Last updated: 2021-01-14
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Knit directory: esoph-micro-cancer-workflow/
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Q1: is there a taxonomic signature shared between the barrett's samples?# in long format
table(dat.16s$sample_type)
0 Barretts Only
19800 1320
EAC-adjacent tissue w/ Barretts History EAC tissues w/ Barretts History
11352 9240 dat <- dat.16s %>% filter(OTU == "Fusobacterium_nucleatum")
table(dat$sample_type)
0 Barretts Only
75 5
EAC-adjacent tissue w/ Barretts History EAC tissues w/ Barretts History
43 35 table(dat$Barretts.)
N Y
71 87 dat.16s.s %>%
filter(sample_type != "0") %>%
dplyr::group_by(sample_type, Genus)%>%
dplyr::summarise(
m = mean(Abundance, na.rm=T)
) %>%
kable(format="html") %>%
kable_styling(full_width = T) %>%
scroll_box()`summarise()` regrouping output by 'sample_type' (override with `.groups` argument)| sample_type | Genus | m |
|---|---|---|
| Barretts Only | g__Campylobacter | 0.0036000 |
| Barretts Only | g__Fusobacterium | 0.0060000 |
| Barretts Only | g__Prevotella | 0.0652000 |
| Barretts Only | g__Streptococcus | 0.4572000 |
| EAC-adjacent tissue w/ Barretts History | g__Campylobacter | 0.0027442 |
| EAC-adjacent tissue w/ Barretts History | g__Fusobacterium | 0.0197694 |
| EAC-adjacent tissue w/ Barretts History | g__Prevotella | 0.0641215 |
| EAC-adjacent tissue w/ Barretts History | g__Streptococcus | 0.2147100 |
| EAC tissues w/ Barretts History | g__Campylobacter | 0.0097714 |
| EAC tissues w/ Barretts History | g__Fusobacterium | 0.0491790 |
| EAC tissues w/ Barretts History | g__Prevotella | 0.0485287 |
| EAC tissues w/ Barretts History | g__Streptococcus | 0.2641698 |
dat.16s.s %>%
filter(sample_type != "0") %>%
dplyr::group_by(sample_type, Phylum)%>%
dplyr::summarise(
m = mean(Abundance, na.rm=T)
) %>%
kable(format="html") %>%
kable_styling(full_width = T) %>%
scroll_box()`summarise()` regrouping output by 'sample_type' (override with `.groups` argument)| sample_type | Phylum | m |
|---|---|---|
| Barretts Only | p__Bacteroidetes | 0.0652000 |
| Barretts Only | p__Firmicutes | 0.4572000 |
| Barretts Only | p__Fusobacteria | 0.0060000 |
| Barretts Only | p__Proteobacteria | 0.0036000 |
| EAC-adjacent tissue w/ Barretts History | p__Bacteroidetes | 0.0641215 |
| EAC-adjacent tissue w/ Barretts History | p__Firmicutes | 0.2147100 |
| EAC-adjacent tissue w/ Barretts History | p__Fusobacteria | 0.0197694 |
| EAC-adjacent tissue w/ Barretts History | p__Proteobacteria | 0.0027442 |
| EAC tissues w/ Barretts History | p__Bacteroidetes | 0.0485287 |
| EAC tissues w/ Barretts History | p__Firmicutes | 0.2641698 |
| EAC tissues w/ Barretts History | p__Fusobacteria | 0.0491790 |
| EAC tissues w/ Barretts History | p__Proteobacteria | 0.0097714 |
# in long format
table(dat.rna$sample_type)
0 EAC-adjacent tissue w/ Barretts History
112176 2337
EAC tissues w/ Barretts History
20254 dat <- dat.rna %>% filter(otu2 == "Fusobacterium nucleatum")
table(dat$sample_type)
0 EAC-adjacent tissue w/ Barretts History
144 3
EAC tissues w/ Barretts History
26 table(dat$Barrett.s.Esophagus.Reported)
No Not Available Yes
113 31 29 dat.rna.s %>%
filter(sample_type != "0") %>%
dplyr::group_by(sample_type, Genus)%>%
dplyr::summarise(
m = mean(Abundance, na.rm=T)
) %>%
kable(format="html") %>%
kable_styling(full_width = T) %>%
scroll_box()`summarise()` regrouping output by 'sample_type' (override with `.groups` argument)| sample_type | Genus | m |
|---|---|---|
| EAC-adjacent tissue w/ Barretts History | Campylobacter | 0.0000000 |
| EAC-adjacent tissue w/ Barretts History | Fusobacterium | 0.0000797 |
| EAC-adjacent tissue w/ Barretts History | Prevotella | 0.0001644 |
| EAC-adjacent tissue w/ Barretts History | Streptococcus | 0.0000554 |
| EAC tissues w/ Barretts History | Campylobacter | 0.0000218 |
| EAC tissues w/ Barretts History | Fusobacterium | 0.0004995 |
| EAC tissues w/ Barretts History | Prevotella | 0.0000900 |
