Hi Emily,

Are you running this on the data from that tutorial? If not, could you provide an object that reproduces the issue?

Hmm, if you downsample the object to say 1k cells do you get the same error?

Closing this now as we have not heard back, but please re-open if you are still having problems

Hi Emily，

have you solved this problem?
I met the same with my data, but I just have around 500 cells from 5 groups(5 96cells/well ).

Hi, in case someone comes across this post, I had the same error message. I saw another issue regarding this, and I made the same comment there too, sorry for posting twice.
So after finding the variable genes with the FindVariableFeatures() function prior to PCA, RunPCA() did not complain any more. Don't know why it did not work without it though.

seu <- FindVariableFeatures(object = seu)
seu <- RunPCA(seu, features = VariableFeatures(object = seu) )

Hi Seurat Team,
I have 98000 cells from 27 samples.

I run the following code

Gastricv2.integrated <- IntegrateData(anchorset = Gastricv1.anchors, features.to.integrate = my.genes,  dims = 1:30)
DefaultAssay(Gastricv2.integrated) <- "integrated"
Gastricv2.integrated <- ScaleData(Gastricv2.integrated, features = my.genes, verbose = FALSE)

and I able to run it.

However when i run the following code:
Gastricv2.integrated <- RunPCA(Gastricv2.integrated, npcs = 30, verbose = FALSE)

I get the following error and warning:

Error in irlba(A = t(x = object), nv = npcs, ...) :
max(nu, nv) must be positive
In addition: Warning message:
In PrepDR(object = object, features = features, verbose = verbose) :
The following 2000 features requested have zero variance (running reduction without them)...

So I tried running codes again using 9 samples and 49000 cells and I could run successfully.

Can you help me to figure out why I am not able to run it when I use 27 samples with 98000 cells?

Thanks very much,
Vikrant

I'm getting the same error in the SCT workflow for data integration, even after down-sampling the whole data set from roughly 90k to 25k cells.

seurat_integrated <- IntegrateData(
  anchorset = seurat_anchors,
  normalization.method = 'SCT'
)

seurat_integrated
# An object of class Seurat 
# 37391 features across 24680 samples within 3 assays 
# Active assay: integrated (3000 features)
#  2 other assays present: RNA, SCT

seurat_integrated <- RunPCA(seurat_integrated)
# Error in irlba(A = t(x = object), nv = npcs, ...) : 
#   max(nu, nv) must be positive
# In addition: Warning message:
# In PrepDR(object = object, features = features, verbose = verbose) :
#   The following 3000 features requested have zero variance (running reduction without them): LYZ, HBA1, HBB, S100A8, HBA2, S100A9, HBD, HBM, CA1, GNLY, RP11-1143G9.4, AHSP, CCL5, CXCL8, HIST1H4C, HLA-DRA, S100A12, CST3, TYROBP, CD74, IGLL1, CA2, FCN1, IGKC, JCHAIN, KLRB1, NKG7, GYPA, IGLC3, LST1, LGALS1, IGLC2, G0S2, GZMB, STMN1, CCL3, FCER1G, AIF1, TUBA1B, CTSS, CCL4, HLA-DPB1, TCL1A, HLA-DPA1, CSTA, HLA-DRB1, FCGR3A, ALAS2, PRDX2, VCAN, MZB1, HMGB2, GZMK, HEMGN, CMC1, IGHM, SNCA, VPREB3, GZMA, HLA-DQA1, FCER1A, TRDC, SAT1, TUBB, SPINK2, GZMH, HLA-DQB1, CD79B, RETN, KIAA0101, S100A11, CD79A, MNDA, GYPB, IFIT1B, LGALS2, BLVRB, COTL1, IFITM3, AZU1, CD14, SERPINA1, CD24, MS4A1, SLC4A1, FGFBP2, TMCC2, TRBC1, CXCL2, SOX4, SLC25A37, IGHA1, IGHG3, IGHG1, CFD, CH17-373J23.1, CCL3L3, LTB, EREG, KLRF1, MS4A6A, FAM178B, IL32, CD8B, KLRD1, HLA-DRB5, IGHD, NEAT1, CST7, MS4A7, S100A4, S100A6, SRGN, FTL, HMGB1, CLIC3, PLAUR, PSAP, UBE2C, IRF8, CTSW, IFI30, RGS2, IFNG, PLD4, PRSS57, VIM, PRF1, HOPX, NA [... truncated]

I also tried to run RunPCA() with the features specified, as suggested by @eregenyi, but that didn't work either.

