Last updated: 2020-10-22
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Knit directory: esoph-micro-cancer-workflow/
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# Transform to relative abundance. Save as new object.
ra.dat = transform_sample_counts(phylo.data.nci.umd, function(x){x / sum(x)})
mphyseq = phyloseq::psmelt(ra.dat)
mphyseq <- subset(mphyseq, Abundance > 0.01)
A <- mphyseq %>%
filter(Genus == "g__Fusobacterium", Abundance > .01)
kable(A[, c("Sample", "Abundance")], style= "html", digits=3) %>%
kable_styling(full_width = T)| Sample | Abundance |
|---|---|
| 205.S2.Jun172016 | 0.776 |
| 71.H10.S94.Jun232016 | 0.651 |
| 74.H03.S87.Jul202017 | 0.498 |
| 73.H04.S88.Jul202017 | 0.471 |
| 16.S38.Jun172016 | 0.381 |
| 204.S1.Jun172016 | 0.351 |
| 156.S42.Jun172016 | 0.322 |
| 173.S66.Jun172016 | 0.198 |
| 135.B06.S18.Jun232016 | 0.193 |
| 114.S41.Jun172016 | 0.150 |
| 97.S45.Jun172016 | 0.148 |
| 1.S37.Jun172016 | 0.124 |
| 158.D06.S42.Jun232016 | 0.122 |
| 174.S76.Jun172016 | 0.110 |
| 124.S93.Jun172016 | 0.092 |
| 27.F09.S69.Jun232016 | 0.092 |
| 175.S88.Jun172016 | 0.086 |
| 55.E03.S51.Jul202017 | 0.076 |
| 46.C04.S28.Jul202017 | 0.068 |
| 24.S92.Jun172016 | 0.066 |
| 87.B01.S13.Jun232016 | 0.058 |
| 127.S6.Jun172016 | 0.054 |
| 103.S58.Jun172016 | 0.049 |
| 38.C09.S33.Jun232016 | 0.044 |
| 186.S77.Jun172016 | 0.038 |
| 112.F02.S62.Jun232016 | 0.032 |
| 167.E05.S53.Jun232016 | 0.028 |
| 81.A02.S2.Jun232016 | 0.024 |
| 199.A11.S11.Jun232016 | 0.022 |
| 172.S65.Jun172016 | 0.022 |
| 130.A02.S2.Jul202017 | 0.020 |
| 104.S23.Jun172016 | 0.017 |
| 113.F01.S61.Jun232016 | 0.015 |
| 128.S5.Jun172016 | 0.014 |
| 234.C07.S31.Jul202017 | 0.012 |
| 179.F01.S61.Jul202017 | 0.012 |
| 184.S61.Jun172016 | 0.012 |
| 139.F07.S67.Jun232016 | 0.010 |
# Transform to relative abundance. Save as new object.
ra.dat = transform_sample_counts(phylo.data.tcga.RNAseq, function(x){x / sum(x)})
mphyseq = phyloseq::psmelt(ra.dat)
A <- mphyseq %>%
filter(Genus == "Fusobacterium", Abundance > .01)
kable(A[, c("Sample", "Abundance")], style= "html", digits=3) %>%
kable_styling(full_width = T)| Sample | Abundance |
|---|---|
| TCGA.L5.A4OT.Tumor.RNAseq.71d | 0.248 |
| TCGA.LN.A49U.Tumor.RNAseq.450 | 0.086 |
| TCGA.S8.A6BW.Tumor.RNAseq.802 | 0.069 |
| TCGA.IG.A50L.Tumor.RNAseq.93f | 0.055 |
| TCGA.L7.A56G.Tumor.RNAseq.70a | 0.045 |
# Transform to relative abundance. Save as new object.
ra.dat = transform_sample_counts(phylo.data.tcga.WGS, function(x){x / sum(x)})
mphyseq = phyloseq::psmelt(ra.dat)
A <- mphyseq %>%
filter(Genus == "Fusobacterium", Abundance > .01)
kable(A[, c("Sample", "Abundance")], style= "html", digits=3) %>%
kable_styling(full_width = T)| Sample | Abundance |
|---|---|
| TCGA.L5.A4OT.Tumor.WGS.7d4 | 0.484 |
| TCGA.LN.A49U.Tumor.WGS.c07 | 0.394 |
| TCGA.L5.A4OG.Tumor.WGS.cef | 0.385 |
| TCGA.IG.A50L.Tumor.WGS.3e7 | 0.243 |
| TCGA.LN.A49V.Tumor.WGS.331 | 0.158 |
| TCGA.L7.A56G.Tumor.WGS.8e8 | 0.094 |
| TCGA.L5.A4OG.Normal.WGS.963 | 0.048 |
| TCGA.IG.A3I8.Tumor.WGS.d45 | 0.045 |
| TCGA.IG.A5S3.Tumor.WGS.736 | 0.041 |
| TCGA.L5.A4OI.Tumor.WGS.61f | 0.036 |
| TCGA.LN.A5U5.Tumor.WGS.d38 | 0.031 |
| TCGA.L5.A891.Tumor.WGS.6a8 | 0.025 |
| TCGA.L5.A4OM.Normal.WGS.6cb | 0.018 |
| TCGA.IG.A5B8.Tumor.WGS.948 | 0.013 |
| TCGA.LN.A4MR.Tumor.WGS.6c3 | 0.012 |
| TCGA.IG.A3I8.Normal.WGS.a07 | 0.011 |
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
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_0.16 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_2.3
[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