R tips：解决monocle的plot_genes_branched_heatmap函数报错
R tips：解决monocle的plot_genes_branched_heatmap函数报错
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发布于 2025-04-13 07:56:00
发布于 2025-04-13 07:56:00
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最近在使用monocle的plotgenesbranched_heatmap函数进行BEAM分支热图绘制时发生了一个报错，如下：
p <-
cds[BEAM_genes,] %>%
plot_genes_branched_heatmap(
branch_point = 1,
num_clusters = 4, # 3,
show_rownames = F,
return_heatmap = T
)
<simpleError in checkwz(wz, M = M, trace = trace, wzepsilon = control$wzepsilon): NAs found in the working weights variable 'wz'>
Error in if (x@family@vfamily %in% c("negbinomial", "negbinomial.size")) { :
the condition has length > 1 老办法，先使用traceback看一下报错的调用栈：
traceback()
11: FUN(X[[i]], ...)
10: lapply(X = X, FUN = FUN, ...)
9: mclapply(models, function(x) {
if (is.null(x)) {
NA
}
else {
if (x@family@vfamily %in% c("negbinomial", "negbinomial.size")) {
predict(x, newdata = newdata, type = response_type)
}
else if (x@family@vfamily %in% c("uninormal")) {
predict(x, newdata = newdata, type = response_type)
}
else {
10^predict(x, newdata = newdata, type = response_type)
}
}
}, mc.cores = cores)
8: responseMatrix(list(model_fits), new_data, response_type = response_type)
7: as.data.frame(responseMatrix(list(model_fits), new_data, response_type = response_type))
6: FUN(newX[, i], ...)
5: apply(exprs(X), MARGIN, FUN, ...)
4: smartEsApply(cds, 1, function(x, trend_formula, expressionFamily,
relative_expr, new_data) {
environment(fit_model_helper) <- environment()
environment(responseMatrix) <- environment()
model_fits <- fit_model_helper(x, modelFormulaStr = trend_formula,
expressionFamily = expressionFamily, relative_expr = relative_expr,
disp_func = cds@dispFitInfo[["blind"]]$disp_func)
if (is.null(model_fits))
expression_curve <- as.data.frame(matrix(rep(NA, nrow(new_data)),
nrow = 1))
else expression_curve <- as.data.frame(responseMatrix(list(model_fits),
new_data, response_type = response_type))
colnames(expression_curve) <- row.names(new_data)
expression_curve
}, convert_to_dense = TRUE, trend_formula = trend_formula, expressionFamily = expressionFamily,
relative_expr = relative_expr, new_data = new_data)
3: genSmoothCurves(new_cds[, ], cores = cores, trend_formula = trend_formula,
relative_expr = T, new_data = rbind(newdataA, newdataB))
2: plot_genes_branched_heatmap(., branch_point = 1, num_clusters = 4,
show_rownames = F, return_heatmap = T)
1: cds[BEAM_genes, ] %>% plot_genes_branched_heatmap(branch_point = 1,
num_clusters = 4, show_rownames = F, return_heatmap = T) 梳理调用栈，并结合各节点的函数源码可以发现报错传递是：
plot_genes_branched_heatmap -> genSmoothCurves -> responseMatrix 先看一下responseMatrix的源码，如下所示，可以发现报错点的if语句那里是有问题的。因为结合最开始的报错提示表明x@family@vfamily的长度可能不为1。
monocle:::responseMatrix
function (models, newdata = NULL, response_type = "response",
cores = 1)
{
res_list <- mclapply(models, function(x) {
if (is.null(x)) {
NA
}
else {
### 报错点 ###
if (x@family@vfamily %in% c("negbinomial", "negbinomial.size")) {
predict(x, newdata = newdata, type = response_type)
}
else if (x@family@vfamily %in% c("uninormal")) {
predict(x, newdata = newdata, type = response_type)
}
else {
