Negative control
fionarhuang
2020-03-15
Last updated: 2020-04-23
Checks: 7 0
Knit directory: treeclimbR_toy_example/
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knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message = FALSE)Data simulation
suppressPackageStartupMessages({
library(ggplot2)
library(ggtree)
library(dplyr)
library(treeclimbR)
library(TreeSummarizedExperiment)
library(ape)
library(TreeHeatmap)
library(ggnewscale)
library(wesanderson)
library(scales)
library(gganimate)
})A random tree is generated.
# random tree
set.seed(1)
n <- 1000
tr <- rtree(n)A count table is generated with each column sampled from the same distribution. Entities in rows and samples in columns. Samples are assigned to two groups.
# generate a random probability of leaves
p <- runif(n = n, 0, 1)
p <- p/sum(p)
names(p) <- tr$tip.label
# group
nSam <- c(30, 30)
gr <- rep(LETTERS[1:2], nSam)
# counts
count <- rmultinom(n = sum(nSam), size = 10000, prob = p)
rownames(count) <- names(p)
colnames(count) <- paste(gr, seq_len(sum(nSam)), sep = "_")The tree and count table are stored as a TSE object.
lse <- TreeSummarizedExperiment(assays = list(count),
colData = data.frame(group = gr),
rowTree = tr)Data aggregation
# all nodes
all_node <- showNode(tree = tr, only.leaf = FALSE)
tse <- aggValue(x = lse, rowLevel = all_node, FUN = sum)Differential analysis
Wilcoxon sum rank test is run on all nodes.
# wilcox.test
test.func <- function (X, Y) {
Y <- as.numeric(factor(Y))
obj <- apply(X, 1, function(x) {
p.value <- suppressWarnings(wilcox.test(x ~ Y)$p.value)
e.sign <- sign(mean(x[Y == 2]) - mean(x[Y == 1]))
c(p.value, e.sign)
})
return(list(p.value=obj[1, ], e.sign=obj[2, ]))
}
Y <- colData(tse)$group
X <- assays(tse)[[1]]
resW <- test.func(X,Y)
outW <- data.frame(node = rowLinks(tse)$nodeNum,
pvalue = resW$p.value,
sign = resW$e.sign)Run treeclimbR
# get candidates
cand <- getCand(tree = rowTree(tse), score_data = outW,
node_column = "node", p_column = "pvalue",
threshold = 0.05,
sign_column = "sign", message = TRUE)# evaluate candidates
best <- evalCand(tree = rowTree(tse), levels = cand$candidate_list,
score_data = outW, node_column = "node",
p_column = "pvalue", sign_column = "sign")
outB <- topNodes(object = best, n = Inf, p_value = 0.05)
infoCand(object = best) t upper_t is_valid method limit_rej level_name best rej_leaf rej_node
1 0.00 0 TRUE BH 0.05 0 TRUE 0 0
2 0.01 0 FALSE BH 0.05 0.01 FALSE 0 0
3 0.02 0 FALSE BH 0.05 0.02 FALSE 0 0
4 0.03 0 FALSE BH 0.05 0.03 FALSE 0 0
5 0.04 0 FALSE BH 0.05 0.04 FALSE 0 0
6 0.05 0 FALSE BH 0.05 0.05 FALSE 0 0
7 0.10 0 FALSE BH 0.05 0.1 FALSE 0 0
8 0.15 0 FALSE BH 0.05 0.15 FALSE 0 0
9 0.20 0 FALSE BH 0.05 0.2 FALSE 0 0
10 0.25 0 FALSE BH 0.05 0.25 FALSE 0 0
11 0.30 0 FALSE BH 0.05 0.3 FALSE 0 0
12 0.35 0 FALSE BH 0.05 0.35 FALSE 0 0
13 0.40 0 FALSE BH 0.05 0.4 FALSE 0 0
14 0.45 0 FALSE BH 0.05 0.45 FALSE 0 0
15 0.50 0 FALSE BH 0.05 0.5 FALSE 0 0
16 0.55 0 FALSE BH 0.05 0.55 FALSE 0 0
17 0.60 0 FALSE BH 0.05 0.6 FALSE 0 0
18 0.65 0 FALSE BH 0.05 0.65 FALSE 0 0
19 0.70 0 FALSE BH 0.05 0.7 FALSE 0 0
20 0.75 0 FALSE BH 0.05 0.75 FALSE 0 0
21 0.80 0 FALSE BH 0.05 0.8 FALSE 0 0
22 0.85 0 FALSE BH 0.05 0.85 FALSE 0 0
23 0.90 0 FALSE BH 0.05 0.9 FALSE 0 0
24 0.95 0 FALSE BH 0.05 0.95 FALSE 0 0
25 1.00 0 FALSE BH 0.05 1 FALSE 0 0Results
Candidates
# number of nodes in each candidate
candL <- cand$candidate_list
unlist(lapply(candL, length)) 0 0.01 0.02 0.03 0.04 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55
