Workflow
Last updated: 2020-12-15
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Knit directory: 20201005_CF_Manuscript_Repo/
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| File | Version | Author | Date | Message |
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| Rmd | fa2a512 | Basil Khuder | 2020-12-15 | Updated dataset for Validation Cohort |
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Load Libraries, Functions and Read in the Data
#All R functions written in this analysis are within the "CF_Manuscript_Functions" file
source("code/CF_Manuscript_Functions.R")
#This function installs packages that aren't installed, and loads them
install_wflow_packages(cran_pkg = c("tidyverse","magrittr","glue","ggrepel",
"ggpubr", "GeneOverlap", "VennDiagram", "DT",
"pheatmap", "viridis"),
bioconductor_pkg = c("GeneOverlap","edgeR"))
pilot_counts <- read_tsv("data/Pilot_cohort_raw_counts_relabeled_for_upload_with_genes.txt")
vali_counts <- read_tsv("data/Validation_cohort_raw_counts_relabeled_for_upload_with_genes.txt")
#Set seed for reproducibility
set.seed(20)Generating PCA Plots
pilot_pca <- pilot_counts %>% dplyr::select(-c(Ensembl_ID,ERCC1, ERCC2))
pilot_pca <- list(pilot_pca, pilot_pca %>% dplyr::select(!contains("CompHet"))) %>%
map(~ column_to_rownames(., var = "Gene_Name"))
pilot_groups <- map(pilot_pca, ~case_when(str_detect(colnames(.x), "F508del_F508del") ~ "F508del",
str_detect(colnames(.x), "CompHet_[\\d]*_F508del") ~ "CompoundHet",
str_detect(colnames(.x), "Control") ~ "Control"))
pilot_groups <- map2(pilot_groups, list(c("CompoundHet", "F508del", "Control"), c("F508del","Control")),
~ factor(.x, levels = .y))
pilot_pca <- imap(pilot_pca, ~ DGEList(counts = .x , group = pilot_groups[[.y]]) %>%
.[filterByExpr(.), , keep.lib.sizes=FALSE] %>%
calcNormFactors(.))
vali_pca <- vali_counts %>% dplyr::select(-c(Ensembl_ID, uhrna1, uhrna2))
vali_pca <- list(vali_pca, vali_pca %>% dplyr::select(!contains("CompHet"))) %>%
map(~ column_to_rownames(., var = "Gene_Name"))
vali_groups <- map(vali_pca, ~case_when(str_detect(colnames(.x), "F508del_F508del") ~ "F508del",
str_detect(colnames(.x), "CompHet_[\\d]*_F508del") ~ "CompoundHet",
str_detect(colnames(.x), "Control") ~ "Control"))
vali_groups <- map2(vali_groups, list(c("CompoundHet", "F508del", "Control"), c("F508del","Control")),
~ factor(.x, levels = .y))
vali_pca <- imap(vali_pca, ~DGEList(counts = .x , group = vali_groups[[.y]]) %>%
.[filterByExpr(.), , keep.lib.sizes=FALSE] %>%
calcNormFactors(.))Pilot Cohort PCA Plots
pilot_pca <-
pmap(list(pilot_pca, pilot_groups, c("CF vs Control", "F508del vs Control")), plot_pca) %>%
map2(list(c("#fcac51", "#ff0000", "#0000ff"),c("#ff0000", "#0000ff")), ~
.x + theme(text = element_text(size = 30)) +
geom_point(size = 5) +
scale_color_manual(values = .y))
