Error in La.svd(x, nu = 0) : infinite or missing values in 'x'

[yueli8]

Hello Yuting,

Thank you for developing so nice software.

Thank you in advance for your great help!

Best,

Yue

> suppressMessages({
+     library(ggplot2)
+     library(CytoTree)
+     library(flowCore)
+     library(stringr)
+ })
> # Read fcs files
> fcs.files <- list.files("~/CyTOF_Data/20220830 PBMC Test2",pattern = '.FCS$', full = TRUE)
> fcs.data <- runExprsMerge(fcs.files, comp = FALSE, transformMethod = "none")
> recol <- c(`Sm147Di<147Sm_CD20>` = "CD20", `Sm154Di<154Sm_CD45>` = "CD45", 
+            `Gd160Di<160Gd_CD14>` = "CD14", `Ho165Di<165Ho_CD16>` = "CD16", 
+            `Er168Di<168Er_CD8>` = "CD8", `Er170Di<170Er_CD3>` = "CD3",
+            `Yb174Di<174Yb_CD4>` = "CD4")
> colnames(fcs.data)[match(names(recol), colnames(fcs.data))] = recol
> fcs.data <- fcs.data[, recol]
> day.list <- c("PBMC_Processed", "PBMC")
> meta.data <- data.frame(cell = rownames(fcs.data),
+                         stage = str_replace(rownames(fcs.data), regex(".FCS.+"), "") )
> meta.data$stage <- factor(as.character(meta.data$stage), levels = day.list)
> markers <- c("CD20","CD45","CD14","CD16","CD8","CD3","CD4")
> 
> # Build the CYT object
> cyt<- createCYT(raw.data = fcs.data, markers = markers,
+                 meta.data = meta.data,
+                 normalization.method = "log",
+                 verbose = TRUE)
2022-10-27 10:18:54 Number of cells in processing: 4000
2022-10-27 10:18:54 rownames of meta.data and raw.data will be named using column cell
2022-10-27 10:18:54 Index of markers in processing
2022-10-27 10:18:54 Creating CYT object.
2022-10-27 10:18:54 Determining normalization factors
2022-10-27 10:18:54 Normalization and log-transformation.
2022-10-27 10:18:54 Build CYT object succeed 
> cyt
CYT Information:
 Input cell number: 4000  cells 
 Enroll marker number: 7  markers 
 Cells after downsampling: 4000  cells 
> 
> set.seed(1)
> cyt <- runCluster(cyt, cluster.method = "som")
Mapping data to SOM

> 
> ## Mapping data to SOM
> # Do not perform downsampling
> set.seed(1)
> cyt <- processingCluster(cyt)
> # run Principal Component Analysis (PCA)
> cyt <- runFastPCA(cyt)
Error in La.svd(x, nu = 0) : infinite or missing values in 'x'

PBMC_Processed.zip
PBMC.zip

[ytdai]

How about other dimensionality reduction methods? Another way to check the data is to use head([email protected]) to verify there are any NA values.