Sample IDs and Relative Abundance Misc.
Last updated: 2020-12-02
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Knit directory: esoph-micro-cancer-workflow/
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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
dat.16s <- filter(dat.16s, OTU == "Fusobacterium_nucleatum")
dat.rna <- filter(dat.rna, otu2 == "Fusobacterium nucleatum")
dat.wgs <- filter(dat.wgs, otu2 == "Fusobacterium nucleatum")
# make tumor vs normal variable
dat.16s$tumor <- factor(dat.16s$tissue, levels=c("BO", "N", "T"), labels = c("Non-Tumor", "Non-Tumor", "Tumor"))
dat.rna$tumor <- factor(dat.rna$SampleType_Level2, levels=c("Normal", "Tumor"), labels = c("Non-Tumor", "Tumor"))
dat.wgs$tumor <- factor(dat.wgs$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)
dat.rna$X <- paste0(dat.rna$source, "-", dat.rna$tumor)
dat.wgs$X <- paste0(dat.wgs$source, "-", dat.wgs$tumor)
# merge data
cls <- c("OTU", "Sample", "Abundance", "tumor", "source", "X")
mydata <- full_join(dat.16s[,cls], dat.rna[,cls])Joining, by = c("OTU", "Sample", "Abundance", "tumor", "source", "X")mydata <- full_join(mydata, dat.wgs[,cls])Joining, by = c("OTU", "Sample", "Abundance", "tumor", "source", "X")Replicating the Analysis
Plot
p <- ggplot(mydata, aes(x=X, y=Abundance)) +
geom_violin() +
geom_jitter(alpha=0.5) +
scale_y_continuous(
trans = "sqrt",
breaks=c(0.002, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 0.8)) +
theme_classic()
p
Fisher Exact Test
Joining, by = c("OTU", "Sample", "Abundance", "tumor", "source", "X")
Joining, by = c("OTU", "Sample", "Abundance", "tumor", "source", "X")Mann-Whitney U-Test
#
wilcox.test(dat.16s$Abundance ~ dat.16s$tumor)
Wilcoxon rank sum test with continuity correction
data: dat.16s$Abundance by dat.16s$tumor
W = 2330, p-value = 0.006126
alternative hypothesis: true location shift is not equal to 0wilcox.test(dat.rna$Abundance ~ dat.rna$tumor)
Wilcoxon rank sum test with continuity correction
data: dat.rna$Abundance by dat.rna$tumor
W = 237, p-value = 0.5319
alternative hypothesis: true location shift is not equal to 0wilcox.test(dat.wgs$Abundance ~ dat.wgs$tumor)
Wilcoxon rank sum test with continuity correction
data: dat.wgs$Abundance by dat.wgs$tumor
W = 1267.5, p-value = 0.0006748
alternative hypothesis: true location shift is not equal to 0Fisher Exact Test.
a <- na.omit(dat.16s[, c("barrett", "tumor")])
fisher.test(a$barrett, a$tumor)
Fisher's Exact Test for Count Data
data: a$barrett and a$tumor
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.5153007 2.0334489
sample estimates:
odds ratio
1.022175 a <- na.omit(dat.rna[, c("barrett", "tumor")])
fisher.test(a$barrett, a$tumor)
Fisher's Exact Test for Count Data
data: a$barrett and a$tumor
p-value = 0.6188
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.07451302 7.42811003
sample estimates:
odds ratio
0.5913395 a <- na.omit(dat.wgs[, c("barrett", "tumor")])
fisher.test(a$barrett, a$tumor)
Fisher's Exact Test for Count Data
data: a$barrett and a$tumor
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.1595714 4.8896940
sample estimates:
odds ratio
0.9158179
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] car_3.0-8 carData_3.0-4 gvlma_1.0.0.3 patchwork_1.0.1
[5] viridis_0.5.1 viridisLite_0.3.0 gridExtra_2.3 xtable_1.8-4
[9] kableExtra_1.1.0 plyr_1.8.6 data.table_1.13.0 readxl_1.3.1
[13] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.1 purrr_0.3.4
[17] readr_1.3.1 tidyr_1.1.1 tibble_3.0.3 ggplot2_3.3.2
[21] tidyverse_1.3.0 lmerTest_3.1-2 lme4_1.1-23 Matrix_1.2-18
[25] vegan_2.5-6 lattice_0.20-41 permute_0.9-5 phyloseq_1.32.0
[29] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] minqa_1.2.4 colorspace_1.4-1 rio_0.5.16
[4] ellipsis_0.3.1 rprojroot_1.3-2 XVector_0.28.0
[7] fs_1.5.0 rstudioapi_0.11 farver_2.0.3
[10] fansi_0.4.1 lubridate_1.7.9 xml2_1.3.2
[13] codetools_0.2-16 splines_4.0.2 knitr_1.29
[16] ade4_1.7-15 jsonlite_1.7.0 nloptr_1.2.2.2
[19] broom_0.7.0 cluster_2.1.0 dbplyr_1.4.4
[22] BiocManager_1.30.10 compiler_4.0.2 httr_1.4.2
[25] backports_1.1.7 assertthat_0.2.1 cli_2.0.2
[28] later_1.1.0.1 htmltools_0.5.0 tools_4.0.2
[31] igraph_1.2.5 gtable_0.3.0 glue_1.4.1
[34] reshape2_1.4.4 Rcpp_1.0.5 Biobase_2.48.0
[37] cellranger_1.1.0 vctrs_0.3.2 Biostrings_2.56.0
[40] multtest_2.44.0 ape_5.4 nlme_3.1-148
[43] iterators_1.0.12 xfun_0.19 openxlsx_4.1.5
[46] rvest_0.3.6 lifecycle_0.2.0 statmod_1.4.34
[49] zlibbioc_1.34.0 MASS_7.3-51.6 scales_1.1.1
[52] hms_0.5.3 promises_1.1.1 parallel_4.0.2
[55] biomformat_1.16.0 rhdf5_2.32.2 curl_4.3
[58] yaml_2.2.1 stringi_1.4.6 S4Vectors_0.26.1
[61] foreach_1.5.0 BiocGenerics_0.34.0 zip_2.0.4
[64] boot_1.3-25 rlang_0.4.7 pkgconfig_2.0.3
[67] evaluate_0.14 Rhdf5lib_1.10.1 tidyselect_1.1.0
[70] magrittr_1.5 R6_2.4.1 IRanges_2.22.2
[73] generics_0.0.2 DBI_1.1.0 foreign_0.8-80
[76] pillar_1.4.6 haven_2.3.1 withr_2.2.0
[79] mgcv_1.8-31 abind_1.4-5 survival_3.2-3
[82] modelr_0.1.8 crayon_1.3.4 rmarkdown_2.5
[85] grid_4.0.2 blob_1.2.1 git2r_0.27.1
[88] reprex_0.3.0 digest_0.6.25 webshot_0.5.2
[91] httpuv_1.5.4 numDeriv_2016.8-1.1 stats4_4.0.2
[94] munsell_0.5.0