satijalab / seurat-wrappers Goto Github PK

Hi, I run the velocyto using Seurat successfully on my dropest matrices. Now, I found there are many doublets in my single-cell data. I want to filter these cells and again run the velocyto to avoid the noise.
Initially, I have dropest matrices with 4000 cells and I want to filter 2000 cells.

Hi,

I recently have a problem when reading in LOOM files produced by the loompy fromfq command:

ldat <- ReadVelocity(file = '/gpfs/output/20200910_test_loompy/test_batch/2002945S10-10XSC3.loom')
reading loom file via hdf5r...
Error in [[.H5File(f, path) :
An object with name layers/ambiguous does not exist in this group

The loom file seems to be ok because importing the same file with scvelo in python does not show a similar problem.

We need to calculate RNA velocity on a large, anchor-merged seurat object and it seems to be difficult to export the filtered/selected (with only cells of interest)/merged/UMAP-mapped cells into python... So intuitively we can do this on the LOOM files instead of using 10xh5 as the raw input, precalculate them in seurat, and export to scvelo. However this does not work so far...

Thanks a lot.
Yi

Hi,
The error as follows:

`

devtools::install_github('satijalab/seurat-wrappers')
Installing package into ‘/R/x86_64-pc-linux-gnu-library/3.6’
(as ‘lib’ is unspecified)

• installing source package ‘SeuratWrappers’ ...
  ** using staged installation
  ** R
  ** byte-compile and prepare package for lazy loading
  ** help
  *** installing help indices
  ** building package indices
  ** testing if installed package can be loaded from temporary location
  ** testing if installed package can be loaded from final location
  sh: line 1: 30033 Aborted (core dumped) '/R/3.6.1-foss-2018b/lib64/R/bin/R' --no-save --slave 2>&1 < '/tmp/RtmpOmL4Pb/file74d1699d03ac'
  ERROR: loading failed
• removing ‘/R/x86_64-pc-linux-gnu-library/3.6/SeuratWrappers’
  Error: Failed to install 'SeuratWrappers' from GitHub:
  (converted from warning) installation of package ‘/tmp/RtmpQK1fjI/file71e8620533bb/SeuratWrappers_0.1.0.tar.gz’ had non-zero exit status

sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux Server 7.6 (Maipo)

Matrix products: default
BLAS/LAPACK: /hpc/software/OpenBLAS/0.3.1-GCC-7.3.0-2.30/lib/libopenblas_haswellp-r0.3.1.so

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
[4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

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

other attached packages:
[1] Seurat_3.1.5 liger_0.5.0 patchwork_1.0.0 Matrix_1.2-18 cowplot_1.0.0

loaded via a namespace (and not attached):
[1] nlme_3.1-147 tsne_0.1-3 bitops_1.0-6 RcppAnnoy_0.0.16
[5] RColorBrewer_1.1-2 httr_1.4.1 rprojroot_1.3-2 backports_1.1.7
[9] sctransform_0.2.1 tools_3.6.1 R6_2.4.1 irlba_2.3.3
[13] KernSmooth_2.23-16 uwot_0.1.8 lazyeval_0.2.2 colorspace_1.4-1
[17] withr_2.2.0 npsurv_0.4-0 prettyunits_1.1.1 tidyselect_1.1.0
[21] gridExtra_2.3 processx_3.4.2 curl_4.3 compiler_3.6.1
[25] cli_2.0.2 plotly_4.9.2.1 caTools_1.18.0 scales_1.1.1
[29] lmtest_0.9-37 ggridges_0.5.2 callr_3.4.3 pbapply_1.4-2
[33] rappdirs_0.3.1 stringr_1.4.0 digest_0.6.25 pkgconfig_2.0.3
[37] htmltools_0.4.0 htmlwidgets_1.5.1 rlang_0.4.6 rstudioapi_0.11
[41] FNN_1.1.3 generics_0.0.2 zoo_1.8-8 riverplot_0.6
[45] jsonlite_1.6.1 ica_1.0-2 mclust_5.4.6 gtools_3.8.2
[49] dplyr_1.0.0 magrittr_1.5 fansi_0.4.1 Rcpp_1.0.4.6
[53] munsell_0.5.0 ape_5.3 reticulate_1.16 lifecycle_0.2.0
[57] stringi_1.4.6 yaml_2.2.1 MASS_7.3-51.5 pkgbuild_1.0.8
[61] gplots_3.0.3 Rtsne_0.15 plyr_1.8.6 grid_3.6.1
[65] parallel_3.6.1 gdata_2.18.0 listenv_0.8.0 ggrepel_0.8.2
[69] crayon_1.3.4 doSNOW_1.0.18 lattice_0.20-41 splines_3.6.1
[73] ps_1.3.3 pillar_1.4.4 igraph_1.2.5 future.apply_1.5.0
[77] reshape2_1.4.4 codetools_0.2-16 leiden_0.3.3 glue_1.4.1
[81] lsei_1.2-0 data.table_1.12.8 remotes_2.1.1 png_0.1-7
[85] vctrs_0.3.1 foreach_1.5.0 gtable_0.3.0 RANN_2.6.1
[89] purrr_0.3.4 tidyr_1.1.0 assertthat_0.2.1 future_1.17.0
[93] ggplot2_3.3.1 rsvd_1.0.3 survival_3.1-12 viridisLite_0.3.0
[97] tibble_3.0.1 snow_0.4-3 iterators_1.0.12 cluster_2.1.0
[101] globals_0.12.5 fitdistrplus_1.0-14 ellipsis_0.3.1 ROCR_1.0-7
`
Thanks.

Hello,
I've installed SeuratWrappers 0.3.0 to covert a Seurat object into a Monocle CDS:
monocle_CDS_RNA <- as.cell_data_set(x = seurat_object, assays = "RNA", default.reduction = "UMAP")

however it seems that the CDS is not adding the info coming from the Seurat object as monocle_CDS_RNA@clusters (among other categories) is empty.
So when I run monocle_CDS_RNA <- learn_graph(monocle_CDS_RNA, use_partition = TRUE) it throws the following error:
Error: No cell clusters for UMAP calculated. Please run cluster_cells with reduction_method = UMAP before running learn_graph.

Any help will be welcome!
Thanks

Hi,

I am unable to understand what "AVP" means in the following code. I am guessing that this code is trying to find cells with a certain value as trajectory starting point cells.

max.avp <- which.max(unlist(FetchData(integrated.sub, "AVP")))
max.avp <- colnames(integrated.sub)[max.avp]
cds <- order_cells(cds, root_cells = max.avp)

Now, If I want the starting point to be all cells from cluster number 2, would it be ok to run

cluster2.cells <- WhichCells(object = seurat_obj_name, idents = 2)
cds <- order_cells(cds, root_cells = cluster2.cells)

Thanks,

Ashu

I have successfully run the example in the vignette. However, when trying on my data, I get the following error at the RunVelocity step:

myse <- RunVelocity(object = myse, deltaT = 1, kCells = 25, fit.quantile = 0.02)
Filtering genes in the spliced matrix
Filtering genes in the unspliced matrix
Calculating embedding distance matrix
error: Mat::init(): requested size is too large
Error in arma_mat_cor(mat) : Mat::init(): requested size is too large

The object has quite some cells, is there a way to be able to run the velocity analysis?

dim(myse)
[1] 28397 85697

I am running this on a machine with 384 GB RAM.

Thank you very much for your help!

Hi,
I am running runVelocity on a Seurat object containing slots for spliced, unspliced and ambiguous counts generated with STAR --soloFeatures Gene Velocyto with the followinf¡g command :
WT <- RunVelocity(object = WT,spliced = "spliced", unspliced = "unspliced", ambiguous = "ambiguous", deltaT = 1, kCells = 25, fit.quantile = 0.02, na.rm = TRUE)
and I am getting this error message :

Filtering genes in the spliced matrix
Filtering genes in the unspliced matrix
Calculating embedding distance matrix
calculating cell knn ... done
calculating convolved matrices ... done
fitting smat-based offsets ... done
fitting gamma coefficients ... Error in quantile.default(df$e, p = c(fit.quantile, 1 - fit.quantile)) :
missing values and NaN's not allowed if 'na.rm' is FALSE
Calls: RunVelocity ... -> lapply -> FUN -> quantile -> quantile.default
Execution halted

I am not able to figure where is the problem exactly...
Thanks in advance !
Best.
Jaime.

OS: Ubuntu 20.04
R: 4.0.0
Seurat 3.1.5
SeuratWrappers: 0.1.0

RunALRA (default parameters) is producing inconsistent results - it often either causes a fatal-error in RStudio or hangs - consuming 12% of CPU (1 core) indefinitely. The latter behavior is also observed when running R on the terminal. It does occasionally run to completion without issue. When it causes a fatal-error, I notice that R begins to consume more and more CPU until it reaches ~95%, at which point it crashes.

I've tested this on the vignette PBMC data, as well as my own (both small and large subsets). It's independent of library size.

Any ideas? What does ALRA depend upon?

Is this an issue with library(future)?

Hi,
I used Seurat to cluster my scRNA data.
This is my clustering result.

