This document has last been compiled on 2021-12-10 18:58:19.
## Reading in metadata from file: results/Year123/data/leaf_meta.txt
## Reading in data from file: results/Year123/data/leaf_normCounts.txt## Reading in metadata from file: results/Year123/data/root_meta.txt
## Reading in data from file: results/Year123/data/root_normCounts.txt## Total number of leaf samples: 750
## Total number of genes in leaf samples: 23113
## No additional bad samples to remove (probably removed during normalization)
## Removing the 72 samples identified as not part of the main experiment, from the leaf samples
## After filtering leaf samples: 678 samples, 23113 genes## Total number of root samples: 758
## Total number of genes in root samples: 24939
## No additional bad samples to remove (probably removed during normalization)
## Removing the 72 samples identified as not part of the main experiment, from the root samples
## After filtering root samples: 686 samples, 24939 genesWe perform a simple DE analysis separately on each time point, i.e. drought versus control on each time point.
A hypothesis we are making is that plant react extremely fast to drought, both in pre- and post-flowering, so this allows us to look at a single week (e.g. week 3) and determine genes that are different only in that week.
We use moanin (in our function DE_timepoints) to calculate the DE for each time point between drought and control. The results are saved in results/Year123/DE_timepoint
For each timepoint, there are results for RT430, BT642, and the genotype difference between them. For each of these results, there are four columns in the output: p-value (_pval), adjusted p-value (_qval), a test statistic value (_stat), and an estimate of the log-fold change (_lfc). Below are printouts of a small subset to demonstrate the column naming conventions:
## First 5 rows and 6 columns of output (for leaf)## Group_BT642_Preflowering.14-Group_BT642_Control.14_pval
## Sobic.001G000200.v3.1 0.30616305
## Sobic.001G000400.v3.1 0.02264834
## Sobic.001G000501.v3.1 0.74560549
## Sobic.001G000700.v3.1 0.39216945
## Sobic.001G000800.v3.1 0.03378754
## Group_BT642_Preflowering.14-Group_BT642_Control.14_qval
## Sobic.001G000200.v3.1 0.8765976
## Sobic.001G000400.v3.1 0.2470620
## Sobic.001G000501.v3.1 1.0000000
## Sobic.001G000700.v3.1 0.9319634
## Sobic.001G000800.v3.1 0.3154974
## Group_BT642_Preflowering.14-Group_BT642_Control.14_stat
## Sobic.001G000200.v3.1 -1.0242298
## Sobic.001G000400.v3.1 -2.2855512
## Sobic.001G000501.v3.1 -0.3245973
## Sobic.001G000700.v3.1 0.8563430
## Sobic.001G000800.v3.1 -2.1277574
## Group_BT642_Preflowering.14-Group_BT642_Control.14_lfc
## Sobic.001G000200.v3.1 -0.13331908
## Sobic.001G000400.v3.1 -0.32049261
## Sobic.001G000501.v3.1 -0.06935862
## Sobic.001G000700.v3.1 0.15484516
## Sobic.001G000800.v3.1 -0.41734988
