Data loading
suppressPackageStartupMessages({
library(SummarizedExperiment)
library(SEtools)
library(edgeR)
library(DT)
library(pheatmap)
library(plotly)
library(dplyr)
library(sva)
library(reshape2)
library(overlapper)
library(ggrepel)
})
source("Functions/EDC_Functions.R")
source("Functions/CriFormatted.R")
load("Data/AllSEcorrected.RData",verbose=T)
## Loading objects:
## SEs
## DEAs
DEGs heatmaps
fetal.chronic <- row.names(DEAs$chronic.fetal)[which((abs(DEAs$chronic.fetal$logFC.EXPO1X)>0.5 | abs(DEAs$chronic.fetal$logFC.EXPO1000X)>0.5) & DEAs$chronic.fetal$FDR<=0.05 & DEAs$chronic.fetal$logCPM>0)]
MixN_fetal_chronic <- SEs$chronic.fetal[,which(SEs$chronic.fetal$EXPO=="CNT"|SEs$chronic.fetal$EXPO=="1X" | SEs$chronic.fetal$EXPO=="1000X")]
sehm(MixN_fetal_chronic, assayName = "corrected", fetal.chronic, do.scale = T, show_rownames = F, anno_columns = c("EXPO2"), main="Chronic fetal DEGs")
1X vs 1000X
FDRTh <- 0.05
LogFCTh <- 0.5
MixN1X <- DEAs$chronic.fetal
MixN1X$genes <- row.names(MixN1X)
MixN1X$logFC <- (MixN1X$logFC.EXPO1X)
MixN1000X <- DEAs$chronic.fetal
MixN1000X$genes <- row.names(MixN1000X)
MixN1000X$logFC <- (MixN1X$logFC.EXPO1000X)
Comp <- compareResultsFCNew(MixN1X, MixN1000X, FDRth=0.05, FCth=2^0.5, FDRceil=1e-10, logCPMth=0, FCceil=2.5,
title='Comparing MixN 1X to 1000X', geneLabel=TRUE, topLab=-16)
## Warning in cor.test.default(AllRes$logFC_A, AllRes$logFC_B, method = corMethod):
## Cannot compute exact p-value with ties
Comp$Scatter
ggsave(Comp$Scatter, filename='Data/ChronicFetal/Scatter.pdf', width=10, height=10)
ggsave(Comp$Scatter, filename='Data/ChronicFetal/Scatter.png', width=10, height=10)
Neurodevelopmental Disorder Genes
load("Data/ASD.RData", verbose = T)
## Loading objects:
## ASD
## SFARI
## SFARIgenes
## NeuropsychiatricDiseases
## PsychencodeNDD
fetal.chronic3 <- row.names(DEAs$chronic.fetal)[which(DEAs$chronic.fetal$FDR<=0.05)]
controlNeg <- row.names(DEAs$chronic.fetal)[-which(DEAs$chronic.fetal$FDR<=0.05)]
#random1 <- sample(row.names(DEAs$chronic.fetal), 10)
#random2 <- sample(row.names(DEAs$chronic.fetal), 100)
#random3 <- sample(row.names(DEAs$chronic.fetal), 1000)
fetal.chronicUp <- row.names(DEAs$chronic.fetal)[which(((DEAs$chronic.fetal$logFC.EXPO1X)>0 & (DEAs$chronic.fetal$logFC.EXPO1000X)>0) & DEAs$chronic.fetal$FDR<=0.05)]
fetal.chronicDown <- row.names(DEAs$chronic.fetal)[which(((DEAs$chronic.fetal$logFC.EXPO1X)<0 & (DEAs$chronic.fetal$logFC.EXPO1000X)<0) & DEAs$chronic.fetal$FDR<=0.05)]