With all cells, I already got an error when trying to split the cells by group using SplitObject() which I couldn't figure out how to solve.

EDIT: I'm using Seurat v3.1.1 on R 3.6.1.

Hi Seurat Community,

I encountered a similar issue when trying to combine a 700-cell object with a previously combined 4800-cell set. When I tried running "FindVariableFeatures" before running pca, I got the error: "Error in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : invalid 'x'" . Also, downsampling 4800-cell set to 2000-cell doesn't solve the issue:\

Sincerely hope someone could share their solution!

Thanks!

I encountered the same problem, then checked that the total variable genes are 'zero' (0) due to one unintentional mistake in the pipeline. So please check if total variable genes are present or not. I am not sure but if the cells are from one cell-types only and having few cells in number might cause this issue too.

I encountered the same problem, then checked that the total variable genes are 'zero' (0) due to one unintentional mistake in the pipeline. So please check if total variable genes are present or not. I am not sure but if the cells are from one cell-types only and having few cells in number might cause this issue too.

Hi Rahul,
Thanks for sharing! May I ask how did you fix when total variable genes not resented if one cell tpe containing few cells?

I encountered the same issue. So there are genes in my data having variance = 0. This creates NA in the sparseMatrix after using log normalization. Then when I used ScaleData, it created a lot of zeros. I guess when Seurat feeded this scaled data to irlba, irlba removed columns/rows having variance = 0 and the final matrix was smaller than their expected number of left/right singular vectors. Fyi, this is my data (a Seurat object). :

sum(data@assays$RNA@meta.features$vst.variance == 0)
# 95
table(is.na(data@assays$RNA@counts@x))
# FALSE
# 19332
table(is.na(data@assays$RNA@data@x))
# FALSE  TRUE
# 19332 12870
data <- Seurat::RunPCA(data, npcs = 50, verbose = FALSE)
# Error in irlba(A = t(x = object), nv = npcs, ...) :
#  max(nu, nv) must be positive

So I replaced the NA in my normalized matrix with 0 (this is not right I know), re-scaled the data, and re-ran PCA. And it worked.

data@assays$RNA@data@x[is.na(data@assays$RNA@data@x)] <- 0
data <- Seurat::ScaleData(data, features = Seurat::VariableFeatures(data))
data <- Seurat::RunPCA(data, npcs = 50, verbose = FALSE)

Any better solution?

I'm getting the same error in the SCT workflow for data integration, even after down-sampling the whole data set from roughly 90k to 25k cells.

seurat_integrated <- IntegrateData(
  anchorset = seurat_anchors,
  normalization.method = 'SCT'
)

seurat_integrated
# An object of class Seurat 
# 37391 features across 24680 samples within 3 assays 
# Active assay: integrated (3000 features)
#  2 other assays present: RNA, SCT

seurat_integrated <- RunPCA(seurat_integrated)
# Error in irlba(A = t(x = object), nv = npcs, ...) : 
#   max(nu, nv) must be positive
# In addition: Warning message:
# In PrepDR(object = object, features = features, verbose = verbose) :
#   The following 3000 features requested have zero variance (running reduction without them): LYZ, HBA1, HBB, S100A8, HBA2, S100A9, HBD, HBM, CA1, GNLY, RP11-1143G9.4, AHSP, CCL5, CXCL8, HIST1H4C, HLA-DRA, S100A12, CST3, TYROBP, CD74, IGLL1, CA2, FCN1, IGKC, JCHAIN, KLRB1, NKG7, GYPA, IGLC3, LST1, LGALS1, IGLC2, G0S2, GZMB, STMN1, CCL3, FCER1G, AIF1, TUBA1B, CTSS, CCL4, HLA-DPB1, TCL1A, HLA-DPA1, CSTA, HLA-DRB1, FCGR3A, ALAS2, PRDX2, VCAN, MZB1, HMGB2, GZMK, HEMGN, CMC1, IGHM, SNCA, VPREB3, GZMA, HLA-DQA1, FCER1A, TRDC, SAT1, TUBB, SPINK2, GZMH, HLA-DQB1, CD79B, RETN, KIAA0101, S100A11, CD79A, MNDA, GYPB, IFIT1B, LGALS2, BLVRB, COTL1, IFITM3, AZU1, CD14, SERPINA1, CD24, MS4A1, SLC4A1, FGFBP2, TMCC2, TRBC1, CXCL2, SOX4, SLC25A37, IGHA1, IGHG3, IGHG1, CFD, CH17-373J23.1, CCL3L3, LTB, EREG, KLRF1, MS4A6A, FAM178B, IL32, CD8B, KLRD1, HLA-DRB5, IGHD, NEAT1, CST7, MS4A7, S100A4, S100A6, SRGN, FTL, HMGB1, CLIC3, PLAUR, PSAP, UBE2C, IRF8, CTSW, IFI30, RGS2, IFNG, PLD4, PRSS57, VIM, PRF1, HOPX, NA [... truncated]