## Group_BT642_Preflowering.21-Group_BT642_Control.21_pval
## Sobic.001G000200.v3.1 0.9966408
## Sobic.001G000400.v3.1 0.8395441
## Sobic.001G000501.v3.1 0.8773106
## Sobic.001G000700.v3.1 0.2116989
## Sobic.001G000800.v3.1 0.1789071
## Group_BT642_Preflowering.21-Group_BT642_Control.21_qval
## Sobic.001G000200.v3.1 1.0000000
## Sobic.001G000400.v3.1 1.0000000
## Sobic.001G000501.v3.1 1.0000000
## Sobic.001G000700.v3.1 0.7782598
## Sobic.001G000800.v3.1 0.7312212## First 5 rows and 6 columns of output (for root)## Group_BT642_Preflowering.14-Group_BT642_Control.14_pval
## Sobic.001G000200.v3.1 0.1868335
## Sobic.001G000400.v3.1 0.1792428
## Sobic.001G000501.v3.1 0.5044362
## Sobic.001G000700.v3.1 0.5792446
## Sobic.001G000800.v3.1 0.8846289
## Group_BT642_Preflowering.14-Group_BT642_Control.14_qval
## Sobic.001G000200.v3.1 0.5225088
## Sobic.001G000400.v3.1 0.5108700
## Sobic.001G000501.v3.1 0.8303116
## Sobic.001G000700.v3.1 0.8719527
## Sobic.001G000800.v3.1 0.9815672
## Group_BT642_Preflowering.14-Group_BT642_Control.14_stat
## Sobic.001G000200.v3.1 -1.3215827
## Sobic.001G000400.v3.1 -1.3447318
## Sobic.001G000501.v3.1 -0.6679464
## Sobic.001G000700.v3.1 -0.5548042
## Sobic.001G000800.v3.1 0.1451684
## Group_BT642_Preflowering.14-Group_BT642_Control.14_lfc
## Sobic.001G000200.v3.1 -0.14451239
## Sobic.001G000400.v3.1 -0.52335949
## Sobic.001G000501.v3.1 -0.17285934
## Sobic.001G000700.v3.1 -0.06125641
## Sobic.001G000800.v3.1 0.01745589
## Group_BT642_Preflowering.21-Group_BT642_Control.21_pval
## Sobic.001G000200.v3.1 0.960151056
## Sobic.001G000400.v3.1 0.001382472
## Sobic.001G000501.v3.1 0.913833141
## Sobic.001G000700.v3.1 0.104681018
## Sobic.001G000800.v3.1 0.166305443
## Group_BT642_Preflowering.21-Group_BT642_Control.21_qval
## Sobic.001G000200.v3.1 0.99815303
## Sobic.001G000400.v3.1 0.01555214
## Sobic.001G000501.v3.1 0.98831655
## Sobic.001G000700.v3.1 0.37377866
## Sobic.001G000800.v3.1 0.49004415
## Group_BT642_Preflowering.21-Group_BT642_Control.21_stat
## Sobic.001G000200.v3.1 0.04998584
## Sobic.001G000400.v3.1 -3.21399303
## Sobic.001G000501.v3.1 0.10825260
## Sobic.001G000700.v3.1 -1.62514218
## Sobic.001G000800.v3.1 1.38593283
## Group_BT642_Preflowering.21-Group_BT642_Control.21_lfc
## Sobic.001G000200.v3.1 0.004521773
## Sobic.001G000400.v3.1 -0.962765095
## Sobic.001G000501.v3.1 0.023376187
## Sobic.001G000700.v3.1 -0.147109831
## Sobic.001G000800.v3.1 0.138093783The column names show the actual contrast that was calculated. For example, Group_BT642_Preflowering.14-Group_BT642_Control.14 means the difference between the BT642 samples under preflowering drought at timepoint 14 and the BT642 samples under control at timepoint 14. The genotype differences have more complicated column names, since their contrasts are differences of differences.
The number of DE genes in leaf in each week.