pilot_pca[[2]] <- pilot_pca[[2]] + scale_x_reverse() + scale_y_reverse()
pilot_pca[[1]] <- pilot_pca[[1]] + scale_shape_manual(values = c(17,15,16))
pilot_pca[[2]] <- pilot_pca[[2]] + scale_shape_manual(values = c(15,16))Validation Cohort PCA Plots
vali_pca <-
pmap(list(vali_pca, vali_groups, c("CF vs Control", "F508del vs Control")), plot_pca) %>%
map2(list(c("#fcac51", "#ff0000", "#0000ff"),c("#ff0000", "#0000ff")), ~
.x + theme(text = element_text(size = 30)) +
geom_point(size = 5) +
scale_color_manual(values = .y))
vali_pca[[1]] <- vali_pca[[1]] + scale_shape_manual(values = c(17,15,16))
vali_pca[[2]] <- vali_pca[[2]] + scale_shape_manual(values = c(15,16))Differential Gene Expression
f508_control_counts_list <- list(pilot_counts %>%
dplyr::select(!contains("CompHet"),-c(Ensembl_ID,ERCC1,ERCC2)),
vali_counts %>%
dplyr::select(!contains("CompHet"),-c(Ensembl_ID, uhrna1, uhrna2))) %>%
map(~ column_to_rownames(., var = "Gene_Name"))
dge_groups <- map(f508_control_counts_list, ~
case_when(str_detect(colnames(.x), "F508del") ~ "F508del",
str_detect(colnames(.x), "Control") ~ "Control")) %>%
map(~factor(.x, levels = c("Control","F508del")))
dge_filtered_norm <- vector(mode = "list", length = 2)
dge_filtered_norm[[1]] <- DGEList(counts = f508_control_counts_list[[1]],
group = dge_groups[[1]])
dge_filtered_norm[[2]] <- DGEList(counts = f508_control_counts_list[[2]],
group = dge_groups[[2]])
dge_filtered_norm[[1]] <-
calcNormFactors(dge_filtered_norm[[1]][filterByExpr(dge_filtered_norm[[1]] ), ,
keep.lib.sizes = FALSE])
dge_filtered_norm[[2]] <-
calcNormFactors(dge_filtered_norm[[2]][filterByExpr(dge_filtered_norm[[2]],
min.count = 5), ,
keep.lib.sizes = FALSE])
dge_design <- imap(dge_groups, ~ model.matrix(~0 + .x) %>%
set_colnames(., levels(dge_groups[[.y]])))
dge_dsp <- map2(dge_filtered_norm, dge_design, ~ estimateDisp(.x, .y, robust = TRUE))
#plotBCV() to assess biological coefficient of variance
dge_contrasts <- map(dge_design, ~ makeContrasts(F508del - Control, levels = .x))
dge_qlf_test <- map2(dge_dsp, dge_design, ~ glmQLFit(.x, .y, robust = TRUE)) %>%
map2(dge_contrasts, ~ glmQLFTest(., contrast = .y))
dge_top_tags <- map(dge_qlf_test, ~topTags(.x, n = "inf"))
map(dge_top_tags, ~head(.x, n = 10))[[1]]
Coefficient: -1*Control 1*F508del
logFC logCPM F PValue FDR
SAMSN1 7.210976 9.683300 69.19179 2.559641e-08 0.0003069777
LAPTM5 5.522935 10.819126 59.57259 9.072889e-08 0.0004531007
CD14 5.525412 8.964458 56.71771 1.360020e-07 0.0004531007
TYROBP 5.887248 9.937166 55.99102 1.511217e-07 0.0004531007
PHACTR1 6.289434 8.461804 54.42953 1.901877e-07 0.0004561842
LCP1 6.131929 10.736899 52.67574 2.476479e-07 0.0004950069
SOD2 4.882542 10.820214 50.11160 3.685403e-07 0.0005642913
PLAUR 5.697250 11.101292 49.20657 4.255171e-07 0.0005642913
AC245128.3 10.045690 8.993026 55.15823 4.394573e-07 0.0005642913
