1 Reading expression data for genes and methylation data for corresponding promoters
I load:
- The result of the DESeq2 analysis performed by Clara to find Differentially Expressed genes at D0, D7 and D14.
- The loss of % of methylation at the same three time points, coming from ‘analysis.Rmd’.
2 Promoters loosing DNA methylation become more susceptible to “noisy” gene expression change
IMPORTANT NOTE: for none of the genes labeled in the plot the gene expression change is significant!
sessionInfo()## R version 4.1.3 (2022-03-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
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## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
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## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=it_IT.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=it_IT.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=it_IT.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=it_IT.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] ggrepel_0.9.2 biomaRt_2.50.3 gridExtra_2.3 ggpubr_0.5.0
## [5] purrr_0.3.5 data.table_1.14.6 reshape2_1.4.4 ggplot2_3.4.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 bit64_4.0.5 filelock_1.0.2
## [4] progress_1.2.2 httr_1.4.4 GenomeInfoDb_1.30.1
## [7] tools_4.1.3 backports_1.4.1 bslib_0.4.1
## [10] utf8_1.2.2 R6_2.5.1 DBI_1.1.3
## [13] BiocGenerics_0.40.0 colorspace_2.0-3 withr_2.5.0
## [16] tidyselect_1.2.0 prettyunits_1.1.1 bit_4.0.5
## [19] curl_4.3.3 compiler_4.1.3 cli_3.4.1
## [22] Biobase_2.54.0 xml2_1.3.3 labeling_0.4.2
## [25] sass_0.4.2 scales_1.2.1 rappdirs_0.3.3
## [28] stringr_1.4.1 digest_0.6.30 rmarkdown_2.18
## [31] XVector_0.34.0 pkgconfig_2.0.3 htmltools_0.5.3
## [34] highr_0.9 dbplyr_2.2.1 fastmap_1.1.0
## [37] rlang_1.0.6 rstudioapi_0.14 RSQLite_2.2.18
## [40] prettydoc_0.4.1 jquerylib_0.1.4 generics_0.1.3
## [43] farver_2.1.1 jsonlite_1.8.3 dplyr_1.0.10
## [46] car_3.1-1 RCurl_1.98-1.9 magrittr_2.0.3
## [49] GenomeInfoDbData_1.2.7 Rcpp_1.0.9 munsell_0.5.0
## [52] S4Vectors_0.32.4 fansi_1.0.3 abind_1.4-5
## [55] lifecycle_1.0.3 stringi_1.7.8 yaml_2.3.6
## [58] carData_3.0-5 zlibbioc_1.40.0 plyr_1.8.8
## [61] BiocFileCache_2.2.1 grid_4.1.3 blob_1.2.3
## [64] crayon_1.5.2 cowplot_1.1.1 Biostrings_2.62.0
## [67] hms_1.1.2 KEGGREST_1.34.0 knitr_1.40
## [70] pillar_1.8.1 ggsignif_0.6.4 codetools_0.2-18
## [73] stats4_4.1.3 XML_3.99-0.12 glue_1.6.2
## [76] evaluate_0.18 png_0.1-7 vctrs_0.5.1
## [79] gtable_0.3.1 tidyr_1.2.1 assertthat_0.2.1
## [82] cachem_1.0.6 xfun_0.35 broom_1.0.1
## [85] rstatix_0.7.1 tibble_3.1.8 AnnotationDbi_1.56.2
## [88] memoise_2.0.1 IRanges_2.28.0 ellipsis_0.3.2