R 주요 트러블슈팅 [21-40]
추천글 : 【R STUDIO】 R 스튜디오 목차
21. Error: could not find function "%>%"
⑴ (package) 해결방법
install.packages("magrittr")
install.packages("dplyr")
library(magrittr)
library(dplyr)
⑵ 레퍼런스
22. Error in is.empty(.) : could not find function "is.empty"
⑴ (package) 해결방법
install.packages('rapportools')
library(rapportools)
23. Error in fill_palette(palette = "npg") : could not find function "fill_palette"
⑴ (package) 해결방법
install.packages('ggpubr')
library(ggpubr)
24. Error: package or namespace load failed for ‘Seurat’ in dyn.load(file, DLLpath = DLLpath, ...): unable to load shared object '/opt/conda/lib/R/library/igraph/libs/igraph.so': libglpk.so.40: cannot open shared object file: No such file or directory
⑴ (package) 원인 : Ubuntu 등의 Linux 서버에 RStudio 서버를 구축하고 Seurat 패키지를 다운받을 때 이 문제가 발생함
⑵ (package) 해결방법
## Linux Ubuntu
sudo apt update
sudo apt install libglpk40
## RStudio
remove.packages('rlang')
install.packages('rlang')
install.packages('lifecycle')
⑶ 세부 설명
① sudo apt update : "E: Unable to locate package" 문제를 해결하기 위함 (ref)
② sudo apt install libglpk40 : libglpk.so.40이 없으니까 이를 설치 (ref)
③ remove.packages('rlang'), install.packages('rlang'), install.packages('lifecycle') : "Error: package or namespace load failed for ‘Seurat’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]): namespace ‘lifecycle’ 1.0.2 is already loaded, but >= 1.0.3 is required" 문제를 해결하기 위함 (ref)
25. The previous R session was abnormally terminated due to an unexpected crash. You may have lost workspace data as a result of this crash. RStudio may not have restored the previously active project as a precaution. You may switch back to it using the Projects menu.
⑴ (system) 원인 : 리소스 부족
26. Error in if (all(cur_arg < 0)) { : missing value where TRUE/FALSE needed
⑴ (grammar) 문제 상황 : SaveH5Seurat(object, filename = "myData.h5Seurat")를 한 이후, Convert("myData.h5Seurat", dest = "h5ad")를 하는 상황에서 해당 오류 메시지가 뜸
⑵ (grammar) 원인 : NM-001001130.3 같은 rowname을 갖는 Seurat object가 주어져 있을 때 object@assays$RNA@data 및 object@assays$RNA@scale.data에 접근하여 NM_001001130.3 같은 rowname을 갖는 매트릭스를 강제로 입력하려고 했을 때, 이 문제가 생김
⑶ (grammar) 해결방법 : NM_001001130.3을 NM-001001130.3으로 바꿀 수 있도록 gsub("_", "-", str)를 사용
27. Error: package or namespace load failed for 'hdf5r' in dyn.load(file, DLLpath = DLLpath, ...): unable to load shared object '/opt/conda/lib/R/library/hdf5r/libs/hdf5r.so': libhdf5_serial_hl.so.100: cannot open shared object file: No such file or directory
⑴ (package) 해결방법
sudo apt-get update
sudo apt-get install libhdf5-dev
sudo apt-get install libhdf5-serial-dev
⑵ 레퍼런스
h5py import error on libhdf5_serial.so.100
I have installed raspbian os on raspberry pi 3 model b. I have to perform a project which involves use of h5py. The os already came preinstalled with python 2.7 and 3.5 With the help of pip, I
stackoverflow.com
28. Error in data %>% gather(CellType, Proportion, -SampleID) : could not find function "%>%"
⑴ (package) 해결방법 1
# If you don't have tidyverse installed, install it first:
# install.packages("tidyverse")
# Then, load it
library(tidyverse)
⑵ (package) 해결방법 2 (ref)
library(tidyr)
29. Error in .subscript.2ary(x, i, j, drop = TRUE) : subscript out of bounds
