Last updated: 2024-11-02

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Knit directory: locust-comparative-genomics/

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  1. Load the libraries

We start by loading all the required R packages.

#(install first from CRAN or Bioconductor)
library("DESeq2")
library("tximport")
library("txdbmaker")
library("knitr")
library("rmdformats")
library("tidyverse")
library("data.table")
library("DT")  # for making interactive search table
library("plotly") # for interactive plots
library("ggthemes") # for theme_calc
library("reshape2")
library("ComplexHeatmap")
library("RColorBrewer")
library("circlize")
library("apeglm")
library("ggpubr")
library("ggplot2")
library("ggrepel")
library("EnhancedVolcano")
library("SARTools")
library("pheatmap")
library("clusterProfiler")
library("sva")
library("cowplot")
library("ashr")
library("vsn")
library("ggdist")
library("ggConvexHull")
library("kableExtra")
library("plotly")

# Path for all species
workDir <- "/Users/maevatecher/Library/Mobile Documents/com~apple~CloudDocs/Documents/GitHub/locust-comparative-genomics/data"
setwd(workDir)

## PARAMETERS for running DEseq2
tresh_logfold <- 1                    # Treshold for log2(foldchange) in final DE-files
tresh_padj <- 0.05                    # Treshold for adjusted p-valued in final DE-files
alpha_DEseq2 <- 0.05                  # threshold of statistical significance
pAdjustMethod_DEseq2 <- "BH"          # p-value adjustment method: "BH" (default) or "BY"
featuresToRemove <- c(NULL)           # names of the features to be removed, NULL if none or if using Idxstats
varInt <- "RearingCondition"          # factor of interest
condRef <- "Isolated"                 # reference biological condition
batch <- NULL                         # blocking factor: NULL (default) or "batch" for example  
fitType <- "parametric"               # mean-variance relationship: "parametric" (default) or "local"
cooksCutoff <- TRUE                   # TRUE/FALSE to perform the outliers detection (default is TRUE)
independentFiltering <- TRUE          # TRUE/FALSE to perform independent filtering (default is TRUE)
typeTrans <- "rlog"                   # transformation for PCA/clustering: "VST" or "rlog"
locfunc <- "median"

STRATEGY 1: One genome S. gregaria

Differential gene expression analysis

gregaria

rawDir <- file.path(workDir, "03-gregaria-DESeq2") 

# Path and name of targetfile containing conditions and file names
species <- "gregaria"
targetFile <- file.path(workDir, "list", paste0("Head", "_", species, ".txt")) 

sampletable <- fread(targetFile)
rownames(sampletable) <- sampletable$SampleName
sampletable$RearingCondition <- as.factor(sampletable$RearingCondition)
sampletable$Tissue <- as.factor(sampletable$Tissue)

## Import count files
satoshi <- DESeqDataSetFromHTSeqCount(sampleTable = sampletable,
                        directory = rawDir,
                        design = ~ RearingCondition )
#satoshi

smallestGroupSize <- 3
keep <- rowSums(counts(satoshi) >= 5) >= smallestGroupSize
satoshi <- satoshi[keep,]
#nrow(satoshi)
satoshi$RearingCondition <- relevel(satoshi$RearingCondition, ref = "Isolated")

# Fit the statistical model
shigeru <- DESeq(satoshi)
#cbind(resultsNames(shigeru))
res_shigeru <- results(shigeru)
sum(res_shigeru$padj < tresh_padj, na.rm = TRUE)
[1] 5697

A total of 5,697 genes out of the pre-filtered 16,305 features were showing significant differences in expression levels. The summary below showed how many were upregulated and downregulated in crowded compared to isolated.

brock <- results(shigeru, name = "RearingCondition_Crowded_vs_Isolated", alpha = alpha_DEseq2)
summary(brock)

