TE analysis in morulae embryos injected and nonInjected w active/dead-Btgh

1 Reading the data

The input files are:

• the output tables made by featureCounts (run in rule ‘count_on_TE’ of the TE_RNASeq.Snakefile), which contain read counts of all repetitive elements (i.e. all repNames) for all samples
• the repetitive elements annotation ../../data/annotations/RepeatMasker_RepeatLibrary20140131_mm10.noGenes.noSimple.bed used for featureCounts command, that is used to retrieve repFamily and repClass for each repName. The annotation I chose is the RepeatMasker most updated library to Nov2020, from which ‘Simple repeats’ and ‘Low complexity regions’ are removed in rule filter_TE_annotation rule of TE_RNASeq.Snakefile
• a table with total number of reads per sample in the raw fastq files
• the samples’ table, containing info on experimental design:

2 Analysis at family level

2.1 FPKM for each family of Repetitive Elements

For each family of Repetitive Elements (in case of elements with no repFamily name or repFamilies belonging to more than one repClass I use repClass) I compute FPKM values, as follows: for each sample:

• I compute the sum of counts for all elements belonging to that repFamily
• I divide this sum by the total number of reads for that sample and multiply by 10⁶
• I divide this number by the total sum of lengths (in Kb) of the elements belonging to that repFamily –> FPKM
• When specified, I subtract from each FPKM the total FPKM of all transposons belonging to the DNA repClass

2.2 Heatmaps

The heatmaps are scaled by rows.

• I exclude samples mA9, mA20 and mC6 from the rest of the TE analysis because more contaminated with DNA transposons.
• No RNA TE family shows deregulation in one of the three experimental groups.

3 DE-Seq analysis of RNA transposons

I include the FPKM of DNA repetitive elements as confounding factor in DESeq2 formula.

Before running the Differential Expression analysis, the data are pre-filtered to remove all repetitive elements with < 10 reads among all samples.

3.1 MA-plots

• The threshold used for a dot to be coloured in the MA-plots is p-value adjusted < 0.05.
• Transposable elements whose mean expression > 10 and log2FoldChange > 0.2 (or < -0.2) are labeled.

active vs dead

baseMean	log2FoldChange	lfcSE	pvalue	padj	repName
806.49139	-0.3778477	0.1193406	0.0000683	0.0116156	MMETn-int
206.40109	-0.3818225	0.1369757	0.0001269	0.0116156	ETnERV-int
3573.42804	-0.3363603	0.1266031	0.0004848	0.0295735	B2_Mm1a
498.50741	-0.3352599	0.1495905	0.0007787	0.0332760	RLTR10-int
2198.03897	-0.3218701	0.1305130	0.0009092	0.0332760	B2_Mm1t
70.57467	0.3044471	0.1306259	0.0016102	0.0453632	L1MdFanc_II
16.97384	-0.2951958	0.1710063	0.0019182	0.0453632	RLTR26
389.54918	-0.2847695	0.1180779	0.0019831	0.0453632	L1MdTf_III

active vs non inj

baseMean	log2FoldChange	lfcSE	pvalue	padj	repName
52.239767	0.5380230	0.1350304	0.0000007	0.0001573	L1_Mur3
31.009136	-0.4685576	0.1483968	0.0000304	0.0034974	RLTR17
198.441948	0.4520685	0.2067209	0.0000526	0.0040305	MER89
57.391712	0.3619053	0.1223337	0.0003116	0.0179177	L1Lx_IV
82.818382	0.3463769	0.1222770	0.0005484	0.0228044	ORR1A3-int
120.961862	-0.3601924	0.1321217	0.0005949	0.0228044	RLTR13B2
1010.357509	0.3837509	0.1613170	0.0007645	0.0251195	MTA_Mm-int
389.549176	-0.3187987	0.1148767	0.0009187	0.0262123	L1MdTf_III
9.499054	-0.3721853	0.1869677	0.0011180	0.0262123	MLTR11B
103.868560	0.3480707	0.1376616	0.0011397	0.0262123	RLTR6-int
14.749991	0.3584921	0.1790419	0.0017506	0.0360284	L1Lx_II
37.170810	0.3497443	0.1597735	0.0019886	0.0360284	MT-int
7.922593	0.3294815	0.2051498	0.0020364	0.0360284	L1_Rod
67.889418	0.3314359	0.1521968	0.0028899	0.0464899	MT2B
61.251019	0.2822648	0.1134049	0.0030320	0.0464899	LTRIS_Mus

dead vs non inj

baseMean	log2FoldChange	lfcSE	pvalue	padj	repName

sessionInfo()