#MixN <- list(fetalDEGs=fetal.chronic3, fetalDEGsUp=fetal.chronicUp, fetalDEGsDown=fetal.chronicDown,NonAffectedGenes=controlNeg)
MixN <- list(fetalDEGs=fetal.chronic3, NonAffectedGenes=controlNeg)
m <- overlapper::multintersect(ll = MixN, ll2 = ASD, universe = row.names(DEAs$chronic.fetal),two.tailed = F)
fetal.chronicLong=fetal.chronic3
save(fetal.chronic,fetal.chronicLong,file = "Data/DEGsFetalChronic.RData")
dotplot.multintersect(m, sizeRange = c(0,15), th=0.05)
ggsave(dotplot.multintersect(m, th=0.05), filename='Data/ChronicFetal/DotplotFetalNDD.pdf', width=10, height=6)
P values of the overlaps
m$prob <- round(m$prob, digits=3)
DT::datatable(m$prob)
NDD psychencode
m <- overlapper::multintersect(ll = MixN, ll2 = PsychencodeNDD, universe = row.names(DEAs$chronic.fetal),two.tailed = F)
dotplot.multintersect(m, th=0.05)
ggsave(dotplot.multintersect(m, th=0.05), filename='Data/ChronicFetal/DotplotFetalNDD.pdf', width=10, height=6)
Gene expression dysregulation of the most important NDD genes
geneStripPairEDCMix(SE = MixN_fetal_chronic,GeneSet = intersect(fetal.chronic, unlist(SFARIgenes)), printExp = FALSE, SampleColors = "Default")
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.ymin` is deprecated. Use `fun.min` instead.
## Warning: `fun.ymax` is deprecated. Use `fun.max` instead.
PVals <- DEAs$chronic.fetal[intersect(fetal.chronic, unlist(SFARIgenes)),]
PVals <- PVals[complete.cases(PVals), ]
PVals
## logFC.EXPO1X logFC.EXPO1000X logCPM F PValue
## RANBP17 0.42942927 0.5046568 4.315729 15.524431 1.368507e-05
## CD38 -0.07818771 -0.5136144 2.317253 10.633310 2.344008e-04
## UPF3B -0.69780169 -0.2505012 2.220196 8.283412 1.095909e-03
## KCNJ2 0.01174969 0.6317999 2.352303 6.680355 3.403851e-03
## FDR
## RANBP17 0.001026474
## CD38 0.006803033
## UPF3B 0.018808919
## KCNJ2 0.038314035
- Genes with conventional PVal > 0.05:
- Genes with conventional PVal < 0.05:
- Genes with conventional FDR < 0.05: RANBP17, CD38, UPF3B, KCNJ2
Gene expression dysregulation of the chronic organoids NDD-DEGs in fetal dataset
load("Data/DEGsOrganoidsChronic.RData", verbose = T)
## Loading objects:
## org.chronic
## org.chronicLong
geneStripPairEDCMix(SE = MixN_fetal_chronic,GeneSet = intersect(org.chronic, union(union(SFARIgenes$score1,SFARIgenes$score2),SFARIgenes$score3)), printExp = FALSE,SampleColors = "Default")
## [1] "Expression values are not available for the following genes: LMX1B CNTN5 FOXP2"
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.ymin` is deprecated. Use `fun.min` instead.