out of 16305 with nonzero total read count
adjusted p-value < 0.05
LFC > 0 (up)       : 2709, 17%
LFC < 0 (down)     : 2988, 18%
outliers [1]       : 99, 0.61%
low counts [2]     : 0, 0%
(mean count < 1)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
#mcols(brock)$description
#head(brock)
kable(cbind(brock)) %>%
  kable_styling(bootstrap_options = "striped", font_size = 10) %>%
  scroll_box(width = "100%", height = "400px")
baseMean log2FoldChange lfcSE stat pvalue padj
LOC126318536 277.030999 0.0378182 0.0721917 0.5238587 0.6003768 0.7156301
LOC126318656 63.373667 0.0909845 0.1567636 0.5803928 0.5616498 0.6829413
LOC126318743 99.581394 0.0188983 0.7549026 0.0250341 0.9800278 0.9874001
LOC126319294 198.137768 -0.3206079 0.1175191 -2.7281337 0.0063694 0.0224445
LOC126319460 594.595828 0.4286836 0.1080481 3.9675272 0.0000726 0.0005497
LOC126320026 87.715679 0.2909248 0.1535866 1.8942072 0.0581975 0.1276423
LOC126320178 191.253746 0.5982409 0.1532922 3.9026181 0.0000952 0.0006894
LOC126320326 51.577705 -0.6578155 0.2522168 -2.6081354 0.0091037 0.0297269
LOC126284887 7.810804 0.0752949 0.4653624 0.1617984 0.8714646 0.9166583
LOC126320407 696.440504 1.0350571 0.1145488 9.0359480 0.0000000 0.0000000
LOC126320662 101.152388 0.3455970 0.1491725 2.3167605 0.0205168 0.0565179
LOC126320757 1891.416031 0.2255601 0.0777386 2.9015182 0.0037136 0.0144774
LOC126320910 3113.035018 -0.1941867 0.0587602 -3.3047329 0.0009507 0.0047728
LOC126322280 514.920299 -0.2876249 0.0899487 -3.1976538 0.0013855 0.0064895
LOC126322412 200.104009 -0.2836114 0.1026485 -2.7629371 0.0057284 0.0205613
LOC126323097 18.014855 0.2605619 0.2702485 0.9641570 0.3349672 0.4719186
LOC126322565 473.733026 0.2775373 0.0958840 2.8945101 0.0037975 0.0147442
LOC126323250 663.257504 0.0782826 0.0625696 1.2511287 0.2108875 0.3368463
LOC126323807 420.986098 -0.1716301 0.0855137 -2.0070484 0.0447445 0.1042602
LOC126323387 307.475664 -0.0454296 0.0705234 -0.6441781 0.5194600 0.6456794
LOC126324212 33.346505 -0.3182415 0.2341276 -1.3592654 0.1740625 0.2920897
LOC126324319 543.266079 -0.1260696 0.1527184 -0.8255034 0.4090858 0.5443058
LOC126325221 1823.836104 0.0116944 0.0515224 0.2269773 0.8204414 0.8818194
LOC126325647 166.517128 1.2891934 0.1345265 9.5831957 0.0000000 0.0000000
LOC126326003 150.164637 -0.1292579 0.1172416 -1.1024917 0.2702480 0.4033700
LOC126326117 1309.924947 0.2092358 0.0415168 5.0397889 0.0000005 0.0000073
LOC126326285 459.401202 0.5910381 0.1902271 3.1070128 0.0018899 0.0084044
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LOC126284877 91.293827 1.1765867 0.1747592 6.7326154 0.0000000 0.0000000
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LOC126284900 13.566664 0.9283542 0.3628386 2.5585874 0.0105098 0.0334296
LOC126364494 3.552945 1.3798583 0.6190933 2.2288374 0.0258247 0.0677356
LOC126284907 5.311726 1.2579206 0.5583651 2.2528641 0.0242677 0.0645488
LOC126266999 4.193426 0.6854099 0.5405318 1.2680288 0.2047877 0.3296425
LOC126328694 566.863582 -0.0443630 0.0682257 -0.6502388 0.5155380 0.6420355
LOC126328850 13.297010 0.2200897 0.3372006 0.6526968 0.5139518 0.6404539
LOC126328952 89.118500 -0.1534793 0.1343928 -1.1420198 0.2534458 0.3851235
LOC126329352 2186.692637 0.0629510 0.0576043 1.0928170 0.2744742 0.4076364
LOC126329909 183.096403 -0.2578416 0.0881157 -2.9261726 0.0034316 0.0136172
LOC126270168 78.554534 0.6366988 0.2412006 2.6397072 0.0082978 0.0276297
LOC126330208 524.538710 0.2430576 0.1009667 2.4073053 0.0160707 0.0465408
LOC126330470 521.289800 -0.3353195 0.0582414 -5.7574056 0.0000000 0.0000002
LOC126331023 80.091001 0.5609238 0.2374972 2.3618123 0.0181858 0.0513270
LOC126331476 101.358127 0.4565402 0.2685709 1.6998868 0.0891522 0.1774504
LOC126331335 504.337856 -0.9754408 0.1145555 -8.5150089 0.0000000 0.0000000
LOC126331179 88.417813 0.2374211 0.2740982 0.8661900 0.3863860 0.5227216
LOC126331633 121.311731 -0.1030851 0.0987386 -1.0440195 0.2964764 0.4315725
LOC126331791 207.579371 -0.3470587 0.1543720 -2.2481976 0.0245636 0.0651305
LOC126331946 137.456551 -0.3324064 0.1126644 -2.9504121 0.0031735 0.0127967
LOC126332104 283.004146 -0.3364025 0.0810187 -4.1521568 0.0000329 0.0002858
LOC126333037 82.120352 -0.3688936 0.1856212 -1.9873458 0.0468841 0.1081109
LOC126333298 80.447878 -0.1036889 0.4078124 -0.2542564 0.7992975 0.8678407
LOC126274441 10.074698 0.1518666 0.3604035 0.4213794 0.6734781 0.7731737
LOC126333611 68.023547 -0.1842804 0.1804385 -1.0212920 0.3071161 0.4430411
LOC126333456 262.182920 0.2142854 0.1670086 1.2830802 0.1994640 0.3233807
LOC126333765 352.607137 -0.0060208 0.0727488 -0.0827614 0.9340412 0.9579631
LOC126284920 57.533683 0.4396634 0.1875402 2.3443689 0.0190593 0.0532423
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LOC126334172 203.114029 0.3313784 0.1378592 2.4037447 0.0162281 0.0468793
LOC126334327 6.803617 0.5874544 0.4654861 1.2620233 0.2069404 0.3320499
