels = c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
    "Campylobacter concisus",
    "Prevotella melaninogenica"
  )
)
dat.rna$OTU1 <- factor(
  dat.rna$otu2,
  levels = c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
    "Campylobacter concisus",
    "Prevotella melaninogenica"),
  labels = c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
    "Campylobacter concisus",
    "Prevotella melaninogenica")
)

dat.wgs$OTU1 <- factor(
  dat.wgs$otu2,
  levels = c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
    "Campylobacter concisus",
    "Prevotella melaninogenica"),
  labels = c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
    "Campylobacter concisus",
    "Prevotella melaninogenica")
)

# rename bacteria
dat.16s$OTU <- factor(
  dat.16s$OTU,
  levels = c(
    "Fusobacterium_nucleatum",
    "Streptococcus_dentisani:Streptococcus_infantis:Streptococcus_mitis:Streptococcus_oligofermentans:Streptococcus_oralis:Streptococcus_pneumoniae:Streptococcus_pseudopneumoniae:Streptococcus_sanguinis",
    "Campylobacter_rectus:Campylobacter_showae",
    "Prevotella_melaninogenica"
  ),
  labels = c(
    "Fusobacterium nucleatum",
    "Streptococcus spp.",
    "Campylobacter concisus",
    "Prevotella melaninogenica"
  )
)
dat.rna$OTU <- factor(
  dat.rna$otu2,
  levels = c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
      "Streptococcus oralis",
      "Streptococcus mitis", 
      "Streptococcus pneumoniae", 
      "Streptococcus parasanguinis", 
      "Streptococcus salivarius",
    "Campylobacter concisus",
    "Prevotella melaninogenica"),
  labels = c(
    "Fusobacterium nucleatum",
    "Streptococcus spp.",
      "Streptococcus spp.",
      "Streptococcus spp.", 
      "Streptococcus spp.", 
      "Streptococcus spp.", 
      "Streptococcus spp.",
    "Campylobacter concisus",
    "Prevotella melaninogenica")
)

dat.wgs$OTU <- factor(
  dat.wgs$otu2,
  levels = c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
      "Streptococcus oralis",
      "Streptococcus mitis", 
      "Streptococcus pneumoniae", 
      "Streptococcus parasanguinis", 
      "Streptococcus salivarius",
    "Campylobacter concisus",
    "Prevotella melaninogenica"),
  labels = c(
    "Fusobacterium nucleatum",
    "Streptococcus spp.",
      "Streptococcus spp.",
      "Streptococcus spp.", 
      "Streptococcus spp.", 
      "Streptococcus spp.", 
      "Streptococcus spp.",
    "Campylobacter concisus",
    "Prevotella melaninogenica")
)



# make tumor vs normal variable
dat.16s$tumor.cat <- factor(dat.16s$tissue, levels=c("BO", "N", "T"), labels = c("Non-Tumor", "Non-Tumor", "Tumor"))
dat.rna$tumor.cat <- factor(dat.rna$SampleType_Level2, levels=c("Normal", "Tumor"), labels = c("Non-Tumor", "Tumor"))
dat.wgs$tumor.cat <- factor(dat.wgs$SampleType_Level2, levels=c("Normal", "Tumor"), labels = c("Non-Tumor", "Tumor"))

# dataset id
dat.16s$source <- "16s"
dat.rna$source <- "rna"
dat.wgs$source <- "wgs"

# plotting ids
dat.16s$X <- paste0(dat.16s$source, "-", dat.16s$tumor.cat)
dat.rna$X <- paste0(dat.rna$source, "-", dat.rna$tumor.cat)
dat.wgs$X <- paste0(dat.wgs$source, "-", dat.wgs$tumor.cat)

# relabel as (0/1) for analysis
dat.16s$tumor <- as.numeric(factor(dat.16s$tissue, levels=c("BO", "N", "T"), labels = c("Non-Tumor", "Non-Tumor", "Tumor"))) - 1
dat.rna$tumor <- as.numeric(factor(dat.rna$SampleType_Level2, levels=c("Normal", "Tumor"), labels = c("Non-Tumor", "Tumor"))) - 1
dat.wgs$tumor <- as.numeric(factor(dat.wgs$SampleType_Level2, levels=c("Normal", "Tumor"), labels = c("Non-Tumor", "Tumor"))) - 1

