list.files()## [1] "00_Slyc-Spimp-final-one2one-orthologs.xlsx"
## [2] "01_MappingRate-info-2024.xlsx"
## [3] "02_Pearson-correlation_vsd-20240324-update.xlsx"
## [4] "03_Normalized-FeatureCounts-averageScored.csv"
## [5] "03_Normalized-FeatureCounts.csv"
## [6] "04_Control.DEGs.csv"
## [7] "04_Control.DEGs_maryam.csv"
## [8] "04_S.lyco.DEGs.csv"
## [9] "04_S.lyco.DEGs_maryam.csv"
## [10] "04_S.pimp.DEGs.csv"
## [11] "04_S.pimp.DEGs_maryam.csv"
## [12] "04_Salt.DEGs.csv"
## [13] "04_Salt.DEGs_maryam.csv"
## [14] "05_Tomato-vsd_PCA_all-genes.csv"
## [15] "06_SpimpSlyco-TPM-clean-MaryamSorted.csv"
## [16] "06_SpimpSlyco-TPM-clean.csv"
## [17] "20240418_RNAseq_Analysis.html"
## [18] "20240418_RNAseq_Analysis.nb.html"
## [19] "20240418_RNAseq_Analysis.Rmd"
## [20] "202404both_neg_gene_list.csv"
## [21] "202404both_neg1000_list.csv"
## [22] "202404both_neg2000_list.csv"
## [23] "202404both_neg3000_list.csv"
## [24] "202404both_neg4000_list.csv"
## [25] "202404both_neg500_list.csv"
## [26] "202404both_pos_gene_list.csv"
## [27] "202404both_pos1000_list.csv"
## [28] "202404both_pos2000_list.csv"
## [29] "202404both_pos3000_list.csv"
## [30] "202404both_pos4000_list.csv"
## [31] "202404both_pos500_list.csv"
## [32] "Seq-summary-based-on-pimp-genome.xlsx"
## [33] "TomatoRNA-Seq-metaData.csv"
## [34] "TomatoRNA-Seq-metaData.xlsx"lyco <- read.csv("04_S.lyco.DEGs_maryam.csv")
pimp <- read.csv("04_S.pimp.DEGs_maryam.csv")
lycopimpmax(pimp$log2FoldChange)## [1] 37.58724min(pimp$log2FoldChange)## [1] -18.2937pimp_pos <- subset(pimp, pimp$log2FoldChange > 0)
pimp_neg <- subset(pimp, pimp$log2FoldChange < 0)
length(unique(pimp_pos$Name))## [1] 2261length(unique(pimp_neg$Name))## [1] 1531lyco_pos <- subset(lyco, lyco$log2FoldChange > 0)
lyco_neg <- subset(lyco, lyco$log2FoldChange < 0)
length(unique(lyco_pos$Name))## [1] 2307length(unique(lyco_neg$Name))## [1] 1725both_pos <- unique(lyco_pos$Name) %in% unique(pimp_pos$Name)
length(which(both_pos, T))## [1] 1467both_neg <- unique(lyco_neg$Name) %in% unique(pimp_neg$Name)
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## [1717] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSElength(which(both_neg, T))## [1] 1009which_genes <- subset(lyco_pos, lyco_pos$Name %in% unique(pimp_pos$Name))
which_geneslength(unique(which_genes$Name))## [1] 1467which_genes2 <- subset(lyco_neg, lyco_neg$Name %in% unique(pimp_neg$Name))
which_genes2write.csv(which_genes, "202404both_pos_gene_list.csv", row.names = FALSE)
write.csv(which_genes2, "202404both_neg_gene_list.csv", row.names = FALSE)Let’s look at specific timepoints:
unique(pimp_pos$Sample)## [1] "pimp_1000min" "pimp_3000min" "pimp_4000min" "pimp_2000min" "pimp_500min"#for 500 min, separates positive genes
pimp500 <- subset(pimp_pos, pimp_pos$Sample == "pimp_500min")
dim(pimp500)## [1] 949 9lyco500 <- subset(lyco_pos, lyco_pos$Sample == "lyco_500min")
dim(lyco500)## [1] 845 9both_pos500 <- unique(pimp500$Name) %in% unique(lyco500$Name)
length(which(both_pos500, T))## [1] 430lyco_both_pos500 <- subset(lyco500, lyco500$Name %in% unique(pimp500$Name))
pimp_both_pos500 <- subset(pimp500, pimp500$Name %in% unique(lyco500$Name))
both_pos500 <- rbind(lyco_both_pos500, pimp_both_pos500)
dim(both_pos500)## [1] 860 9both_pos500write.csv(both_pos500, "202404both_pos500_list.csv", row.names = FALSE)#for 500 min, separates negative genes
pimp500 <- subset(pimp_neg, pimp_neg$Sample == "pimp_500min")
dim(pimp500)## [1] 847 9lyco500 <- subset(lyco_neg, lyco_neg$Sample == "lyco_500min")
dim(lyco500)## [1] 1144 9both_neg500 <- unique(pimp500$Name) %in% unique(lyco500$Name)
length(which(both_neg500, T))## [1] 597lyco_both_neg500 <- subset(lyco500, lyco500$Name %in% unique(pimp500$Name))
pimp_both_neg500 <- subset(pimp500, pimp500$Name %in% unique(lyco500$Name))
both_neg500 <- rbind(lyco_both_neg500, pimp_both_neg500)
dim(both_neg500)## [1] 1194 9both_neg500write.csv(both_neg500, "202404both_neg500_list.csv", row.names = FALSE)I have CSV files for both up and down common genes…now I need to generate / extract the unique gene list in each time point for up and down.