998 998 998 998 998 998 997 997 995 993 991 990 990 987 987 986
0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
982 979 978 978 976 976 973 969 964 # tree
leaf <- showNode(tree = tr, only.leaf = TRUE)
nleaf <- length(leaf)
# the candidate list
t <- names(candL)
nt <- length(candL)
mm <- matrix(NA, nrow = nleaf, ncol = nt)
colnames(mm) <- paste("row_", seq_len(nt), sep = "")
#
path <- matTree(tree = tr)
r1 <- lapply(leaf, FUN = function(x) {
which(path == x, arr.ind = TRUE)[, "row"]
})
for (j in seq_len(nt)) {
rj <- lapply(candL[[j]], FUN = function(x) {
which(path == x, arr.ind = TRUE)[, "row"]
})
for (i in seq_len(nleaf)) {
# leaf i: which row of `path`
ni <- r1[[i]]
ul <- lapply(rj, FUN = function(x) {
any(ni %in% x)
})
# the ancestor of leaf i: which node in candidate j
ll <- which(unlist(ul))
if (length(ll) == 1) {
mm[i, j] <- ll
}
}}
nn <- lapply(seq_len(ncol(mm)), FUN = function(x) {
mx <- mm[, x]
xx <- candL[[x]][mx]
cbind.data.frame(xx, rep(t[x], length(xx)),
stringsAsFactors = FALSE)
})
df <- do.call(rbind.data.frame, nn)
colnames(df) <- c("node", "threshold")
head(df)
fig_1 <- ggtree(tr = tr, layout = "circular",
branch.length = "none", size = 0.3)
pd <- df %>%
left_join(y = fig_1$data, by = "node") %>%
select(threshold, x, y) %>%
mutate(t = factor(threshold, levels = names(candL)))
gif_neg <- fig_1 +
geom_point(data = pd, aes(x, y),
color = "navy", size = 4) +
theme(plot.title = element_text(size = 25)) +
transition_states(states = t,
state_length = 8,
transition_length = 2,
wrap = FALSE) +
shadow_wake(wake_length = 0.1, alpha = FALSE,
wrap = FALSE) +
labs(title = "t = {closest_state}") +
enter_fade() +
exit_fade()
anim_save("output/negative_cands.gif", gif_neg, height = 800, width = 800)
Nodes that are detected to be differential abundant (DA) between two groups are labeled as red points. Here, treeclimbR doesn’t detect any nodes.
# detected nodes
(loc <- outB$node)integer(0)fig_1 <- ggtree(tr = tr, layout = "circular",
branch.length = "none", size = 0.3)
fig_1 +
geom_point2(aes(subset = node %in% loc), color = "red")
| Version | Author | Date |
|---|---|---|
| 76414e2 | fionarhuang | 2020-04-23 |
Histogram of P-values
pL <- lapply(seq_along(candL), FUN = function(x) {
p.x <- outW[outW$node %in% candL[[x]], ][["pvalue"]]
df.x <- cbind.data.frame(pvalue = p.x, t = names(candL)[x])
return(df.x)
})
df <- do.call(rbind, pL)
ggplot(df, aes(x = pvalue, y = ..density..)) +
geom_histogram(bins = 20, fill = "grey", alpha = 0.8) +
geom_density(color = "blue") +
facet_wrap(. ~ t, nrow = 6) +
theme(plot.margin = unit(c(0, 0, 0, 0), "cm")) +
ylim(0, 1.5)
| Version | Author | Date |
|---|---|---|
| 76414e2 | fionarhuang | 2020-04-23 |
prettify <- theme_bw(base_size = 9) + theme(
aspect.ratio = 1,
#plot.margin = unit(c(0, 0, 0.2, 0), "cm"),
panel.grid = element_blank(),
panel.spacing = unit(0, "lines"),
axis.text = element_text(color = "black"),
axis.text.x = element_text(angle = 45, hjust = 1),
legend.key.size= unit(1.5, "mm"),
plot.title = element_text(hjust = 0.5),
legend.text = element_text(size = 7),
legend.position="bottom",
legend.margin = margin(0, 0, 0, 0),
legend.box.margin=margin(-5,-5,-10,-5),
strip.background = element_rect(colour = "black", fill = "gray90"),
strip.text.x = element_text(color = "black", size = 8),
strip.text.y = element_text(color = "black", size = 8))
pal <- wes_palette("Zissou1", 25, type = "continuous")
