在做单细胞分析时，无论是用monocle3还是2，都会出现各种各样的问题，记录一下解决过程
monocle3：
问题1
2.png
在用
cluster_cells和find_gene_modules时，会遇到和 leiden相关的问题。解决办法：
虽然用
cluster_cells(cds, cluster_method = c('louvain')) 可以暂时跳过这个问题，但是需要设置 resolution的时候就会报错，因为默认只有leiden才能设置这个参数。所以这个问题还是得解决解决办法可以参考 Github的这个tissue
这个问题一般是包的版本问题
1, 下载leidenbase
devtools::install_github("cole-trapnell-lab/leidenbase")2, 从Archive/igraph下载 igraph 并且本地安装
devtools::install_local("igraph_1.5.0.tar.gz")环境:
• R version 4.2.0
• igraph version 1.5.0
• leidenbase version 0.1.25
• monocle3 version 1.3.4
monocle2：
问题2
2真的各种各样的问题，猜测是包更新了和之前的代码不兼容。
可以参考这两个帖子
1，链接1
2，链接2
报错点：
1， 运行ordercell的时候报错：
Error in if (class(projection) != “matrix”) projection <- as.matrix(projection) : the condition has length > 1
2， 运行BEAM的时候报错： 'length(x) = 2 > 1' in coercion to 'logical(1)'
3， 运行BEAM的时候报错：Error in if (progenitor_method == “duplicate”) { : the condition has length > 1
2.26版本（只解决了第一个问题，后面因为2.26的太繁琐了，就不想改了，换成2.24了）
解决办法：
- 将链接1的代码下载下来，做以下修改
○ 把整段reduceDimension函数注释掉
○ 将reducedDimk改成reducedDimK（小写k改成大写K）
○ 将orderCells函数中的reducedDimK(cds) <- K_old和reducedDimW(cds) <- old_W注释掉，即1158和1162行注释 - 安装igraph包，并且library
- 重新source该代码
2.24版本：
- 卸载原来的monocle包，remove.packages("monocle")
- 自己去搜索相关网站下载各个版本的安装包（此处是0.24）
- 修改里面的R文件：
monocle/R/order_cell.R
从if(class(projection) != 'matrix') projection <- as.matrix(projection)改为
projection <- as.matrix(projection)
monocle/R /BEAM.R
从if (progenitor_method == "duplicate") { } else if (progenitor_method == "sequential_split") { }改为if ('duplicate' %in% progenitor_method){} else if('sequential_split' %in% progenitor_method){}
monocle/R /utils.R
从class(x) %in% c("dgCMatrix", "dgTMatrix")改为any(class(x) %in% c("dgCMatrix", "dgTMatrix"))
并且将这两行注释：
if (class(cellData) != "matrix" && isSparseMatrix(cellData) == FALSE){ stop("Error: argument cellData must be a matrix (either sparse from the Matrix package or dense)") } - 压缩
tar zcvf monocle_2.24.0.tar.gz monocle_2.24.0 - 本地安装
devtools::install_local("monocle_2.24.0.tar.gz")，跳过更新
另外，跑BEAM的plot_genes_branched_heatmap时候也会遇到问题，我就懒得改包里得代码了，直接重新写一个函数调用就好，可以参考文末代码
2.6和2.4的解决方案还会有一点不一样，但解决思路基本是一样的，就是把出错的函数一步一步拆解，去运行，直到找到报错的那一行代码
授人以鱼不如授人以渔，代码文件就不上传了，谢绝伸手党，谢谢。
new_plot_genes_branched_heatmap <- function (cds_subset, branch_point = 1, branch_states = NULL,
branch_labels = c("Cell fate 1", "Cell fate 2"), cluster_rows = TRUE,
hclust_method = "ward.D2", num_clusters = 6, hmcols = NULL,
branch_colors = c("#979797", "#F05662", "#7990C8"), add_annotation_row = NULL,
add_annotation_col = NULL, show_rownames = FALSE, use_gene_short_name = TRUE,
scale_max = 3, scale_min = -3, norm_method = c("log", "vstExprs"),
trend_formula = "~sm.ns(Pseudotime, df=3) * Branch", return_heatmap = FALSE,
cores = 1, ...)