Now, I want to apply Monocle3 on clusters 3,4,6, and 7.
I extract these clusters.
Seurat_object_subset <- subset(Seurat_object, idents = c(7,3,4,6))
I am following this example: https://github.com/satijalab/seurat-wrappers/blob/feat/monocle3/docs/monocle3.Rmd

should I have to normalize this Seurat_object_subset? Because in code they used integrated data as an example.
I am stuck at this point. Please guide me on this.
Thanks.

Hi,
I used Seurat to cluster my scRNA data.
This is my clustering result.

Now, I want to apply Monocle3 on clusters 3,4,6, and 7.
I extract these clusters.
Seurat_object_subset <- subset(Seurat_object, idents = c(7,3,4,6))
I am following this example: https://github.com/satijalab/seurat-wrappers/blob/feat/monocle3/docs/monocle3.Rmd

should I have to normalize this Seurat_object_subset? Because in code they used integrated data as an example.
I am stuck at this point. Please guide me on this.
Thanks.

I got this error when running
show.velocity.on.embedding.cor(emb = Embeddings(object = endo_sub, reduction = "umap"),
vel = Tool(object = endo_sub, slot = "RunVelocity"), n = 200, scale = "sqrt", cell.colors = ac(x = cell.colors, alpha = 0.5),
cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 1,
do.par = FALSE, cell.border.alpha = 0.1)

Error in seq.default(rx[1], rx[2], length.out = grid.n) :
'from' must be a finite number

I've read comments of NaN values but I'm not sure where to start checking

Hi all,

I am using R version 3.6.0 and Seurat_3.1.1. During installation I get

Error: object ‘IsGlobal’ is not exported by 'namespace:Seurat'
Execution halted
ERROR: lazy loading failed for package ‘SeuratWrappers

Do 'SeuratWrappers' work only with version equal/greater than Seurat_3.1.4 ?

Thanks in advance and best wishes,
Vikas

Dear all,

I am trying to run velocity analysis on my sample, following the seurat-wrappers/velocyto.R tutorial.

Everything goes smoothly till the step where I have to plot the velocity vector:

show.velocity.on.embedding.cor(emb = Embeddings(object = bm, reduction = "umap"), vel = Tool(object = bm,
slot = "RunVelocity"), n = 200, scale = "sqrt", cell.colors = ac(x = cell.colors, alpha = 0.5),
cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 1,
do.par = FALSE, cell.border.alpha = 0.1)

Error I get :
delta projections ... sqrt
libgomp: Out of memory allocating 463856469312 bytes

Can anyone help me solve this ?

Many thanks.

Best,
Moheb

p.s I am running R in windows linux subsystem,
R version 3.6.3 (2020-02-29) -- "Holding the Windsock"
Platform: x86_64-pc-linux-gnu (64-bit)

Hi, I am trying to merge several Seurat objects includes RNA velocity, and here is the error:

Error in GetAssay.Seurat(object = object, assay = assay) :
RNA is not an assay present in the given object. Available assays are: spliced, unspliced, ambiguous, SCT

How should I fix it ?

here is the error message

Installing package into ‘/usr/local/lib/R/site-library’
(as ‘lib’ is unspecified)
* installing *source* package ‘SeuratWrappers’ ...
** using staged installation
** R
** byte-compile and prepare package for lazy loading
Error: object ‘LogSeuratCommand’ is not exported by 'namespace:Seurat'
Execution halted
ERROR: lazy loading failed for package ‘SeuratWrappers’
* removing ‘/usr/local/lib/R/site-library/SeuratWrappers’
Error in i.p(...) : 
  (converted from warning) installation of package ‘/tmp/RtmpjqWJ0V/file302df65dbd/SeuratWrappers_0.1.0.tar.gz’ had non-zero exit status

here is the R sessionInfo

R version 3.6.0 (2019-04-26) 
Platform: x86_64-pc-linux-gnu (64-bit) 
Running under: Debian GNU/Linux 9 (stretch)  
Matrix products: default 
BLAS/LAPACK: /usr/lib/libopenblasp-r0.2.19.so  
locale:  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8     [6] LC_MESSAGES=C              LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C     

attached base packages: [1] grid      stats     graphics  grDevices utils     datasets  methods   base       
other attached packages:  [1] clustree_0.4.0  ggraph_1.0.2    cowplot_0.9.4   tictoc_1.0      forcats_0.4.0   stringr_1.4.0   dplyr_0.8.1     purrr_0.3.2     readr_1.3.1     [10] tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1 velocyto.R_0.6  Matrix_1.2-17   Seurat_3.0.0     

oaded via a namespace (and not attached):   
[1] Rtsne_0.15          colorspace_1.4-1    ggridges_0.5.1      rprojroot_1.3-2     fs_1.3.1            rstudioapi_0.10     listenv_0.7.0        
[8] farver_1.1.0        npsurv_0.4-0        remotes_2.0.4       ggrepel_0.8.1       lubridate_1.7.4     xml2_1.2.0          codetools_0.2-16     
[15] splines_3.6.0       R.methodsS3_1.7.1   lsei_1.2-0          knitr_1.22          pkgload_1.0.2       polyclip_1.10-0     jsonlite_1.6         
[22] packrat_0.5.0       broom_0.5.2         ica_1.0-2           cluster_2.0.8       png_0.1-7           R.oo_1.22.0         ggforce_0.2.2       
[29] sctransform_0.2.0   compiler_3.6.0      httr_1.4.0          backports_1.1.4     assertthat_0.2.1    lazyeval_0.2.2      cli_1.1.0            
[36] tweenr_1.0.1        prettyunits_1.0.2   htmltools_0.3.6     tools_3.6.0         rsvd_1.0.0          igraph_1.2.4.1      gtable_0.3.0         
[43] glue_1.3.1          RANN_2.6.1          reshape2_1.4.3      Rcpp_1.0.1          Biobase_2.44.0      cellranger_1.1.0    gdata_2.18.0         
[50] ape_5.3             nlme_3.1-139        gbRd_0.4-11         lmtest_0.9-37       xfun_0.7            ps_1.3.0            globals_0.12.4       
[57] testthat_2.1.1      rvest_0.3.3         irlba_2.3.3         devtools_2.0.2      gtools_3.8.1        future_1.13.0       MASS_7.3-51.4        
[64] zoo_1.8-5           scales_1.0.0        pcaMethods_1.76.0   hms_0.4.2           parallel_3.6.0      RColorBrewer_1.1-2  curl_3.3             
[71] yaml_2.2.0          memoise_1.1.0       reticulate_1.12     pbapply_1.4-0       gridExtra_2.3       stringi_1.4.3       desc_1.2.0           
[78] caTools_1.17.1.2    BiocGenerics_0.30.0 pkgbuild_1.0.3      bibtex_0.4.2        Rdpack_0.11-0       SDMTools_1.1-221.1  rlang_0.3.4          
[85] pkgconfig_2.0.2     bitops_1.0-6        evaluate_0.13       lattice_0.20-38     ROCR_1.0-7          htmlwidgets_1.3     processx_3.3.1       
[92] tidyselect_0.2.5    plyr_1.8.4          magrittr_1.5        R6_2.4.0            gplots_3.0.1.1      generics_0.0.2      pillar_1.4.0         
[99] haven_2.1.0         withr_2.1.2         mgcv_1.8-28         fitdistrplus_1.0-14 survival_2.44-1.1   future.apply_1.2.0  tsne_0.1-3          
[106] modelr_0.1.4        crayon_1.3.4        KernSmooth_2.23-15  plotly_4.9.0        rmarkdown_1.12      usethis_1.5.0       viridis_0.5.1       
[113] readxl_1.3.1        data.table_1.12.2   callr_3.2.0         metap_1.1           digest_0.6.18       R.utils_2.8.0       munsell_0.5.0       
[120] viridisLite_0.3.0   sessioninfo_1.1.1

Dear Seurat authors,

many many thanks for publishing the script that integrates Seurat3 and Monocle3 :

https://htmlpreview.github.io/?https://github.com/satijalab/seurat-wrappers/blob/master/docs/monocle3.html

i am following the script, and everything works well until the line of the R code :

cds <- as.cell_data_set(integrated)

when Seurat3 sends a message " could not find function "as.cell_data_set""

Shall i use "as.CellDataSet" (as in Seurat3 manual), the message is :

"Please install monocle from Bioconductor before converting to a CellDataSet object"

Any suggestions would be welcome. thanks a lot !

