Results DESeq2 per species

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Last updated: 2024-11-01

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Knit directory: locust-comparative-genomics/

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File	Version	Author	Date	Message
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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")

# 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

[1] "mean of normalized counts for all samples"                   
[2] "log2 fold change (MLE): RearingCondition Crowded vs Isolated"
[3] "standard error: RearingCondition Crowded vs Isolated"        
[4] "Wald statistic: RearingCondition Crowded vs Isolated"        
[5] "Wald test p-value: RearingCondition Crowded vs Isolated"     
[6] "BH adjusted p-values"                                        

head(brock)

log2 fold change (MLE): RearingCondition Crowded vs Isolated 
Wald test p-value: RearingCondition Crowded vs Isolated 
DataFrame with 6 rows and 6 columns
              baseMean log2FoldChange     lfcSE       stat      pvalue
             <numeric>      <numeric> <numeric>  <numeric>   <numeric>
LOC126318536  277.0310      0.0378182 0.0721917  0.5238587 6.00377e-01
LOC126318656   63.3737      0.0909845 0.1567636  0.5803928 5.61650e-01
LOC126318743   99.5814      0.0188983 0.7549026  0.0250341 9.80028e-01
LOC126319294  198.1378     -0.3206079 0.1175191 -2.7281337 6.36938e-03
LOC126319460  594.5958      0.4286836 0.1080481  3.9675272 7.26222e-05
LOC126320026   87.7157      0.2909248 0.1535866  1.8942072 5.81975e-02
                    padj
               <numeric>
LOC126318536 0.715630080
LOC126318656 0.682941257
LOC126318743 0.987400090
LOC126319294 0.022444476
LOC126319460 0.000549704
LOC126320026 0.127642289

Plots

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

Past versions of "gregaria2 plot-1.png"

Version	Author	Date
f01f1cf	Maeva TECHER	2024-11-01

itadori2 <- itadori$gg + ggtitle("Transformation with ntd")
itadori2

Past versions of "gregaria2 plot-2.png"

Version	Author	Date
f01f1cf	Maeva TECHER	2024-11-01

megumi <- meanSdPlot(assay(shigeru_vst))

Past versions of "gregaria2 plot-3.png"

Version	Author	Date
f01f1cf	Maeva TECHER	2024-11-01

megumi2 <- megumi$gg + ggtitle("Transformation with vst")
megumi2

Past versions of "gregaria2 plot-4.png"

Version	Author	Date
f01f1cf	Maeva TECHER	2024-11-01

nobara <- meanSdPlot(assay(shigeru_rlog))

Past versions of "gregaria2 plot-5.png"

Version	Author	Date
f01f1cf	Maeva TECHER	2024-11-01

nobara2 <-nobara$gg + ggtitle("Transformation with rlog")
nobara2

Past versions of "gregaria2 plot-6.png"

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") 

Past versions of "gregaria2 plot-7.png"

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") 

Past versions of "gregaria2 plot-8.png"

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")

Past versions of "gregaria2 plot-9.png"

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")

Past versions of "gregaria2 plot-10.png"

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")

Past versions of "gregaria2 plot-11.png"

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")

Past versions of "gregaria2 plot-12.png"

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")

Past versions of "gregaria2 plot-13.png"

Version	Author	Date
f01f1cf	Maeva TECHER	2024-11-01

# 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=14),axis.text.y = element_text(size=13),axis.title.x = element_text(size=4),axis.title.y = element_text(size=17),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")
maplot

Past versions of "gregaria2 plot-14.png"

Version	Author	Date
f01f1cf	Maeva TECHER	2024-11-01

#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)] <-'darkgray' 
names(keyvals)[keyvals == "#B31B21"] <-'Upregulated' 
names(keyvals)[keyvals == "#1465AC"] <-'Downregulated' 
names(keyvals)[keyvals == 'darkgray'] <-'NS'
EnhancedVolcano(res_shigeru,
                lab = rownames(res_shigeru),
                x = 'log2FoldChange',
                y = 'padj',
                pCutoff = 0.05,
                FCcutoff = 1,
                pointSize = 2,
                labSize = 3,
                colAlpha = 4/5,
                colCustom = keyvals,
                drawConnectors = TRUE)

Past versions of "gregaria2 plot-15.png"