LOC126334429 288.713467 0.2243595 0.1134088 1.9783260 0.0478919 0.1098877
LOC126334511 682.922352 0.1074279 0.0708975 1.5152564 0.1297075 0.2349697
LOC126334525 1087.851196 0.5530857 0.1167302 4.7381559 0.0000022 0.0000269
LOC126334532 485.328363 0.6007165 0.0825360 7.2782341 0.0000000 0.0000000
LOC126334539 287.797003 -0.3108349 0.0977049 -3.1813647 0.0014658 0.0068086
LOC126334578 205.392664 0.0564130 0.0897028 0.6288875 0.5294227 0.6546985
LOC126334570 253.530977 -0.0823578 0.0927813 -0.8876547 0.3747265 0.5109219
LOC126334603 15.810188 0.7083910 0.2860576 2.4763930 0.0132717 0.0401122
LOC126334620 283.233382 -0.0843341 0.2350321 -0.3588195 0.7197301 0.8094668
LOC126334628 68.221872 -0.5155691 0.1768659 -2.9150292 0.0035566 0.0140150
LOC126334652 203.510088 0.1123293 0.1120188 1.0027714 0.3159712 0.4521526
LOC126334644 59.971736 -0.4139543 0.1771184 -2.3371618 0.0194308 0.0539481
LOC126334636 170.127842 -0.6623756 0.1162850 -5.6961407 0.0000000 0.0000003
LOC126334676 250.868272 -0.2891749 0.1066462 -2.7115354 0.0066972 0.0233711
LOC126334687 132.526124 -0.1854206 0.1461177 -1.2689812 0.2044478 0.3292766
LOC126334692 658.119738 -0.3559353 0.0827876 -4.2993788 0.0000171 0.0001618
LOC126279107 18.003889 0.5389013 0.2641519 2.0401193 0.0413384 0.0982592
LOC126334712 8572.576549 0.1597073 0.0593102 2.6927433 0.0070867 0.0244980
LOC126334738 84.160429 -0.1745307 0.1594103 -1.0948517 0.2735816 0.4066043
LOC126334732 53.643935 -0.0784712 0.1763100 -0.4450752 0.6562654 0.7594571
LOC126334747 584.794691 0.1576220 0.1124021 1.4023052 0.1608241 0.2750729
LOC126334754 50.505178 1.3837273 0.5863725 2.3598094 0.0182843 0.0515422
LOC126328798 86.717375 -0.3588969 0.1265933 -2.8350390 0.0045820 0.0171651
LOC126334766 407.675239 0.0248923 0.0664533 0.3745831 0.7079706 0.8013250
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LOC126330262 18.575149 1.0171146 0.2866460 3.5483306 0.0003877 0.0022500
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LOC126334819 448.972889 0.1252587 0.0816315 1.5344403 0.1249214 0.2285219
LOC126334861 82.782649 0.2315588 0.1790957 1.2929334 0.1960341 0.3189312
LOC126334868 80.051109 0.1229160 0.2536988 0.4844959 0.6280340 0.7372646
LOC126334872 21.782252 0.6222938 0.3515136 1.7703265 0.0766728 0.1578454
LOC126284981 33.966096 0.4807881 0.2992891 1.6064336 0.1081787 0.2049501
LOC126334722 4.794244 0.6951273 0.6385842 1.0885446 0.2763548 0.4099035
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LOC126333040 251.586403 -0.2801208 0.3014713 -0.9291789 0.3527964 0.4895469
LOC126333044 13.352338 -3.6407805 1.1031291 -3.3004120 0.0009654 0.0048394
LOC126333039 163.063236 6.1925956 1.6217142 3.8185493 NA NA
LOC126333050 34.830076 2.7743376 0.5657466 4.9038522 0.0000009 0.0000135
LOC126333051 451.293444 0.8823473 0.1711557 5.1552315 0.0000003 0.0000044
LOC126333266 36.133123 -0.5977865 0.3059000 -1.9541891 0.0506789 0.1148192
LOC126333260 52.967701 1.7120707 0.6963817 2.4585234 0.0139510 0.0416985
LOC126333261 68.722826 1.5539091 0.6892899 2.2543622 0.0241734 0.0644014
LOC126333262 166.221230 1.1044045 0.2775151 3.9796195 0.0000690 0.0005247
LOC126333258 3.768324 -0.4150239 1.2032173 -0.3449285 0.7301482 0.8176327
LOC126333328 153.809506 0.5104225 0.2428657 2.1016658 0.0355826 0.0871632
LOC126333326 9.473856 -0.6204914 0.7531270 -0.8238868 0.4100039 0.5452140
LOC126333323 249.678812 0.7830866 0.3613787 2.1669419 0.0302393 0.0766674
LOC126333327 3.995226 -1.4401733 1.2159066 -1.1844440 0.2362373 0.3667812
LOC126333359 3.794834 -4.0379961 1.0672680 -3.7834882 0.0001546 0.0010356
LOC126333367 830.198835 1.2130122 0.2606450 4.6538866 0.0000033 0.0000387
LOC126333366 16.172001 -1.3333265 1.3010713 -1.0247913 0.3054617 0.4414404
LOC126333368 71.050246 2.7616914 1.0934103 2.5257595 0.0115448 0.0360007
LOC126333372 15.057521 -0.5551751 1.1377266 -0.4879688 0.6255719 0.7355985
LOC126333374 13.186547 1.0513608 0.5382422 1.9533228 0.0507814 0.1149755
LOC126333383 5.443564 -0.6898877 1.3561837 -0.5086979 0.6109640 0.7236193
LOC126333385 8292.740067 1.0565012 0.1941916 5.4405085 0.0000001 0.0000011
LOC126333384 35.677318 -0.4461512 0.3435940 -1.2984836 0.1941212 0.3164725
LOC126333389 5.253177 -1.5667302 1.4223796 -1.1014853 0.2706855 0.4036302
LOC126333399 9.701169 -0.9146782 1.3858471 -0.6600138 0.5092450 0.6368903
LOC126333395 23.464999 0.6125320 0.7144728 0.8573203 0.3912679 0.5272649
LOC126333400 5.012164 -0.1818634 0.8398768 -0.2165358 0.8285701 0.8878398
LOC126333396 22.120175 2.7865229 1.1171749 2.4942585 0.0126221 0.0386022
LOC126333415 47.853397 -0.4347786 0.2872394 -1.5136454 0.1301158 0.2355515
LOC126333409 4.530668 0.2257833 0.8462624 0.2668006 0.7896227 0.8605666
LOC126333422 550.347250 -0.1099901 0.1943178 -0.5660320 0.5713720 0.6911339
LOC126333425 1.893084 -2.9590821 1.1142661 -2.6556333 0.0079160 0.0266818