I also tried to run RunPCA() with the features specified, as suggested by @eregenyi, but that didn't work either.

With all cells, I already got an error when trying to split the cells by group using SplitObject() which I couldn't figure out how to solve.

EDIT: I'm using Seurat v3.1.1 on R 3.6.1.

Hi romanhaa,

Did you ever figure out the solution to this issue? I'm experiencing the exact same thing on a dataset of 9k cells.

Thanks,
Will

I encountered the same issue. Any news on this? Thanks

I have the same problem with RunPCA

pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
Error in irlba(A = t(x = object), nv = npcs, ...) :
max(nu, nv) must be strictly less than min(nrow(A), ncol(A))

Someone have the solution?????

One has to do the following steps before running a PCA:

GEX <- NormalizeData(GEX, normalization.method = "LogNormalize", scale.factor = 10000)
GEX <- ScaleData(GEX, features = rownames(GEX))
GEX <- FindVariableFeatures(GEX, selection.method = "vst", nfeatures = 2000)
GEX <- RunPCA(GEX, features = VariableFeatures(object = GEX))

I encountered the same error running FindTransferAnchors() on 2 objects: one of 16512 cells and the other of 8536 cells. I suspect they stem from the same bug so I'm reporting here. In my case, both objects had variable features calculated and 20 dimensions for each PCA, in the integrated assay for each. Even running with reference.assay = "integrated" and query.assay = "integrated" didn't solve the issue as I had hoped.

However, running DefaultAssay(seurat_object) <- "integrated" for both objects before running FindTransferAnchors() resolved the issue. Maybe DefaultAssay() needs to be set to the assay with PCs calculated explicitly before calling RunPCA() on that object. Calling functions like NormalizeData(), ScaleData(), or FindVariableFeatures() resets the DefaultAssay of an object, so that will also work, but are unnecessary if you've already called them and are encountering this error.

You can check the assay used to calculated PCs with the following line:
seurat_object@reductions$pca@assay.used

a1.anchors <- FindTransferAnchors(reference = a1, 
                                                 query = a2, 
                                                 dims = 1:20,
                                                 reference.assay = "integrated",
                                                 query.assay = "integrated",
                                                 project.query = F)
#Performing PCA on the provided reference using 0 features as input.
#Error in irlba(A = t(x = object), nv = npcs, ...) : 
#  max(nu, nv) must be positive

DefaultAssay(a1) <- "integrated"
DefaultAssay(a2) <- "integrated"
a1.anchors <- FindTransferAnchors(reference = a1, 
                                                 query = a2, 
                                                 dims = 1:20,
                                                 reference.assay = "integrated",
                                                 query.assay = "integrated",
                                                 project.query = F)
#Performing PCA on the provided reference using 2000 features as input.
#Projecting PCA
#Finding neighborhoods
#Finding anchors
#	Found 15636 anchors
#Filtering anchors
#	Retained 8526 anchors

Hi Seurat Team,
I have 98000 cells from 27 samples.

I run the following code

Gastricv2.integrated <- IntegrateData(anchorset = Gastricv1.anchors, features.to.integrate = my.genes,  dims = 1:30)
DefaultAssay(Gastricv2.integrated) <- "integrated"
Gastricv2.integrated <- ScaleData(Gastricv2.integrated, features = my.genes, verbose = FALSE)

and I able to run it.

