Hi!

Thanks for your package.

I am trying to follow the following case study https://carmonalab.github.io/ProjecTILs_CaseStudies/SadeFeldman_ortho.html using my scRNAseq data (CD3+ T cells).

However, when I run make.projection on my Seurat object I get the following error:

Error in predictTilState(sce, human = human) : Too many genes not found In addition: Warning message: In predictTilState(sce, human = human) : The following genes were not found in the dataset provided CD2,CD3D,CD3E,CD247,LCK,CD8B,CD8A,CD4,...

When I run: c("CD3D", "CD3E", "CD247", "LCK") %in% row.names(sobj) TRUE TRUE TRUE TRUE

sobj here is my seurat object.

I noticed that in the query_example_seurat the gene name format is with lower cases (eg "Cd3d"). However even if I change the format of my gene names I get the same error.

Could you please help me?

Thanks a lot

Francesco

Thanks again for this great package which is a great enrichment for the single cell RNA-seq community I think and helped me in better understanding T cell clusters.

@mass-a Would it be possible to provide the full data of the reference atlases, that means including count data, variable genes etc. (e.g. via figashare)?

The background is that with larger datasets integration via Seurat I have memory issues (with 64GB RAM available), plus it takes really long. Additionally, in several datasets integration fails with Error in idx[i, ] <- res[[i]][[1]]: number of items to replace is not a multiple of replacement length. I read issue 16, but in my dataset I have mostly T cells (between ~60-90% based on the filtering).

[1] "Using assay SCT for N_COVID_CSF"
[1] "196 out of 1524 ( 13 % ) non-pure T cells removed.  Use filter.cells=FALSE to avoid pre-filtering (NOT RECOMMENDED)"
[1] "Transforming expression matrix into space of mouse orthologs"
[1] "Aligning N_COVID_CSF to reference map for batch-correction..."
[1] "DIRECTLY projecting query onto Reference PCA space"
[1] "DIRECTLY projecting query onto Reference UMAP space"
[1] "Using assay SCT for PostCOVID_blood"
[1] "1234 out of 3667 ( 34 % ) non-pure T cells removed.  Use filter.cells=FALSE to avoid pre-filtering (NOT RECOMMENDED)"
[1] "Transforming expression matrix into space of mouse orthologs"
[1] "Aligning PostCOVID_blood to reference map for batch-correction..."
[1] "DIRECTLY projecting query onto Reference PCA space"
[1] "DIRECTLY projecting query onto Reference UMAP space"
Pre-filtering of T cells (TILPRED classifier)...
Genes in the gene sets NOT available in the dataset: 
	B.cell: 	6 (12% of 50)
	CAF: 	12 (24% of 49)
	Endo.: 	11 (24% of 46)
	Macrophage: 	5 (11% of 46)
	Mal: 	8 (18% of 45)
Computing within dataset neighborhoods
Finding all pairwise anchors
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
	Found 144 anchors
Alignment failed due to: Error in idx[i, ] <- res[[i]][[1]]: number of items to replace is not a multiple of replacement length
 

Warning: alignment of query dataset failed - Trying direct projection...
Pre-filtering of T cells (TILPRED classifier)...
Genes in the gene sets NOT available in the dataset: 
	B.cell: 	6 (12% of 50)
	CAF: 	12 (24% of 49)
	Endo.: 	11 (24% of 46)
	Macrophage: 	5 (11% of 46)
	Mal: 	8 (18% of 45)
Computing within dataset neighborhoods
Finding all pairwise anchors
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
	Found 124 anchors
Alignment failed due to: Error in idx[i, ] <- res[[i]][[1]]: number of items to replace is not a multiple of replacement length
 

Warning: alignment of query dataset failed - Trying direct projection...
[1] "Using assay SCT for PostCOVID_CSF"
[1] "1497 out of 5878 ( 25 % ) non-pure T cells removed.  Use filter.cells=FALSE to avoid pre-filtering (NOT RECOMMENDED)"
[1] "Transforming expression matrix into space of mouse orthologs"
[1] "Aligning PostCOVID_CSF to reference map for batch-correction..."
[1] "DIRECTLY projecting query onto Reference PCA space"
[1] "DIRECTLY projecting query onto Reference UMAP space"
[1] "Using assay SCT for IIH_CSF"
[1] "1159 out of 4887 ( 24 % ) non-pure T cells removed.  Use filter.cells=FALSE to avoid pre-filtering (NOT RECOMMENDED)"
[1] "Transforming expression matrix into space of mouse orthologs"
[1] "Aligning IIH_CSF to reference map for batch-correction..."