{
# cds_subset = cdsc[row.names(subset(BEAM_res,qval < 1e-4)),]
# branch_states = NULL
# branch_point = 1
# num_clusters = 4
#
# progenitor_method = "duplicate"
# branch_labels = c("Cell fate 1", "Cell fate 2")
# cluster_rows = TRUE
# hclust_method = "ward.D2"
# hmcols = NULL
# branch_colors = c("#979797", "#F05662", "#7990C8")
# add_annotation_row = NULL
# add_annotation_col = NULL
# show_rownames = FALSE
# use_gene_short_name = TRUE
# scale_max = 3
# scale_min = -3
# norm_method = c("log")
# trend_formula = "~sm.ns(Pseudotime, df=3) * Branch"
# return_heatmap = FALSE
# cores = 1
cds <- NA
new_cds <- buildBranchCellDataSet(cds_subset, branch_states = branch_states,
branch_point = branch_point, progenitor_method = "duplicate")
new_cds@dispFitInfo <- cds_subset@dispFitInfo
if (is.null(branch_states)) {
progenitor_state <- subset(pData(cds_subset), Pseudotime ==
0)[, "State"]
branch_states <- setdiff(pData(cds_subset)$State, progenitor_state)
}
col_gap_ind <- 101
newdataA <- data.frame(Pseudotime = seq(0, 100, length.out = 100),
Branch = as.factor(unique(as.character(pData(new_cds)$Branch))[1]))
newdataB <- data.frame(Pseudotime = seq(0, 100, length.out = 100),
Branch = as.factor(unique(as.character(pData(new_cds)$Branch))[2]))
BranchAB_exprs <- genSmoothCurves(new_cds[, ], cores = cores,
trend_formula = trend_formula, relative_expr = T, new_data = rbind(newdataA,
newdataB))
BranchA_exprs <- BranchAB_exprs[, 1:100]
BranchB_exprs <- BranchAB_exprs[, 101:200]
common_ancestor_cells <- row.names(pData(new_cds)[pData(new_cds)$State ==
setdiff(pData(new_cds)$State, branch_states), ])
BranchP_num <- (100 - floor(max(pData(new_cds)[common_ancestor_cells,
"Pseudotime"])))
BranchA_num <- floor(max(pData(new_cds)[common_ancestor_cells,
"Pseudotime"]))
BranchB_num <- BranchA_num
########## problem is here
# norm_method <- match.arg(norm_method)
########## problem solve end
if (norm_method == "vstExprs") {
BranchA_exprs <- vstExprs(new_cds, expr_matrix = BranchA_exprs)
BranchB_exprs <- vstExprs(new_cds, expr_matrix = BranchB_exprs)
}else if (norm_method == "log") {
BranchA_exprs <- log10(BranchA_exprs + 1)
BranchB_exprs <- log10(BranchB_exprs + 1)
}
heatmap_matrix <- cbind(BranchA_exprs[, (col_gap_ind - 1):1],
BranchB_exprs)
heatmap_matrix = heatmap_matrix[!apply(heatmap_matrix, 1,
sd) == 0, ]
heatmap_matrix = Matrix::t(scale(Matrix::t(heatmap_matrix),
center = TRUE))
heatmap_matrix = heatmap_matrix[is.na(row.names(heatmap_matrix)) ==
FALSE, ]
heatmap_matrix[is.nan(heatmap_matrix)] = 0
heatmap_matrix[heatmap_matrix > scale_max] = scale_max
heatmap_matrix[heatmap_matrix < scale_min] = scale_min
heatmap_matrix_ori <- heatmap_matrix
heatmap_matrix <- heatmap_matrix[is.finite(heatmap_matrix[,
1]) & is.finite(heatmap_matrix[, col_gap_ind]), ]
row_dist <- as.dist((1 - cor(Matrix::t(heatmap_matrix)))/2)
row_dist[is.na(row_dist)] <- 1
exp_rng <- range(heatmap_matrix)