However when i run the following code:
Gastricv2.integrated <- RunPCA(Gastricv2.integrated, npcs = 30, verbose = FALSE)

I get the following error and warning:

Error in irlba(A = t(x = object), nv = npcs, ...) :
max(nu, nv) must be positive
In addition: Warning message:
In PrepDR(object = object, features = features, verbose = verbose) :
The following 2000 features requested have zero variance (running reduction without them)...

So I tried running codes again using 9 samples and 49000 cells and I could run successfully.

Can you help me to figure out why I am not able to run it when I use 27 samples with 98000 cells?

Thanks very much,
Vikrant

Hi Vikrant,
Did you figure out how to get around this? I am having a similar issue.
Thanks!
Anu

I have encountered the same error when integrating 36 samples. I figured out decreasing k.weight in 'IntegrateData' help solve the problem.

The previous code that went wrong was:
IntegrateData(anchorset = anchors)

I changed it into this and it works:
IntegrateData(anchorset = anchors,k.weight=50)

I haven't tried it yet but I guess changing other parameters in 'FindIntegrationAnchors' and 'IntegrateData' related to number of anchors (k.*) may also help.

Maybe it's worthwhile to automatically determine the number of anchors in Seurat program so that the function does not break down? Or determine the optimal number based on data itself?

I had the same problem and I solved it by changing the npcs value.
It's set to 50 by default. I changed it to npcs = a value that's smaller than my column numbers and it worked. Hope this helps.

I had the same problem. For me it seems to always work for integration of dataset pairs, but fails when adding the 3th dataset on a merged pair, regardless of data order/which batches are used. I am working on a small test dataset so may not be very variable - I have tried some of the above suggestions, but that did not help (I made sure to add HVGs, changed dims and k.weight, and removing less variable datasets).

R version 4.1.3 (2022-03-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /mnt/home/icb/karin.hrovatin/miniconda3/envs/scib-pipeline/lib/libmkl_rt.so.2

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] matrixStats_0.61.0 reticulate_1.22    SeuratObject_4.0.4 Seurat_4.1.0      
[5] optparse_1.7.1    

loaded via a namespace (and not attached):
  [1] nlme_3.1-157          spatstat.sparse_2.1-0 RcppAnnoy_0.0.19     
  [4] RColorBrewer_1.1-3    httr_1.4.2            repr_1.1.4           
  [7] sctransform_0.3.3     tools_4.1.3           utf8_1.2.2           
 [10] R6_2.5.1              irlba_2.3.5           rpart_4.1.16         
 [13] KernSmooth_2.23-20    uwot_0.1.11           mgcv_1.8-40          
 [16] DBI_1.1.2             lazyeval_0.2.2        colorspace_2.0-3     
 [19] gridExtra_2.3         tidyselect_1.1.2      compiler_4.1.3       
 [22] cli_3.2.0             plotly_4.10.0         scales_1.1.1         
 [25] spatstat.data_2.1-4   lmtest_0.9-40         ggridges_0.5.3       
 [28] pbapply_1.5-0         goftest_1.2-3         stringr_1.4.0        
 [31] pbdZMQ_0.3-7          digest_0.6.29         spatstat.utils_2.3-0 
 [34] base64enc_0.1-3       pkgconfig_2.0.3       htmltools_0.5.2      
 [37] parallelly_1.30.0     fastmap_1.1.0         htmlwidgets_1.5.4    
 [40] rlang_1.0.2           shiny_1.7.1           generics_0.1.2       
 [43] zoo_1.8-9             jsonlite_1.8.0        spatstat.random_2.2-0
 [46] ica_1.0-2             dplyr_1.0.8           magrittr_2.0.3       
 [49] patchwork_1.1.1       Matrix_1.4-1          Rcpp_1.0.8.3         
 [52] IRkernel_1.3          munsell_0.5.0         fansi_1.0.3          
 [55] abind_1.4-5           lifecycle_1.0.1       stringi_1.7.6        
 [58] MASS_7.3-56           Rtsne_0.15            plyr_1.8.7           
 [61] grid_4.1.3            parallel_4.1.3        listenv_0.8.0        
 [64] promises_1.2.0.1      ggrepel_0.9.1         crayon_1.5.1         
 [67] deldir_1.0-6          miniUI_0.1.1.1        lattice_0.20-45      
 [70] IRdisplay_1.1         cowplot_1.1.1         splines_4.1.3        
 [73] tensor_1.5            pillar_1.7.0          igraph_1.3.0         
 [76] uuid_1.0-4            spatstat.geom_2.4-0   reshape2_1.4.4       
 [79] future.apply_1.8.1    codetools_0.2-18      leiden_0.3.9         
 [82] glue_1.6.2            evaluate_0.15         data.table_1.14.2    
 [85] png_0.1-7             vctrs_0.4.0           httpuv_1.6.5         
 [88] polyclip_1.10-0       spatstat.core_2.4-2   gtable_0.3.0         
 [91] getopt_1.20.3         RANN_2.6.1            purrr_0.3.4          
 [94] tidyr_1.2.0           scattermore_0.8       future_1.24.0        
 [97] assertthat_0.2.1      ggplot2_3.3.5         mime_0.12            
[100] xtable_1.8-4          later_1.3.0           survival_3.3-1       
[103] viridisLite_0.4.0     tibble_3.1.6          cluster_2.1.3        
[106] globals_0.14.0        fitdistrplus_1.1-8    ellipsis_0.3.2       
[109] ROCR_1.0-11     