10^predict(x, newdata = newdata, type = response_type)
}
}
}, mc.cores = cores)
res_list_lengths <- lapply(res_list[is.na(res_list) == FALSE],
length)
stopifnot(length(unique(res_list_lengths)) == 1)
num_na_fits <- length(res_list[is.na(res_list)])
if (num_na_fits > 0) {
na_matrix <- matrix(rep(rep(NA, res_list_lengths[[1]]),
num_na_fits), nrow = num_na_fits)
row.names(na_matrix) <- names(res_list[is.na(res_list)])
non_na_matrix <- Matrix::t(do.call(cbind, lapply(res_list[is.na(res_list) ==
FALSE], unlist)))
row.names(non_na_matrix) <- names(res_list[is.na(res_list) ==
FALSE])
res_matrix <- rbind(non_na_matrix, na_matrix)
res_matrix <- res_matrix[names(res_list), ]
}
else {
res_matrix <- Matrix::t(do.call(cbind, lapply(res_list,
unlist)))
row.names(res_matrix) <- names(res_list[is.na(res_list) ==
FALSE])
}
res_matrix
}
可以再看一下genSmoothCurves函数源码：
genSmoothCurves
function (cds, new_data, trend_formula = "~sm.ns(Pseudotime, df = 3)",
relative_expr = T, response_type = "response", cores = 1)
{
expressionFamily <- cds@expressionFamily
if (cores > 1) {
expression_curve_matrix <- mcesApply(cds, 1, function(x,
trend_formula, expressionFamily, relative_expr, new_data,
fit_model_helper, responseMatrix, calculate_NB_dispersion_hint,
calculate_QP_dispersion_hint) {
environment(fit_model_helper) <- environment()
environment(responseMatrix) <- environment()
model_fits <- fit_model_helper(x, modelFormulaStr = trend_formula,
expressionFamily = expressionFamily, relative_expr = relative_expr,
disp_func = cds@dispFitInfo[["blind"]]$disp_func)
if (is.null(model_fits))
expression_curve <- as.data.frame(matrix(rep(NA,
nrow(new_data)), nrow = 1))
else expression_curve <- as.data.frame(responseMatrix(list(model_fits),
newdata = new_data, response_type = response_type))
colnames(expression_curve) <- row.names(new_data)
expression_curve
}, required_packages = c("BiocGenerics", "Biobase", "VGAM",
"plyr"), cores = cores, trend_formula = trend_formula,
expressionFamily = expressionFamily, relative_expr = relative_expr,
new_data = new_data, fit_model_helper = fit_model_helper,
responseMatrix = responseMatrix, calculate_NB_dispersion_hint = calculate_NB_dispersion_hint,
calculate_QP_dispersion_hint = calculate_QP_dispersion_hint)
expression_curve_matrix <- as.matrix(do.call(rbind, expression_curve_matrix))
return(expression_curve_matrix)
}
else {
expression_curve_matrix <- smartEsApply(cds, 1, function(x,
trend_formula, expressionFamily, relative_expr, new_data) {
environment(fit_model_helper) <- environment()
environment(responseMatrix) <- environment()
model_fits <- fit_model_helper(x, modelFormulaStr = trend_formula,
expressionFamily = expressionFamily, relative_expr = relative_expr,
disp_func = cds@dispFitInfo[["blind"]]$disp_func)
if (is.null(model_fits))
expression_curve <- as.data.frame(matrix(rep(NA,
nrow(new_data)), nrow = 1))
##### 下面的responseMatrix是traceback提示的报错点 #####
else expression_curve <- as.data.frame(responseMatrix(list(model_fits),
new_data, response_type = response_type))
colnames(expression_curve) <- row.names(new_data)
expression_curve
}, convert_to_dense = TRUE, trend_formula = trend_formula,
expressionFamily = expressionFamily, relative_expr = relative_expr,
