This document has last been compiled on 2021-12-14 23:00:10.
## Reading in metadata from file: results/BT642Year2/data/leaf_meta.txt
## Reading in data from file: results/BT642Year2/data/leaf_normCounts.txt## Reading in metadata from file: results/BT642Year2/data/root_meta.txt
## Reading in data from file: results/BT642Year2/data/root_normCounts.txt## Total number of leaf samples: 125
## Total number of genes in leaf samples: 22181
## No additional bad samples to remove (probably removed during normalization)
## Removing the 13 samples identified as not part of the main experiment, from the leaf samples
## After filtering leaf samples: 112 samples, 22181 genes## Total number of root samples: 133
## Total number of genes in root samples: 23916
## No additional bad samples to remove (probably removed during normalization)
## Removing the 13 samples identified as not part of the main experiment, from the root samples
## After filtering root samples: 120 samples, 23916 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/BT642Year2/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
## SbiBTX642.01G000200.v1.1 0.05945598
## SbiBTX642.01G000400.v1.1 0.53403775
## SbiBTX642.01G000500.v1.1 0.80305656
## SbiBTX642.01G000600.v1.1 0.69352380
## SbiBTX642.01G000900.v1.1 0.17234639
## Group_BT642_Preflowering.14-Group_BT642_Control.14_qval
## SbiBTX642.01G000200.v1.1 0.4037070
## SbiBTX642.01G000400.v1.1 0.8829866
## SbiBTX642.01G000500.v1.1 0.9643082
## SbiBTX642.01G000600.v1.1 0.9371594
## SbiBTX642.01G000900.v1.1 0.6202195
## Group_BT642_Preflowering.14-Group_BT642_Control.14_stat
## SbiBTX642.01G000200.v1.1 1.9154482
## SbiBTX642.01G000400.v1.1 -0.6248978
## SbiBTX642.01G000500.v1.1 0.2503264
## SbiBTX642.01G000600.v1.1 -0.3956822
## SbiBTX642.01G000900.v1.1 1.3785750
## Group_BT642_Preflowering.14-Group_BT642_Control.14_lfc
## SbiBTX642.01G000200.v1.1 0.32922021
## SbiBTX642.01G000400.v1.1 -0.13809515
## SbiBTX642.01G000500.v1.1 0.06978366
## SbiBTX642.01G000600.v1.1 -0.10575986
## SbiBTX642.01G000900.v1.1 0.51066268
## Group_BT642_Preflowering.21-Group_BT642_Control.21_pval
## SbiBTX642.01G000200.v1.1 0.8008064
## SbiBTX642.01G000400.v1.1 0.7565265
## SbiBTX642.01G000500.v1.1 0.6662575
## SbiBTX642.01G000600.v1.1 0.3234248
## SbiBTX642.01G000900.v1.1 0.8499590
## Group_BT642_Preflowering.21-Group_BT642_Control.21_qval
## SbiBTX642.01G000200.v1.1 0.9638949
## SbiBTX642.01G000400.v1.1 0.9533418
## SbiBTX642.01G000500.v1.1 0.9291587
## SbiBTX642.01G000600.v1.1 0.7680977
## SbiBTX642.01G000900.v1.1 0.9738268## First 5 rows and 6 columns of output (for root)## Group_BT642_Preflowering.14-Group_BT642_Control.14_pval
## SbiBTX642.01G000200.v1.1 0.5042095
## SbiBTX642.01G000400.v1.1 0.8502438
## SbiBTX642.01G000500.v1.1 0.4023447
## SbiBTX642.01G000600.v1.1 0.5608048
## SbiBTX642.01G000900.v1.1 0.1796280
## Group_BT642_Preflowering.14-Group_BT642_Control.14_qval
## SbiBTX642.01G000200.v1.1 0.7759297
## SbiBTX642.01G000400.v1.1 0.9498205
## SbiBTX642.01G000500.v1.1 0.7008183
## SbiBTX642.01G000600.v1.1 0.8118630
## SbiBTX642.01G000900.v1.1 0.4590994
## Group_BT642_Preflowering.14-Group_BT642_Control.14_stat
## SbiBTX642.01G000200.v1.1 -0.6709580
## SbiBTX642.01G000400.v1.1 -0.1894290
## SbiBTX642.01G000500.v1.1 -0.8419851
## SbiBTX642.01G000600.v1.1 -0.5841251
## SbiBTX642.01G000900.v1.1 1.3539350
## Group_BT642_Preflowering.14-Group_BT642_Control.14_lfc
## SbiBTX642.01G000200.v1.1 -0.13732235
## SbiBTX642.01G000400.v1.1 -0.09038904