bks <- seq(exp_rng[1] - 0.1, exp_rng[2] + 0.1, by = 0.1)
if (is.null(hmcols)) {
hmcols <- colorRamps::blue2green2red(length(bks) - 1)
}
ph <- pheatmap::pheatmap(heatmap_matrix, useRaster = T, cluster_cols = FALSE,
cluster_rows = TRUE, show_rownames = F, show_colnames = F,
clustering_distance_rows = row_dist, clustering_method = hclust_method,
cutree_rows = num_clusters, silent = TRUE, filename = NA,
breaks = bks, color = hmcols)
annotation_row <- data.frame(Cluster = factor(cutree(ph$tree_row,
num_clusters)))
if (!is.null(add_annotation_row)) {
annotation_row <- cbind(annotation_row, add_annotation_row[row.names(annotation_row),
])
}
colnames(heatmap_matrix) <- c(1:ncol(heatmap_matrix))
annotation_col <- data.frame(row.names = c(1:ncol(heatmap_matrix)),
`Cell Type` = c(rep(branch_labels[1], BranchA_num),
rep("Pre-branch", 2 * BranchP_num), rep(branch_labels[2],
BranchB_num)))
colnames(annotation_col) <- "Cell Type"
if (!is.null(add_annotation_col)) {
annotation_col <- cbind(annotation_col, add_annotation_col[fData(cds[row.names(annotation_col),
])$gene_short_name, 1])
}
names(branch_colors) <- c("Pre-branch", branch_labels[1],
branch_labels[2])
annotation_colors = list(`Cell Type` = branch_colors)
names(annotation_colors$`Cell Type`) = c("Pre-branch", branch_labels)
if (use_gene_short_name == TRUE) {
if (is.null(fData(cds_subset)$gene_short_name) == FALSE) {
feature_label <- as.character(fData(cds_subset)[row.names(heatmap_matrix),
"gene_short_name"])
feature_label[is.na(feature_label)] <- row.names(heatmap_matrix)
row_ann_labels <- as.character(fData(cds_subset)[row.names(annotation_row),
"gene_short_name"])
row_ann_labels[is.na(row_ann_labels)] <- row.names(annotation_row)
}
else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
}else {
feature_label <- row.names(heatmap_matrix)
row_ann_labels <- row.names(annotation_row)
}
row.names(heatmap_matrix) <- feature_label
row.names(annotation_row) <- row_ann_labels
ph_res <- pheatmap::pheatmap(heatmap_matrix[, ], useRaster = T, cluster_cols = FALSE,
cluster_rows = TRUE, show_rownames = show_rownames,
show_colnames = F, clustering_distance_rows = row_dist,
clustering_method = hclust_method, cutree_rows = num_clusters,
annotation_row = annotation_row, annotation_col = annotation_col,
annotation_colors = annotation_colors, gaps_col = col_gap_ind,
treeheight_row = 20, breaks = bks, fontsize = 6, color = hmcols,
border_color = NA, silent = TRUE)
grid::grid.rect(gp = grid::gpar("fill", col = NA))
grid::grid.draw(ph_res$gtable)
if (return_heatmap) {
return(list(BranchA_exprs = BranchA_exprs, BranchB_exprs = BranchB_exprs,
heatmap_matrix = heatmap_matrix, heatmap_matrix_ori = heatmap_matrix_ori,
ph = ph, col_gap_ind = col_gap_ind, row_dist = row_dist,
hmcols = hmcols, annotation_colors = annotation_colors,
annotation_row = annotation_row, annotation_col = annotation_col,
ph_res = ph_res))
}
# return(ph_res)
}