LOC126333421 391.863392 -2.0426254 1.0029239 -2.0366704 NA NA
LOC126333437 192.155864 0.5201868 0.1953384 2.6630037 0.0077447 0.0262353
LOC126333435 203.482705 1.6312054 0.3776634 4.3192039 0.0000157 0.0001498
LOC126333432 8.920769 1.7868250 0.9218359 1.9383330 0.0525826 0.1181577
LOC126333436 102.041577 2.1970279 0.7929991 2.7705302 0.0055965 0.0201790
LOC126333433 5.841324 1.3515186 0.8382557 1.6122988 0.1068969 0.2031199
LOC126333452 22.258922 -1.1811434 0.4658588 -2.5354104 0.0112316 0.0351999
LOC126333458 412.904528 1.9675663 0.4898641 4.0165554 0.0000591 0.0004608
LOC126333465 122.334668 0.8350594 0.7081201 1.1792624 0.2382937 0.3687727
LOC126333469 104.214511 2.0296263 0.7946875 2.5539928 0.0106495 0.0337658
LOC126333480 1601.210813 -0.2458790 1.1165329 -0.2202166 NA NA
LOC126333476 19.051771 1.7486893 0.5576676 3.1357198 0.0017143 0.0077583
LOC126333483 5.310614 0.3096908 1.5896704 0.1948145 0.8455382 0.8980971
LOC126333486 4.520685 0.2966796 1.2037168 0.2464696 0.8053187 0.8722761
LOC126333498 3.155340 0.0568127 1.0179294 0.0558120 0.9554916 0.9720168
LOC126333491 1453.871506 0.4701126 0.2869722 1.6381818 0.1013838 0.1955983
LOC126333492 49.479601 1.2278236 0.5700718 2.1538050 0.0312555 0.0786043
LOC126333500 2.293007 -2.2237343 1.1209144 -1.9838574 0.0472717 0.1088345
LOC126333505 2.477561 -1.2080577 0.9277001 -1.3022072 0.1928456 0.3149189
LOC126333504 7.987962 -3.5769961 0.9701248 -3.6871503 0.0002268 0.0014356
LOC126333501 7.093371 -1.7705103 0.6299570 -2.8105257 0.0049461 0.0182297
LOC126333580 22.742569 0.3456790 1.0215992 0.3383705 0.7350840 0.8211740
LOC126333583 7.645910 2.1448981 0.6753935 3.1757753 0.0014944 0.0069233
LOC126333585 7.202387 1.3218979 0.7130494 1.8538658 0.0637584 0.1368748
LOC126333584 47.918848 1.7590938 1.5812431 1.1124753 0.2659339 0.3984214
LOC126333615 85.051124 -0.4594488 0.7454048 -0.6163749 0.5376471 0.6621164
LOC126333617 44.609994 -5.3427111 1.1500372 -4.6456854 0.0000034 0.0000402
LOC126333619 293.587964 -1.2655208 0.7451262 -1.6983979 0.0894327 0.1778994
LOC126333630 44.405406 -0.9019534 0.3729448 -2.4184638 0.0155862 0.0455117
LOC126333624 117.337859 1.0628439 0.2339266 4.5434923 0.0000055 0.0000615
LOC126333623 7.738218 0.6060216 2.6226246 0.2310745 NA NA
LOC126333626 5.413620 1.9152866 0.7390017 2.5917215 0.0095497 0.0308722
LOC126333622 14.057629 -2.4020689 0.8802887 -2.7287285 0.0063579 0.0224167
LOC126333625 23.048872 0.8552451 1.0920758 0.7831371 NA NA
LOC126333621 47.664916 0.3640578 0.7750151 0.4697428 0.6385388 0.7451152
LOC126333634 10.356946 -1.6204049 0.6720038 -2.4113033 0.0158956 0.0461326
LOC126333639 11.284896 -0.9465009 0.5686249 -1.6645435 0.0960039 0.1879032
LOC126333633 122.945720 0.2120863 0.4739987 0.4474406 0.6545570 0.7580517
LOC126333644 86.067587 -1.5300980 1.0699442 -1.4300728 0.1526961 0.2648891
LOC126333645 14.223375 1.8337978 1.7349655 1.0569650 NA NA
LOC126333643 4.052594 0.7148508 1.6682453 0.4285046 0.6682838 0.7691908
LOC126333667 102.340454 1.2657855 0.4881575 2.5929859 0.0095147 0.0308071
LOC126333666 10.703608 -2.8018445 1.1632135 -2.4087104 0.0160090 0.0463952
LOC126333663 110.753614 1.6810267 0.4892258 3.4360955 0.0005902 0.0031934
LOC126333778 6.164369 -2.5595127 0.7424336 -3.4474636 0.0005659 0.0030877
LOC126333783 5.602663 -2.0221345 0.9432436 -2.1438093 0.0320482 0.0801501
LOC126333785 3.017569 1.2778124 0.9730001 1.3132706 0.1890918 0.3103440
LOC126333789 168.216663 0.4260950 0.4509740 0.9448326 0.3447443 0.4819224
LOC126333788 138.538221 0.8326731 0.1988319 4.1878250 0.0000282 0.0002495
LOC126333800 73.939203 0.4331060 0.9631865 0.4496595 0.6529560 0.7567621
LOC126333801 865.819331 1.1720135 0.2435401 4.8124051 0.0000015 0.0000201
LOC126333809 2047.134564 -1.0715154 1.0436541 -1.0266960 NA NA
LOC126333812 3.434489 -0.5002127 1.0442154 -0.4790321 0.6319158 0.7402286
LOC126333854 3.627303 1.9414423 2.2053786 0.8803216 NA NA
LOC126333865 62.558883 0.2209427 0.1775554 1.2443593 0.2133674 0.3394024
LOC126333850 2.273619 -1.4234165 0.9957305 -1.4295198 0.1528549 0.2650510
LOC126333927 2.621411 0.2826429 1.3620137 0.2075184 0.8356050 0.8918477
LOC126333935 213.088009 0.3510734 1.2279389 0.2859046 NA NA
LOC126333966 24.841377 1.7472535 0.8909754 1.9610570 0.0498724 0.1134360
LOC126333965 355.412017 1.8701536 0.3506702 5.3330834 0.0000001 0.0000019
LOC126333973 1.877210 -1.1752638 1.1947333 -0.9837039 0.3252611 0.4621006
LOC126333975 4.605525 1.4482579 0.8484271 1.7069916 0.0878236 0.1754091
LOC126333974 23.251829 2.4895301 1.8890696 1.3178604 NA NA
LOC126333995 4.310483 -1.1657445 0.8948519 -1.3027233 0.1926692 0.3146944
LOC126333999 106.911613 1.1725989 0.2586307 4.5338742 0.0000058 0.0000639
LOC126333998 5.126934 2.7548627 1.2401818 2.2213377 0.0263281 0.0687739
LOC126334004 2.329007 -0.5313300 2.1700877 -0.2448427 0.8065783 0.8729403
LOC126334005 7.159003 1.8194984 0.6352423 2.8642588 0.0041799 0.0159232