PFKFB3 6.056730 9.967869 48.58057 4.705172e-07 0.0005642913
[[2]]
Coefficient: -1*Control 1*F508del
logFC logCPM F PValue FDR
FER1L4 -2.8093945 3.272270 60.40035 2.574141e-08 0.0004494965
GOLGA8A -2.0433944 2.314476 36.87317 1.852423e-06 0.0161735073
TUBGCP6 -0.9657729 4.654906 31.24315 6.585431e-06 0.0272449891
CDK10 -1.0893249 3.931612 30.72597 7.446812e-06 0.0272449891
AHSA2P -1.7046242 3.490967 29.31874 1.046729e-05 0.0272449891
CD8A -2.8912358 2.844155 28.83821 1.178214e-05 0.0272449891
SGSM2 -0.8275227 5.327414 27.65926 1.582422e-05 0.0272449891
TTLL3 -1.4031078 3.235304 27.19394 1.781117e-05 0.0272449891
TENT5B 1.9858803 4.506881 26.11420 2.353580e-05 0.0272449891
HOPX 3.1652495 6.538092 25.64382 2.746478e-05 0.0272449891Pilot Cohort Top 50 Differentially Expressed Genes
datatable(head(dge_top_tags[[1]]$table, n = 50))Validation Cohort Top 50 Differentially Expressed Genes
datatable(head(dge_top_tags[[2]]$table, n = 50))Summary of Regulation of Genes
dge_qlf_test %>% map(~summary(decideTests(.x)))[[1]]
-1*Control 1*F508del
Down 320
NotSig 11105
Up 568
[[2]]
-1*Control 1*F508del
Down 403
NotSig 15225
Up 1834logFC Plot of DEGs
top_tags_tibble <- map(dge_top_tags, ~ as_tibble(.x$table, rownames = "Genes")) %>%
map2(c("Pilot logFC","Validation logFC"), ~ dplyr::rename(.x, !!.y := logFC)) %>%
map2(c("Sig_Pilot", "Sig_Vali"), ~mutate(.x, !!.y := if_else(FDR <= .05, "Sig","NS"))) %>%
map(~ dplyr::select(.x, -c(logCPM,F,PValue,FDR))) %>%
purrr::reduce(~inner_join(.x, .y, by = "Genes")) %>%
mutate(Significance = case_when(
Sig_Pilot == "Sig" & Sig_Vali == "Sig" ~ "Both",
Sig_Pilot == "Sig" & Sig_Vali == "NS" ~ "Pilot",
Sig_Vali == "Sig" & Sig_Pilot == "NS" ~ "Validation",
Sig_Vali == "NS" & Sig_Pilot == "NS" ~ "Neither")) %>%
mutate(Significance = factor(Significance, levels = c("Neither", "Pilot", "Validation","Both"))) %>%
arrange(Significance, .by_group = TRUE)ggplot(data = top_tags_tibble, aes(x = `Pilot logFC`,y = `Validation logFC`,
color = Significance)) +
geom_point(size = 1.5) +
theme(text = element_text(size = 30)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
scale_color_manual(values=c("dark gray", "#40cdd0", "#cb86fe","#f8766d")) +
ylim(-12,12) +
xlim(-12,12)
Volcano Plots
map2(dge_top_tags, c("pilot", "vali"), ~plot_volcano(.x, .y))[[1]]
[[2]]
Hypergeometric Test Between Gene Overlaps
DEG_overlap <- newGeneOverlap(rownames(dge_top_tags[[1]]$table[dge_top_tags[[1]]$table$FDR <= .05,]),
rownames(dge_top_tags[[2]]$table[dge_top_tags[[2]]$table$FDR <= .05,]),
genome.size = length(pilot_counts$Gene_Name))
testGeneOverlap(DEG_overlap)GeneOverlap object:
listA size=888
listB size=2237
Intersection size=453
Overlapping p-value=0e+00
Jaccard Index=0.2Venn Diagram of Overlap
windowsFonts(Arial=windowsFont("Arial"))
draw.pairwise.venn(area1 = 1341,
area2 = 2690,
cross.area = 453,
fontfamily = "Arial",
