Split-Splines with contrasts in Moanin (leaf, preflowering, RT430Year3)
This document has last been compiled on 2021-12-15 00:29:47.
Data Entry
## Reading in metadata from file: results/RT430Year3/data/leaf_meta.txt
## Reading in data from file: results/RT430Year3/data/leaf_normCounts.txt## Total number of leaf samples: 171
## Total number of genes in leaf samples: 21737
## No additional bad samples to remove (probably removed during normalization)
## Removing the 46 samples identified as not part of the main experiment, from the leaf samples
## After filtering leaf samples: 125 samples, 21737 genesIn this document, we find genes that are globally DE, meaning that we search for any difference between the expression of the gene. We do this via splines, where for each gene we fit a spline function to the expression levels within each condition and compare whether those splines are statistically different between the conditions.
Here we use “split-splines” meaning we fit a different spline function to before and after the watering change (first time point after Day 56 for Preflowering and first time point after Day 63 for post-flowering).
(Note that currently we are not expanding the postflowering condition points into control like Nelle did…)
Create Moanin Object
## Dimensions of data matrix restricting to designated timepoints: 21737 x 87
## The unique time points are:
## 21, 28, 35, 42, 49, 56, 58, 59, 61, 63, 70, 77, 84, 91, 105
## The conditions and number of samples are:
##
## Control.RT430 Preflowering.RT430
## 44 43## Moanin object on 87 samples containing the following information:
## Group variable given by 'Group' with the following levels:
## Control.RT430 Preflowering.RT430
## 44 43
## Time variable given by 'Time.Point'
## Basis matrix with 16 basis_matrix functions
## Basis matrix was constructed with the following spline_formula
## ~Group:discont_basis(Time.Point, dfPre = 3, dfPost = 3, discont = pre_watering_time, intercept = TRUE, type = "bs") + 0
##
## Information about the data (a SummarizedExperiment object):
## class: SummarizedExperiment
## dim: 21737 87
## metadata(0):
## assays(1): ''
## rownames(21737): SbiRTX430.01G000100.v2.1 SbiRTX430.01G000400.v2.1 ...
## SbiRTX430.K011000.v2.1 SbiRTX430.K011600.v2.1
## rowData names(0):
## colnames(87): 0710183L11.1 0717184L06.1 ... 1002185L06 1002185L18
## colData names(66): Barcode Genotype ... Group WeeklyGroupWe separate out the results into genotype-differences, RT430, and BT642 results.
We save results into comma-deliminated file in results/RT430Year3/DE_splitsplines in the following files:
## Saving matrix of results on 21737 genes to comma-deliminated file in results/RT430Year3/DE_splitsplines.## [1] "leaf_RT430_Preflowering_splitsplinesDE_all.csv"Summary of significant genes
## Number of significant genes:## RT430: 10242Visualization of BT642 Results
Visualization of RT430 Results
## Contrast matrix:
## Contrasts
## Levels Preflowering.RT430-Control.RT430
## Control.RT430 -1
## Preflowering.RT430 1Volcano plots
We will plot volcano plots of the genes based on the calculated log-fold-change (lfc) and the adjusted p-value (qval).
volcano_plot_moanin(moanin_results_all, target, lfc_thres=1, pval_thres=0.05)
Splines for top 20 genes
Here we plot the spline fits for the top 20 genes.