Hello, I am having the same issue of the RUNPCA command,
Is there any solution to it?
My data is the CMO from Cell Plex and I am using the intial codes from 10X Genomics and then continuing with the integrated codes from Seurat,
pbmc.singlet <- RunPCA(pbmc.singlet, features = VariableFeatures(pbmc.singlet))
Error in irlba(A = t(x = object), nv = npcs, ...) :
max(nu, nv) must be positive

I have tried all the solutions mentioned in this thread but still receiving this error.
Is there any way I can resolve this error please?

Thank you

Hi, Seurat Team and all on this post,

Honestly, I have no idea how I fixed this. After trying to figure out what was happening for a few hours, I decided to reapproach it fresh in the morning. I restarted R, cleared all of the history and etc, and reran the commands I saved from the previous night.

Surprisingly, it did not encounter the same error as before. Maybe a hard reset of the program might be helpful for those still stuck.

I hope that helps...!

Hello all,

Had a similar problem. When I was checking the data slot for the integrated data, turns out some of the datasets were all NaN. Followed the discussion from #4036 it seems to do with the naming of the assays across the different objects.
#4036

Reset the naming and now it works fine!
Note: you have to also rename the SCT model umi.assay:
seurat.obj = RenameAssays(seurat.obj, old.assay = new.assay)
seurat.obj@assays$SCT@SCTModel.list$model1@umi.assay = new.assay

Hope it helps!

我遇到了同样的问题。因此，我的数据中有一些基因的方差 = 0。这将在使用日志规范化后在 sparseMatrix 中创建。然后当我使用 时，它创建了很多零。我猜当将此缩放数据馈送到 时，删除了方差 = 0 的列/行，并且最终矩阵小于其预期的左/右奇异向量数量。仅供参考，这是我的（修拉对象）。:NA``ScaleData``Seurat``irlba``irlba``data

sum(data@assays$RNA@meta.features$vst.variance == 0)
# 95
table(is.na(data@assays$RNA@counts@x))
# FALSE
# 19332
table(is.na(data@assays$RNA@data@x))
# FALSE  TRUE
# 19332 12870
data <- Seurat::RunPCA(data, npcs = 50, verbose = FALSE)
# Error in irlba(A = t(x = object), nv = npcs, ...) :
#  max(nu, nv) must be positive

因此，我将归一化矩阵中的矩阵替换为 0（我知道这是不正确的），重新缩放了数据，并重新运行了 PCA。它奏效了。NA

data@assays$RNA@data@x[is.na(data@assays$RNA@data@x)] <- 0
data <- Seurat::ScaleData(data, features = Seurat::VariableFeatures(data))
data <- Seurat::RunPCA(data, npcs = 50, verbose = FALSE)

还有更好的解决方案吗？

I also found some problems after reading the whole data frame, which may be caused by some problems in the previous standardization. assay$RNA$var.feature is empty, which may affect the subsequent varfeature analysis. I believe that there are many people who may also have this question, please look for the answer in the previous standardization step.