Projecting corrected query onto Reference PCA space
[1] "Projecting corrected query onto Reference UMAP space"
[1] "Using assay SCT for IIH_blood"
[1] "1750 out of 8326 ( 21 % ) non-pure T cells removed.  Use filter.cells=FALSE to avoid pre-filtering (NOT RECOMMENDED)"
[1] "Transforming expression matrix into space of mouse orthologs"
[1] "Aligning IIH_blood to reference map for batch-correction..."

Projecting corrected query onto Reference PCA space
[1] "Projecting corrected query onto Reference UMAP space"
Pre-filtering of T cells (TILPRED classifier)...
Genes in the gene sets NOT available in the dataset: 
	B.cell: 	6 (12% of 50)
	CAF: 	12 (24% of 49)
	Endo.: 	11 (24% of 46)
	Macrophage: 	5 (11% of 46)
	Mal: 	8 (18% of 45)
Computing within dataset neighborhoods
Finding all pairwise anchors
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
	Found 118 anchors
Alignment failed due to: Error in idx[i, ] <- res[[i]][[1]]: number of items to replace is not a multiple of replacement length
 

Warning: alignment of query dataset failed - Trying direct projection...
Pre-filtering of T cells (TILPRED classifier)...
Genes in the gene sets NOT available in the dataset: 
	B.cell: 	6 (12% of 50)
	CAF: 	12 (24% of 49)
	Endo.: 	11 (24% of 46)
	Macrophage: 	5 (11% of 46)
	Mal: 	8 (18% of 45)
Computing within dataset neighborhoods
Finding all pairwise anchors
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
	Found 129 anchors
Pre-filtering of T cells (TILPRED classifier)...
Genes in the gene sets NOT available in the dataset: 
	B.cell: 	6 (12% of 50)
	CAF: 	12 (24% of 49)
	Endo.: 	11 (24% of 46)
	Macrophage: 	5 (11% of 46)
	Mal: 	8 (18% of 45)
Computing within dataset neighborhoods
Finding all pairwise anchors
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
	Found 150 anchors

Therefore, I would like to use Harmony orSymphony which are much faster and need much less memory and are, in my experience, equally good or even better than Seurat integration ... although I am huge Seurat fan.

Maybe you could also think about using Harmony or Symphony because I think your visualizations are unique ... but probably that would mean completely rewriting the package .. so it would be great if you could provide the entire full atlases so that users can use these reference atlases with other algorithms that need the count data/variable genes.

Thank you, Mischko

EDIT: When integrating selected CD8 clusters of different conditions (between 400-900 cells) (both with and witout filter) to the beautiful ref_LCMV_Atlas, they all fail with the same error: Error in idx[i, ] <- res[[i]][[1]]: number of items to replace is not a multiple of replacement length. I lowered seurat.k.filter = 20 without any effect.

Sorry I have used mine Seurat object for projection but I get this error

> query.projected <- make.projection(cancer, ref=ref,human.ortho = T)
[1] "Using assay RNA for query"
Pre-filtering of T cells (TILPRED classifier)...
Genes in the gene sets NOT available in the dataset: 
  B.cell: 	7 (14% of 50)
CAF: 	2 (4% of 49)
Endo.: 	5 (11% of 46)
Macrophage: 	5 (11% of 46)
Mal: 	1 (2% of 45)
[1] "12566 out of 18338 ( 69 % ) non-pure T cells removed.  Use filter.cells=FALSE to avoid pre-filtering (NOT RECOMMENDED)"
[1] "Transforming expression matrix into space of mouse orthologs"
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
  [----|----|----|----|----|----|----|----|----|----|
     **************************************************|
     [1] "Aligning query to reference map for batch-correction..."
   |========================================================================================================| 100%
   Computing within dataset neighborhoods
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s  
   Finding all pairwise anchors
   |                                                  | 0 % ~calculating  Finding neighborhoods
   Finding anchors
   Found 616 anchors
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=09s  
   
