T1D_patient
scottzijiezhang
2020-03-26
Last updated: 2020-09-09
Checks: 6 0
Knit directory: T1D_epitranscriptome/
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Type 1 diabetes patient samples
load( "~/Rohit_T1D/stim_Patient_islets/allSample_RADAR.RData")
T1D_patient_sample <- c("2","3","9","T1D_patient1", "T1D_patient3" )
T1D_patient_RADAR <- select(allSampleRADAR, T1D_patient_sample)
T1D_patient_RADAR <- normalizeLibrary(T1D_patient_RADAR)
T1D_patient_RADAR <- adjustExprLevel(T1D_patient_RADAR)
variable(T1D_patient_RADAR) <- data.frame( disease = c("Ctl","Ctl","Ctl","T1D","T1D")
)
T1D_patient_RADAR <- filterBins(T1D_patient_RADAR)
save(T1D_patient_RADAR, file = "~/Rohit_T1D/stim_Patient_islets/T1D_patient_RADAR.RData")Check confounding factors
library(RADAR)
load( "~/Rohit_T1D/stim_Patient_islets/T1D_patient_RADAR.RData")
plotPCAfromMatrix(T1D_patient_RADAR@ip_adjExpr_filtered, variable(T1D_patient_RADAR)$disease ) + scale_color_discrete(name = "Disease")
DM tests with RADAR
T1D_patient_RADAR <- diffIP_parallel(T1D_patient_RADAR, thread = 20)
T1D_patient_RADAR <- reportResult(T1D_patient_RADAR, cutoff = 0.05, threads = 20)
save(T1D_patient_RADAR, file = "~/Rohit_T1D/stim_Patient_islets/T1D_patient_RADAR.RData")
write.table(results(T1D_patient_RADAR), file = "~/Rohit_T1D/stim_Patient_islets/T1D_patient_diffPeaks_FDR0.05.xls", sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE)Differentially methylated m6A sites at FDR 5% threshold.
library(RADAR)
load("~/Rohit_T1D/stim_Patient_islets/T1D_patient_RADAR.RData")
DT::datatable( results(T1D_patient_RADAR) , rownames = FALSE )There are 2076 reported differential loci at FDR < 0.05 and logFoldChange > 0.5.Distribution of differential m6A
DMG_result <- results(T1D_patient_RADAR)There are 2076 reported differential loci at FDR < 0.05 and logFoldChange > 0.5.ggplot(DMG_result, aes( x = logFC) )+geom_histogram(color="black", fill="dark gray",bins = 60)+xlab("Log fold change")+theme_bw() + ylab("Count")+ theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black",size = 0.8),axis.ticks = element_line(colour = "black",size = 0.8),
axis.text = element_text(size = 20,colour = "black"),axis.text.y = element_text(angle = 0) ,axis.title=element_text(size=25,) )+scale_y_continuous(expand = c(0,0) )
| Version | Author | Date |
|---|---|---|
| 41633ea | scottzijiezhang | 2020-09-09 |
There are some sparse large values up to ~ 20, so the x axis extended rightwards. For simplicity, I plot another histogram with cropped x-axis.
ggplot(DMG_result, aes( x = logFC) )+geom_histogram(color="black", fill="dark gray",bins = 60)+xlab("Log fold change")+theme_bw() + ylab("Count")+ theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black",size = 0.8),axis.ticks = element_line(colour = "black",size = 0.8),
axis.text = element_text(size = 20,colour = "black"),axis.text.y = element_text(angle = 0) ,axis.title=element_text(size=25,) )+scale_y_continuous(expand = c(0,0) )+scale_x_continuous(limits = c(-6,6))
| Version | Author | Date |
|---|---|---|
| 41633ea | scottzijiezhang | 2020-09-09 |
ggplot(DMG_result, aes( x = logFC, y = -log10(p_value) ) )+geom_point()+xlab("Log fold change")+theme_bw() + ylab("- Log10(P-value)")+ theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black",size = 0.8),axis.ticks = element_line(colour = "black",size = 0.8),
axis.text = element_text(size = 20,colour = "black"),axis.text.y = element_text(angle = 0) ,axis.title=element_text(size=25,) )+scale_y_continuous(expand = c(0,0), limits = c(0,14) )
| Version | Author | Date |
|---|---|---|
| 41633ea | scottzijiezhang | 2020-09-09 |
Note this is not a standard volcano plot because only data points passing the threshold were plotted. This is just to visualize the P-values corresponding to the fold changes.
