J. Taroni 2018
Here, we’ll compare what latent variables are differentially expressed in each of the ANCA-associated vasculitis data sets. An advantage of the multiPLIER approach is that we don’t have to compare across multiple models. We’ll use a cutoff of FDR < 0.05. Recall that we used “BH” correction for multiple testing in each case.
At first, we’ll take the naive approach of just looking at the overlapping sets of significant LVs. There’s no guarantee that the directionality will be in agreement this way.
# magrittr pipe
`%>%` <- dplyr::`%>%`# plot and result directory setup for this notebook
plot.dir <- file.path("plots", "21")
dir.create(plot.dir, recursive = TRUE, showWarnings = FALSE)
results.dir <- file.path("results", "21")
dir.create(results.dir, recursive = TRUE, showWarnings = FALSE)# files with all the limma results
nares.file <- file.path("results", "18",
"NARES_recount2_model_LV_limma_results.tsv")
blood.file <- file.path("results", "19",
"GPA_blood_recount2_model_LV_limma_results.tsv")
kidney.file <- file.path("results", "20",
"ERCB_glom_recount2_model_LV_limma_results.tsv")nares.df <- readr::read_tsv(nares.file)Parsed with column specification:
cols(
.default = col_double(),
LV = col_character()
)
See spec(...) for full column specifications.blood.df <- readr::read_tsv(blood.file)Parsed with column specification:
cols(
LV = col_character(),
Control...GPAneg = col_double(),
Control...GPApos = col_double(),
GPAneg...GPApos = col_double(),
AveExpr = col_double(),
F = col_double(),
P.Value = col_double(),
adj.P.Val = col_double()
)kidney.df <- readr::read_tsv(kidney.file)Parsed with column specification:
cols(
LV = col_character(),
Living.donor.control...Nephrotic.syndrome = col_double(),
Living.donor.control...Vasculitis = col_double(),
Nephrotic.syndrome...Vasculitis = col_double(),
AveExpr = col_double(),
F = col_double(),
P.Value = col_double(),
adj.P.Val = col_double()
)# initialize list to hold significant pathways for each of the datasets -- this
# is what VennDiagram functions generally take as an argument
sig.list <- list()
sig.list[["nares"]] <- nares.df$LV[which(nares.df$adj.P.Val < 0.05)]
sig.list[["blood"]] <- blood.df$LV[which(blood.df$adj.P.Val < 0.05)]
sig.list[["kidney"]] <- kidney.df$LV[which(kidney.df$adj.P.Val < 0.05)]overlap <- VennDiagram::calculate.overlap(sig.list)
overlap.file <- file.path(results.dir, "AAV_FDR_0.05_overlap.RDS")
saveRDS(overlap, file = overlap.file)vd.file <- file.path(plot.dir, "AAV_FDR_0.05_overlap.png")
VennDiagram::venn.diagram(x = sig.list, filename = vd.file,
imagetype = "png",
resolution = 600,
category.names = c("NARES", "GPA blood",
"ERCB glomeruli"))[1] 1# which element of the overlap list are we looking for?
lapply(overlap, length)$a5
[1] 22
$a2
[1] 19
$a4
[1] 57
$a6
[1] 72
$a1
[1] 53
$a3
[1] 79
$a7
[1] 308overlap$a5 [1] "10,SVM Macrophages M0" "38,PID_PLK1_PATHWAY" "LV 53" "LV 55" "LV 63" "LV 113"
[7] "LV 122" "LV 139" "LV 332" "LV 346" "LV 390" "LV 470"
[13] "LV 488" "524,IRIS_Neutrophil-Resting" "LV 624" "LV 631" "LV 745" "LV 793"
[19] "LV 834" "LV 887" "LV 909" "937,PID_HIF1_TFPATHWAY" # remove files & data.frame no longer needed once we have the overlap
rm(list = setdiff(ls(), c("%>%", "overlap", "plot.dir", "results.list")))lvs.to.plot <- overlap$a5We’ll use these, from the individual dataset notebooks, for plotting.
nares.file <- file.path("results", "18",
"NARES_recount2_model_B_long_sample_info.tsv")
nares.df <- readr::read_tsv(nares.file)Parsed with column specification:
cols(
.default = col_character(),
Value = col_double(),
Batch = col_integer(),
Age = col_integer(),
`Disease_Duration (yrs)` = col_integer(),
Flares = col_integer(),
PGA = col_integer(),
BVAS = col_integer(),
VDI = col_integer(),
Smoking_pkyrs = col_integer(),
Steroids_daily_pred_mg = col_integer(),
Steroids_Cat = col_integer(),
GC_or_Immune = col_integer(),
Immune_or_Nasal = col_integer(),
Any_Immune = col_integer()
)
See spec(...) for full column specifications.
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|================================================================================================================================================================================| 100% 9 MBnumber of columns of result is not a multiple of vector length (arg 1)147063 parsing failures.
row [38;5;246m# A tibble: 5 x 5[39m col row col expected actual file expected [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m actual [38;5;250m1[39m [4m1[24m975 Disease_Duration (yrs) no trailing characters .75 'results/18/NARES_recount2_model_B_long_sample_info.tsv' file [38;5;250m2[39m [4m1[24m976 Disease_Duration (yrs) no trailing characters .75 'results/18/NARES_recount2_model_B_long_sample_info.tsv' row [38;5;250m3[39m [4m1[24m977 Disease_Duration (yrs) no trailing characters .75 'results/18/NARES_recount2_model_B_long_sample_info.tsv' col [38;5;250m4[39m [4m1[24m978 Disease_Duration (yrs) no trailing characters .75 'results/18/NARES_recount2_model_B_long_sample_info.tsv' expected [38;5;250m5[39m [4m1[24m979 Disease_Duration (yrs) no trailing characters .75 'results/18/NARES_recount2_model_B_long_sample_info.tsv'
... ................................. ... ..................................................................................................................... ........ .................................................................................................................................................................................................................................................. ...... .............................................................................................................................................. .... .............................................................................................................................................. ... .............................................................................................................................................. ... .............................................................................................................................................. ........ ..............................................................................................................................................