## Preflowering BT642
## condition genotype timepoint count
## 1 Preflowering BT642 Day014 1641
## 2 Preflowering BT642 Day021 147
## 3 Preflowering BT642 Day028 85
## 4 Preflowering BT642 Day035 3949
## 5 Preflowering BT642 Day042 4232
## 6 Preflowering BT642 Day049 2124
## 7 Preflowering BT642 Day056 4949
## 8 Preflowering BT642 Day058 3638
## 9 Preflowering BT642 Day059 1690
## 10 Preflowering BT642 Day061 947
## 11 Preflowering BT642 Day063 1531
## 12 Preflowering BT642 Day070 708
## 13 Preflowering BT642 Day077 477
## 14 Preflowering BT642 Day084 468
## 15 Preflowering BT642 Day091 427
## 16 Preflowering BT642 Day098 89
## 17 Preflowering BT642 Day105 755
## 18 Preflowering BT642 Day119 40
## Preflowering RT430
## condition genotype timepoint count
## 1 Preflowering RT430 Day014 1558
## 2 Preflowering RT430 Day021 118
## 3 Preflowering RT430 Day028 172
## 4 Preflowering RT430 Day035 2423
## 5 Preflowering RT430 Day042 2903
## 6 Preflowering RT430 Day049 2297
## 7 Preflowering RT430 Day056 4463
## 8 Preflowering RT430 Day058 3572
## 9 Preflowering RT430 Day059 2829
## 10 Preflowering RT430 Day061 651
## 11 Preflowering RT430 Day063 1077
## 12 Preflowering RT430 Day070 781
## 13 Preflowering RT430 Day077 293
## 14 Preflowering RT430 Day084 370
## 15 Preflowering RT430 Day091 221
## 16 Preflowering RT430 Day098 58
## 17 Preflowering RT430 Day105 250
## 18 Preflowering RT430 Day112 142
## 19 Preflowering RT430 Day119 34
## Preflowering Both
## condition genotype timepoint count
## 1 Preflowering Both Day014 96
## 2 Preflowering Both Day021 18
## 3 Preflowering Both Day028 17
## 4 Preflowering Both Day035 231
## 5 Preflowering Both Day042 436
## 6 Preflowering Both Day049 256
## 7 Preflowering Both Day056 559
## 8 Preflowering Both Day058 420
## 9 Preflowering Both Day059 226
## 10 Preflowering Both Day061 181
## 11 Preflowering Both Day063 170
## 12 Preflowering Both Day070 84
## 13 Preflowering Both Day077 82
## 14 Preflowering Both Day084 79
## 15 Preflowering Both Day091 72
## 16 Preflowering Both Day098 27
## 17 Preflowering Both Day105 117
## 18 Preflowering Both Day119 21
## Postflowering BT642
## condition genotype timepoint count
## 1 Postflowering BT642 Day063 3
## 2 Postflowering BT642 Day065 7
## 3 Postflowering BT642 Day066 18
## 4 Postflowering BT642 Day068 22
## 5 Postflowering BT642 Day070 230
## 6 Postflowering BT642 Day077 2641
## 7 Postflowering BT642 Day084 3199
## 8 Postflowering BT642 Day091 2036
## 9 Postflowering BT642 Day098 145
## 10 Postflowering BT642 Day105 3124
## 11 Postflowering BT642 Day119 1178
## Postflowering RT430
## condition genotype timepoint count
## 1 Postflowering RT430 Day063 11
## 2 Postflowering RT430 Day065 56
## 3 Postflowering RT430 Day066 15
## 4 Postflowering RT430 Day068 32
## 5 Postflowering RT430 Day070 857
## 6 Postflowering RT430 Day077 3702
## 7 Postflowering RT430 Day084 4321
## 8 Postflowering RT430 Day091 4730
## 9 Postflowering RT430 Day098 423
## 10 Postflowering RT430 Day105 2976
## 11 Postflowering RT430 Day119 1706
## Postflowering Both
## condition genotype timepoint count
## 1 Postflowering Both Day063 2
## 2 Postflowering Both Day065 9
## 3 Postflowering Both Day066 0
## 4 Postflowering Both Day068 8
## 5 Postflowering Both Day070 52
## 6 Postflowering Both Day077 391
## 7 Postflowering Both Day084 326
## 8 Postflowering Both Day091 680
## 9 Postflowering Both Day098 26
## 10 Postflowering Both Day105 584
## 11 Postflowering Both Day119 159The number of DE genes in root in each week.