> sessionInfo()
R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS

Matrix products: default
BLAS:   /home/bogdan/R/R-4.0.0.install/lib/R/lib/libRblas.so
LAPACK: /home/bogdan/R/R-4.0.0.install/lib/R/lib/libRlapack.so

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

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] hcabm40k.SeuratData_3.0.0   magrittr_1.5               
 [3] patchwork_1.0.0             ggplot2_3.3.1              
 [5] SeuratWrappers_0.1.0        pbmc3k.SeuratData_3.1.4    
 [7] SeuratData_0.2.1            Seurat_3.1.5               
 [9] monocle3_0.2.2              SingleCellExperiment_1.10.1
[11] SummarizedExperiment_1.18.1 DelayedArray_0.14.0        
[13] matrixStats_0.56.0          GenomicRanges_1.40.0       
[15] GenomeInfoDb_1.24.2         IRanges_2.22.2             
[17] S4Vectors_0.26.1            Biobase_2.48.0             
[19] BiocGenerics_0.34.0        

loaded via a namespace (and not attached):
  [1] Rtsne_0.15             colorspace_1.4-1       ellipsis_0.3.1        
  [4] ggridges_0.5.2         rprojroot_1.3-2        XVector_0.28.0        
  [7] fs_1.4.1               farver_2.0.3           leiden_0.3.3          
 [10] listenv_0.8.0          remotes_2.1.1          ggrepel_0.8.2         
 [13] RSpectra_0.16-0        fansi_0.4.1            codetools_0.2-16      
 [16] splines_4.0.0          pkgload_1.1.0          jsonlite_1.6.1        
 [19] ica_1.0-2              cluster_2.1.0          png_0.1-7             
 [22] uwot_0.1.8             sctransform_0.2.1      BiocManager_1.30.10   
 [25] compiler_4.0.0         httr_1.4.1             backports_1.1.8       
 [28] assertthat_0.2.1       Matrix_1.2-18          lazyeval_0.2.2        
 [31] cli_2.0.2              htmltools_0.5.0        prettyunits_1.1.1     
 [34] tools_4.0.0            rsvd_1.0.3             igraph_1.2.5          
 [37] gtable_0.3.0           glue_1.4.1             GenomeInfoDbData_1.2.3
 [40] RANN_2.6.1             reshape2_1.4.4         dplyr_1.0.0           
 [43] rappdirs_0.3.1         Rcpp_1.0.4.6           vctrs_0.3.1           
 [46] ape_5.4                nlme_3.1-148           lmtest_0.9-37         
 [49] stringr_1.4.0          globals_0.12.5         ps_1.3.3              
 [52] testthat_2.3.2         lifecycle_0.2.0        irlba_2.3.3           
 [55] devtools_2.3.0         future_1.17.0          zlibbioc_1.34.0       
 [58] MASS_7.3-51.6          zoo_1.8-8              scales_1.1.1          
 [61] RColorBrewer_1.1-2     curl_4.3               memoise_1.1.0         
 [64] reticulate_1.16        pbapply_1.4-2          gridExtra_2.3         
 [67] stringi_1.4.6          desc_1.2.0             pkgbuild_1.0.8        
 [70] rlang_0.4.6            pkgconfig_2.0.3        bitops_1.0-6          
 [73] lattice_0.20-41        ROCR_1.0-11            purrr_0.3.4           
 [76] labeling_0.3           htmlwidgets_1.5.1      cowplot_1.0.0         
 [79] processx_3.4.2         tidyselect_1.1.0       RcppAnnoy_0.0.16      
 [82] plyr_1.8.6             R6_2.4.1               generics_0.0.2        
 [85] pillar_1.4.4           withr_2.2.0            fitdistrplus_1.1-1    
 [88] survival_3.2-3         RCurl_1.98-1.2         tibble_3.0.1          
 [91] future.apply_1.5.0     tsne_0.1-3             crayon_1.3.4          
 [94] KernSmooth_2.23-17     plotly_4.9.2.1         viridis_0.5.1         
 [97] usethis_1.6.1          grid_4.0.0             data.table_1.12.8     
[100] callr_3.4.3            digest_0.6.25          tidyr_1.1.0           
[103] munsell_0.5.0          viridisLite_0.3.0      sessioninfo_1.1.1   

Hi!
I've been playing with this Seurat wrapper for a while and I found something that confuses me a little.

velocyto.R::gene.relative.velocity.estimates() expects the input matrices to be raw count matrices (Well, at least I think so. But It really looks like that, both from the docs and from the fact that it starts with adding pseudocount and log transforming the matrices).

Now RunVelocity() uses GetAssayData() to pull data matrices from our "spliced" and "unspliced" assays. GetAssayData() returns the "data" matrix (i.e. cells@assays$spliced@data), which is identical to the "counts" matrix (i.e. cells@assays$spliced@counts), but only as long as the user did not run any normalization procedure.

If the user runs some normalization, then RunVelocity() will use the normalized matrix as an input for velocity calculation, which I think is incorrect. Things can get even wilder in a pretty imaginable scenario where the user normalizes and scales the "spliced" assay first, say to cluster the cells, but does not touch the "unspliced" assay, and the runs RunVelocity(). In that case, RunVelocity() will feed velocyto.R::gene.relative.velocity.estimates() with normalized "spliced" matrix and raw counts "unspliced" matrix.

This is not so much of a problem when users use Seurat::SCTransform(), like in the Vignette. SCTransform creates a new assay and then the untouched old assays are fed into RunVelocity(). But if one uses some different normalization procedure, the "data" matrix gets changed, which can lead to weird results, even tough the untouched "counts" matrix is still there and could have been used.

This got me confused for a while so I was thinking that using the "counts" matrix or writing a note into the documentation could prevent such confusion. Or is it actually meaningful to use the normalized matrix instead of raw counts? Thank you.

Hi,
Thank you for the great functions for Seurat and making life more easy. Is it possible to run alra on integrated data by preserving the conditions, ie something like RunALRA(object, split.by="Treatment") ?

Hello! I have an integrated Seurat object (R 3.5.1, Seurat_3.1.5) which I am trying to use with SeuratWrapper's Monocle3 trajectory analysis (R 3.6.1, Seurat_3.1.5). Following the tutorial, I get following error running cluster_cells command. I also tried updating Seurat object using UpdateSeuratObject, but keep getting the same error. I can always re-run integration pipeline but I want to use the UMAP coordinates & cluster ids from my existing integration results. Please suggest a solution. Thanks!

> cds <- as.cell_data_set(integrated)
Warning: Monocle 3 trajectories require cluster partitions, which Seurat does not calculate. Please run 'cluster_cells' on your cell_data_set object
> cds <- cluster_cells(cds)
Error: No dimensionality reduction for UMAP calculated. Please run reduce_dimensions with reduction_method = UMAP before running cluster_cells

Hi, I am using SeuratWrappers to run RNA velocity with Seurat. The result is great, but I want to plot the graph with my own UMAP projections. I've tried assigning the projections directly to the embedding and CreateDimReducObject() to do the job, but nothing worked. Is there a way to use custom reduction in this situation?

I'm having this error (title of the issue) when trying to run RunQuantileAlignSNF and I can't find the cause of the issue

> sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3

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

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

other attached packages:
[1] liger_0.5.0.9000     patchwork_1.0.0      Matrix_1.2-18        cowplot_1.0.0        SeuratWrappers_0.1.0
[6] dplyr_0.8.4          Seurat_3.1.4  

Any help?
Thanks

Dear Seurat Team,
Introducing RNA-velocyto workflow in seurat is very helpful. Can you please help us with image quality. showembeddings function saves .TIFF image but the quality is very poor.

Any Suggestions ?

Cheers

Hi,

I've used RunFastMNN() to integrate two batches following this link:
http://htmlpreview.github.io/?https://github.com/satijalab/seurat-wrappers/blob/master/docs/fast_mnn.html
It works quite well.

Now I want to get the integrated assay to do a trajectory analysis. However, I've noticed that after running RunFastMNN(), there is only one active assay "RNA".
I think I should use the integrated assay for a trajectory analysis. Is there a way to get the integrated assay and use it to do a trajectory analysis?

Thanks!

Hi
I have a plot after running Velocyto using Seurat.
Filtered_cells_S1_velocyto_dim20.pdf
I want to zoom into the upper clusters, where the dimensions are following UMAP_1 >-5 and UMAP_2 >5.
I tried to save these dimensions like this way.

umap <- data.frame(bm@reductions$[email protected], row.names = rownames(bm@reductions$[email protected])

Could you plz tell me how can I plot only a specific dimension in Velocyto?
Thanks

Hi. I'd like to create a bioconda package for the project, and having a version tag to be able to reference a release would be very helpful. Any chance of that?

Hi,
Thanks for developing the great tools and continuing having new additions.
My question is how to properly implement RNA velocity to Seurat objects. Specifically, what reference should be use for mapping: cDNA/introns info are required for velocity but I think mapping to the whole transcriptome may be more accurate. In this case, can we generate velocity with a seurat object consisting the spliced/unspliced assays and then transfer the calculated velocity info to another seurat object generated by mapping to the transcriptome by matching the cell identities? If so, how to do it? If not, what could be a better strategy?
Thanks much!
Xiao

Hi there Seurat team!

I'm sorry if this is the wrong place to post this question - please let me know. I'm merging several Seurat objects with FindIntegrationAnchors and IntegrateData.

One of these objects came from a .loom velocyto output, and I wonder if I'll be able to run RunVelocity and plot the RNA velocity only for the cells belonging to that original dataset, even on a merged-dataset embedding.

Do you have any recommendations on how to proceed?

Hi,
I was wondering what would be the best approach to perform clustering on a subset of cells pulled out from a MNN-batch-corrected object.
I used fastMNN from SeuratWrappers to perform a MNN batch-correction and perform an integrated analysis.
Then I subsetted a cluster of cells and wanted to perform re-clustering.
Should I proceed with a Seurat workflow (standard or SCT) or do I need to perform another round of MNN batch-correction?
Thank you.