#for 1000 min, separates positive genes
pimp1000 <- subset(pimp_pos, pimp_pos$Sample == "pimp_1000min")
dim(pimp1000)## [1] 837 9lyco1000 <- subset(lyco_pos, lyco_pos$Sample == "lyco_1000min")
dim(lyco1000)## [1] 754 9both_pos1000 <- unique(pimp1000$Name) %in% unique(lyco1000$Name)
length(which(both_pos1000, T))## [1] 393lyco_both_pos1000 <- subset(lyco1000, lyco1000$Name %in% unique(pimp1000$Name))
pimp_both_pos1000 <- subset(pimp1000, pimp1000$Name %in% unique(lyco1000$Name))
both_pos1000 <- rbind(lyco_both_pos1000, pimp_both_pos1000)
dim(both_pos1000)## [1] 786 9both_pos1000write.csv(both_pos1000, "202404both_pos1000_list.csv", row.names = FALSE)#for 1000 min, separates negative genes
pimp1000 <- subset(pimp_neg, pimp_neg$Sample == "pimp_1000min")
dim(pimp1000)## [1] 689 9lyco1000 <- subset(lyco_neg, lyco_neg$Sample == "lyco_1000min")
dim(lyco1000)## [1] 732 9both_neg1000 <- unique(pimp1000$Name) %in% unique(lyco1000$Name)
length(which(both_neg1000, T))## [1] 357lyco_both_neg1000 <- subset(lyco1000, lyco1000$Name %in% unique(pimp1000$Name))
pimp_both_neg1000 <- subset(pimp1000, pimp1000$Name %in% unique(lyco1000$Name))
both_neg1000 <- rbind(lyco_both_neg1000, pimp_both_neg1000)
dim(both_neg1000)## [1] 714 9both_neg1000write.csv(both_neg1000, "202404both_neg1000_list.csv", row.names = FALSE)#for 2000 min, separates positive genes
pimp2000 <- subset(pimp_pos, pimp_pos$Sample == "pimp_2000min")
dim(pimp2000)## [1] 958 9lyco2000 <- subset(lyco_pos, lyco_pos$Sample == "lyco_2000min")
dim(lyco2000)## [1] 1053 9both_pos2000 <- unique(pimp2000$Name) %in% unique(lyco2000$Name)
length(which(both_pos2000, T))## [1] 634lyco_both_pos2000 <- subset(lyco2000, lyco2000$Name %in% unique(pimp2000$Name))
pimp_both_pos2000 <- subset(pimp2000, pimp2000$Name %in% unique(lyco2000$Name))
both_pos2000 <- rbind(lyco_both_pos2000, pimp_both_pos2000)
dim(both_pos2000)## [1] 1268 9both_pos2000write.csv(both_pos2000, "202404both_pos2000_list.csv", row.names = FALSE)#for 2000 min, separates negative genes
pimp2000 <- subset(pimp_neg, pimp_neg$Sample == "pimp_2000min")
dim(pimp2000)## [1] 426 9lyco2000 <- subset(lyco_neg, lyco_neg$Sample == "lyco_2000min")
dim(lyco2000)## [1] 517 9both_neg2000 <- unique(pimp2000$Name) %in% unique(lyco2000$Name)
length(which(both_neg2000, T))## [1] 242lyco_both_neg2000 <- subset(lyco2000, lyco2000$Name %in% unique(pimp2000$Name))
pimp_both_neg2000 <- subset(pimp2000, pimp2000$Name %in% unique(lyco2000$Name))
both_neg2000 <- rbind(lyco_both_neg2000, pimp_both_neg2000)
dim(both_neg2000)## [1] 484 9both_neg2000write.csv(both_neg2000, "202404both_neg2000_list.csv", row.names = FALSE)#for 3000 min, separates positive genes