## Warning: `fun.ymax` is deprecated. Use `fun.max` instead.
PVals <- DEAs$chronic.fetal[intersect(org.chronic, union(union(SFARIgenes$score1,SFARIgenes$score2),SFARIgenes$score3)),]
PVals <- PVals[complete.cases(PVals), ]
PVals
## logFC.EXPO1X logFC.EXPO1000X logCPM F PValue
## SCN9A -0.02379698 -0.22817909 4.0165210 8.5764810 0.0008975184
## CACNA1E -0.23420581 0.01300326 0.1448921 1.0890090 0.3473611846
## SCN2A -0.18687998 -0.07101465 0.2608272 0.3449099 0.7105950709
## SPARCL1 -0.14589917 -0.04707601 3.5113849 0.5094489 0.6050859597
## SLC6A1 -0.01639811 -0.23519283 3.7305288 2.0112246 0.1485613035
## NRXN3 0.08392746 0.01466756 6.1478405 0.7129638 0.4969681974
## CACNB2 0.21680279 0.01476147 4.0805953 4.7232387 0.0150695434
## KDM6B -0.06400076 0.07252019 3.9159704 0.8225561 0.4473804131
## KIRREL3 0.05531668 -0.19737441 1.9492403 3.8168693 0.0313621709
## FDR
## SCN9A 0.01661891
## CACNA1E 0.56800767
## SCN2A 0.83594250
## SPARCL1 0.76628869
## SLC6A1 0.35460420
## NRXN3 0.69077479
## CACNB2 0.09452263
## KDM6B 0.65057903
## KIRREL3 0.14465401
- Genes with conventional PVal > 0.05: CACNA1E, SCN2A, SPARCL1, SLC6A1, NRXN3, KDM6B
- Genes with conventional PVal < 0.05: CACNB2, KIRREL3
- Genes with conventional FDR < 0.05: SCN9A
Gene expression dysregulation of the genes found in the acute expo
load("Data/DEGsFetalAcute.RData", verbose = T)
## Loading objects:
## fet.acute
geneStripPairEDCMix(SE = MixN_fetal_chronic,GeneSet = intersect(fet.acute,fetal.chronic), printExp = FALSE,SampleColors = "Default")
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.ymin` is deprecated. Use `fun.min` instead.
## Warning: `fun.ymax` is deprecated. Use `fun.max` instead.
PVals <- DEAs$chronic.fetal[intersect(fet.acute,fetal.chronic),]
PVals <- PVals[complete.cases(PVals), ]
PVals
## logFC.EXPO1X logFC.EXPO1000X logCPM F PValue FDR
## DHRS3 -0.7391126 -1.2919270 2.356456 8.279411 1.098918e-03 0.018828021
## LGI4 -0.6400688 -0.5527407 3.324905 14.129351 2.942914e-05 0.001699696
- Genes with conventional PVal > 0.05:
- Genes with conventional PVal < 0.05:
- Genes with conventional FDR < 0.05: DHRS3, LGI4
geneStripPairEDCMix(SE = MixN_fetal_chronic,GeneSet = c("CLSTN2", "EPHB2"), printExp = FALSE,SampleColors = "Default")
## Warning: `fun.y` is deprecated. Use `fun` instead.
## Warning: `fun.ymin` is deprecated. Use `fun.min` instead.
## Warning: `fun.ymax` is deprecated. Use `fun.max` instead.
PVals <- DEAs$chronic.fetal[c("CLSTN2", "EPHB2"),]
PVals <- PVals[complete.cases(PVals), ]
PVals
## logFC.EXPO1X logFC.EXPO1000X logCPM F PValue FDR
## CLSTN2 -0.43177041 -0.36208302 3.102171 5.3919031 0.008939188 0.06884648
## EPHB2 -0.08307527 -0.06479717 6.840971 0.5195956 0.599145992 0.76229730
- Genes with conventional PVal > 0.05: EPHB2
- Genes with conventional PVal < 0.05: CLSTN2
- Genes with conventional FDR < 0.05:
Data praparation