Hey!
I generated a loom file using velocyto, trying to read it into seurat but getting this error:

> ldat <- ReadVelocity(file = "~/UT/possorted_genome_bam_DYCR7.loom")
reading loom file via hdf5r...
Error in H5File.open(filename, mode, file_create_pl, file_access_pl) : 
  HDF5-API Errors:
    error #000: H5F.c in H5Fopen(): line 509: unable to open file
        class: HDF5
        major: File accessibilty
        minor: Unable to open file

    error #001: H5Fint.c in H5F_open(): line 1498: unable to open file: time = Wed Oct 16 11:20:43 2019
, name = '/Users/gordonbeattie/UT/possorted_genome_bam_DYCR7.loom', tent_flags = 0
        class: HDF5
        major: File accessibilty
        minor: Unable to open file

    error #002: H5FD.c in H5FD_open(): line 734: open failed
        class: HDF5
        major: Virtual File Layer
        minor: Unable to initialize object

    error #003: H5FDsec2.c in H5FD_sec2_open(): line 346: unable to open file: name = '/Users/gordonbeattie/UT/possorted_genome_bam_DYCR7.loom', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0
        class: HDF5
        major: File accessibilty
        minor: Unable to open file

Thanks in advance for any assistance!

Hello,
I have been working with the vignette integrating monocle3 and Seurat, which works fine, but I run into errors when trying to run other functions of monocle3.
I get this error with the graph_test function:

graph <- graph_test(cds, neighbor_graph="principal_graph", cores=4)
Error in names(sf) <- colnames(SingleCellExperiment::counts(cds)) :
attempt to set an attribute on NULL

I also cannot plot genes with the plot_cells function of monocle3 :

genes <- c("ACTG2","ACTA2")
plot_cells(cds, genes=genes, label_cell_groups=FALSE, show_trajectory_graph=FALSE)
Error in plot_cells(cds, genes = genes, label_cell_groups = FALSE, show_trajectory_graph = FALSE) :
None of the provided genes were found in the cds

The plot_cells function otherwise works fine with other arguments (partitions, metadata from seurat, pseudotime, etc). Finding gene names seems to be the issue

Do you have any suggestions ?
Thanks for your help !

Dear all,
I followed the vignette https://htmlpreview.github.io/?https://github.com/satijalab/seurat.wrappers/blob/master/docs/fast_mnn.html on a previously saved seurat object.
After the first steps of Normalize and FindVariableFeatures I could see the VariableFeatures by giving

VariableFeatures(myobject).

However after running fastMNN if I give again

VariableFeatures(myobject)
logical(0).

Is there a bug or a reason for this?
Thanks
Roberta

Hi,
I´ve an issue regarding the monocle 3 pipeline with Seuat objects.
This is the error I got:

cds_rep1 <- as.CellDataSet(integrated_rep1)
Error in [[<-.data.frame(*tmp*, i, value = numeric(0)) :
replacement has 0 rows, data has 7346

sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)

Matrix products: default

locale:
[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 LC_MONETARY=German_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=German_Germany.1252

attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets methods base

other attached packages:
[1] patchwork_1.0.1 metap_1.4 multtest_2.44.0 scales_1.1.1 cowplot_1.0.0
[6] RColorBrewer_1.1-2 ggplot2_3.3.2 gplots_3.0.4 iTALK_0.1.0 dplyr_1.0.2
[11] Seurat_3.2.0 monocle3_0.2.3.0 SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.2 DelayedArray_0.14.0
[16] matrixStats_0.56.0 GenomicRanges_1.40.0 GenomeInfoDb_1.24.2 IRanges_2.22.2 S4Vectors_0.26.1
[21] Biobase_2.48.0 BiocGenerics_0.34.0

loaded via a namespace (and not attached):
[1] rappdirs_0.3.1 SparseM_1.78 maxLik_1.4-0 tidyr_1.1.1 bit64_4.0.2
[6] multcomp_1.4-13 irlba_2.3.3 Rook_1.1-1 data.table_1.13.0 rpart_4.1-15
[11] RCurl_1.98-1.2 generics_0.0.2 TH.data_1.0-10 callr_3.4.3 usethis_1.6.1
[16] RSQLite_2.2.0 RANN_2.6.1 VGAM_1.1-3 combinat_0.0-8 future_1.18.0
[21] bit_4.0.4 mutoss_0.1-12 spatstat.data_1.4-3 httpuv_1.5.4 assertthat_0.2.1
[26] viridis_0.5.1 hms_0.5.3 RMTstat_0.3 promises_1.1.1 progress_1.2.2
[31] fansi_0.4.1 caTools_1.18.0 tmvnsim_1.0-2 igraph_1.2.5 DBI_1.1.0
[36] geneplotter_1.66.0 htmlwidgets_1.5.1 sparsesvd_0.2 RcppArmadillo_0.9.900.2.0 purrr_0.3.4
[41] ellipsis_0.3.1 RSpectra_0.16-0 backports_1.1.7 V8_3.2.0 DDRTree_0.1.5
[46] gbRd_0.4-11 annotate_1.66.0 deldir_0.1-28 vctrs_0.3.2 remotes_2.2.0
[51] quantreg_5.61 Cairo_1.5-12.2 ROCR_1.0-11 abind_1.4-5 withr_2.2.0
[56] bdsmatrix_1.3-4 sctransform_0.2.1 gamlss_5.1-7 prettyunits_1.1.1 mnormt_2.0.1
[61] goftest_1.2-2 cluster_2.1.0 ape_5.4-1 lazyeval_0.2.2 crayon_1.3.4
[66] genefilter_1.70.0 labeling_0.3 edgeR_3.30.3 pkgconfig_2.0.3 slam_0.1-47
[71] nlme_3.1-148 pkgload_1.1.0 nnet_7.3-14 devtools_2.3.1 rlang_0.4.7
[76] globals_0.12.5 lifecycle_0.2.0 miniUI_0.1.1.1 MatrixModels_0.4-1 sandwich_2.5-1
[81] gamlss.data_5.1-4 extRemes_2.0-12 mathjaxr_1.0-1 rsvd_1.0.3 rprojroot_1.3-2
[86] polyclip_1.10-0 distillery_1.1 lmtest_0.9-37 Matrix_1.2-18 zoo_1.8-8
[91] ggridges_0.5.2 GlobalOptions_0.1.2 processx_3.4.3 pheatmap_1.0.12 png_0.1-7
[96] viridisLite_0.3.0 rjson_0.2.20 bitops_1.0-6 Lmoments_1.3-1 KernSmooth_2.23-17
[101] blob_1.2.1 shape_1.4.4 stringr_1.4.0 brew_1.0-6 memoise_1.1.0
[106] magrittr_1.5 plyr_1.8.6 ica_1.0-2 bibtex_0.4.2.2 gdata_2.18.0
[111] zlibbioc_1.34.0 compiler_4.0.2 HSMMSingleCell_1.8.0 miscTools_0.6-26 bbmle_1.0.23.1
[116] plotrix_3.7-8 pcaMethods_1.80.0 DESeq2_1.28.1 fitdistrplus_1.1-1 cli_2.0.2
[121] XVector_0.28.0 listenv_0.8.0 pbapply_1.4-3 ps_1.3.4 MASS_7.3-51.6
[126] mgcv_1.8-31 tidyselect_1.1.0 MAST_1.14.0 stringi_1.4.6 densityClust_0.3
[131] locfit_1.5-9.4 ggrepel_0.8.2 grid_4.0.2 randomcoloR_1.1.0.1 tools_4.0.2
[136] future.apply_1.6.0 circlize_0.4.10 rstudioapi_0.11 gamlss.dist_5.1-7 monocle_2.16.0
[141] gridExtra_2.3 farver_2.0.3 Rtsne_0.15 DEsingle_1.8.2 digest_0.6.25
[146] FNN_1.1.3 shiny_1.5.0 qlcMatrix_0.9.7 Rcpp_1.0.5 pscl_1.5.5
[151] later_1.1.0.1 RcppAnnoy_0.0.16 httr_1.4.2 AnnotationDbi_1.50.3 Rdpack_1.0.0
[156] colorspace_1.4-1 XML_3.99-0.4 fs_1.4.2 tensor_1.5 reticulate_1.16
[161] splines_4.0.2 uwot_0.1.8 sn_1.6-2 conquer_1.0.1 spatstat.utils_1.17-0
[166] flexmix_2.3-15 plotly_4.9.2.1 sessioninfo_1.1.1 xtable_1.8-4 jsonlite_1.7.0
[171] spatstat_1.64-1 modeltools_0.2-23 testthat_2.3.2 R6_2.4.1 TFisher_0.2.0
[176] pillar_1.4.6 htmltools_0.5.0 mime_0.9 glue_1.4.1 fastmap_1.0.1
[181] BiocParallel_1.22.0 codetools_0.2-16 pkgbuild_1.1.0 mvtnorm_1.1-1 lattice_0.20-41
[186] tibble_3.0.3 network_1.16.0 numDeriv_2016.8-1.1 curl_4.3 leiden_0.3.3
[191] gtools_3.8.2 scde_2.16.0 survival_3.1-12 limma_3.44.3 docopt_0.7.1
[196] desc_1.2.0 fastICA_1.2-2 munsell_0.5.0 GenomeInfoDbData_1.2.3 reshape2_1.4.4
[201] gtable_0.3.0

It would be nice if anybody can help me.