See problems(...) for more details.gpa.file <- file.path("results", "19",
"GPA_blood_recount2_model_B_long_sample_info.tsv")
gpa.df <- readr::read_tsv(gpa.file)Parsed with column specification:
cols(
LV = col_character(),
Sample = col_character(),
Value = col_double(),
Name = col_character(),
GPA_signature = col_character(),
Cell_type = col_character()
)kidney.file <- file.path("results", "20",
"ERCB_glom_recount2_model_B_long_sample_info.tsv")
kidney.df <- readr::read_tsv(kidney.file)Parsed with column specification:
cols(
LV = col_character(),
Sample = col_character(),
Value = col_double(),
Diagnosis = col_character()
)We’ll want the plots to be in the same general order and for the x-axis labels to display well.
# Nasal brushing data
nares.df <- nares.df %>%
dplyr::mutate(Classification =
dplyr::recode(Classification,
`GPA with active nasal disease` = "GPA active",
`GPA with prior nasal disease` = "GPA prior",
`GPA (no prior nasal disease)` = "GPA none"
)) %>%
dplyr::mutate(Classification =
factor(Classification,
levels = c("GPA active",
"GPA prior",
"GPA none",
"EGPA",
"Allergic Rhinitis",
"Sarcoidosis",
"Healthy")))# GPA PBMC data
gpa.df <- gpa.df %>%
dplyr::mutate(GPA_signature = dplyr::recode(GPA_signature,
GPApos = "GPA-positive",
GPAneg = "GPA-negative",
Control = "Control")) %>%
dplyr::mutate(GPA_signature = factor(GPA_signature,
levels = c("GPA-positive",
"GPA-negative",
"Control"))) kidney.df <- kidney.df %>%
dplyr::mutate(Diagnosis = factor(Diagnosis,
levels = c("Vasculitis",
"Nephrotic syndrome",
"Living donor control")))mean_ci <- function(x) ggplot2::mean_se(x, mult = 2)
# custom ggplot2 theme
custom_theme <- function() {
ggplot2::theme_bw() +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45,
hjust = 1),
legend.position = "none",
plot.title = ggplot2::element_text(hjust = 0.5,
face = "bold"),
axis.title.x = ggplot2::element_blank())
}# wrapper function for plotting the three datasets -- this is only intended to
# be used in this environment
MultiPlotWrapper <- function(lv.name) {
# lv.name: string to be passed to dplyr::filter -- one LV plotted at a time
# returns a 3 panel plot with strip charts from the three tissues, the output
# of cowplot::plot_grid
#### NARES ####
nares.p <- nares.df %>%
dplyr::filter(LV == lv.name) %>%
ggplot2::ggplot(ggplot2::aes(x = Classification, y = Value)) +
ggplot2::geom_jitter(width = 0.2, alpha = 0.6,
ggplot2::aes(colour = Classification)) +
ggplot2::stat_summary(geom = "pointrange",
fun.data = mean_ci) +
ggplot2::labs(y = lv.name, title = "NARES") +
ggplot2::scale_color_manual(values = c("#2F4F4F", "#191970", "#20B2AA",
"#666666", "#CDCD00", "#FF4500",
"#FF8C69")) +
custom_theme()
#### Glomeruli ####
glom.p <- kidney.df %>%
dplyr::filter(LV == lv.name) %>%
ggplot2::ggplot(ggplot2::aes(x = Diagnosis, y = Value)) +
ggplot2::geom_jitter(width = 0.2, alpha = 0.6,
ggplot2::aes(colour = Diagnosis)) +
ggplot2::stat_summary(geom = "pointrange",
fun.data = mean_ci) +
ggplot2::labs(y = lv.name, title = "Glomeruli") +
ggplot2::scale_color_manual(values = c("#2F4F4F", "#666666", "#FF8C69")) +
custom_theme()
#### PBMC ####
pbmc.p <- gpa.df %>%
dplyr::filter(LV == lv.name) %>%
ggplot2::ggplot(ggplot2::aes(x = GPA_signature, y = Value)) +
ggplot2::geom_jitter(width = 0.2, alpha = 0.6,
ggplot2::aes(colour = GPA_signature)) +
ggplot2::stat_summary(geom = "pointrange",
fun.data = mean_ci) +
ggplot2::labs(y = lv.name, title = "GPA PBMCs") +
ggplot2::scale_color_manual(values = c("#2F4F4F", "#20B2AA", "#FF8C69")) +
custom_theme()
#### Return multipanel plot ####
return(cowplot::plot_grid(nares.p, glom.p, pbmc.p, align = "h", ncol = 3))
}# list to hold the multipanel plots
plot.list <- lapply(lvs.to.plot, MultiPlotWrapper)plot.file <- file.path(plot.dir, "significant_LVs_3_AAV_sets.pdf")
pdf(plot.file, width = 8, height = 6, onefile = TRUE)
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[[22]]dev.off()null device
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