KEGG pathway analysis of DMG
library(clusterProfiler)
eg.PatDMG_DMG <- bitr( unique(results(T1D_patient_RADAR)$name), fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")There are 2076 reported differential loci at FDR < 0.05 and logFoldChange > 0.5.KEGG_PatDMG_DMG <- enrichKEGG(eg.PatDMG_DMG$ENTREZID,organism = "hsa",pAdjustMethod = "fdr", pvalueCutoff = 0.1,minGSSize = 3)No enriched termed found in KEGG pathway search. Plot coverage for selected genes
T1D_patient_RADAR <- PrepCoveragePlot(T1D_patient_RADAR)
plotGeneCov(T1D_patient_RADAR, geneName = "GAD2", libraryType = "opposite", center = mean, adjustExprLevel = TRUE )+ggtitle("GAD2")
plotGeneCov(T1D_patient_RADAR, geneName = "GAD2", libraryType = "opposite", center = mean, ZoomIn = c(26216007, 26216656), adjustExprLevel = TRUE )+ggtitle("GAD2 zoom in")
From the coverage plot, we can tell that the T1D samples have flat-shaped coverage, which indicates there many reads are likely identical and may result from PCR duplicates. Thus I would be cautious about the result of this gene, especially given our sample size are small here.
plotGeneCov(T1D_patient_RADAR, geneName = "HLA-A", libraryType = "opposite", center = mean, adjustExprLevel = TRUE )+ggtitle("HLA-A")
plotGeneCov(T1D_patient_RADAR, geneName = "HLA-B", libraryType = "opposite", center = mean, adjustExprLevel = TRUE )+ggtitle("HLA-B")
plotGeneCov(T1D_patient_RADAR, geneName = "HLA-B", libraryType = "opposite", center = mean,ZoomIn = c(31355280, 31355729), adjustExprLevel = TRUE )+ggtitle("HLA-B zoom in")
plotGeneCov(T1D_patient_RADAR, geneName = "PTPRN", libraryType = "opposite", center = mean, adjustExprLevel = TRUE )+ggtitle("PTPRN")
plotGeneCov(T1D_patient_RADAR, geneName = "PTPRN", libraryType = "opposite", center = mean,ZoomIn = c(219299509, 219299958), adjustExprLevel = TRUE )+ggtitle("PTPRN zoom in")
This gene also show signs of bad data quality according to coverage of the T1D samples.
plotGeneCov(T1D_patient_RADAR, geneName = "PTPRN2", libraryType = "opposite", center = mean, adjustExprLevel = TRUE )+ggtitle("PTPRN2")
plotGeneCov(T1D_patient_RADAR, geneName = "PTPRN2", libraryType = "opposite", center = mean,ZoomIn = c(157540400, 157540849), adjustExprLevel = TRUE )+ggtitle("PTPRN2 zoom in")
plotGeneCov(T1D_patient_RADAR, geneName = "INS", libraryType = "opposite", center = mean, adjustExprLevel = TRUE )+ggtitle("INS")
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 17.10
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] org.Hs.eg.db_3.7.0 clusterProfiler_3.10.1
[3] RADAR_0.2.3 qvalue_2.14.1