   Projecting corrected query onto Reference PCA space
   [1] "Projecting corrected query onto Reference UMAP space"
   Warning message:
     In predictTilState(sce, human = human) :
     The following genes were not found in the dataset provided  NAPSB,NCF1C,IGLL5,NCF1B,ELK2AP,CYBASC3,IGJ,WISP2,CTGF,PTRF,CYR61,C10orf10,DARC,FAM26F,GPX1,CTSL1,IL8,SEPP1 . Doesn't look too bad but prediction performance might be affected.
> query.projected
An object of class Seurat 
30365 features across 5772 samples within 2 assays 
Active assay: integrated (707 features, 707 variable features)
 1 other assay present: RNA
 2 dimensional reductions calculated: pca, umap
> cellstate.predict(ref=ref, query=query.projected)
Error: $ operator not defined for this S4 class
> plot.states.radar(ref, query=query.projected, min.cells = 20)
Error in intI(j, n = x@Dim[2], dn[[2]], give.dn = FALSE) : 
  'NA' indices are not (yet?) supported for sparse Matrices
> 
> 

Any help please

Dear authors,

congrats on this awesome utility. I'm wondering if you have implemented the calculation of the cycling score on ProjectTILs as with TILPRED. I have not seen ProjectTILs return this value.

Thanks for your time.

Best,

Juan L.

Dear all,

Thank you for developing ProjecTILs!

While I am trying out your package, I noticed that classes of the existing metadata are changed after projection.

ref = ProjecTILs::load.reference.map()
sc_projectils = ProjecTILs::make.projection(sc, ref=ref, skip.normalize=TRUE)

Maybe this is something you can fix?

Best wishes and thanks a lot for your work! Katrin

I run: `> library(ProjecTILs)

ref <- load.reference.map() [1] "Loading Default Reference Atlas..." [1] "/home/ubuntu/TIL/ref_TILAtlas_mouse_v1.rds" [1] "Loaded Reference map ref_TILAtlas_mouse_v1" querydata <- ProjecTILs::query_example_seurat query.projected <- make.projection(querydata, ref=ref) [1] "Using assay RNA for query" Pre-filtering of T cells (TILPRED classifier)... Error in value[3L] : invalid subscript 'e' in 'altExp(, type="character", ...)': 'RNA' not in 'altExpNames()' But I got the error here.Error in value[3L] : invalid subscript 'e' in 'altExp(, type="character", ...)': 'RNA' not in 'altExpNames()' ` What should I do?

If I follow the tutorial:

library(ProjecTILs)
ref <- load.reference.map()
querydata <- ProjecTILs::query_example_seurat
query.projected <- make.projection(querydata, ref = ref, filter.cells = TRUE)

It interrupts with the following error:

Error in value[[3L]](cond) : 
  invalid subscript 'e' in 'altExp(<SingleCellExperiment>, type="character", ...)':
  'RNA' not in 'altExpNames(<SingleCellExperiment>)'

traceback:

17: stop("invalid subscript '", substr, "' in '", funstr, "(<", class(x), 
        ">, type=\"character\", ...)':\n  ", "'", index, "' not in '", 
        namestr, "(<", class(x), ">)'")
16: value[[3L]](cond)
15: tryCatchOne(expr, names, parentenv, handlers[[1L]])
14: tryCatchList(expr, classes, parentenv, handlers)
13: tryCatch({
        internals[, index]
    }, error = function(err) {
        stop("invalid subscript '", substr, "' in '", funstr, "(<", 
            class(x), ">, type=\"character\", ...)':\n  ", "'", index, 
            "' not in '", namestr, "(<", class(x), ">)'")
    })
12: .get_internal_character(x, e, getfun = int_colData, key = .alt_key, 
        funstr = "altExp", substr = "e", namestr = "altExpNames")
11: .local(x, e, ...)
10: altExp(x, name, withColData = withColData)
9: altExp(x, name, withColData = withColData)
8: SingleCellExperiment::swapAltExp(x = sce, name = assayn, saved = orig.exp.name)
7: as.SingleCellExperiment.Seurat(query.object)
6: as.SingleCellExperiment(query.object)
5: filterCells(query, human = human.ortho)
4: projection.helper(query = query.list[[i]], ref = ref, filter.cells = filter.cells, 
       query.assay = query.assay, direct.projection = direct.projection, 
       seurat.k.filter = seurat.k.filter, skip.normalize = skip.normalize, 
       id = names(query.list)[i])
3: FUN(X[[i]], ...)
2: lapply(X = 1:length(query.list), FUN = function(i) {
       res <- projection.helper(query = query.list[[i]], ref = ref, 
           filter.cells = filter.cells, query.assay = query.assay, 
           direct.projection = direct.projection, seurat.k.filter = seurat.k.filter, 
           skip.normalize = skip.normalize, id = names(query.list)[i])
       return(res)
   })
1: make.projection(querydata, ref = ref, filter.cells = TRUE)

If I turn of filter cells by setting filter.cells = FALSE, everything works fine.