LOC126334006 564.520054 0.3101636 0.2530009 1.2259387 0.2202217 0.3478338
LOC126334017 739.074093 1.2456461 0.2766372 4.5028151 0.0000067 0.0000722
LOC126334022 13.919678 -1.5997916 0.6704217 -2.3862469 0.0170213 0.0487363
LOC126334018 274.965008 0.5349303 0.3448809 1.5510578 0.1208878 0.2231839
LOC126334015 2.946831 -2.7688861 1.1352493 -2.4390114 0.0147275 0.0434981
LOC126334016 671.619863 2.0802010 0.7723809 2.6932320 NA NA
LOC126334023 3.231543 -1.5336174 1.1224929 -1.3662602 0.1718573 0.2892729
LOC126334028 6.999747 -1.7242791 0.7522439 -2.2921809 0.0218952 0.0595940
LOC126334025 63.000393 1.6738527 0.3984069 4.2013653 0.0000265 0.0002373
LOC126334027 2.297900 -0.9095709 0.9416488 -0.9659343 0.3340771 0.4709920
LOC126334029 46.831098 0.0460535 0.4431831 0.1039153 0.9172365 0.9474793
LOC126334042 23.005771 -2.3586632 0.7385642 -3.1935789 0.0014052 0.0065666
LOC126334052 4.743749 -2.3756474 0.9615520 -2.4706386 0.0134872 0.0406195
LOC126334057 138.851627 0.9266021 0.5246932 1.7659883 0.0773978 0.1589686
LOC126334058 3.328024 2.2785337 0.7564665 3.0120749 0.0025947 0.0108880
LOC126334055 5.063135 0.5172774 0.8377536 0.6174577 0.5369329 0.6616633
LOC126334051 2.004108 -1.3276085 1.2758569 -1.0405622 0.2980788 0.4333601
LOC126334120 16.968327 -1.1582639 1.0148479 -1.1413178 NA NA
LOC126334124 275.051419 0.6255870 0.4266066 1.4664259 0.1425323 0.2519957
LOC126334123 5.003121 3.3290581 0.9054737 3.6765926 0.0002364 0.0014856
LOC126334125 66.266921 -0.9709264 0.5578960 -1.7403359 0.0818000 0.1657272
LOC126334129 41.503577 2.6548564 0.6969229 3.8093977 0.0001393 0.0009490
LOC126334134 28.267573 -3.6453955 1.4199015 -2.5673580 NA NA
LOC126334131 14.176951 3.4315152 0.7180290 4.7790758 0.0000018 0.0000228
LOC126334135 35.096825 -0.0128097 0.3494621 -0.0366555 0.9707597 0.9821533
LOC126334130 3.199904 2.9898909 0.9565762 3.1256172 0.0017743 0.0079741
LOC126334157 169.010841 -1.4427932 0.6355590 -2.2701169 0.0232005 0.0623941
LOC126334156 246.640475 -0.3474029 0.4547501 -0.7639424 0.4449016 0.5785184
LOC126334162 40.758397 0.1331213 0.5356679 0.2485145 0.8037363 0.8711428
LOC126334166 10.023530 -2.5838544 1.1502163 -2.2464073 0.0246779 0.0653161
LOC126334161 41.336709 1.2721081 0.6745037 1.8859913 0.0592961 0.1293864
LOC126334160 9.855029 -1.7059006 0.6420042 -2.6571486 0.0078805 0.0265843
LOC126334168 1409.616351 0.9661221 0.3526471 2.7396286 0.0061509 0.0218072
LOC126334174 515.372285 -0.5860416 0.5673409 -1.0329620 0.3016217 0.4374513
LOC126334169 3101.175728 0.4996551 0.4576012 1.0919007 0.2748767 0.4081221
LOC126334173 48.348383 -0.2582003 0.4364332 -0.5916146 0.5541087 0.6765418
LOC126334177 665.971870 1.7587140 0.6545095 2.6870717 0.0072081 0.0248015
LOC126334181 10.419187 0.5185640 0.6155649 0.8424197 0.3995531 0.5353582
LOC126334176 1443.891623 -0.7393094 0.6393274 -1.1563862 0.2475232 0.3791815
LOC126334247 1026.051472 -0.0808190 0.5201130 -0.1553873 0.8765160 0.9201204
LOC126334249 189.414033 -1.2522683 0.3837764 -3.2630155 0.0011023 0.0053971
LOC126334246 2791.637132 -0.4880904 0.5089620 -0.9589917 0.3375629 0.4745448
LOC126334257 6.468650 -3.7112653 1.1226372 -3.3058456 0.0009469 0.0047583
LOC126334253 21.770664 -1.0301590 0.6523621 -1.5791216 0.1143082 0.2136160
LOC126334256 7.536602 -3.9713826 1.1403226 -3.4826835 0.0004964 0.0027722
LOC126334252 43.250607 -2.9590119 1.0149261 -2.9154951 0.0035512 0.0140028
LOC126334299 309.712849 0.1462739 0.3036432 0.4817295 0.6299981 0.7390336
LOC126334298 147.243094 6.1935241 1.6104316 3.8458785 NA NA
LOC126334301 175.464582 -1.4733028 0.6082664 -2.4221341 0.0154297 0.0451930
LOC126334302 511.112528 0.0865799 0.9796030 0.0883826 0.9295726 0.9555375
LOC126334307 5.601430 -0.0055027 0.7334284 -0.0075027 0.9940137 0.9956726
LOC126334319 96.837971 -0.9824717 0.3708694 -2.6491041 0.0080705 0.0270845
LOC126334321 337.883305 1.3019097 0.2502224 5.2030112 0.0000002 0.0000035
LOC126334324 15.487784 -0.7901620 0.4687224 -1.6857782 0.0918385 0.1815264
LOC126334320 724.205665 0.5042794 0.2491227 2.0242212 0.0429474 0.1010785
LOC126334328 2305.473684 0.9516047 0.1992885 4.7750098 0.0000018 0.0000232
LOC126334333 2.452883 -1.1620209 1.2438007 -0.9342501 0.3501749 0.4871337
LOC126334370 5.699682 0.3953387 0.6608846 0.5981962 0.5497090 0.6726506
LOC126334375 25.396400 -0.2746589 0.6529745 -0.4206273 0.6740273 0.7734395
LOC126334427 2.064963 1.4219410 1.3913251 1.0220048 0.3067786 0.4427907
LOC126334428 31.383234 -4.4486528 1.4405408 -3.0881825 0.0020138 0.0088686
LOC126334457 46.634377 1.1162358 0.7367474 1.5150862 0.1297506 0.2350144
LOC126334460 68.027719 0.0422496 0.5686236 0.0743015 0.9407705 0.9627979
LOC126334458 89.761237 2.7256392 1.0750908 2.5352642 0.0112363 0.0352078
NNL47_mgp12 3.177528 -0.8225948 0.7598095 -1.0826329 0.2789714 0.4126139
NNL47_mgp09 7.517627 -0.4589627 0.5586863 -0.8215034 0.4113596 0.5467027