⑴ (package) 문제상황 : br.sp_subset <- subset(br.sp, features = top_genes[valid_genes])
⑵ (package) 해결방법 1. Seurat 버전을 5에서 4로 downgrade
devtools::install_github("satijalab/seurat", ref = "v4.3.0")
⑶ (package) 해결방법 2. Seurat v5는 subset의 문법이 바뀜 (ref)
# Seurat v4
br.sp_subset <- subset(br.sp, features = top_genes)
# Seurat v5
br.sp[["RNAsub"]] <- subset(br.sp[["RNA"]], features = top_genes)
DefaultAssay(br.sp) <- "RNAsub"
30. Warning in (function (A, nv = 5, nu = nv, maxit = 1000, work = nv + 7, reorth = TRUE, : You're computing too large a percentage of total singular values, use a standard svd instead. Error in irlba::irlba(Matrix::t(preproc_res), nv = min(num_dim, min(dim(FM)) - : function 'as_cholmod_sparse' not provided by package 'Matrix'
⑴ (package) 문제상황 : cds1 <- preprocess_cds(cds1, method='LSI')
⑵ (package) 해결방법 : preprocess_cds를 새로 정의 (ref). 기타 필요한 함수는 ref1, ref2, ref3, ref4에서 정의
preprocess_cds <- function(cds,
method = c('PCA', "LSI"),
num_dim = 50,
norm_method = c("log", "size_only", "none"),
use_genes = NULL,
pseudo_count = NULL,
scaling = TRUE,
verbose = FALSE,
build_nn_index = FALSE,
nn_control = list()) {
assertthat::assert_that(
tryCatch(expr = ifelse(match.arg(method) == "",TRUE, TRUE),
error = function(e) FALSE),
msg = "method must be one of 'PCA' or 'LSI'")
method <- match.arg(method)
assertthat::assert_that(
tryCatch(expr = ifelse(match.arg(norm_method) == "",TRUE, TRUE),
error = function(e) FALSE),
msg = "norm_method must be one of 'log', 'size_only' or 'none'")
norm_method <- match.arg(norm_method)
assertthat::assert_that(assertthat::is.count(num_dim))
if(!is.null(use_genes)) {
assertthat::assert_that(is.character(use_genes))
assertthat::assert_that(all(use_genes %in% row.names(rowData(cds))),
msg = paste("use_genes must be NULL, or all must",
"be present in the row.names of rowData(cds)"))
}
assertthat::assert_that(!is.null(size_factors(cds)),
msg = paste("You must call estimate_size_factors before calling",
"preprocess_cds."))
assertthat::assert_that(sum(is.na(size_factors(cds))) == 0,
msg = paste("One or more cells has a size factor of",
"NA."))
if(build_nn_index) {
nn_control <- set_nn_control(mode=1,
nn_control=nn_control,
nn_control_default=get_global_variable('nn_control_annoy_cosine'),
nn_index=NULL,
k=NULL,
verbose=verbose)
}
#ensure results from RNG sensitive algorithms are the same on all calls
set.seed(2016)
FM <- normalize_expr_data(cds, norm_method, pseudo_count)
if (nrow(FM) == 0) {
stop("all rows have standard deviation zero")
}
if (!is.null(use_genes)) {
FM <- FM[use_genes, ]
}
fm_rowsums = Matrix::rowSums(FM)
FM <- FM[is.finite(fm_rowsums) & fm_rowsums != 0, ]
#
# Notes:
# o the functions save_transform_models/load_transform_models
# expect that the reduce_dim_aux slot consists of a S4Vectors::SimpleList
# that stores information about methods with the elements
# reduce_dim_aux[[method]][['model']] for the transform elements
# reduce_dim_aux[[method]][[nn_method]] for the nn index
# and depends on the elements within model and nn_method.
#
if(method == 'PCA') {
cds <- initialize_reduce_dim_metadata(cds, 'PCA')
cds <- initialize_reduce_dim_model_identity(cds, 'PCA')
if (verbose) message("Remove noise by PCA ...")