ProjecTILs_1.0.0 Matrix_1.3-3 TILPRED_1.0.2 umap_0.2.7.0 SeuratObject_4.0.1 Seurat_4.0.2

hello,

Thank you very much for creating such great software !!!

I download the images and run following the suggestions:

docker run --rm -p 8787:8787 -e PASSWORD=pass mandrea1/projectils_demo:1.0.0
remotes::install_github("carmonalab/UCell")

But I met some erros:

Error in file(filename, "r", encoding = encoding) : 
  cannot open the connection
Calls: source -> file
In addition: Warning message:
In file(filename, "r", encoding = encoding) :
  cannot open file 'renv/activate.R': No such file or directory
Execution halted
ERROR: lazy loading failed for package ‘BiocGenerics’
* removing ‘/home/rstudio/renv/library/R-4.0/x86_64-pc-linux-gnu/BiocGenerics’
* installing *binary* package ‘bitops’ ...
* DONE (bitops)
* installing *source* package ‘GenomeInfoDbData’ ...
** using staged installation
** data
** inst
** help
*** installing help indices
** building package indices
Error in file(filename, "r", encoding = encoding) : 
  cannot open the connection
Calls: source -> file
In addition: Warning message:
In file(filename, "r", encoding = encoding) :
  cannot open file 'renv/activate.R': No such file or directory
Execution halted

Best

Hi,

I should also report that the test data failed as well.

I should have mentioned that I am running R 4.2.0

Thanks

Output and error below:

library(ProjecTILs) data(query_example_seurat)

Run.ProjecTILs(query_example_seurat) [1] "Loading Default Reference Atlas..." [1] "/Seurat/ref_TILAtlas_mouse_v1.rds" [1] "Loaded Reference map ref_TILAtlas_mouse_v1" [1] "Using assay RNA for query" Pre-filtering cells with scGate... No scGate model specified: using default filter for T cells

Detected a total of 34 non-pure cells for Target (2.27% of total)

[1] "34 out of 1501 ( 2% ) non-pure cells removed. Use filter.cells=FALSE to avoid pre-filtering" Performing log-normalization 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| [1] "Aligning query to reference map for batch-correction..." |======================================================================| 100% Computing within dataset neighborhoods Finding all pairwise anchors Finding neighborhoods Finding anchors Found 645 anchors SD on anchor distances: 0.088

Projecting corrected query onto Reference PCA space

Projecting corrected query onto Reference UMAP space Error in cellstate.predict(ref = ref, query = x, reduction = reduction, : trying to get slot "misc" from an object of a basic class ("NULL") with no slots

Somehow, ProjectTILs (version 3) doesn't return functional.cluster to the query after make.projection.

query_example_seurat <- make.projection(query_example_seurat, ref=ref)
plot.states.radar(query_example_seurat )
Error: Cannot find 'functional.cluster' in this Seurat object

names([email protected])
[1] "Sample"             "Barcode"            "SampleLab"          "nCount_RNA"         "nFeature_RNA"       "cycling.score"     
[7] "cycling.score.G1_S" "cycling.score.G2_M" "anchor.score"

Has anyone encounter the same issue? Is there any way to fix it? Thank you in advance for any tips.

Hi,

Thank you very much for this wonderful package. I follow the tutorial and use the example file provided to run the "make.projection" function.

But I met this error:

> ref <- load.reference.map()
[1] "Loading Default Reference Atlas..."
[1] "/sbgenomics/workspace/ref_TILAtlas_mouse_v1.rds"
[1] "Loaded Reference map ref_TILAtlas_mouse_v1"

> querydata <- ProjecTILs::query_example_seurat
> query.projected <- make.projection(querydata, ref=ref)

Error: BiocParallel errors
  1 remote errors, element index: 1
  0 unevaluated and other errors
  first remote error: worker evaluation failed:
  option type has NULL value

Here is my session info.