# presence- absence
dat.16s$pres <- ifelse(dat.16s$Abundance > 0, 1, 0)
dat.16s$pres[is.na(dat.16s$pres)] <- 0
dat.rna$pres <- ifelse(dat.rna$Abundance > 0, 1, 0)
dat.rna$pres[is.na(dat.rna$pres)] <- 0
dat.wgs$pres <- ifelse(dat.wgs$Abundance > 0, 1, 0)
dat.wgs$pres[is.na(dat.wgs$pres)] <- 0

# blood exclusions
dat.16s$bloodWGS <- 0
dat.rna$bloodWGS <- dat.rna$WGS_Blood_Normal
dat.wgs$bloodWGS <- dat.wgs$WGS_Blood_Normal

# merge data
cls <- c("OTU", "OTU1", "Sample", "Abundance", "tumor.cat", "tumor", "source", "X", "pres", "bloodWGS")
mydata <- full_join(dat.16s[,cls], dat.rna[,cls])

Joining, by = c("OTU", "OTU1", "Sample", "Abundance", "tumor.cat", "tumor", "source", "X", "pres", "bloodWGS")

mydata <- full_join(mydata, dat.wgs[,cls])

Joining, by = c("OTU", "OTU1", "Sample", "Abundance", "tumor.cat", "tumor", "source", "X", "pres", "bloodWGS")

Replicating the Analysis

Plot

p <- ggplot(mydata, aes(x=X, y=Abundance)) +
  geom_violin() +
  geom_jitter(alpha=0.5) +
  facet_wrap(.~OTU)+
  scale_y_continuous(
    trans = "sqrt",
    breaks=c(0.002, 0.01, 0.05, 0.1, 0.25, 0.5, 0.8)) +
  labs(x=NULL, y="Relative Abundance")+
  theme_classic() +
  theme(
    axis.text.x = element_text(angle = 30,vjust=0.95, hjust=0.95)
  )
p

Warning: Removed 1107 rows containing non-finite values (stat_ydensity).

Warning: Removed 1107 rows containing missing values (geom_point).

Statistical Tests

Fusobacterium nucleatum

# No additional subsetting/exclusions
d <- mydata %>%
  filter(OTU == "Fusobacterium nucleatum",
         source == "16s")
fisher.test(d$pres, d$tumor)


    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.1487
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.8126368 3.2144776
sample estimates:
odds ratio 
  1.610386

d <- mydata %>%
  filter(OTU == "Fusobacterium nucleatum",
         source == "wgs")
fisher.test(d$pres, d$tumor)


    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.02533
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 1.065529 4.759083
sample estimates:
odds ratio 
  2.232631

d <- mydata %>%
  filter(OTU == "Fusobacterium nucleatum",
         source == "rna")
fisher.test(d$pres, d$tumor)


    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.04707
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.05593801 1.12690374
sample estimates:
odds ratio 
 0.2723903

# Excluding blood != 0
d <- mydata %>%
  filter(OTU == "Fusobacterium nucleatum",
         source == "wgs",
         bloodWGS==0)
fisher.test(d$pres, d$tumor)


    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.7626
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.3753539 5.6058176
sample estimates:
odds ratio 
  1.432327

Streptococcus spp.

difficult to determine which species were used

# No additional subsetting/exclusions
d <- mydata %>%
  filter(OTU == "Streptococcus spp.",
         source == "16s")
fisher.test(d$pres, d$tumor)


    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.2189448 7.8326544
sample estimates:
odds ratio 
  1.173049

d <- mydata %>%
  filter(OTU == "Streptococcus spp.",
         source == "wgs")
fisher.test(d$pres, d$tumor)


    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 1.413e-06
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 1.487867 2.643073
sample estimates:
odds ratio 
  1.981098

d <- mydata %>%
  filter(OTU == "Streptococcus spp.",
         source == "rna")
fisher.test(d$pres, d$tumor)


    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 8.651e-07
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.1589282 0.4818080
sample estimates:
odds ratio 
 0.2795264

# Excluding blood != 0
d <- mydata %>%
  filter(OTU == "Streptococcus spp.",
         source == "wgs",
         bloodWGS==0)
fisher.test(d$pres, d$tumor)


    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.5998557 1.6670677
sample estimates:
odds ratio 
         1

Streptococcus sanguinis