## SbiBTX642.01G000500.v1.1 -0.22072677
## SbiBTX642.01G000600.v1.1 -0.10571662
## SbiBTX642.01G000900.v1.1 0.50965939
## Group_BT642_Preflowering.21-Group_BT642_Control.21_pval
## SbiBTX642.01G000200.v1.1 0.215128614
## SbiBTX642.01G000400.v1.1 0.000186387
## SbiBTX642.01G000500.v1.1 0.901393638
## SbiBTX642.01G000600.v1.1 0.006194042
## SbiBTX642.01G000900.v1.1 0.381253279
## Group_BT642_Preflowering.21-Group_BT642_Control.21_qval
## SbiBTX642.01G000200.v1.1 0.507963676
## SbiBTX642.01G000400.v1.1 0.003414142
## SbiBTX642.01G000500.v1.1 0.968163491
## SbiBTX642.01G000600.v1.1 0.048651163
## SbiBTX642.01G000900.v1.1 0.682906474
## Group_BT642_Preflowering.21-Group_BT642_Control.21_stat
## SbiBTX642.01G000200.v1.1 1.2496450
## SbiBTX642.01G000400.v1.1 -3.9219838
## SbiBTX642.01G000500.v1.1 -0.1243009
## SbiBTX642.01G000600.v1.1 2.8129835
## SbiBTX642.01G000900.v1.1 -0.8805244
## Group_BT642_Preflowering.21-Group_BT642_Control.21_lfc
## SbiBTX642.01G000200.v1.1 0.18902425
## SbiBTX642.01G000400.v1.1 -1.22033150
## SbiBTX642.01G000500.v1.1 -0.02397383
## SbiBTX642.01G000600.v1.1 0.37420886
## SbiBTX642.01G000900.v1.1 -0.24759644The 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 113
## 2 Preflowering BT642 Day021 29
## 3 Preflowering BT642 Day028 62
## 4 Preflowering BT642 Day035 215
## 5 Preflowering BT642 Day042 1113
## 6 Preflowering BT642 Day049 1931
## 7 Preflowering BT642 Day056 1232
## 8 Preflowering BT642 Day058 1578
## 9 Preflowering BT642 Day059 756
## 10 Preflowering BT642 Day061 385
## 11 Preflowering BT642 Day063 379
## 12 Preflowering BT642 Day070 403
## 13 Preflowering BT642 Day077 143
## 14 Preflowering BT642 Day084 132
## 15 Preflowering BT642 Day091 236
## 16 Preflowering BT642 Day105 257
## Postflowering BT642
## condition genotype timepoint count
## 1 Postflowering BT642 Day063 6
## 2 Postflowering BT642 Day065 5
## 3 Postflowering BT642 Day068 19
## 4 Postflowering BT642 Day070 103
## 5 Postflowering BT642 Day077 1547
## 6 Postflowering BT642 Day084 1059
## 7 Postflowering BT642 Day091 230
## 8 Postflowering BT642 Day105 1399The number of DE genes in root in each week.
## Preflowering BT642
## condition genotype timepoint count
## 1 Preflowering BT642 Day014 84
## 2 Preflowering BT642 Day021 2623
## 3 Preflowering BT642 Day028 3668
## 4 Preflowering BT642 Day035 3661
## 5 Preflowering BT642 Day042 5880
## 6 Preflowering BT642 Day049 6727
## 7 Preflowering BT642 Day056 6123
## 8 Preflowering BT642 Day058 6567
## 9 Preflowering BT642 Day059 5610
## 10 Preflowering BT642 Day061 3791
## 11 Preflowering BT642 Day063 2363
## 12 Preflowering BT642 Day070 1363
## 13 Preflowering BT642 Day077 1304
## 14 Preflowering BT642 Day084 467
## 15 Preflowering BT642 Day091 1577
## 16 Preflowering BT642 Day105 766
## Postflowering BT642
## condition genotype timepoint count
## 1 Postflowering BT642 Day063 13
## 2 Postflowering BT642 Day065 964
## 3 Postflowering BT642 Day068 4365
## 4 Postflowering BT642 Day070 2624
## 5 Postflowering BT642 Day077 3363
## 6 Postflowering BT642 Day084 3673
## 7 Postflowering BT642 Day091 1901
## 8 Postflowering BT642 Day105 4434We save results of each contrast (including contrasts of genotype differences) into a separate comma-delimnated file in results/BT642Year2/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/BT642Year2/DE_timepoint
## Saving root results in results/BT642Year2/DE_timepoint## [1] "2021-12-14 23:02:16 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