pimp3000 <- subset(pimp_pos, pimp_pos$Sample == "pimp_3000min")
dim(pimp3000)## [1] 981 9lyco3000 <- subset(lyco_pos, lyco_pos$Sample == "lyco_3000min")
dim(lyco3000)## [1] 1040 9both_pos3000 <- unique(pimp3000$Name) %in% unique(lyco3000$Name)
length(which(both_pos3000, T))## [1] 570lyco_both_pos3000 <- subset(lyco3000, lyco3000$Name %in% unique(pimp3000$Name))
pimp_both_pos3000 <- subset(pimp3000, pimp3000$Name %in% unique(lyco3000$Name))
both_pos3000 <- rbind(lyco_both_pos3000, pimp_both_pos3000)
dim(both_pos3000)## [1] 1140 9both_pos3000write.csv(both_pos3000, "202404both_pos3000_list.csv", row.names = FALSE)#for 3000 min, separates negative genes
pimp3000 <- subset(pimp_neg, pimp_neg$Sample == "pimp_3000min")
dim(pimp3000)## [1] 371 9lyco3000 <- subset(lyco_neg, lyco_neg$Sample == "lyco_3000min")
dim(lyco3000)## [1] 441 9both_neg3000 <- unique(pimp3000$Name) %in% unique(lyco3000$Name)
length(which(both_neg3000, T))## [1] 217lyco_both_neg3000 <- subset(lyco3000, lyco3000$Name %in% unique(pimp3000$Name))
pimp_both_neg3000 <- subset(pimp3000, pimp3000$Name %in% unique(lyco3000$Name))
both_neg3000 <- rbind(lyco_both_neg3000, pimp_both_neg3000)
dim(both_neg3000)## [1] 434 9both_neg3000write.csv(both_neg3000, "202404both_neg3000_list.csv", row.names = FALSE)#for 4000 min, separates positive genes
pimp4000 <- subset(pimp_pos, pimp_pos$Sample == "pimp_4000min")
dim(pimp4000)## [1] 1056 9lyco4000 <- subset(lyco_pos, lyco_pos$Sample == "lyco_4000min")
dim(lyco4000)## [1] 1436 9both_pos4000 <- unique(pimp4000$Name) %in% unique(lyco4000$Name)
length(which(both_pos4000, T))## [1] 687lyco_both_pos4000 <- subset(lyco4000, lyco4000$Name %in% unique(pimp4000$Name))
pimp_both_pos4000 <- subset(pimp4000, pimp4000$Name %in% unique(lyco4000$Name))
both_pos4000 <- rbind(lyco_both_pos4000, pimp_both_pos4000)
dim(both_pos4000)## [1] 1374 9both_pos4000write.csv(both_pos4000, "202404both_pos4000_list.csv", row.names = FALSE)#for 4000 min, separates negative genes
pimp4000 <- subset(pimp_neg, pimp_neg$Sample == "pimp_4000min")
dim(pimp4000)## [1] 575 9lyco4000 <- subset(lyco_neg, lyco_neg$Sample == "lyco_4000min")
dim(lyco4000)## [1] 582 9both_neg4000 <- unique(pimp4000$Name) %in% unique(lyco4000$Name)
length(which(both_neg4000, T))## [1] 253lyco_both_neg4000 <- subset(lyco4000, lyco4000$Name %in% unique(pimp4000$Name))
pimp_both_neg4000 <- subset(pimp4000, pimp4000$Name %in% unique(lyco4000$Name))
both_neg4000 <- rbind(lyco_both_neg4000, pimp_both_neg4000)
dim(both_neg4000)## [1] 506 9both_neg4000write.csv(both_neg4000, "202404both_neg4000_list.csv", row.names = FALSE)