## Preflowering BT642
## condition genotype timepoint count
## 1 Preflowering BT642 Day014 665
## 2 Preflowering BT642 Day021 1347
## 3 Preflowering BT642 Day028 5175
## 4 Preflowering BT642 Day035 9563
## 5 Preflowering BT642 Day042 12857
## 6 Preflowering BT642 Day049 9124
## 7 Preflowering BT642 Day056 10439
## 8 Preflowering BT642 Day058 7370
## 9 Preflowering BT642 Day059 5678
## 10 Preflowering BT642 Day061 3888
## 11 Preflowering BT642 Day063 4058
## 12 Preflowering BT642 Day070 1906
## 13 Preflowering BT642 Day077 1664
## 14 Preflowering BT642 Day084 1217
## 15 Preflowering BT642 Day091 2199
## 16 Preflowering BT642 Day098 89
## 17 Preflowering BT642 Day105 1452
## 18 Preflowering BT642 Day119 135
## Preflowering RT430
## condition genotype timepoint count
## 1 Preflowering RT430 Day014 588
## 2 Preflowering RT430 Day021 342
## 3 Preflowering RT430 Day028 5596
## 4 Preflowering RT430 Day035 10195
## 5 Preflowering RT430 Day042 13384
## 6 Preflowering RT430 Day049 9912
## 7 Preflowering RT430 Day056 13024
## 8 Preflowering RT430 Day058 7408
## 9 Preflowering RT430 Day059 5870
## 10 Preflowering RT430 Day061 4887
## 11 Preflowering RT430 Day063 5135
## 12 Preflowering RT430 Day070 2655
## 13 Preflowering RT430 Day077 3516
## 14 Preflowering RT430 Day084 3333
## 15 Preflowering RT430 Day091 1959
## 16 Preflowering RT430 Day098 209
## 17 Preflowering RT430 Day105 2457
## 18 Preflowering RT430 Day112 299
## 19 Preflowering RT430 Day119 335
## Preflowering Both
## condition genotype timepoint count
## 1 Preflowering Both Day014 63
## 2 Preflowering Both Day021 108
## 3 Preflowering Both Day028 385
## 4 Preflowering Both Day035 765
## 5 Preflowering Both Day042 1466
## 6 Preflowering Both Day049 1076
## 7 Preflowering Both Day056 1467
## 8 Preflowering Both Day058 813
## 9 Preflowering Both Day059 822
## 10 Preflowering Both Day061 436
## 11 Preflowering Both Day063 879
## 12 Preflowering Both Day070 310
## 13 Preflowering Both Day077 550
## 14 Preflowering Both Day084 719
## 15 Preflowering Both Day091 222
## 16 Preflowering Both Day098 31
## 17 Preflowering Both Day105 393
## 18 Preflowering Both Day119 57
## Postflowering BT642
## condition genotype timepoint count
## 1 Postflowering BT642 Day063 11
## 2 Postflowering BT642 Day065 3152
## 3 Postflowering BT642 Day066 816
## 4 Postflowering BT642 Day068 2753
## 5 Postflowering BT642 Day070 5447
## 6 Postflowering BT642 Day077 6633
## 7 Postflowering BT642 Day084 7134
## 8 Postflowering BT642 Day091 5064
## 9 Postflowering BT642 Day098 414
## 10 Postflowering BT642 Day105 6003
## 11 Postflowering BT642 Day119 2786
## Postflowering RT430
## condition genotype timepoint count
## 1 Postflowering RT430 Day063 7
## 2 Postflowering RT430 Day065 1494
## 3 Postflowering RT430 Day066 559
## 4 Postflowering RT430 Day068 1273
## 5 Postflowering RT430 Day070 4065
## 6 Postflowering RT430 Day077 7017
## 7 Postflowering RT430 Day084 6061
## 8 Postflowering RT430 Day091 6779
## 9 Postflowering RT430 Day098 1128
## 10 Postflowering RT430 Day105 7316
## 11 Postflowering RT430 Day112 2238
## 12 Postflowering RT430 Day119 4596
## Postflowering Both
## condition genotype timepoint count
## 1 Postflowering Both Day063 12
## 2 Postflowering Both Day065 168
## 3 Postflowering Both Day066 35
## 4 Postflowering Both Day068 1697
## 5 Postflowering Both Day070 282
## 6 Postflowering Both Day077 710
## 7 Postflowering Both Day084 631
## 8 Postflowering Both Day091 699
## 9 Postflowering Both Day098 129
## 10 Postflowering Both Day105 748
## 11 Postflowering Both Day119 298We save results of each contrast (including contrasts of genotype differences) into a separate comma-delimnated file in results/Year123/DE_timepoint and combine them by condition of drought, for example:
leaf_Preflowering_tpDE_all.csv
leaf_GenoDiff_Preflowering_tpDE_all.csv
and similarly for root.