category = c("Pilot Cohort", "Validation Cohort"),
fill = c("#58d8d9", "#ffcd78"),
cat.fontfamily = "Arial",
cat.cex = c(2, 2),
cex = c(2, 2, 2),
alpha = c(0.6,.6),
cat.pos = 180)
(polygon[GRID.polygon.263], polygon[GRID.polygon.264], polygon[GRID.polygon.265], polygon[GRID.polygon.266], text[GRID.text.267], text[GRID.text.268], text[GRID.text.269], text[GRID.text.270], text[GRID.text.271]) mRNA CPM Plots
mrna_genes <- map2(list(pilot_counts, vali_counts), list(c("ERCC1","ERCC2"),c("uhrna1","uhrna2")),
~ dplyr::select(.x, !contains("CompHet"), -c(all_of(.y), Ensembl_ID))) %>%
map(~cpmGeneExpr(.x, gene.col = "Gene_Name", goi = c("CFTR","FOXI1","ASCL3")) %>%
mutate(Samples = case_when(
str_detect(Samples, "F508del") ~ "F508del",
str_detect(Samples, "Control") ~ "Control")))Pilot mRNA CPM Plots
pilot_mrna <- map(c("CFTR","FOXI1","ASCL3"), ~ mrna_cpm_plot(mrna_genes[[1]], gene = .x) + theme(text = element_text(size = 10)))
map(pilot_mrna, ~ assign_in(.x, list(2,2,"aes_params", 1), 1))[[1]]
[[2]]
[[3]]
Validation mRNA CPM Plots
validation_mrna <- map(c("CFTR","FOXI1","ASCL3"), ~ mrna_cpm_plot(mrna_genes[[2]], gene = .x) + theme(text = element_text(size = 20)))
map(validation_mrna, ~ assign_in(.x, list(2,2,"aes_params", 1), 1))[[1]]
[[2]]
[[3]]
Heatmap of DEGs
dge_sig_genes <- map(dge_qlf_test, ~ as.logical(abs(decideTests(.))))
cpm_list <- map2(dge_filtered_norm, dge_sig_genes, ~.x[.y,] %>%
edgeR::cpm(.) %>%
as_tibble(rownames = "Genes"))
cpm_mat_list <- map(cpm_list, ~ dplyr::select(., -Genes) %>%
as.matrix() %>%
t() %>%
scale %>%
t())
cpm_mat_list <- map2(cpm_mat_list, cpm_list, ~ set_rownames(.x,.y$Genes))
group_annot <- map2(dge_groups, cpm_mat_list, ~data.frame(groups = .x) %>%
set_rownames(colnames(.y)))plot_heatmap(cpm_mat_list[[1]], group_annot[[1]], "Pilot")
plot_heatmap(cpm_mat_list[[2]], group_annot[[2]], "Validation")
Gene Ontology
dge_qlf_test_entrez <- map(dge_qlf_test, ~ genesToEntrez(., return_DGE_Obj = TRUE))
go_qlf <- map(dge_qlf_test_entrez, ~ goana(.x, geneid = .x$Entrez, species = "Hs"))Pilot Validation
topGO(go_qlf[[1]], n=10, sort = "up") Term Ont N Up Down
GO:0002376 immune system process BP 1907 263 22
GO:0006955 immune response BP 1284 212 14
GO:0001775 cell activation BP 945 177 5
GO:0045321 leukocyte activation BP 859 160 4
GO:0050896 response to stimulus BP 5178 432 96
GO:0002274 myeloid leukocyte activation BP 507 117 1
GO:0002252 immune effector process BP 839 151 5
GO:0007154 cell communication BP 3628 338 72
GO:0002366 leukocyte activation involved in immune response BP 531 114 4
GO:0002263 cell activation involved in immune response BP 535 114 4
P.Up P.Down
GO:0002376 4.020796e-63 1.0000000
GO:0006955 4.014909e-62 0.9999971
GO:0001775 6.753134e-59 1.0000000
GO:0045321 2.900781e-52 1.0000000
GO:0050896 2.270427e-50 1.0000000
GO:0002274 2.976527e-47 0.9999996