Thanks,
Tony

https://bioconductor.org/packages/devel/bioc/html/batchelor.html

MarioniLab/scran#31

fastMNN is not in scran anymore (at least as of 1.13.10)

I am trying to use RNAvelocity. I am not sure that I started correctly.

I converted Seurat object to loom using as.loom function but the loom object that is produced doesn't have spliced and unspliced assays needed for RunVelocity function.

Could you please help?

Hi,

I am following the scVelo vignette with the following seurat Object. After creating h5ad file and importing it as an anndata object, it seems that, assays other than RNA is not written to h5ad. If I use assays argument in Convert function, it only allows one assay, not multiple assays. Can you help me solve this? Thanks in advance!

I am not sure whether I should put this here or in SeuratDisk github page.

>Fib
An object of class Seurat 
152625 features across 13143 samples within 6 assays 
Active assay: RNA (25697 features, 0 variable features)
 5 other assays present: SCT, integrated, spliced, unspliced, ambiguous
 4 dimensional reductions calculated: pca, umap, harm_umap, harmony

In R

>DefaultAssay(Fib) <- "RNA"

>SaveH5Seurat(Fib, filename = "Fibroblast_for_scVelo_complete.h5Seurat")
Creating h5Seurat file for version 3.1.2
Adding counts for RNA
Adding data for RNA
No variable features found for RNA
No feature-level metadata found for RNA
Adding counts for SCT
Adding data for SCT
Adding scale.data for SCT
No variable features found for SCT
No feature-level metadata found for SCT
Adding data for integrated
Adding scale.data for integrated
Adding variable features for integrated
No feature-level metadata found for integrated
Adding counts for spliced
Adding data for spliced
No variable features found for spliced
No feature-level metadata found for spliced
Adding counts for unspliced
Adding data for unspliced
No variable features found for unspliced
No feature-level metadata found for unspliced
Adding counts for ambiguous
Adding data for ambiguous
No variable features found for ambiguous
No feature-level metadata found for ambiguous
Adding cell embeddings for pca
Adding loadings for pca
No projected loadings for pca
Adding standard deviations for pca
No JackStraw data for pca
Adding cell embeddings for umap
No loadings for umap
No projected loadings for umap
No standard deviations for umap
No JackStraw data for umap
Adding cell embeddings for harm_umap
No loadings for harm_umap
No projected loadings for harm_umap
No standard deviations for harm_umap
No JackStraw data for harm_umap
Adding cell embeddings for harmony
No loadings for harmony
No projected loadings for harmony
No standard deviations for harmony
No JackStraw data for harmony

>Convert("Fibroblast_for_scVelo_complete.h5Seurat", dest = "h5ad")
Validating h5Seurat file
Adding data from RNA as X
Adding counts from RNA as raw
Transfering meta.data to obs
Adding dimensional reduction information for umap (global)

In python

import scvelo as scv
adata = scv.read("/mnt/DATA1/Fibrosis/Full Scale Analysis/scVelo/Fibroblast_integrated_for_scVelo_complete.h5ad")
adata
Out[8]: 
AnnData object with n_obs × n_vars = 13143 × 25697 
    obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA', 'GC_Sample_Number', 'Location', 'Segment', 'Patient_Type', 'Biopsy_method', 'Age', 'Patient_no', 'Sample', 'Sample_type', 'Gender', 'percent.mt', 'nCount_SCT', 'nFeature_SCT', 'integrated_snn_res.2', 'seurat_clusters', 'Harmony_Clusters', 'integrated_snn_res.1', 'integrated_snn_res.0.6', 'integrated_snn_res.0.4', 'integrated_snn_res.0.3', 'integrated_snn_res.0.2', 'integrated_snn_res.0.15', 'subtype', 'colors', 'nCount_spliced', 'nFeature_spliced', 'nCount_unspliced', 'nFeature_unspliced', 'nCount_ambiguous', 'nFeature_ambiguous', 'initial_size'
    var: 'features'
    obsm: 'X_umap'

notice that var: only shows 'features' as opposed to var: 'features', 'ambiguous_features', 'spliced_features', 'unspliced_features' shwon in the vignette

I'm new to RNA velocyto and have a question on Velocyto

do we always have to go back to original raw file to estimate/calculate spliced and unspliced reads? or are there anyways that we can calculate them on subsetted seurat objects?

Hello. I wish to thank you for your work in developing your very efficient and user-friendly package Seurat. To begin I wish to mention that this issue number #1948: Remove background in FeaturePlot has not been able to help me because adding the do.return = TRUE argument does not help the problem.

Inspired by the method in the walk-around function in this post issue #528 , I have successfully plotted the expression of several genes at the same time on my clusters. However I want to remove the default background from the FeaturePlot function by manipulationg the ggplot object that should be returned but I only obtain the same unmodified plot and the value NULL returned when I add additional geom layers.

Seurat::FeaturePlot(object = object, features.plot = "fibrotic.set.score", do.return = T)+ theme_classic()

I get in return the same default plot and NULL returned.

Furthermore, I just noticed that FeaturePlot function in my case returns a list and not a ggplot object.

> class(Seurat::FeaturePlot(nacl_bleo_sub,
+                     features.plot = "fibrotic.set.score",
+                     do.return = T))
[1] "list"

I will be very grateful for any hints to overcome these issues.

session info:

R version 3.5.1 (2018-07-02)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

Matrix products: default

locale:
[1] LC_COLLATE=German_Germany.1252  LC_CTYPE=German_Germany.1252    LC_MONETARY=German_Germany.1252
[4] LC_NUMERIC=C                    LC_TIME=German_Germany.1252    

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] Seurat_2.3.4                DESeq2_1.22.2               forcats_0.4.0               stringr_1.4.0              
 [5] purrr_0.3.2                 readr_1.3.1                 tidyr_0.8.3                 tibble_2.1.3               
 [9] tidyverse_1.2.1             BisqueRNA_1.0               cowplot_1.0.0               SingleCellExperiment_1.4.1 
[13] SummarizedExperiment_1.12.0 DelayedArray_0.8.0          BiocParallel_1.16.6         matrixStats_0.54.0         
[17] GenomicRanges_1.34.0        GenomeInfoDb_1.18.2         IRanges_2.16.0              S4Vectors_0.20.1           
[21] xlsx_0.6.1                  openxlsx_4.1.0.1            dplyr_0.8.3                 cellrangerRkit_1.1.0       
[25] Rmisc_1.5                   plyr_1.8.4                  lattice_0.20-35             bit64_0.9-7                
[29] bit_1.1-14                  ggplot2_3.2.0               RColorBrewer_1.1-2          Biobase_2.42.0             
[33] BiocGenerics_0.28.0         Matrix_1.2-14              

loaded via a namespace (and not attached):
  [1] reticulate_1.13        R.utils_2.9.0          tidyselect_0.2.5       RSQLite_2.1.2          AnnotationDbi_1.44.0  
  [6] htmlwidgets_1.3        grid_3.5.1             Rtsne_0.15             munsell_0.5.0          codetools_0.2-15      
 [11] ica_1.0-2              future_1.14.0          withr_2.1.2            colorspace_1.4-1       knitr_1.23            
 [16] rstudioapi_0.10        ROCR_1.0-7             dtw_1.21-3             robustbase_0.93-5      rJava_0.9-11          
 [21] gbRd_0.4-11            listenv_0.7.0          lars_1.2               Rdpack_0.11-0          labeling_0.3          
 [26] GenomeInfoDbData_1.2.0 pheatmap_1.0.12        rhdf5_2.26.2           vctrs_0.2.0            generics_0.0.2        
 [31] xfun_0.8               diptest_0.75-7         R6_2.4.0               rsvd_1.0.2             locfit_1.5-9.1        
 [36] flexmix_2.3-15         hdf5r_1.2.0            bitops_1.0-6           assertthat_0.2.1       SDMTools_1.1-221.1    
 [41] scales_1.0.0           nnet_7.3-12            gtable_0.3.0           npsurv_0.4-0           globals_0.12.4        
 [46] rlang_0.4.0            zeallot_0.1.0          genefilter_1.64.0      splines_3.5.1          lazyeval_0.2.2        
 [51] acepack_1.4.1          broom_0.5.2            checkmate_1.9.4        BiocManager_1.30.4     yaml_2.2.0            
 [56] reshape2_1.4.3         modelr_0.1.4           backports_1.1.4        Hmisc_4.2-0            tools_3.5.1           
 [61] gplots_3.0.1.1         proxy_0.4-23           ggridges_0.5.1         Rcpp_1.0.2             base64enc_0.1-3       
 [66] zlibbioc_1.28.0        RCurl_1.95-4.12        rpart_4.1-13           pbapply_1.4-1          zoo_1.8-6             
 [71] haven_2.1.1            ggrepel_0.8.1          cluster_2.0.7-1        magrittr_1.5           data.table_1.12.2     
 [76] lmtest_0.9-37          RANN_2.6.1             fitdistrplus_1.0-14    hms_0.5.0              xlsxjars_0.6.1        
 [81] lsei_1.2-0             xtable_1.8-4           XML_3.98-1.20          mclust_5.4.5           readxl_1.3.1          
 [86] gridExtra_2.3          compiler_3.5.1         KernSmooth_2.23-15     crayon_1.3.4           R.oo_1.22.0           
 [91] htmltools_0.3.6        segmented_1.0-0        snow_0.4-3             Formula_1.2-3          geneplotter_1.60.0    
 [96] RcppParallel_4.4.3     lubridate_1.7.4        DBI_1.0.0              fpc_2.2-3              MASS_7.3-50           
[101] cli_1.1.0              R.methodsS3_1.7.1      gdata_2.18.0           metap_1.1              igraph_1.2.4.1        
[106] pkgconfig_2.0.2        foreign_0.8-71         plotly_4.9.0           foreach_1.4.7          xml2_1.2.1            
[111] annotate_1.60.1        XVector_0.22.0         bibtex_0.4.2           rvest_0.3.4            digest_0.6.20         
[116] sctransform_0.2.0      RcppAnnoy_0.0.12       tsne_0.1-3             cellranger_1.1.0       leiden_0.3.1          
[121] htmlTable_1.13.1       uwot_0.1.3             kernlab_0.9-27         modeltools_0.2-22      gtools_3.8.1          
[126] nlme_3.1-137           jsonlite_1.6           Rhdf5lib_1.4.3         viridisLite_0.3.0      pillar_1.4.2          
[131] DEoptimR_1.0-8         httr_1.4.0             survival_2.42-3        glue_1.3.1             zip_2.0.3             
[136] prabclus_2.3-1         iterators_1.0.12       png_0.1-7              mixtools_1.1.0         class_7.3-14          
[141] stringi_1.4.3          blob_1.2.0             doSNOW_1.0.18          latticeExtra_0.6-28    caTools_1.17.1.2      
[146] memoise_1.1.0          irlba_2.3.3            future.apply_1.3.0     ape_5.3 