top_de_genes <- plot_top_de_genes(moaninObj = moaninObj, moanin_results = moanin_results_all, target = target, n_top = 20,lfc_thres=1, pval_thres=0.05,drought=condition)## 180 selected after filtering based on p-value and log fold change
## Splines for the top 20 genes in RT430Year3, RT430 Preflowering:top_de_genes## pval qval lfc
## SbiRTX430.02G144400.v2.1 5.539061e-32 1.204026e-27 3.600441
## SbiRTX430.09G120900.v2.1 3.610115e-19 1.401305e-16 3.139180
## SbiRTX430.01G069600.v2.1 7.110107e-17 1.379932e-14 2.815923
## SbiRTX430.06G008200.v2.1 5.917303e-15 6.878311e-13 2.605799
## SbiRTX430.01G234000.v2.1 2.260520e-24 4.565317e-21 2.549066
## SbiRTX430.03G185700.v2.1 3.768860e-14 3.427770e-12 2.340325
## SbiRTX430.06G008400.v2.1 4.133315e-15 5.104879e-13 2.109237
## SbiRTX430.09G228800.v2.1 6.553382e-17 1.295008e-14 2.097827
## SbiRTX430.07G119100.v2.1 1.176323e-23 1.826409e-20 2.095523
## SbiRTX430.03G254700.v2.1 1.947258e-19 8.638276e-17 2.070924
## SbiRTX430.01G439900.v2.1 3.232625e-18 9.369010e-16 2.069805
## SbiRTX430.02G270400.v2.1 8.080692e-19 2.879508e-16 2.015351
## SbiRTX430.05G164300.v2.1 6.493174e-18 1.764277e-15 1.993485
## SbiRTX430.09G100800.v2.1 7.745679e-31 8.418391e-27 1.957615
## SbiRTX430.04G311000.v2.1 2.310277e-24 4.565317e-21 1.949514
## SbiRTX430.06G191200.v2.1 2.370218e-12 1.192626e-10 1.927074
## SbiRTX430.04G352800.v2.1 2.186477e-19 9.319110e-17 1.921824
## SbiRTX430.03G088100.v2.1 8.636003e-09 1.534921e-07 1.910636
## SbiRTX430.03G140400.v2.1 6.338929e-20 3.444733e-17 1.891594
## SbiRTX430.02G284500.v2.1 4.650518e-13 2.973186e-11 1.867644
Visualization of Differences between genotypes
Session Info
## [1] "2021-12-15 00:30:45 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 splines stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] moanin_1.1.2 topGO_2.44.0
## [3] SparseM_1.81 GO.db_3.13.0
## [5] AnnotationDbi_1.56.1 graph_1.72.0
## [7] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [9] GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
## [11] IRanges_2.28.0 S4Vectors_0.32.2
## [13] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
## [15] matrixStats_0.61.0 MASS_7.3-54
## [17] reshape2_1.4.4 forcats_0.5.1
## [19] stringr_1.4.0 purrr_0.3.4
## [21] readr_2.0.2 tidyr_1.1.4
## [23] tibble_3.1.6 tidyverse_1.3.1
## [25] dplyr_1.0.7 ggplot2_3.3.5
## [27] knitr_1.36 rmarkdown_2.11
## [29] SCF_4.1.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 fs_1.5.0 bit64_4.0.5
## [4] lubridate_1.8.0 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 gridExtra_2.3 tidyselect_1.1.1
## [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] highr_0.9 limma_3.50.0 dbplyr_2.1.1
## [31] fastmap_1.1.0 rlang_0.4.12 readxl_1.3.1
## [34] rstudioapi_0.13 RSQLite_2.2.8 jquerylib_0.1.4
## [37] generics_0.1.1 jsonlite_1.7.2 gtools_3.9.2
## [40] RCurl_1.98-1.5 magrittr_2.0.1 GenomeInfoDbData_1.2.7
## [43] Matrix_1.3-4 Rcpp_1.0.7 munsell_0.5.0
## [46] fansi_0.5.0 viridis_0.6.2 lifecycle_1.0.1
## [49] edgeR_3.36.0 stringi_1.7.5 yaml_2.2.1
## [52] ClusterR_1.2.5 zlibbioc_1.40.0 plyr_1.8.6
## [55] blob_1.2.2 grid_4.1.2 crayon_1.4.2
## [58] lattice_0.20-45 Biostrings_2.60.2 haven_2.4.3
## [61] KEGGREST_1.34.0 hms_1.1.1 locfit_1.5-9.4
## [64] pillar_1.6.4 reprex_2.0.1 glue_1.5.0
## [67] evaluate_0.14 modelr_0.1.8 png_0.1-7
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## [79] memoise_2.0.0 gmp_0.6-2.1 ellipsis_0.3.2