GO:0002252 4.760254e-47 0.9999996
GO:0007154 1.120875e-42 0.9999316
GO:0002366 1.244801e-42 0.9998447
GO:0002263 2.743383e-42 0.9998591topGO(go_qlf[[1]], n=10, sort = "down") Term Ont N Up Down
GO:0005929 cilium CC 382 10 46
GO:0003341 cilium movement BP 49 0 14
GO:0031514 motile cilium CC 104 1 19
GO:0035082 axoneme assembly BP 44 1 13
GO:0005930 axoneme CC 89 1 17
GO:0097014 ciliary plasm CC 90 1 17
GO:0060271 cilium assembly BP 294 9 30
GO:0044782 cilium organization BP 303 9 30
GO:0001578 microtubule bundle formation BP 63 1 13
GO:0032838 plasma membrane bounded cell projection cytoplasm CC 154 7 19
P.Up P.Down
GO:0005929 0.9941977 2.849650e-17
GO:0003341 1.0000000 3.965164e-11
GO:0031514 0.9955450 6.690231e-11
GO:0035082 0.8981393 1.253724e-10
GO:0005930 0.9902425 3.327317e-10
GO:0097014 0.9907391 3.999092e-10
GO:0060271 0.9647728 8.512924e-10
GO:0044782 0.9726074 1.755313e-09
GO:0001578 0.9621197 1.563430e-08
GO:0032838 0.6659782 5.881904e-08topGO(go_qlf[[2]], n=10, sort = "up") Term Ont N Up Down
GO:0002376 immune system process BP 2354 571 66
GO:0006955 immune response BP 1599 426 51
GO:0001775 cell activation BP 1171 342 31
GO:0002274 myeloid leukocyte activation BP 590 220 11
GO:0043299 leukocyte degranulation BP 490 196 6
GO:0002444 myeloid leukocyte mediated immunity BP 507 200 8
GO:0045321 leukocyte activation BP 1051 314 27
GO:0002446 neutrophil mediated immunity BP 463 186 7
GO:0002366 leukocyte activation involved in immune response BP 625 223 10
GO:0042119 neutrophil activation BP 464 186 7
P.Up P.Down
GO:0002376 8.021656e-65 0.4452284
GO:0006955 3.199435e-58 0.1426465
GO:0001775 6.417686e-56 0.6109977
GO:0002274 7.862417e-55 0.9353670
GO:0043299 2.268667e-54 0.9932689
GO:0002444 2.452310e-54 0.9705804
GO:0045321 1.887067e-53 0.6690325
GO:0002446 6.674400e-52 0.9726579
GO:0002366 9.804140e-52 0.9792673
GO:0042119 9.832625e-52 0.9731086topGO(go_qlf[[2]], n=10, sort = "down") Term Ont N Up Down
GO:0005815 microtubule organizing center CC 687 82 49
GO:0005813 centrosome CC 486 57 38
GO:0044782 cilium organization BP 357 27 31
GO:0060271 cilium assembly BP 342 27 30
GO:0042101 T cell receptor complex CC 16 2 7
GO:0015630 microtubule cytoskeleton CC 1086 132 60
GO:0034451 centriolar satellite CC 28 3 8
GO:0120031 plasma membrane bounded cell projection assembly BP 514 71 34
GO:0005929 cilium CC 496 35 33
GO:0042105 alpha-beta T cell receptor complex CC 5 1 4
P.Up P.Down
GO:0005815 0.7419361 8.258130e-10
GO:0005813 0.7582599 6.430677e-09
GO:0044782 0.9993465 1.546623e-08
GO:0060271 0.9983196 2.112881e-08
GO:0042101 0.6203806 1.030180e-07
GO:0015630 0.7185523 1.429524e-07
GO:0034451 0.7074933 5.776358e-07
GO:0120031 0.2336155 2.062687e-06
GO:0005929 0.9999857 2.585079e-06
GO:0042105 0.4923379 2.736092e-06Roast Analysis
entrez_chaperome <- read_csv("data/chaperome_entrez.csv", col_names = FALSE) %>% pull(X1)