R version 4.1.2 (2021-11-01) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 20.04.3 LTS

Matrix products: default BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.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=C
[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] stats4 stats graphics grDevices utils datasets methods base

other attached packages: [1] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0 GenomicRanges_1.46.1
[4] GenomeInfoDb_1.30.0 IRanges_2.28.0 S4Vectors_0.32.3
[7] MatrixGenerics_1.6.0 matrixStats_0.61.0 GEOquery_2.62.2
[10] Biobase_2.54.0 BiocGenerics_0.40.0 ProjecTILs_2.2.1
[13] pracma_2.3.6 ggplot2_3.3.5 gridExtra_2.3
[16] BiocParallel_1.28.3 Matrix_1.4-0 umap_0.2.7.0
[19] scGate_1.0.2 reshape2_1.4.4 ggridges_0.5.3
[22] patchwork_1.1.1 dplyr_1.0.7 data.table_1.14.2
[25] SeuratObject_4.0.4 Seurat_4.0.6 UCell_1.3.1

loaded via a namespace (and not attached): [1] plyr_1.8.6 igraph_1.2.10 lazyeval_0.2.2
[4] splines_4.1.2 listenv_0.8.0 scattermore_0.7
[7] digest_0.6.29 htmltools_0.5.2 fansi_1.0.2
[10] magrittr_2.0.1 tensor_1.5 cluster_2.1.2
[13] ROCR_1.0-11 tzdb_0.2.0 limma_3.50.0
[16] remotes_2.4.2 globals_0.14.0 readr_2.1.1
[19] askpass_1.1 spatstat.sparse_2.1-0 prettyunits_1.1.1
[22] colorspace_2.0-2 ggrepel_0.9.1 xfun_0.29
[25] RCurl_1.98-1.5 callr_3.7.0 crayon_1.4.2
[28] jsonlite_1.7.3 spatstat.data_2.1-2 survival_3.2-13
[31] zoo_1.8-9 glue_1.6.0 polyclip_1.10-0
[34] gtable_0.3.0 zlibbioc_1.40.0 XVector_0.34.0
[37] leiden_0.3.9 DelayedArray_0.20.0 pkgbuild_1.3.1
[40] future.apply_1.8.1 abind_1.4-5 scales_1.1.1
[43] DBI_1.1.2 miniUI_0.1.1.1 Rcpp_1.0.8
[46] viridisLite_0.4.0 xtable_1.8-4 reticulate_1.22
[49] spatstat.core_2.3-2 htmlwidgets_1.5.4 httr_1.4.2
[52] RColorBrewer_1.1-2 ellipsis_0.3.2 ica_1.0-2
[55] pkgconfig_2.0.3 farver_2.1.0 uwot_0.1.11
[58] deldir_1.0-6 utf8_1.2.2 tidyselect_1.1.1
[61] labeling_0.4.2 rlang_0.4.12 later_1.3.0
[64] munsell_0.5.0 tools_4.1.2 cli_3.1.1
[67] generics_0.1.1 evaluate_0.14 stringr_1.4.0
[70] fastmap_1.1.0 yaml_2.2.1 goftest_1.2-3
[73] processx_3.5.2 knitr_1.37 fitdistrplus_1.1-6
[76] purrr_0.3.4 RANN_2.6.1 pbapply_1.5-0
[79] future_1.23.0 nlme_3.1-155 mime_0.12
[82] xml2_1.3.3 compiler_4.1.2 rstudioapi_0.13
[85] plotly_4.10.0 curl_4.3.2 png_0.1-7
[88] spatstat.utils_2.3-0 tibble_3.1.6 stringi_1.7.6
[91] ps_1.6.0 RSpectra_0.16-0 lattice_0.20-45
[94] vctrs_0.3.8 pillar_1.6.4 lifecycle_1.0.1
[97] BiocManager_1.30.16 spatstat.geom_2.3-1 lmtest_0.9-39
[100] RcppAnnoy_0.0.19 bitops_1.0-7 cowplot_1.1.1
[103] irlba_2.3.5 httpuv_1.6.4 R6_2.5.1
[106] promises_1.2.0.1 KernSmooth_2.23-20 parallelly_1.30.0
[109] codetools_0.2-18 MASS_7.3-55 assertthat_0.2.1
[112] openssl_1.4.6 rprojroot_2.0.2 withr_2.4.3
[115] sctransform_0.3.2 GenomeInfoDbData_1.2.7 mgcv_1.8-38
[118] parallel_4.1.2 hms_1.1.1 grid_4.1.2
[121] rpart_4.1-15 tidyr_1.1.4 rmarkdown_2.11
[124] Rtsne_0.15 shiny_1.7.1

Any help would be appreciated.

Thank you very much.

Hi, I have been trying to install these libraries into R-Studio, but I keep getting the following errors:

remotes::install_github("carmonalab/scGate") Downloading GitHub repo carmonalab/scGate@HEAD These packages have more recent versions available. It is recommended to update all of them. Which would you like to update?