# Initialize variables
preproc_res <- NULL
rotation_matrix <- NULL
sdev_values <- NULL
# Determine the dimension of the feature matrix
dim_FM <- min(dim(FM)) - 1
# Use irlba if the number of dimensions is less than 20% of the matrix dimension, else use svd
if (num_dim <= 0.2 * dim_FM) {
irlba_res <- irlba::irlba(Matrix::t(FM), n = min(num_dim, dim_FM), center = scaling, scale. = scaling)
preproc_res <- irlba_res$x
irlba_rotation <- irlba_res$rotation
rotation_matrix <- irlba_res$rotation
sdev_values <- irlba_res$sdev
} else {
svd_res <- svd(Matrix::t(FM))
irlba_res = svd_res
preproc_res <- svd_res$u[, 1:num_dim] %*% diag(svd_res$d[1:num_dim])
irlba_rotation <- svd_res$v[, 1:num_dim]
rotation_matrix <- svd_res$v[, 1:num_dim]
sdev_values <- svd_res$d[1:num_dim]
}
row.names(preproc_res) <- colnames(cds)
SingleCellExperiment::reducedDims(cds)[[method]] <- as.matrix(preproc_res)
row.names(rotation_matrix) <- rownames(FM)
# we need svd_v downstream so
# calculate gene_loadings in cluster_cells.R
cds@reduce_dim_aux[['PCA']][['model']][['num_dim']] <- num_dim
cds@reduce_dim_aux[['PCA']][['model']][['norm_method']] <- norm_method
cds@reduce_dim_aux[['PCA']][['model']][['use_genes']] <- use_genes
cds@reduce_dim_aux[['PCA']][['model']][['pseudo_count']] <- pseudo_count
cds@reduce_dim_aux[['PCA']][['model']][['svd_v']] <- irlba_rotation
cds@reduce_dim_aux[['PCA']][['model']][['svd_sdev']] <- irlba_res$sdev
cds@reduce_dim_aux[['PCA']][['model']][['svd_center']] <- irlba_res$center
cds@reduce_dim_aux[['PCA']][['model']][['svd_scale']] <- irlba_res$svd_scale
# Note that prop_var_expl is the fraction of variance explained by the retained
# PCs, not the fraction of total variance.
cds@reduce_dim_aux[['PCA']][['model']][['prop_var_expl']] <- irlba_res$sdev^2 / sum(irlba_res$sdev^2)
matrix_id <- get_unique_id(SingleCellExperiment::reducedDims(cds)[['PCA']])
counts_identity <- get_counts_identity(cds)
cds <- set_reduce_dim_matrix_identity(cds, 'PCA',
'matrix:PCA',
matrix_id,
counts_identity[['matrix_type']],
counts_identity[['matrix_id']],
'matrix:PCA',
matrix_id)
cds <- set_reduce_dim_model_identity(cds, 'PCA',
'matrix:PCA',
matrix_id,
'none',
'none')
if( build_nn_index ) {
nn_index <- make_nn_index(subject_matrix=SingleCellExperiment::reducedDims(cds)[[method]], nn_control=nn_control, verbose=verbose)
cds <- set_cds_nn_index(cds=cds, reduction_method=method, nn_index=nn_index, verbose=verbose)
}
else
cds <- clear_cds_nn_index(cds=cds, reduction_method=method, nn_method='all')
} else if(method == "LSI") {
cds <- initialize_reduce_dim_metadata(cds, 'LSI')
cds <- initialize_reduce_dim_model_identity(cds, 'LSI')