Version	Author	Date
f01f1cf	Maeva TECHER	2024-11-01

Figure XX: XXX

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

Session information

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] distributional_0.5.0     car_3.1-3                GO.db_3.19.1            
 [43] Matrix_1.7-1             fansi_1.0.6              abind_1.4-8             
 [46] R.methodsS3_1.8.2        lifecycle_1.0.4          whisker_0.4.1           
 [49] yaml_2.3.10              carData_3.0-5            qvalue_2.36.0           
 [52] SparseArray_1.4.8        BiocFileCache_2.12.0     blob_1.2.4              
 [55] promises_1.3.0           crayon_1.5.3             bdsmatrix_1.3-7         
 [58] lattice_0.22-6           annotate_1.82.0          KEGGREST_1.44.1         
 [61] pillar_1.9.0             fgsea_1.30.0             rjson_0.2.23            
 [64] codetools_0.2-20         fastmatch_1.1-4          glue_1.8.0              
 [67] ggfun_0.1.7              treeio_1.28.0            vctrs_0.6.5             
 [70] png_0.1-8                gtable_0.3.6             emdbook_1.3.13          
 [73] cachem_1.1.0             xfun_0.49                S4Arrays_1.4.1          
 [76] tidygraph_1.3.1          coda_0.19-4.1            survival_3.7-0          
 [79] iterators_1.0.14         statmod_1.5.0            ggtree_3.12.0           
 [82] bit64_4.5.2              progress_1.2.3           filelock_1.0.3          
 [85] rprojroot_2.0.4          bslib_0.8.0              affyio_1.74.0           
 [88] irlba_2.3.5.1            colorspace_2.1-1         DBI_1.2.3               
 [91] tidyselect_1.2.1         bit_4.5.0                compiler_4.4.1          
 [94] curl_5.2.3               git2r_0.35.0             httr2_1.0.5             
 [97] xml2_1.3.6               ggdendro_0.2.0           DelayedArray_0.30.1     
[100] shadowtext_0.1.4         bookdown_0.41            rtracklayer_1.64.0      
[103] scales_1.3.0             hexbin_1.28.4            affy_1.82.0             
[106] rappdirs_0.3.3           digest_0.6.37            rmarkdown_2.28          
[109] XVector_0.44.0           htmltools_0.5.8.1        pkgconfig_2.0.3         
[112] highr_0.11               dbplyr_2.5.0             fastmap_1.2.0           
[115] rlang_1.1.4              GlobalOptions_0.1.2      htmlwidgets_1.6.4       
[118] UCSC.utils_1.0.0         farver_2.1.2             jquerylib_0.1.4         
[121] jsonlite_1.8.9           GOSemSim_2.30.2          R.oo_1.26.0             
[124] RCurl_1.98-1.16          magrittr_2.0.3           ggplotify_0.1.2         
[127] Formula_1.2-5            GenomeInfoDbData_1.2.12  patchwork_1.3.0         
[130] munsell_0.5.1            Rcpp_1.0.13              ape_5.8                 
[133] viridis_0.6.5            stringi_1.8.4            ggraph_2.2.1            
[136] zlibbioc_1.50.0          MASS_7.3-61              plyr_1.8.9              
[139] ggstats_0.7.0            parallel_4.4.1           graphlayouts_1.2.0      
[142] Biostrings_2.72.1        splines_4.4.1            hms_1.1.3               
[145] locfit_1.5-9.10          igraph_2.1.1             ggsignif_0.6.4          
[148] biomaRt_2.60.1           XML_3.99-0.17            evaluate_1.0.1          
[151] BiocManager_1.30.25      tweenr_2.0.3             tzdb_0.4.0              
[154] foreach_1.5.2            httpuv_1.6.15            polyclip_1.10-7         
[157] clue_0.3-65              ggforce_0.4.2            broom_1.0.7             
[160] xtable_1.8-4             restfulr_0.0.15          tidytree_0.4.6          
[163] rstatix_0.7.2            later_1.3.2              viridisLite_0.4.2       
[166] truncnorm_1.0-9          aplot_0.2.3              memoise_2.0.1           
[169] GenomicAlignments_1.40.0 cluster_2.1.6            workflowr_1.7.1         
[172] timechange_0.3.0