-- Column specification ----------------------------------------------------------------
cols(
X1 = col_double()
)entrez_index <- map(dge_dsp, ~ genesToEntrez(.x, return_DGE_Obj = FALSE) %>%
.$NCBI_ID %in% entrez_chaperome)pmap(list(dge_dsp, entrez_index, dge_design, dge_contrasts), ~
roast.DGEList(..1, ..2, ..3, ..4, nrot = 10000000))[[1]]
Active.Prop P.Value
Down 0.4194757 0.00219935
Up 0.1011236 0.99780070
UpOrDown 0.4194757 0.00439870
Mixed 0.5205993 0.00490890
[[2]]
Active.Prop P.Value
Down 0.3026706 0.031090598
Up 0.2700297 0.968909452
UpOrDown 0.3026706 0.062181194
Mixed 0.5727003 0.007242199map2(dge_qlf_test_entrez, entrez_index, ~ barcodeplot(.x$table$logFC, index = .y, xlab = "logFC"))
[[1]]
NULL
[[2]]
NULL
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17134)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] edgeR_3.30.3 limma_3.44.3 viridis_0.5.1
[4] viridisLite_0.3.0 pheatmap_1.0.12 DT_0.15
[7] VennDiagram_1.6.20 futile.logger_1.4.3 GeneOverlap_1.24.0
[10] ggpubr_0.4.0 ggrepel_0.8.2 glue_1.4.2
[13] magrittr_1.5 forcats_0.5.0 stringr_1.4.0
[16] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
[19] tidyr_1.1.2 tibble_3.0.3 ggplot2_3.3.2
[22] tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] colorspace_1.4-1 ggsignif_0.6.0 ellipsis_0.3.1
[4] rio_0.5.16 rprojroot_1.3-2 fs_1.5.0
[7] rstudioapi_0.11 farver_2.0.3 bit64_4.0.5
[10] AnnotationDbi_1.50.3 fansi_0.4.1 lubridate_1.7.9
[13] xml2_1.3.2 splines_4.0.2 knitr_1.30
[16] jsonlite_1.7.1 workflowr_1.6.2 broom_0.7.0
[19] GO.db_3.11.4 dbplyr_1.4.4 BiocManager_1.30.10
[22] compiler_4.0.2 httr_1.4.2 backports_1.1.10
[25] assertthat_0.2.1 cli_2.0.2 later_1.1.0.1
[28] formatR_1.7 htmltools_0.5.0.9003 tools_4.0.2
[31] gtable_0.3.0 Rcpp_1.0.5 Biobase_2.48.0
[34] carData_3.0-4 cellranger_1.1.0 jquerylib_0.1.2
[37] vctrs_0.3.4 crosstalk_1.1.0.1 xfun_0.19
[40] openxlsx_4.2.2 rvest_0.3.6 lifecycle_0.2.0
[43] gtools_3.8.2 statmod_1.4.34 rstatix_0.6.0
[46] org.Hs.eg.db_3.11.4 scales_1.1.1 hms_0.5.3
[49] promises_1.1.1.9001 parallel_4.0.2 lambda.r_1.2.4
[52] RColorBrewer_1.1-2 yaml_2.2.1 curl_4.3
[55] memoise_1.1.0 gridExtra_2.3 sass_0.2.0.9005
[58] RSQLite_2.2.0 stringi_1.5.3 S4Vectors_0.26.1
[61] BiocGenerics_0.34.0 caTools_1.18.0 zip_2.1.1
[64] rlang_0.4.8.9001 pkgconfig_2.0.3 bitops_1.0-6
[67] evaluate_0.14 lattice_0.20-41 htmlwidgets_1.5.1
[70] labeling_0.3 bit_4.0.4 tidyselect_1.1.0
[73] R6_2.4.1 IRanges_2.22.2 gplots_3.1.0
[76] generics_0.0.2 DBI_1.1.0 pillar_1.4.6
[79] haven_2.3.1 whisker_0.4 foreign_0.8-80
[82] withr_2.3.0 abind_1.4-5 modelr_0.1.8
[85] crayon_1.3.4 car_3.0-10 futile.options_1.0.1
[88] KernSmooth_2.23-17 rmarkdown_2.6.0 locfit_1.5-9.4
[91] readxl_1.3.1 data.table_1.13.0 blob_1.2.1
[94] git2r_0.27.1 reprex_0.3.0 digest_0.6.26
[97] httpuv_1.5.4 stats4_4.0.2 munsell_0.5.0
[100] bslib_0.2.2.9000