For details on data filtering, normalization, batch correction and differential expression analysis, see here
For details on the preparation of Neurodevelopmental disorder genes, see here
Authors
Nicolò Caporale: nicolo.caporale@ieo.it, nicolo.caporale@unimi.it
Cristina Cheroni: cristina.cheroni@ieo.it
Pierre-Luc Germain: pierre-luc.germain@ieo.it
Giuseppe Testa: giuseppe.testa@ieo.it
Lab: http://www.testalab.eu/
‘Date: December 01, 2021’
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggrepel_0.9.1 overlapper_0.99.1
## [3] reshape2_1.4.4 sva_3.38.0
## [5] BiocParallel_1.24.1 genefilter_1.72.1
## [7] mgcv_1.8-34 nlme_3.1-152
## [9] dplyr_1.0.5 plotly_4.9.3
## [11] ggplot2_3.3.3 pheatmap_1.0.12
## [13] DT_0.17 edgeR_3.32.1
## [15] limma_3.46.0 SEtools_1.4.0
## [17] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [19] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2
## [21] IRanges_2.24.1 S4Vectors_0.28.1
## [23] BiocGenerics_0.36.0 MatrixGenerics_1.2.1
## [25] matrixStats_0.58.0
##
## loaded via a namespace (and not attached):
## [1] circlize_0.4.12 plyr_1.8.6 lazyeval_0.2.2
## [4] shinydashboard_0.7.1 splines_4.0.3 crosstalk_1.1.1
## [7] digest_0.6.27 foreach_1.5.1 htmltools_0.5.1.1
## [10] fansi_0.4.2 magrittr_2.0.1 memoise_2.0.0
## [13] cluster_2.1.1 openxlsx_4.2.3 ComplexHeatmap_2.6.2
## [16] annotate_1.68.0 colorspace_2.0-0 blob_1.2.1
## [19] xfun_0.21 crayon_1.4.1 RCurl_1.98-1.2
## [22] jsonlite_1.7.2 survival_3.2-7 iterators_1.0.13
## [25] glue_1.4.2 registry_0.5-1 gtable_0.3.0
## [28] zlibbioc_1.36.0 XVector_0.30.0 UpSetR_1.4.0
## [31] GetoptLong_1.0.5 DelayedArray_0.16.1 V8_3.4.0
## [34] shape_1.4.5 scales_1.1.1 futile.options_1.0.1
## [37] DBI_1.1.1 randomcoloR_1.1.0.1 Rcpp_1.0.6
## [40] viridisLite_0.3.0 xtable_1.8-4 clue_0.3-58
## [43] bit_4.0.4 htmlwidgets_1.5.3 httr_1.4.2
## [46] RColorBrewer_1.1-2 ellipsis_0.3.1 farver_2.1.0
## [49] pkgconfig_2.0.3 XML_3.99-0.5 sass_0.3.1
## [52] locfit_1.5-9.4 utf8_1.2.1 labeling_0.4.2
## [55] tidyselect_1.1.0 rlang_0.4.10 later_1.1.0.1
## [58] AnnotationDbi_1.52.0 munsell_0.5.0 tools_4.0.3
## [61] cachem_1.0.4 generics_0.1.0 RSQLite_2.2.3
## [64] evaluate_0.14 stringr_1.4.0 fastmap_1.1.0
## [67] yaml_2.2.1 knitr_1.31 bit64_4.0.5
## [70] shinycssloaders_1.0.0 zip_2.1.1 purrr_0.3.4
## [73] mime_0.10 formatR_1.7 compiler_4.0.3
## [76] curl_4.3 png_0.1-7 tibble_3.1.0
## [79] bslib_0.2.4 stringi_1.5.3 highr_0.8
## [82] futile.logger_1.4.3 lattice_0.20-41 Matrix_1.3-2
## [85] ggsci_2.9 vctrs_0.3.7 pillar_1.5.1
## [88] lifecycle_1.0.0 jquerylib_0.1.3 GlobalOptions_0.1.2
## [91] cowplot_1.1.1 data.table_1.14.0 bitops_1.0-6
## [94] seriation_1.2-9 httpuv_1.5.5 R6_2.5.0
## [97] promises_1.2.0.1 TSP_1.1-10 gridExtra_2.3
## [100] codetools_0.2-18 lambda.r_1.2.4 assertthat_0.2.1
## [103] rjson_0.2.20 withr_2.4.1 GenomeInfoDbData_1.2.4
## [106] VennDiagram_1.6.20 grid_4.0.3 tidyr_1.1.3
## [109] rmarkdown_2.7 Cairo_1.5-12.2 Rtsne_0.15
## [112] shiny_1.6.0