Now we will make the different plots: PCA, MA and Volcano.

# Try with the data transformation
shigeru_vst <- vst(shigeru)
shigeru_rlog <- rlog(shigeru)
shigeru_ntd <- normTransform(shigeru)
itadori <- meanSdPlot(assay(shigeru_ntd))

Version Author Date
f01f1cf Maeva TECHER 2024-11-01 
itadori2 <- itadori$gg + ggtitle("Transformation with ntd")
itadori2

Version Author Date
f01f1cf Maeva TECHER 2024-11-01 
megumi <- meanSdPlot(assay(shigeru_vst))

Version Author Date
f01f1cf Maeva TECHER 2024-11-01 
megumi2 <- megumi$gg + ggtitle("Transformation with vst")
megumi2

Version Author Date
f01f1cf Maeva TECHER 2024-11-01 
nobara <- meanSdPlot(assay(shigeru_rlog))

Version Author Date
f01f1cf Maeva TECHER 2024-11-01 
nobara2 <-nobara$gg + ggtitle("Transformation with rlog")
nobara2

Version Author Date
f01f1cf Maeva TECHER 2024-11-01 
# Create the pca on the defined groups
pcaData1 <- plotPCA(object = shigeru_rlog, intgroup = c("RearingCondition"),returnData=TRUE)
percentVar <- round(100 * attr(pcaData1, "percentVar"))
pcaData1$RearingCondition<-factor(pcaData1$RearingCondition,levels=c("Crowded","Isolated"), labels=c("Crowded S. gregaria","Isolated S. gregaria"))
#levels(pcaData1$RearingCondition)
p1 <- ggplot(pcaData1, aes(PC1, PC2, color= RearingCondition)) +
  geom_point(size=6) +
  xlab(paste0("PC1: ", percentVar[1], "% variance")) +
  ylab(paste0("PC2: ", percentVar[2], "% variance")) + 
  scale_color_manual(values = c("blue", "red")) +
  #coord_fixed() +
  theme_bw() +
  theme(legend.title = element_blank()) + 
  theme(legend.text = element_text(face="bold", size=16)) +
  theme(axis.text = element_text(size=14)) +
  theme(axis.title = element_text(size=14))
p1 + geom_convexhull(aes(fill = RearingCondition, color = RearingCondition), alpha = 0.5) +
    scale_fill_manual(values = c("blue", "red"))+ 
    ggtitle("PCA on S. gregaria Head tissues", subtitle = "rlog transformation")

Version Author Date
f01f1cf Maeva TECHER 2024-11-01 
pcaData2 <- plotPCA(object = shigeru_vst, intgroup = c("RearingCondition"),returnData=TRUE)
percentVar <- round(100 * attr(pcaData2, "percentVar"))
pcaData2$RearingCondition<-factor(pcaData2$RearingCondition,levels=c("Crowded","Isolated"), labels=c("Crowded S. gregaria","Isolated S. gregaria"))
#levels(pcaData2$RearingCondition)
p2 <-ggplot(pcaData2, aes(PC1, PC2, color= RearingCondition)) +
  geom_point(size=6) +
  xlab(paste0("PC1: ", percentVar[1], "% variance")) +
  ylab(paste0("PC2: ", percentVar[2], "% variance")) + 
  scale_color_manual(values = c("blue", "red")) +
  #coord_fixed() +
  theme_bw() +
  theme(legend.title = element_blank()) + 
  theme(legend.text = element_text(face="bold", size=16)) +
  theme(axis.text = element_text(size=14)) +
  theme(axis.title = element_text(size=14))
p2 + geom_convexhull(aes(fill = RearingCondition, color = RearingCondition), alpha = 0.5) +
    scale_fill_manual(values = c("blue", "red"))+ 
    ggtitle("PCA on S. gregaria Head tissues", subtitle = "vst transformation")

Version Author Date
f01f1cf Maeva TECHER 2024-11-01 
select <- order(rowMeans(counts(shigeru,normalized=TRUE)),
                decreasing=TRUE)[1:12]
df <- as.data.frame(colData(shigeru)[,c("RearingCondition","Tissue")])


# Count matrix
pheatmap(assay(shigeru_ntd)[select,], cluster_rows=FALSE, show_rownames=FALSE,
         cluster_cols=FALSE, annotation_col=df, main = "Count Matrix after norm transformation")