[5] RcppArmadillo_0.9.400.2.0 Rcpp_1.0.1
[7] RColorBrewer_1.1-2 gplots_3.0.1.1
[9] doParallel_1.0.14 iterators_1.0.10
[11] foreach_1.4.4 ggplot2_3.1.1
[13] Rsamtools_1.34.1 Biostrings_2.50.2
[15] XVector_0.22.0 GenomicFeatures_1.34.8
[17] AnnotationDbi_1.44.0 Biobase_2.42.0
[19] GenomicRanges_1.34.0 GenomeInfoDb_1.18.2
[21] IRanges_2.16.0 S4Vectors_0.20.1
[23] BiocGenerics_0.28.0
loaded via a namespace (and not attached):
[1] backports_1.1.4 Hmisc_4.2-0
[3] fastmatch_1.1-0 workflowr_1.3.0
[5] plyr_1.8.4 igraph_1.2.4.1
[7] lazyeval_0.2.2 splines_3.5.3
[9] BiocParallel_1.16.6 crosstalk_1.0.0
[11] urltools_1.7.3 digest_0.6.18
[13] htmltools_0.3.6 GOSemSim_2.8.0
[15] viridis_0.5.1 GO.db_3.7.0
[17] gdata_2.18.0 magrittr_1.5
[19] checkmate_1.9.1 memoise_1.1.0
[21] cluster_2.0.7-1 annotate_1.60.1
[23] matrixStats_0.54.0 enrichplot_1.2.0
[25] prettyunits_1.0.2 colorspace_1.4-1
[27] blob_1.1.1 ggrepel_0.8.0
[29] xfun_0.6 dplyr_0.8.0.1
[31] crayon_1.3.4 RCurl_1.95-4.12
[33] jsonlite_1.6 genefilter_1.64.0
[35] survival_2.44-1.1 glue_1.3.1
[37] polyclip_1.10-0 gtable_0.3.0
[39] zlibbioc_1.28.0 UpSetR_1.3.3
[41] DelayedArray_0.8.0 scales_1.0.0
[43] DOSE_3.8.2 DBI_1.0.0
[45] viridisLite_0.3.0 xtable_1.8-4
[47] progress_1.2.0 htmlTable_1.13.1
[49] gridGraphics_0.3-0 foreign_0.8-71
[51] bit_1.1-14 europepmc_0.3
[53] Formula_1.2-3 DT_0.5.1
[55] htmlwidgets_1.3 httr_1.4.0
[57] fgsea_1.8.0 acepack_1.4.1
[59] pkgconfig_2.0.2 XML_3.98-1.19
[61] farver_1.1.0 nnet_7.3-12
[63] locfit_1.5-9.1 ggplotify_0.0.3
[65] tidyselect_0.2.5 labeling_0.3
[67] rlang_0.4.0 reshape2_1.4.3
[69] later_0.8.0 munsell_0.5.0
[71] tools_3.5.3 RSQLite_2.1.1
[73] ggridges_0.5.1 evaluate_0.13
[75] stringr_1.4.0 yaml_2.2.0
[77] knitr_1.22 bit64_0.9-7
[79] fs_1.3.0 caTools_1.17.1.2
[81] purrr_0.3.2 ggraph_1.0.2
[83] whisker_0.3-2 mime_0.6
[85] xml2_1.2.0 DO.db_2.9
[87] biomaRt_2.38.0 compiler_3.5.3
[89] rstudioapi_0.10 tibble_2.1.1
[91] tweenr_1.0.1 geneplotter_1.60.0
[93] stringi_1.4.3 lattice_0.20-38
[95] Matrix_1.2-17 pillar_1.3.1
[97] triebeard_0.3.0 data.table_1.12.2
[99] cowplot_0.9.4 bitops_1.0-6
[101] httpuv_1.5.1 rtracklayer_1.42.2
[103] R6_2.4.0 latticeExtra_0.6-28
[105] promises_1.0.1 KernSmooth_2.23-15
[107] gridExtra_2.3 codetools_0.2-16
[109] MASS_7.3-51.4 gtools_3.8.1
[111] assertthat_0.2.1 SummarizedExperiment_1.12.0
[113] DESeq2_1.22.2 rprojroot_1.3-2
[115] withr_2.1.2 GenomicAlignments_1.18.1
[117] GenomeInfoDbData_1.2.0 hms_0.4.2
[119] grid_3.5.3 rpart_4.1-13
[121] tidyr_0.8.3 rvcheck_0.1.3
[123] rmarkdown_1.12 git2r_0.25.2
[125] ggforce_0.2.2 shiny_1.3.2
[127] base64enc_0.1-3