## R version 4.1.3 (2022-03-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
## 
## 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
## 
## 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] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] ggpubr_0.5.0                ggrepel_0.9.2              
##  [3] gridExtra_2.3               DESeq2_1.34.0              
##  [5] SummarizedExperiment_1.24.0 Biobase_2.54.0             
##  [7] MatrixGenerics_1.6.0        matrixStats_0.62.0         
##  [9] GenomicRanges_1.46.1        GenomeInfoDb_1.30.1        
## [11] IRanges_2.28.0              S4Vectors_0.32.4           
## [13] BiocGenerics_0.40.0         pheatmap_1.0.12            
## [15] data.table_1.14.6           ggplot2_3.4.0              
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-7           bit64_4.0.5            RColorBrewer_1.1-3    
##  [4] httr_1.4.4             tools_4.1.3            backports_1.4.1       
##  [7] bslib_0.4.1            irlba_2.3.5.1          utf8_1.2.2            
## [10] R6_2.5.1               DBI_1.1.3              colorspace_2.0-3      
## [13] withr_2.5.0            tidyselect_1.2.0       bit_4.0.5             
## [16] compiler_4.1.3         cli_3.4.1              DelayedArray_0.20.0   
## [19] labeling_0.4.2         sass_0.4.2             scales_1.2.1          
## [22] SQUAREM_2021.1         genefilter_1.76.0      mixsqp_0.3-48         
## [25] stringr_1.4.1          digest_0.6.30          rmarkdown_2.18        
## [28] XVector_0.34.0         pkgconfig_2.0.3        htmltools_0.5.3       
## [31] invgamma_1.1           highr_0.9              fastmap_1.1.0         
## [34] rlang_1.0.6            rstudioapi_0.14        RSQLite_2.2.18        
## [37] prettydoc_0.4.1        farver_2.1.1           jquerylib_0.1.4       
## [40] generics_0.1.3         jsonlite_1.8.3         BiocParallel_1.28.3   
## [43] car_3.1-1              dplyr_1.0.10           RCurl_1.98-1.9        
## [46] magrittr_2.0.3         GenomeInfoDbData_1.2.7 Matrix_1.5-3          
## [49] Rcpp_1.0.9             munsell_0.5.0          fansi_1.0.3           
## [52] abind_1.4-5            lifecycle_1.0.3        stringi_1.7.8         
## [55] yaml_2.3.6             carData_3.0-5          zlibbioc_1.40.0       
## [58] grid_4.1.3             blob_1.2.3             parallel_4.1.3        
## [61] crayon_1.5.2           lattice_0.20-45        Biostrings_2.62.0     
## [64] splines_4.1.3          annotate_1.72.0        KEGGREST_1.34.0       
## [67] locfit_1.5-9.6         knitr_1.40             pillar_1.8.1          
## [70] ggsignif_0.6.4         codetools_0.2-18       geneplotter_1.72.0    
## [73] XML_3.99-0.12          glue_1.6.2             evaluate_0.18         
## [76] png_0.1-7              vctrs_0.5.1            gtable_0.3.1          
## [79] purrr_0.3.5            tidyr_1.2.1            assertthat_0.2.1      
## [82] ashr_2.2-54            cachem_1.0.6           xfun_0.35             
## [85] xtable_1.8-4           broom_1.0.1            rstatix_0.7.1         
## [88] survival_3.2-13        truncnorm_1.0-8        tibble_3.1.8          
## [91] AnnotationDbi_1.56.2   memoise_2.0.1