Version Author Date
f01f1cf Maeva TECHER 2024-11-01 
pheatmap(assay(shigeru_vst)[select,], cluster_rows=FALSE, show_rownames=FALSE,
         cluster_cols=FALSE, annotation_col=df, main = "Count Matrix after vst transformation")

Version Author Date
f01f1cf Maeva TECHER 2024-11-01 
pheatmap(assay(shigeru_rlog)[select,], cluster_rows=FALSE, show_rownames=FALSE,
         cluster_cols=FALSE, annotation_col=df, main = "Count Matrix after rlog transformation")

Version Author Date
f01f1cf Maeva TECHER 2024-11-01 
# calculate between-sample distance matrix
metadata <- sampletable[,c("RearingCondition", "Tissue")]
rownames(metadata) <- sampletable$SampleName
sampleDistMatrix.rlog <- as.matrix(dist(t(assay(shigeru_rlog))))
pheatmap(sampleDistMatrix.rlog, annotation_col=metadata, main = "Head tissue heatmap distance matrix, rlog transformation")

Version Author Date
f01f1cf Maeva TECHER 2024-11-01 
sampleDistMatrix.vst<- as.matrix(dist(t(assay(shigeru_vst))))
pheatmap(sampleDistMatrix.vst, annotation_col=metadata, main = "Head tissue heatmap distance matrix, rlog transformation")

Version Author Date
f01f1cf Maeva TECHER 2024-11-01

The following plots are interactive and we can hover or Zoom on the locus of interest.

# Ma plot parameters after shrinkage
de_shrink <- lfcShrink(dds = shigeru, coef="RearingCondition_Crowded_vs_Isolated", type="apeglm")
#head(de_shrink)
maplot <-ggmaplot(de_shrink, fdr = 0.05, fc = 1, size = 2, palette = c("#B31B21", "#1465AC", "darkgray"), genenames = as.vector(rownames(de_shrink$name)), top = 0,legend="top",label.select = NULL) +
  coord_cartesian(xlim = c(0, 20)) + 
  scale_y_continuous(limits=c(-12, 12)) + 
  theme(axis.text.x = element_text(size=12),axis.text.y = element_text(size=12),axis.title.x = element_text(size=14),axis.title.y = element_text(size=14),axis.line = element_line(size = 1, colour="gray20"),axis.ticks = element_line(size = 1, colour="gray20")) +
  guides(color = guide_legend(override.aes = list(size = c(3,3,3)))) + 
  theme(legend.position = c(0.70, 0.12),legend.text=element_text(size=14,face="bold"),legend.background = element_rect(fill="transparent")) +
  theme(plot.title = element_text(size=18, colour="gray30", face="bold",hjust=0.06, vjust=-5)) +
  labs(title="MA-plot for the shrunken log2 fold changes in the Head tissues")
interactive_maplot <- ggplotly(maplot)
interactive_maplot
#Volcano plot
keyvals <-ifelse( 
  res_shigeru$log2FoldChange >= 1 & res_shigeru$padj <= 0.05, '#B31B21', 
  ifelse(res_shigeru$log2FoldChange <= -1 & res_shigeru$padj <= 0.05, '#1465AC', 'darkgray')) 

keyvals[is.na(keyvals)] <-'lightgray' 
names(keyvals)[keyvals == "#B31B21"] <-'Upregulated' 
names(keyvals)[keyvals == "#1465AC"] <-'Downregulated' 
names(keyvals)[keyvals == 'darkgray'] <-'NS'

res_shigeru$color <- keyvals
volcano_plot <- ggplot(res_shigeru, aes(x = log2FoldChange, y = -log10(padj), 
                                        color = color,  # Use the color column with keyvals
                                        text = rownames(res_shigeru))) +
  geom_point(size = 3, alpha = 0.8) +
  scale_color_identity() +  # Directly use the color values from `keyvals`
  guides(color = "none") +  # Hide the color legend
  labs(title = "Volcano Plot DEG Head S. gregaria", x = "log2 Fold Change", y = "-log10 Adjusted P-Value") +
  theme_minimal()

# Convert to interactive plot with hover text for gene names
interactive_volcano <- ggplotly(volcano_plot, tooltip = "text") %>%
  layout(hoverlabel = list(namelength = -1))

# Display the interactive plot
interactive_volcano

piceifrons

cancellata

americana

cubense

nitens

STRATEGY 2: Own RefSeq genome

DEseq2

This follows the same code as for STRATEGY 1 except that we will change the RefSeq to the transcript species genome path.

DEGs

gregaria

piceifrons

cancellata

americana

cubense

nitens


sessionInfo()
R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Asia/Tokyo
tzcode source: internal

attached base packages:
[1] grid      stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] ggConvexHull_0.1.0          ggdist_3.3.2               
 [3] vsn_3.72.0                  cowplot_1.1.3              
 [5] sva_3.52.0                  BiocParallel_1.38.0        
 [7] genefilter_1.86.0           mgcv_1.9-1                 
 [9] nlme_3.1-166                clusterProfiler_4.12.6     
[11] pheatmap_1.0.12             SARTools_1.8.1             
[13] kableExtra_1.4.0            edgeR_4.2.2                
[15] limma_3.60.6                ashr_2.2-63                
[17] EnhancedVolcano_1.22.0      ggrepel_0.9.6              
[19] ggpubr_0.6.0                apeglm_1.26.1              
[21] circlize_0.4.16             RColorBrewer_1.1-3         
[23] ComplexHeatmap_2.20.0       reshape2_1.4.4             
[25] ggthemes_5.1.0              plotly_4.10.4              
[27] DT_0.33                     data.table_1.16.2          
[29] lubridate_1.9.3             forcats_1.0.0              
[31] stringr_1.5.1               dplyr_1.1.4                
[33] purrr_1.0.2                 readr_2.1.5                
[35] tidyr_1.3.1                 tibble_3.2.1               
[37] ggplot2_3.5.1               tidyverse_2.0.0            
[39] rmdformats_1.0.4            knitr_1.48                 
[41] txdbmaker_1.0.1             GenomicFeatures_1.56.0     
[43] AnnotationDbi_1.66.0        tximport_1.32.0            
[45] DESeq2_1.44.0               SummarizedExperiment_1.34.0
[47] Biobase_2.64.0              MatrixGenerics_1.16.0      
[49] matrixStats_1.4.1           GenomicRanges_1.56.2       
[51] GenomeInfoDb_1.40.1         IRanges_2.38.1             
[53] S4Vectors_0.42.1            BiocGenerics_0.50.0        