Hello,

I am trying to install seurat-wrappers and running into an error, could you please help?

devtools::install_github('satijalab/seurat-wrappers')
Downloading GitHub repo satijalab/seurat-wrappers@master
✓ checking for file ‘/private/var/folders/k6/kc8fk3c56j5d_msh9ttjvjxr0000gn/T/RtmpwyP1Vv/remotesc4b97ff971c7/satijalab-seurat-wrappers-1e814d1/DESCRIPTION’ ...
─ preparing ‘SeuratWrappers’:
✓ checking DESCRIPTION meta-information
─ checking for LF line-endings in source and make files and shell scripts
─ checking for empty or unneeded directories
─ building ‘SeuratWrappers_0.1.0.tar.gz’

• installing source package ‘SeuratWrappers’ ...
  ** using staged installation
  ** R
  ** byte-compile and prepare package for lazy loading
  sh: line 1: 64739 Killed: 9 R_TESTS= '/Library/Frameworks/R.framework/Resources/bin/R' --no-save --slave 2>&1 < '/var/folders/k6/kc8fk3c56j5d_msh9ttjvjxr0000gn/T//RtmpsUkqSj/filefcdc4d4df839'
  ERROR: lazy loading failed for package ‘SeuratWrappers’
• removing ‘/Library/Frameworks/R.framework/Versions/3.6/Resources/library/SeuratWrappers’
  Error: Failed to install 'SeuratWrappers' from GitHub:
  (converted from warning) installation of package ‘/var/folders/k6/kc8fk3c56j5d_msh9ttjvjxr0000gn/T//RtmpwyP1Vv/filec4b97073c4c9/SeuratWrappers_0.1.0.tar.gz’ had non-zero exit status

session_info()
─ Session info ────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 3.6.3 (2020-02-29)
os macOS Catalina 10.15.3
system x86_64, darwin15.6.0
ui RStudio
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2020-03-23

─ Packages ────────────────────────────────────────────────────────────────────────────────────────
package * version date lib source
ape 5.3 2019-03-17 [1] CRAN (R 3.6.0)
assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.6.0)
backports 1.1.5 2019-10-02 [1] CRAN (R 3.6.0)
bibtex 0.4.2.2 2020-01-02 [1] CRAN (R 3.6.0)
Biobase 2.46.0 2019-10-29 [1] Bioconductor
BiocGenerics 0.32.0 2019-10-29 [1] Bioconductor
BiocManager 1.30.10 2019-11-16 [1] CRAN (R 3.6.0)
bitops 1.0-6 2013-08-17 [1] CRAN (R 3.6.0)
callr 3.4.2 2020-02-12 [1] CRAN (R 3.6.0)
caTools 1.18.0 2020-01-17 [1] CRAN (R 3.6.0)
cli 2.0.2 2020-02-28 [1] CRAN (R 3.6.0)
cluster 2.1.0 2019-06-19 [1] CRAN (R 3.6.3)
codetools 0.2-16 2018-12-24 [1] CRAN (R 3.6.3)
colorspace 1.4-1 2019-03-18 [1] CRAN (R 3.6.0)
cowplot 1.0.0 2019-07-11 [1] CRAN (R 3.6.0)
crayon 1.3.4 2017-09-16 [1] CRAN (R 3.6.0)
curl 4.3 2019-12-02 [1] CRAN (R 3.6.0)
data.table 1.12.8 2019-12-09 [1] CRAN (R 3.6.0)
desc 1.2.0 2018-05-01 [1] CRAN (R 3.6.0)
devtools * 2.2.2 2020-02-17 [1] CRAN (R 3.6.0)
digest 0.6.25 2020-02-23 [1] CRAN (R 3.6.0)
dplyr * 0.8.5 2020-03-07 [1] CRAN (R 3.6.0)
ellipsis 0.3.0 2019-09-20 [1] CRAN (R 3.6.0)
fansi 0.4.1 2020-01-08 [1] CRAN (R 3.6.0)
farver 2.0.3 2020-01-16 [1] CRAN (R 3.6.0)
fitdistrplus 1.0-14 2019-01-23 [1] CRAN (R 3.6.0)
fs 1.3.2 2020-03-05 [1] CRAN (R 3.6.0)
future 1.16.0 2020-01-16 [1] CRAN (R 3.6.0)
future.apply 1.4.0 2020-01-07 [1] CRAN (R 3.6.0)
gbRd 0.4-11 2012-10-01 [1] CRAN (R 3.6.0)
gdata 2.18.0 2017-06-06 [1] CRAN (R 3.6.0)
ggplot2 3.3.0 2020-03-05 [1] CRAN (R 3.6.0)
ggrepel 0.8.2 2020-03-08 [1] CRAN (R 3.6.0)
ggridges 0.5.2 2020-01-12 [1] CRAN (R 3.6.0)
globals 0.12.5 2019-12-07 [1] CRAN (R 3.6.0)
glue 1.3.2 2020-03-12 [1] CRAN (R 3.6.0)
gplots 3.0.3 2020-02-25 [1] CRAN (R 3.6.0)
gridExtra 2.3 2017-09-09 [1] CRAN (R 3.6.0)
gtable 0.3.0 2019-03-25 [1] CRAN (R 3.6.0)
gtools 3.8.1 2018-06-26 [1] CRAN (R 3.6.0)
htmltools 0.4.0 2019-10-04 [1] CRAN (R 3.6.0)
htmlwidgets 1.5.1 2019-10-08 [1] CRAN (R 3.6.0)
httr 1.4.1 2019-08-05 [1] CRAN (R 3.6.0)
ica 1.0-2 2018-05-24 [1] CRAN (R 3.6.0)
igraph 1.2.5 2020-03-19 [1] CRAN (R 3.6.3)
irlba 2.3.3 2019-02-05 [1] CRAN (R 3.6.0)
jsonlite 1.6.1 2020-02-02 [1] CRAN (R 3.6.0)
KernSmooth 2.23-16 2019-10-15 [1] CRAN (R 3.6.3)
labeling 0.3 2014-08-23 [1] CRAN (R 3.6.0)
lattice 0.20-40 2020-02-19 [1] CRAN (R 3.6.0)
lazyeval 0.2.2 2019-03-15 [1] CRAN (R 3.6.0)
leiden 0.3.3 2020-02-04 [1] CRAN (R 3.6.0)
lifecycle 0.2.0 2020-03-06 [1] CRAN (R 3.6.0)
listenv 0.8.0 2019-12-05 [1] CRAN (R 3.6.0)
lmtest 0.9-37 2019-04-30 [1] CRAN (R 3.6.0)
lsei 1.2-0 2017-10-23 [1] CRAN (R 3.6.0)
magrittr 1.5 2014-11-22 [1] CRAN (R 3.6.0)
MASS 7.3-51.5 2019-12-20 [1] CRAN (R 3.6.3)
Matrix 1.2-18 2019-11-27 [1] CRAN (R 3.6.3)
memoise 1.1.0 2017-04-21 [1] CRAN (R 3.6.0)
metap 1.3 2020-01-23 [1] CRAN (R 3.6.0)
mnormt 1.5-6 2020-02-03 [1] CRAN (R 3.6.0)
multcomp 1.4-12 2020-01-10 [1] CRAN (R 3.6.0)
multtest 2.42.0 2019-10-29 [1] Bioconductor
munsell 0.5.0 2018-06-12 [1] CRAN (R 3.6.0)
mutoss 0.1-12 2017-12-04 [1] CRAN (R 3.6.0)
mvtnorm 1.1-0 2020-02-24 [1] CRAN (R 3.6.0)
nlme 3.1-145 2020-03-04 [1] CRAN (R 3.6.0)
npsurv 0.4-0 2017-10-14 [1] CRAN (R 3.6.0)
numDeriv 2016.8-1.1 2019-06-06 [1] CRAN (R 3.6.0)
patchwork 1.0.0 2019-12-01 [1] CRAN (R 3.6.0)
pbapply 1.4-2 2019-08-31 [1] CRAN (R 3.6.0)
pillar 1.4.3 2019-12-20 [1] CRAN (R 3.6.0)
pkgbuild 1.0.6 2019-10-09 [1] CRAN (R 3.6.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 3.6.0)
pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.6.0)
plotly 4.9.2 2020-02-12 [1] CRAN (R 3.6.0)
plotrix 3.7-7 2019-12-05 [1] CRAN (R 3.6.0)
plyr 1.8.6 2020-03-03 [1] CRAN (R 3.6.0)
png 0.1-7 2013-12-03 [1] CRAN (R 3.6.0)
prettyunits 1.1.1 2020-01-24 [1] CRAN (R 3.6.0)
processx 3.4.2 2020-02-09 [1] CRAN (R 3.6.0)
ps 1.3.2 2020-02-13 [1] CRAN (R 3.6.0)
purrr 0.3.3 2019-10-18 [1] CRAN (R 3.6.0)
R6 2.4.1 2019-11-12 [1] CRAN (R 3.6.0)
RANN 2.6.1 2019-01-08 [1] CRAN (R 3.6.0)
RColorBrewer 1.1-2 2014-12-07 [1] CRAN (R 3.6.0)
Rcpp 1.0.4 2020-03-17 [1] CRAN (R 3.6.0)
RcppAnnoy 0.0.16 2020-03-08 [1] CRAN (R 3.6.0)
Rdpack 0.11-1 2019-12-14 [1] CRAN (R 3.6.0)
remotes 2.1.1 2020-02-15 [1] CRAN (R 3.6.0)
reshape2 1.4.3 2017-12-11 [1] CRAN (R 3.6.0)
reticulate * 1.14 2019-12-17 [1] CRAN (R 3.6.3)
rlang 0.4.5 2020-03-01 [1] CRAN (R 3.6.0)
ROCR 1.0-7 2015-03-26 [1] CRAN (R 3.6.0)
rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.6.0)
RSpectra 0.16-0 2019-12-01 [1] CRAN (R 3.6.0)
rstudioapi 0.11 2020-02-07 [1] CRAN (R 3.6.0)
rsvd 1.0.3 2020-02-17 [1] CRAN (R 3.6.0)
Rtsne 0.15 2018-11-10 [1] CRAN (R 3.6.0)
sandwich 2.5-1 2019-04-06 [1] CRAN (R 3.6.0)
scales 1.1.0 2019-11-18 [1] CRAN (R 3.6.0)
sctransform 0.2.1 2019-12-17 [1] CRAN (R 3.6.0)
sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.6.0)
Seurat * 3.1.4 2020-02-26 [1] CRAN (R 3.6.0)
sn 1.5-5 2020-01-30 [1] CRAN (R 3.6.0)
stringi 1.4.6 2020-02-17 [1] CRAN (R 3.6.0)
stringr 1.4.0 2019-02-10 [1] CRAN (R 3.6.0)
survival 3.1-11 2020-03-07 [1] CRAN (R 3.6.0)
testthat 2.3.2 2020-03-02 [1] CRAN (R 3.6.0)
TFisher 0.2.0 2018-03-21 [1] CRAN (R 3.6.0)
TH.data 1.0-10 2019-01-21 [1] CRAN (R 3.6.0)
tibble 2.1.3 2019-06-06 [1] CRAN (R 3.6.0)
tidyr 1.0.2 2020-01-24 [1] CRAN (R 3.6.0)
tidyselect 1.0.0 2020-01-27 [1] CRAN (R 3.6.0)
tsne 0.1-3 2016-07-15 [1] CRAN (R 3.6.0)
usethis * 1.5.1 2019-07-04 [1] CRAN (R 3.6.0)
utf8 1.1.4 2018-05-24 [1] CRAN (R 3.6.0)
uwot 0.1.8 2020-03-16 [1] CRAN (R 3.6.0)
vctrs 0.2.4 2020-03-10 [1] CRAN (R 3.6.0)
viridisLite 0.3.0 2018-02-01 [1] CRAN (R 3.6.0)
withr 2.1.2 2018-03-15 [1] CRAN (R 3.6.0)
zoo 1.8-7 2020-01-10 [1] CRAN (R 3.6.0)