fig <- ggplot(df, aes(x= pvalue, group = t, color = t)) +
stat_ecdf(geom = "step") +
geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
scale_color_manual(values = pal) +
prettify +
labs(y = NULL,
title = "CDF of p values on each candidate") +
theme(legend.position = "right",
legend.key.size= unit(3, "mm"),
legend.box.margin=margin(0,0,0,0),
axis.text.x = element_text(angle = 0, hjust = 1))
fig
| Version | Author | Date |
|---|---|---|
| 76414e2 | fionarhuang | 2020-04-23 |
ggsave(filename = "output/Supplementary_negative_unif.eps",
width = 5, height = 5, units = "in")
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] gganimate_1.0.4 scales_1.1.0
[3] wesanderson_0.3.6 ggnewscale_0.4.0
[5] TreeHeatmap_0.1.0 ape_5.3
[7] treeclimbR_0.1.1 TreeSummarizedExperiment_1.3.0
[9] SingleCellExperiment_1.8.0 SummarizedExperiment_1.16.0
[11] DelayedArray_0.12.0 BiocParallel_1.20.0
[13] matrixStats_0.55.0 Biobase_2.46.0
[15] GenomicRanges_1.38.0 GenomeInfoDb_1.22.0
[17] IRanges_2.20.0 S4Vectors_0.24.0
[19] BiocGenerics_0.32.0 dplyr_0.8.5
[21] ggtree_2.1.6 ggplot2_3.3.0
[23] workflowr_1.5.0
loaded via a namespace (and not attached):
[1] backports_1.1.6 circlize_0.4.8
[3] diffcyt_1.6.1 plyr_1.8.5
[5] igraph_1.2.4.1 ConsensusClusterPlus_1.50.0
[7] lazyeval_0.2.2 splines_3.6.1
[9] flowCore_1.52.0 fda_2.4.8
[11] TH.data_1.0-10 digest_0.6.25
[13] htmltools_0.4.0 viridis_0.5.1
[15] fansi_0.4.1 magrittr_1.5
[17] CytoML_1.12.0 cluster_2.1.0
[19] ks_1.11.6 limma_3.42.0
[21] ComplexHeatmap_2.2.0 RcppParallel_4.4.4
[23] R.utils_2.9.0 sandwich_2.5-1
[25] flowWorkspace_3.34.0 prettyunits_1.1.1
[27] colorspace_1.4-1 rrcov_1.4-7
[29] xfun_0.11 crayon_1.3.4
[31] RCurl_1.95-4.12 jsonlite_1.6.1
[33] hexbin_1.28.0 graph_1.64.0
[35] lme4_1.1-21 dirmult_0.1.3-4
[37] survival_2.44-1.1 zoo_1.8-6
[39] glue_1.4.0 flowClust_3.24.0
[41] gtable_0.3.0 zlibbioc_1.32.0
[43] XVector_0.26.0 GetoptLong_0.1.7
[45] ggcyto_1.14.0 IDPmisc_1.1.19
[47] Rgraphviz_2.30.0 shape_1.4.4
[49] DEoptimR_1.0-8 mvtnorm_1.0-11
[51] edgeR_3.28.0 Rcpp_1.0.4
[53] progress_1.2.2 viridisLite_0.3.0
[55] clue_0.3-57 tidytree_0.3.3
[57] openCyto_1.24.0 mclust_5.4.5
[59] FlowSOM_1.18.0 tsne_0.1-3
[61] RColorBrewer_1.1-2 ellipsis_0.3.0
[63] farver_2.0.3 pkgconfig_2.0.3
[65] XML_3.98-1.20 R.methodsS3_1.7.1
[67] flowViz_1.50.0 locfit_1.5-9.1
[69] labeling_0.3 reshape2_1.4.3
[71] flowStats_3.44.0 tidyselect_1.0.0
[73] rlang_0.4.5 later_1.0.0
[75] munsell_0.5.0 tools_3.6.1
[77] cli_2.0.2 gifski_0.8.6
[79] evaluate_0.14 stringr_1.4.0
[81] yaml_2.2.0 knitr_1.26
[83] fs_1.3.1 robustbase_0.93-5
[85] purrr_0.3.3 RBGL_1.62.1
[87] nlme_3.1-142 whisker_0.4
[89] R.oo_1.23.0 aplot_0.0.4
[91] compiler_3.6.1 png_0.1-7
[93] treeio_1.11.2 tweenr_1.0.1
[95] tibble_3.0.0 pcaPP_1.9-73
[97] stringi_1.4.6 lattice_0.20-38
[99] Matrix_1.2-17 nloptr_1.2.1
[101] vctrs_0.2.4 pillar_1.4.3
[103] lifecycle_0.2.0 BiocManager_1.30.10
[105] GlobalOptions_0.1.1 data.table_1.12.6
[107] cowplot_1.0.0 bitops_1.0-6
[109] corpcor_1.6.9 httpuv_1.5.2
[111] patchwork_1.0.0 R6_2.4.1
[113] latticeExtra_0.6-28 promises_1.1.0
[115] KernSmooth_2.23-15 gridExtra_2.3
[117] codetools_0.2-16 boot_1.3-23
[119] MASS_7.3-51.4 gtools_3.8.1
[121] assertthat_0.2.1 rprojroot_1.3-2
[123] rjson_0.2.20 withr_2.1.2
[125] mnormt_1.5-5 multcomp_1.4-10
[127] GenomeInfoDbData_1.2.2 hms_0.5.2
[129] ncdfFlow_2.32.0 grid_3.6.1
[131] minqa_1.2.4 tidyr_1.0.2
[133] rmarkdown_1.17 rvcheck_0.1.8
[135] git2r_0.26.1 base64enc_0.1-3
[137] ellipse_0.4.1