# preproc_res <- tfidf(FM)
tfidf_res <- tfidf(FM)
preproc_res <- tfidf_res[['tf_idf_counts']]
num_col <- ncol(preproc_res)
irlba_res <- irlba::irlba(Matrix::t(preproc_res),
nv = min(num_dim,min(dim(FM)) - 1))
preproc_res <- irlba_res$u %*% diag(irlba_res$d)
row.names(preproc_res) <- colnames(cds)
SingleCellExperiment::reducedDims(cds)[[method]] <- as.matrix(preproc_res)
irlba_rotation = irlba_res$v
row.names(irlba_rotation) = rownames(FM)
cds@reduce_dim_aux[['LSI']][['model']][['num_dim']] <- num_dim
cds@reduce_dim_aux[['LSI']][['model']][['norm_method']] <- norm_method
cds@reduce_dim_aux[['LSI']][['model']][['use_genes']] <- use_genes
cds@reduce_dim_aux[['LSI']][['model']][['pseudo_count']] <- pseudo_count
cds@reduce_dim_aux[['LSI']][['model']][['log_scale_tf']] <- tfidf_res[['log_scale_tf']]
cds@reduce_dim_aux[['LSI']][['model']][['frequencies']] <- tfidf_res[['frequencies']]
cds@reduce_dim_aux[['LSI']][['model']][['scale_factor']] <- tfidf_res[['scale_factor']]
cds@reduce_dim_aux[['LSI']][['model']][['col_sums']] <- tfidf_res[['col_sums']]
cds@reduce_dim_aux[['LSI']][['model']][['row_sums']] <- tfidf_res[['row_sums']]
cds@reduce_dim_aux[['LSI']][['model']][['num_cols']] <- tfidf_res[['num_cols']]
cds@reduce_dim_aux[['LSI']][['model']][['svd_v']] <- irlba_rotation
cds@reduce_dim_aux[['LSI']][['model']][['svd_sdev']] <- irlba_res$d/sqrt(max(1, num_col - 1))
# we need svd_v downstream so
# calculate gene_loadings in cluster_cells.R
matrix_id <- get_unique_id(SingleCellExperiment::reducedDims(cds)[['LSI']])
counts_identity <- get_counts_identity(cds)
cds <- set_reduce_dim_matrix_identity(cds, 'LSI',
'matrix:LSI',
matrix_id,
counts_identity[['matrix_type']],
counts_identity[['matrix_id']],
'matrix:LSI',
matrix_id)
cds <- set_reduce_dim_model_identity(cds, 'LSI',
'matrix:LSI',
matrix_id,
'none',
'none')
if( build_nn_index ) {
nn_index <- make_nn_index(subject_matrix=SingleCellExperiment::reducedDims(cds)[[method]], nn_control=nn_control, verbose=verbose)
cds <- set_cds_nn_index(cds=cds, reduction_method=method, nn_index=nn_index, verbose=verbose)
}
else
cds <- clear_cds_nn_index(cds=cds, reduction_method=method, nn_method='all')
}
if(!is.null(cds@reduce_dim_aux[['Aligned']]) && !is.null(cds@reduce_dim_aux[['Aligned']][['model']][['beta']])) {
cds@reduce_dim_aux[['Aligned']][['model']][['beta']] <- NULL
}
cds
}
# Helper function to normalize the expression data prior to dimensionality
# reduction
normalize_expr_data <- function(cds,
norm_method = c("log", "size_only", "none"),
pseudo_count = NULL) {
norm_method <- match.arg(norm_method)
FM <- SingleCellExperiment::counts(cds)