[1] /Library/Frameworks/R.framework/Versions/3.6/Resources/library

I've been using RunFastMNN to align partially overlapping datasets. It works great in this context, but I run into an issue not in downstream analyses, but in downstream presentations like heat maps and other exploratory plots discussed here.

> seuratObj.mnn <- RunFastMNN(object.list = by.patient.list)
> ProjectDim(seuratObj.mnn, reduction  = "mnn", dims.print = 1:5)
Error in data.use %*% cell.embeddings : non-conformable arguments
> ProjectDim(seuratObj.mnn, reduction  = "umap", dims.print = 1:2)
Error in data.use %*% cell.embeddings : non-conformable arguments
#And the be thorough
> DimHeatmap(object = seuratObj.mnn, reduction = "mnn", dims = 1, balanced = TRUE)
Error in Loadings(object = object, projected = projected, ...)[, dim,  : 
  subscript out of bounds

But integrated objects following this approach seem to work fine:

> ProjectDim(otherSeuratObj, reduction  = "umap", dims.print = 1:2)
UMAP_ 1 
Positive:  TAGLN, JUN, DCN, DNAJB1, FOS, LUM, JUNB, IGFBP5, MYL9, EGR1 
	   GADD45B, ACTA2, CYR61, CRYAB, TPM2, ATF3, HSPA6, MEG3, GEM, ADIRF 
Negative:  TMSB4X, CD74, B2M, SRGN, HLA-DRA, HLA-DRB1, IFI27, HLA-C, HLA-B, RBP1 
	   TM4SF1, GSTA1, HLA-A, HLA-DPA1, TMSB10, HLA-DPB1, CXCR4, CLU, HLA-DQB1, ACKR1 
UMAP_ 2 
Positive:  B2M, TM4SF1, HLA-C, HLA-B, HLA-A, SRGN, CD74, SPARCL1, CLU, GADD45B 
	   IGFBP7, ANXA1, CCL5, HSPA1A, HLA-DRB1, CXCR4, SOCS3, JUN, ACKR1, UBC 
Negative:  RBP1, GSTA1, SERPINE2, STAR, AMH, TNNI3, FHL2, MAGED2, IQCG, DCN 
	   SOX4, RPL3, LUM, RPS25, RPL7, RPS18, GATM, ARID5B, RPL41, RPS8 
An object of class Seurat 
41602 features across 53746 samples within 3 assays 
Active assay: RNA (20004 features)
 2 other assays present: SCT, integrated
 3 dimensional reductions calculated: pca, umap, tsne
```

Any guesses where to look? It is, of course, possible to go directly to fastMNN and to construct the appropriate reduced dimensionality object. But it would be nice to use RunFastMNN....and I feel like I'm probably missing something obvious about the dimensionality of what's stored in the output object of RunFastMNN.

Thanks

Hello,

I am confused about how to best prepare a Seurat object for Velocyto after integration. I have been able to run both velocyto and scvelo by taking the loom file created via standard velocyto pipeline and then filter cells / map onto the UMAP that was calculated on the integrated Seurat analysis.

However, I wonder whether merging the original integrated Seurat object with the loom file containing spliced / unspliced info would be better. But I'm not able to add that data before running Velocyto (Seurat wrapper). Could you please advice? The vignette is not informative for this specific situation.

Thank you very much for your hard word and best wishes.

Hi,

I follow this tutorial to generate loom file with spliced and unsplied counts, followed by converting loom to Seurat object.

Here are my scripts:
ldat <- ReadVelocity(file = "path/to/loom/file")
bm <- as.Seurat(x = ldat)
saveRDS(bm, file = "path/to/rds/file")
object <- readRDS(file= "path/to/rds/file")
object[['RNA']] <- object[['unspliced']]

My questions are 1) Is it normal that the spliced/unspliced counts are much lower than the numbers I usually got from standard Seurat pipeline(without spliced/unspliced assay)? 2) Is there a way to filter out low quality cells (e.g. low nCounts_RNA, low_nFeature_RNA, high percent.mt) before I run SCTransform?

Thank you!

I'm using 'Signac' for scATAC-Seq data analysis and i wondered why we should filter nucleosome signal upper than four? I had look up many papers but i still have no idea for the reason. Or maybe it just match the fragments size distribution condition? Does that mean that I could change the standard number which suit my own data?
Best,
Ariel.

First of all, I'm really enjoying Seurat and all the new integration tools! Thanks a bunch, team!
Y'all are amazing!

I've been working with velocyto integration lately and while it works great on individual objects, how can I use it on combined files that otherwise I'd be integrating in Seurat? I did this, because I read somewhere that I should begin from a combined loom files and convert to Seurat. This works well, except I'm not sure how to set ident in each files before combining. Any help on that?

I want to be able to see the velocity on the clusters, but I would also want to know where the cells are coming from. Assuming that this can be done prior to combining, I realize that this maybe a loom related question, not Seurat. But I'm a biologist, not a computational person and I feel like there might be a very easy solution to this that you might still be able to help me with.

Hello,

I'm trying to use RunPrestoAll(), but I'm getting warnings that end up in no DE being detected.

I'm creating a Seurat object SmallRO following the vignette and doing SCTransform, although I'm calling the "RNA" assay for the DE calculation (see below).
https://satijalab.org/seurat/v3.2/pbmc3k_tutorial.html
With ~1k PBMC's from 10X public datasets.

First, I tried to run RunPrestoAll with all default parameters but got error: error in evaluating the argument 'x' in selecting a method for function 't': Cannot find '0.01' in this Seurat object

> so.RunPrestoAll <- RunPrestoAll(object = SmallRO)
Calculating cluster 0
Calculating cluster 1
...etc
Calculating cluster 12
Warning: No DE genes identified
Warning: The following tests were not performed: 
Warning: When testing 0 versus all:
	error in evaluating the argument 'x' in selecting a method for function 't': Cannot find '0.01' in this Seurat object

Then, I indicated the assay assay = "RNA" but I got Please only specify either assay or reduction. warnings and the result is empty.

> so.RunPrestoAll <- RunPrestoAll(object = SmallRO, assay = "RNA")
Calculating cluster 0
Calculating cluster 1
... etc
Calculating cluster 12
Warning: No DE genes identified
Warning: The following tests were not performed: 
Warning: When testing 0 versus all:
	Please only specify either assay or reduction.
... etc
Warning: When testing 12 versus all:
	Please only specify either assay or reduction.
> so.RunPrestoAll
data frame with 0 columns and 0 rows

Then, I added reduction = NULL. The warning is gone but the results are still empty.