loaded via a namespace (and not attached):
  [1] fs_1.6.5                 bitops_1.0-9             enrichplot_1.24.4       
  [4] httr_1.4.7               doParallel_1.0.17        numDeriv_2016.8-1.1     
  [7] tools_4.4.1              backports_1.5.0          utf8_1.2.4              
 [10] R6_2.5.1                 lazyeval_0.2.2           GetoptLong_1.0.5        
 [13] withr_3.0.2              prettyunits_1.2.0        GGally_2.2.1            
 [16] gridExtra_2.3            preprocessCore_1.66.0    cli_3.6.3               
 [19] scatterpie_0.2.4         labeling_0.4.3           sass_0.4.9              
 [22] SQUAREM_2021.1           mvtnorm_1.3-1            mixsqp_0.3-54           
 [25] Rsamtools_2.20.0         systemfonts_1.1.0        yulab.utils_0.1.7       
 [28] gson_0.1.0               DOSE_3.30.5              svglite_2.1.3           
 [31] R.utils_2.12.3           invgamma_1.1             bbmle_1.0.25.1          
 [34] rstudioapi_0.17.1        RSQLite_2.3.7            gridGraphics_0.5-1      
 [37] generics_0.1.3           shape_1.4.6.1            BiocIO_1.14.0           
 [40] crosstalk_1.2.1          distributional_0.5.0     car_3.1-3               
 [43] GO.db_3.19.1             Matrix_1.7-1             fansi_1.0.6             
 [46] abind_1.4-8              R.methodsS3_1.8.2        lifecycle_1.0.4         
 [49] whisker_0.4.1            yaml_2.3.10              carData_3.0-5           
 [52] qvalue_2.36.0            SparseArray_1.4.8        BiocFileCache_2.12.0    
 [55] blob_1.2.4               promises_1.3.0           crayon_1.5.3            
 [58] bdsmatrix_1.3-7          lattice_0.22-6           annotate_1.82.0         
 [61] KEGGREST_1.44.1          pillar_1.9.0             fgsea_1.30.0            
 [64] rjson_0.2.23             codetools_0.2-20         fastmatch_1.1-4         
 [67] glue_1.8.0               ggfun_0.1.7              treeio_1.28.0           
 [70] vctrs_0.6.5              png_0.1-8                gtable_0.3.6            
 [73] emdbook_1.3.13           cachem_1.1.0             xfun_0.49               
 [76] S4Arrays_1.4.1           tidygraph_1.3.1          coda_0.19-4.1           
 [79] survival_3.7-0           iterators_1.0.14         statmod_1.5.0           
 [82] ggtree_3.12.0            bit64_4.5.2              progress_1.2.3          
 [85] filelock_1.0.3           rprojroot_2.0.4          bslib_0.8.0             
 [88] affyio_1.74.0            irlba_2.3.5.1            colorspace_2.1-1        
 [91] DBI_1.2.3                tidyselect_1.2.1         bit_4.5.0               
 [94] compiler_4.4.1           curl_5.2.3               git2r_0.35.0            
 [97] httr2_1.0.5              xml2_1.3.6               ggdendro_0.2.0          
[100] DelayedArray_0.30.1      shadowtext_0.1.4         bookdown_0.41           
[103] rtracklayer_1.64.0       scales_1.3.0             hexbin_1.28.4           
[106] affy_1.82.0              rappdirs_0.3.3           digest_0.6.37           
[109] rmarkdown_2.28           XVector_0.44.0           htmltools_0.5.8.1       
[112] pkgconfig_2.0.3          highr_0.11               dbplyr_2.5.0            
[115] fastmap_1.2.0            rlang_1.1.4              GlobalOptions_0.1.2     
[118] htmlwidgets_1.6.4        UCSC.utils_1.0.0         farver_2.1.2            
[121] jquerylib_0.1.4          jsonlite_1.8.9           GOSemSim_2.30.2         
[124] R.oo_1.26.0              RCurl_1.98-1.16          magrittr_2.0.3          
[127] ggplotify_0.1.2          Formula_1.2-5            GenomeInfoDbData_1.2.12 
[130] patchwork_1.3.0          munsell_0.5.1            Rcpp_1.0.13             
[133] ape_5.8                  viridis_0.6.5            stringi_1.8.4           
[136] ggraph_2.2.1             zlibbioc_1.50.0          MASS_7.3-61             
[139] plyr_1.8.9               ggstats_0.7.0            parallel_4.4.1          
[142] graphlayouts_1.2.0       Biostrings_2.72.1        splines_4.4.1           
[145] hms_1.1.3                locfit_1.5-9.10          igraph_2.1.1            
[148] ggsignif_0.6.4           biomaRt_2.60.1           XML_3.99-0.17           
[151] evaluate_1.0.1           BiocManager_1.30.25      tweenr_2.0.3            
[154] tzdb_0.4.0               foreach_1.5.2            httpuv_1.6.15           
[157] polyclip_1.10-7          clue_0.3-65              ggforce_0.4.2           
[160] broom_1.0.7              xtable_1.8-4             restfulr_0.0.15         
[163] tidytree_0.4.6           rstatix_0.7.2            later_1.3.2             
[166] viridisLite_0.4.2        truncnorm_1.0-9          aplot_0.2.3             
[169] memoise_2.0.1            GenomicAlignments_1.40.0 cluster_2.1.6           
[172] workflowr_1.7.1          timechange_0.3.0