new_data = new_data)
expression_curve_matrix <- as.matrix(do.call(rbind, expression_curve_matrix))
row.names(expression_curve_matrix) <- row.names(fData(cds))
return(expression_curve_matrix)
}
}
如果debug到genSmoothCurves函数，手动走一下fitmodelhelper，如下所示，我们直接让smartEsApply返回fitmodelhelper函数拟合的model，并保存到modelfitsls2，可以发现确实有些基因的model的vfamily有两个：
model_fits_ls2 <- smartEsApply(cds, 1, function(x,
trend_formula, expressionFamily, relative_expr, new_data) {
environment(fit_model_helper) <- environment()
environment(responseMatrix) <- environment()
# 下面的fit_model_helper是traceback提示的报错点
model_fits <- fit_model_helper(x, modelFormulaStr = trend_formula,
expressionFamily = expressionFamily, relative_expr = relative_expr,
disp_func = cds@dispFitInfo[["blind"]]$disp_func)
model_fits
}, convert_to_dense = TRUE, trend_formula = trend_formula,
expressionFamily = expressionFamily, relative_expr = relative_expr,
new_data = new_data)
# 发现部分model的vfamily有两个
m <- model_fits_ls2$EVX2
m@family@vfamily
[1] "negbinomial" "VGAMcategorical"
既然知道了报错所在，那就可以解决它，把monocle的源码下载了，重新安装：
# 下载monocle的bioconductor的包
https://bioconductor.org/packages/release/bioc/html/monocle.html
wget https://bioconductor.org/packages/release/bioc/src/contrib/monocle_2.34.0.tar.gz
tar -xvzf monocle_2.34.0.tar.gz
# 在源码里面找到responseMatrix函数，可以看到responseMatrix函数的定义在expr_models.R里面
grep responseMatrix monocle/R/*
# R/clustering.R:#' expression_curve_matrix <- responseMatrix(full_model_fits)
# R/expr_models.R:responseMatrix <- function(models, newdata = NULL, response_type="response", cores = 1) {
# R/expr_models.R:#' the corresponding response matrix. This function is build on other functions (fit_models and responseMatrix) and used in calILRs and calABCs functions
# R/expr_models.R: expression_curve_matrix <- mcesApply(cds, 1, function(x, trend_formula, expressionFamily, relative_expr, new_data, fit_model_helper, responseMatrix,
# R/expr_models.R: environment(responseMatrix) <- environment()
# R/expr_models.R: expression_curve <- as.data.frame(responseMatrix(list(model_fits), newdata = new_data, response_type=response_type))
# R/expr_models.R: fit_model_helper = fit_model_helper, responseMatrix = responseMatrix, calculate_NB_dispersion_hint = calculate_NB_dispersion_hint,
# R/expr_models.R: environment(responseMatrix) <- environment()
# R/expr_models.R: expression_curve <- as.data.frame(responseMatrix(list(model_fits), new_data, response_type=response_type))
# R/expr_models.R: environment(responseMatrix) <- environment()
# R/plotting.R:#' expression_curve_matrix <- responseMatrix(full_model_fits)
# 修改编辑相应的文件：
vi monocle/R/expr_models.R
# 将下面代码
if (x@family@vfamily %in% c("negbinomial", "negbinomial.size")) {
# 改为下面代码即可
if (any(x@family@vfamily %in% c("negbinomial", "negbinomial.size"))) {
# 在R中使用devtools重新安装
devtools::install('monocle')
回到R中重新载入monocle包即可：
detach("package:monocle", unload = TRUE)
library(monocle)
# 不再报错
p <-
cds[BEAM_genes,] %>%
plot_genes_branched_heatmap(
branch_point = 1,
num_clusters = 4, # 3,
show_rownames = F,
return_heatmap = T
) 本文参与 腾讯云自媒体同步曝光计划，分享自微信公众号。
原始发表：2025-04-11，如有侵权请联系 cloudcommunity@tencent.com 删除
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