> so.RunPrestoAll <- RunPrestoAll(object = SmallRO, assay = "RNA", reduction = NULL)
Calculating cluster 0
Calculating cluster 1
... etc
Calculating cluster 12
Warning: No DE genes identified
> so.RunPrestoAll
data frame with 0 columns and 0 rows

Just to note, if I use FindAllMarkers() with the same object I get DE.

so.FindAllMarkers <- FindAllMarkers(object = SmallRO, assay = "RNA", only.pos = F)
> head(so.FindAllMarkers)
            p_val   avg_logFC pct.1 pct.2    p_val_adj cluster gene
TRAC 1.606406e-52  10.1013930 0.952 0.322 2.100215e-48       0 TRAC
IL32 3.388294e-52 -41.6934132 0.959 0.332 4.429856e-48       0 IL32
IL7R 2.395640e-51  12.4201524 0.816 0.217 3.132060e-47       0 IL7R

My questions are:

1. May the conflict of RunPrestoAll(..., assay = "RNA") when not specifying reduction be a bug? Is reduction related to PCA, etc? I couldn't find reduction in the RunPrestoAll() Usage or Arguments. What reduction would I be using if I set reduction=NULL?

2. What may be wrong in my commands, since I can run FindAllMarkers() but not RunPrestoAll() with basically the same parameters?

This is my session info:

R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS High Sierra 10.13.6

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8

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

other attached packages:
[1] Seurat_3.2.1         SeuratWrappers_0.3.0

Thanks.

Hi,
I want to know how arrows are generated in RNA velocity? I read papers but I didn't get clear view of it.
Second thing, can I apply RNA Velocity on the data that has no time points? I have data on adult stage of a species.
Thanks in advance.

In a previous analysis with SeuratWrappers_0.1.0 I ran as.cell_data_set() on my Seurat object. Later I used names(cds_from_seurat@reducedDims)[1] <- "PCA" to set the names of the reducedDims.

When I run exactly the same code with the same Seurat object now with SeuratWrappers_0.2.0 it throws an error:

Error in names(cds_from_seurat@reducedDims)[1] <- "PCA" :
no slot of name "reducedDims" for this object of class "cell_data_set"

My Seurat object has 2 dimensional reductions calculated: pca, umap.
In the documentation of the function it says that "Cell emebeddings are transferred over to the reducedDims slot." So I thought this would be done automatically, when calling the function.

Could you let me know if there were any major changes from 0.1.0 to 0.2.0 in this function, so I can figure out what to do?

Hello, Seurat-Wrapper package works very well for me. My question is:
Is there a way to number the cell clusters (like cluster 1,2, 3, 4,......) in velocity map produced by Seurat-Wrapper. That will be helpful to compare RNA velocity of different cell clusters in different conditions.
Thanks a lot for your help!

Hi,

I am following this link to find trajectories in my scRNA data.
https://github.com/satijalab/seurat-wrappers/blob/master/docs/monocle3.Rmd

SO.subset <- subset(SO, idents = c(7,3,4,6))
SO.subset<- FindNeighbors(SO.subset, dims = 1:10)
SO.subset <- RunUMAP(SO.subsete, dims = 1:10, reduction.name = "UMAP")
SO.subset<- FindClusters(SO.subset, resolution = 0.5)
cds <- as.cell_data_set(SO.subset)
cds <- cluster_cells(cds)
int.sub <- subset(as.Seurat(cds), monocle3_partitions == 1)
Error in CellsByIdentities(object = object, cells = cells) :
Cannot find cells provided

How I can fix this issue?

My cds object has not select 2000 variable genes. See below attached picture

Thanks for your great work and recent update of seurat-wrappers.
I followed the vignette of liger to test the new function RunQuantileNorm

 library(liger)
 library(Seurat)
 library(SeuratData)
 library(SeuratWrappers)
 InstallData("pbmcsca")
 data("pbmcsca")
 pbmcsca <- NormalizeData(pbmcsca)
 pbmcsca <- FindVariableFeatures(pbmcsca)
 pbmcsca <- ScaleData(pbmcsca, split.by = "Method", do.center = FALSE)
 pbmcsca <- RunOptimizeALS(pbmcsca, k = 20, lambda = 5, split.by = "Method")

When I go to next step, the new fuction has issue:

 > pbmcsca <- RunQuantileNorm(pbmcsca, split.by = "Method")
 Error in ncol(object@H[[1]]) : 
   trying to get slot "H" from an object of a basic class ("list") with no slots

I test the data ifnb in vignette and my own data. It turns out to be the same error.

Here is sessionInfo() output:

 R version 4.0.0 (2020-04-24)
 Platform: x86_64-w64-mingw32/x64 (64-bit)
 Running under: Windows 10 x64 (build 14393)
 
 Matrix products: default
 
 locale:
 [1] LC_COLLATE=Chinese (Simplified)_China.936  LC_CTYPE=Chinese (Simplified)_China.936    LC_MONETARY=Chinese (Simplified)_China.936
 [4] LC_NUMERIC=C                               LC_TIME=Chinese (Simplified)_China.936    
 
 attached base packages:
 [1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
  [1] ifnb.SeuratData_3.1.0       SeuratWrappers_0.1.0        pbmcsca.SeuratData_3.0.0    SeuratData_0.2.1           
  [5] Seurat_3.1.5                liger_0.5.0.9000            patchwork_1.0.0             Matrix_1.2-18              
  [9] cowplot_1.0.0               monocle3_0.2.1              SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
 [13] DelayedArray_0.14.0         matrixStats_0.56.0          GenomicRanges_1.40.0        GenomeInfoDb_1.24.0        
 [17] IRanges_2.22.2              S4Vectors_0.26.1            Biobase_2.48.0              BiocGenerics_0.34.0        

Hi,
I am trying to use conos to integrate 18 samples and the workflow fails at the conversion step from conos to Seurat:

seurat.merged <- as.Seurat(seurats.con)
Merging 18 samples
Adding pairwise alignments to 'conos.pairs' in miscellaneous data
Adding graph as 'RNA_mnn'
Error in intI(i, n = d[1], dn[[1]], give.dn = FALSE) : 
  invalid character indexing
In addition: Warning message:
In CheckDuplicateCellNames(object.list = objects) :
  Some cell names are duplicated across objects provided. Renaming to enforce unique cell names.

The example code from SeuratWrappers conos vignette works fine.

Directly merging the same 18 seurat objects (same list from which the conos workflow starts) works fine as well.

seurat.merged <- merge(seurats[[1]], seurats[2:18], merge.data = F, project = "Merged")
Warning message:
In CheckDuplicateCellNames(object.list = objects) :
  Some cell names are duplicated across objects provided. Renaming to enforce unique cell names.

Any suggestions as to why conos workflow fails on my data?

Thanks,
Vinko