## Saving leaf results in results/Year123/DE_timepoint
## Saving root results in results/Year123/DE_timepoint## [1] "2021-12-10 19:12:44 PST"## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] forcats_0.5.1 purrr_0.3.4
## [3] readr_2.0.2 tidyr_1.1.4
## [5] tibble_3.1.6 ggplot2_3.3.5
## [7] tidyverse_1.3.1 stringr_1.4.0
## [9] moanin_1.1.2 topGO_2.44.0
## [11] SparseM_1.81 GO.db_3.13.0
## [13] AnnotationDbi_1.56.1 graph_1.72.0
## [15] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [17] GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
## [19] IRanges_2.28.0 S4Vectors_0.32.2
## [21] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
## [23] matrixStats_0.61.0 dplyr_1.0.7
## [25] rmarkdown_2.11 knitr_1.36
## [27] SCF_4.1.0
##
## loaded via a namespace (and not attached):
## [1] fs_1.5.0 bitops_1.0-7 lubridate_1.8.0
## [4] bit64_4.0.5 httr_1.4.2 tools_4.1.2
## [7] backports_1.3.0 bslib_0.3.1 utf8_1.2.2
## [10] R6_2.5.1 DBI_1.1.1 colorspace_2.0-2
## [13] withr_2.4.2 tidyselect_1.1.1 gridExtra_2.3
## [16] bit_4.0.4 compiler_4.1.2 cli_3.1.0
## [19] rvest_1.0.2 xml2_1.3.2 DelayedArray_0.20.0
## [22] sass_0.4.0 scales_1.1.1 digest_0.6.28
## [25] XVector_0.34.0 pkgconfig_2.0.3 htmltools_0.5.2
## [28] dbplyr_2.1.1 fastmap_1.1.0 limma_3.50.0
## [31] readxl_1.3.1 rlang_0.4.12 rstudioapi_0.13
## [34] RSQLite_2.2.8 jquerylib_0.1.4 generics_0.1.1
## [37] jsonlite_1.7.2 gtools_3.9.2 RCurl_1.98-1.5
## [40] magrittr_2.0.1 GenomeInfoDbData_1.2.7 Matrix_1.3-4
## [43] Rcpp_1.0.7 munsell_0.5.0 fansi_0.5.0
## [46] viridis_0.6.2 lifecycle_1.0.1 stringi_1.7.5
## [49] yaml_2.2.1 edgeR_3.36.0 ClusterR_1.2.5
## [52] zlibbioc_1.40.0 grid_4.1.2 blob_1.2.2
## [55] crayon_1.4.2 lattice_0.20-45 haven_2.4.3
## [58] Biostrings_2.60.2 splines_4.1.2 hms_1.1.1
## [61] KEGGREST_1.34.0 locfit_1.5-9.4 pillar_1.6.4
## [64] reprex_2.0.1 glue_1.5.0 evaluate_0.14
## [67] modelr_0.1.8 tzdb_0.2.0 png_0.1-7
## [70] vctrs_0.3.8 cellranger_1.1.0 gtable_0.3.0
## [73] assertthat_0.2.1 cachem_1.0.6 xfun_0.28
## [76] broom_0.7.10 viridisLite_0.4.0 memoise_2.0.0
## [79] gmp_0.6-2.1 ellipsis_0.3.2