| EAC tissues w/ Barretts History | Streptococcus | 0.0001505 |
dat.rna.s %>%
filter(sample_type != "0") %>%
dplyr::group_by(sample_type, Phylum)%>%
dplyr::summarise(
m = mean(Abundance, na.rm=T)
) %>%
kable(format="html") %>%
kable_styling(full_width = T) %>%
scroll_box()`summarise()` regrouping output by 'sample_type' (override with `.groups` argument)| sample_type | Phylum | m |
|---|---|---|
| EAC-adjacent tissue w/ Barretts History | Bacteroidetes | 0.0001644 |
| EAC-adjacent tissue w/ Barretts History | Firmicutes | 0.0000554 |
| EAC-adjacent tissue w/ Barretts History | Fusobacteria | 0.0000797 |
| EAC-adjacent tissue w/ Barretts History | Proteobacteria | 0.0000000 |
| EAC tissues w/ Barretts History | Bacteroidetes | 0.0000900 |
| EAC tissues w/ Barretts History | Firmicutes | 0.0001505 |
| EAC tissues w/ Barretts History | Fusobacteria | 0.0004995 |
| EAC tissues w/ Barretts History | Proteobacteria | 0.0000218 |
# in long format
table(dat.wgs$sample_type)
0 EAC-adjacent tissue w/ Barretts History
100491 4674
EAC tissues w/ Barretts History
3116 dat <- dat.wgs %>% filter(otu2 == "Fusobacterium nucleatum")
table(dat$sample_type)
0 EAC-adjacent tissue w/ Barretts History
129 6
EAC tissues w/ Barretts History
4 table(dat$Barrett.s.Esophagus.Reported)
No Not Available Yes
54 47 10 dat.wgs.s %>%
filter(sample_type != "0") %>%
dplyr::group_by(sample_type, Genus)%>%
dplyr::summarise(
m = mean(Abundance, na.rm=T)
) %>%
kable(format="html") %>%
kable_styling(full_width = T) %>%
scroll_box()`summarise()` regrouping output by 'sample_type' (override with `.groups` argument)| sample_type | Genus | m |
|---|---|---|
| EAC-adjacent tissue w/ Barretts History | Campylobacter | 0.0000441 |
| EAC-adjacent tissue w/ Barretts History | Fusobacterium | 0.0012641 |
| EAC-adjacent tissue w/ Barretts History | Prevotella | 0.0014830 |
| EAC-adjacent tissue w/ Barretts History | Streptococcus | 0.0031405 |
| EAC tissues w/ Barretts History | Campylobacter | 0.0000303 |
| EAC tissues w/ Barretts History | Fusobacterium | 0.0005612 |
| EAC tissues w/ Barretts History | Prevotella | 0.0009216 |
| EAC tissues w/ Barretts History | Streptococcus | 0.0006834 |
dat.wgs.s %>%
filter(sample_type != "0") %>%
dplyr::group_by(sample_type, Phylum)%>%
dplyr::summarise(
m = mean(Abundance, na.rm=T)
) %>%
kable(format="html") %>%
kable_styling(full_width = T) %>%
scroll_box()`summarise()` regrouping output by 'sample_type' (override with `.groups` argument)| sample_type | Phylum | m |
|---|---|---|
| EAC-adjacent tissue w/ Barretts History | Bacteroidetes | 0.0014830 |
| EAC-adjacent tissue w/ Barretts History | Firmicutes | 0.0031405 |
| EAC-adjacent tissue w/ Barretts History | Fusobacteria | 0.0012641 |
| EAC-adjacent tissue w/ Barretts History | Proteobacteria | 0.0000441 |
| EAC tissues w/ Barretts History | Bacteroidetes | 0.0009216 |
| EAC tissues w/ Barretts History | Firmicutes | 0.0006834 |
| EAC tissues w/ Barretts History | Fusobacteria | 0.0005612 |
| EAC tissues w/ Barretts History | Proteobacteria | 0.0000303 |
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 fansi_0.4.1
[10] lubridate_1.7.9 xml2_1.3.2 codetools_0.2-16
[13] splines_4.0.2 knitr_1.29 ade4_1.7-15
[16] jsonlite_1.7.0 nloptr_1.2.2.2 broom_0.7.0
[19] cluster_2.1.0 dbplyr_1.4.4 BiocManager_1.30.10
[22] compiler_4.0.2 httr_1.4.2 backports_1.1.7
[25] assertthat_0.2.1 cli_2.0.2 later_1.1.0.1
[28] htmltools_0.5.0 tools_4.0.2 igraph_1.2.5
[31] gtable_0.3.0 glue_1.4.1 reshape2_1.4.4
[34] Rcpp_1.0.5 Biobase_2.48.0 cellranger_1.1.0
[37] vctrs_0.3.2 Biostrings_2.56.0 multtest_2.44.0
[40] ape_5.4 nlme_3.1-148 iterators_1.0.12
[43] xfun_0.19 openxlsx_4.1.5 rvest_0.3.6
[46] lifecycle_0.2.0 statmod_1.4.34 zlibbioc_1.34.0
[49] MASS_7.3-51.6 scales_1.1.1 hms_0.5.3
[52] promises_1.1.1 parallel_4.0.2 biomformat_1.16.0
[55] rhdf5_2.32.2 curl_4.3 yaml_2.2.1
[58] stringi_1.4.6 highr_0.8 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