# If we're going to be using log, and the user hasn't given us a
# pseudocount set it to 1 by default.
if (is.null(pseudo_count)){
if(norm_method == "log")
pseudo_count <- 1
else
pseudo_count <- 0
}
if (norm_method == "log") {
# If we are using log, normalize by size factor before log-transforming
FM <- Matrix::t(Matrix::t(FM)/size_factors(cds))
if (pseudo_count != 1 || is_sparse_matrix(SingleCellExperiment::counts(cds)) == FALSE){
FM <- FM + pseudo_count
FM <- log2(FM)
} else {
FM@x = log2(FM@x + 1)
}
} else if (norm_method == "size_only") {
FM <- Matrix::t(Matrix::t(FM)/size_factors(cds))
FM <- FM + pseudo_count
}
return (FM)
}
# Andrew's tfidf
tfidf <- function(count_matrix, frequencies=TRUE, log_scale_tf=TRUE,
scale_factor=100000, block_size=2000e6) {
# Use either raw counts or divide by total counts in each cell
if (frequencies) {
# "term frequency" method
col_sums <- Matrix::colSums(count_matrix)
tf <- Matrix::t(Matrix::t(count_matrix) / col_sums)
} else {
# "raw count" method
col_sums <- NA
tf <- count_matrix
}
# Either TF method can optionally be log scaled
if (log_scale_tf) {
if (frequencies) {
tf@x = log1p(tf@x * scale_factor)
} else {
tf@x = log1p(tf@x * 1)
}
}
# IDF w/ "inverse document frequency smooth" method
num_cols <- ncol(count_matrix)
row_sums <- Matrix::rowSums(count_matrix > 0)
idf = log(1 + num_cols / row_sums)
# Try to just to the multiplication and fall back on delayed array
# TODO hopefully this actually falls back and not get jobs killed in SGE
tf_idf_counts = tryCatch({
tf_idf_counts = tf * idf
tf_idf_counts
}, error = function(e) {
print(paste("TF*IDF multiplication too large for in-memory, falling back",
"on DelayedArray."))
options(DelayedArray.block.size=block_size)
DelayedArray:::set_verbose_block_processing(TRUE)
tf = DelayedArray::DelayedArray(tf)
idf = as.matrix(idf)
tf_idf_counts = tf * idf
tf_idf_counts
})
rownames(tf_idf_counts) = rownames(count_matrix)
colnames(tf_idf_counts) = colnames(count_matrix)
tf_idf_counts = methods::as(tf_idf_counts, "sparseMatrix")
return(list(tf_idf_counts=tf_idf_counts, frequencies=frequencies, log_scale_tf=log_scale_tf, scale_factor=scale_factor, col_sums=col_sums, row_sums=row_sums, num_cols=num_cols))
}
① 위 함수들을 다음과 같이 간단하게 불러올 수 있음
source("https://github.com/JB243/nate9389/blob/main/RStudio/preprocess_cds_and_as_cholmod_sparse.R?raw=true")
31. Warning: No layers found matching search pattern provided Error in FetchData.Assay5(object = object[[DefaultAssay(object = object)]], : layer "data" is not found in the object
⑴ (grammar) 원인 : Seurat ver. 5의 경우 별도의 normalization을 해야 SpatialFeaturePlot에 에러가 나지 않음
⑵ (grammar) 해결방법
# before
library(Seurat)
br.sp = Load10X_Spatial('~/Downloads/sample', slice= 'slice1')
SpatialFeaturePlot(br.sp, "Slc2a1") # error occurs
# after
library(Seurat)
br.sp = Load10X_Spatial('~/Downloads/sample', slice= 'slice1')
br.sp <- SCTransform(br.sp, assay = "Spatial", verbose = FALSE, variable.features.n = 1000)
SpatialFeaturePlot(br.sp, "Slc2a1")
32. Error in fill_alpha(data$fill %||% "black", data$alpha): could not find function “fill_alpha"
⑴ (package) 문제상황 : SpatialDimPlot(tnbc.merge, label = TRUE, label.size = 3)를 할 때 에러가 발생
⑵ (package) 해결방법 : ggplot2 3.4.4을 ggplot2 3.5.0으로 업데이트
⑶ 레퍼런스
Show error message : "fill_alpha" can't find
When run the code, below error message popped. Anyone can help ? Error Message: Error in fill_alpha(data$fill %||% "grey20", data$alpha) : could not find function "fill_alpha"...
stackoverflow.com
33. data layers are not joined. Please run JoinLayers
⑴ (grammar) 문제상황 : FindAllMarkers(tnbc.merge, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
⑵ (grammar) 해결방법 : tnbc.merge = JoinLayers(tnbc.merge)을 먼저 한 다음에 위 코드를 실행
⑶ 레퍼런스
Integrative analysis in Seurat v5
Seurat
satijalab.org
입력: 2023.06.09 15:38
수정: 2023.12.27 12:29
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