1: All
2: CRAN packages only
3: None
4: stringi (1.7.6 -> 1.7.8) [CRAN]

Enter one or more numbers, or an empty line to skip updates: 1 stringi (1.7.6 -> 1.7.8) [CRAN] Skipping 1 packages not available: UCell Installing 2 packages: stringi, UCell Installing packages into ‘C:/Users/ashka/Documents/renv/library/R-4.1/x86_64-w64-mingw32’ (as ‘lib’ is unspecified)

There is a binary version available but the source version is later: binary source needs_compilation stringi 1.7.6 1.7.8 TRUE

Binaries will be installed trying URL 'https://cloud.r-project.org/bin/windows/contrib/4.1/stringi_1.7.6.zip' Content type 'application/zip' length 16449819 bytes (15.7 MB) downloaded 15.7 MB

package ‘stringi’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in C:\Users\ashka\AppData\Local\Temp\Rtmpc50GBN\downloaded_packages Running R CMD build...

• checking for file 'C:\Users\ashka\AppData\Local\Temp\Rtmpc50GBN\remotes73b047e96ee2\carmonalab-scGate-1b23084/DESCRIPTION' ... OK
• preparing 'scGate':
• checking DESCRIPTION meta-information ... OK
• checking for LF line-endings in source and make files and shell scripts
• checking for empty or unneeded directories
• building 'scGate_1.2.0.tar.gz' Installing package into ‘C:/Users/ashka/Documents/renv/library/R-4.1/x86_64-w64-mingw32’ (as ‘lib’ is unspecified) ERROR: dependency 'UCell' is not available for package 'scGate'
• removing 'C:/Users/ashka/Documents/renv/library/R-4.1/x86_64-w64-mingw32/scGate' Warning messages: 1: In untar2(tarfile, files, list, exdir, restore_times) : skipping pax global extended headers 2: In untar2(tarfile, files, list, exdir, restore_times) : skipping pax global extended headers 3: package ‘UCell’ is not available for this version of R

A version of this package for your version of R might be available elsewhere, see the ideas at https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages 4: In i.p(...) : installation of package ‘C:/Users/ashka/AppData/Local/Temp/Rtmpc50GBN/file73b0491424d0/scGate_1.2.0.tar.gz’ had non-zero exit status

remotes::install_github("carmonalab/ProjecTILs") Downloading GitHub repo carmonalab/ProjecTILs@HEAD These packages have more recent versions available. It is recommended to update all of them. Which would you like to update?

1: All
2: CRAN packages only
3: None
4: stringi (1.7.6 -> 1.7.8) [CRAN]

Enter one or more numbers, or an empty line to skip updates: Running R CMD build...

• checking for file 'C:\Users\ashka\AppData\Local\Temp\Rtmpc50GBN\remotes73b014782336\carmonalab-ProjecTILs-370abd8/DESCRIPTION' ... OK
• preparing 'ProjecTILs':
• checking DESCRIPTION meta-information ... OK
• checking for LF line-endings in source and make files and shell scripts
• checking for empty or unneeded directories
• building 'ProjecTILs_3.0.0.tar.gz' Installing package into ‘C:/Users/ashka/Documents/renv/library/R-4.1/x86_64-w64-mingw32’ (as ‘lib’ is unspecified) ERROR: dependencies 'UCell', 'scGate' are not available for package 'ProjecTILs'
• removing 'C:/Users/ashka/Documents/renv/library/R-4.1/x86_64-w64-mingw32/ProjecTILs' Warning messages: 1: In untar2(tarfile, files, list, exdir, restore_times) : skipping pax global extended headers 2: In untar2(tarfile, files, list, exdir, restore_times) : skipping pax global extended headers 3: In i.p(...) : installation of package ‘C:/Users/ashka/AppData/Local/Temp/Rtmpc50GBN/file73b07ff54eff/ProjecTILs_3.0.0.tar.gz’ had non-zero exit status

I have been able to successfully install the packages on another computer a few months ago, so I'm not sure what the problem is now. Can someone help me with this? Thanks!

Dear all,

really exciting package to me! I am now exploring its functionalities. I am checking arbitrary details of the functions and I wonder: How to generate a conversion table for mouse <----> human gene orthologs elegantly? Did you do it with Biomart, somehow?

I just noticed that ZNF683 (human) is not included in the default table but a mouse ortholog is described: Znf683. I did not conduct a systematic check for a large number of genes, yet. Maybe the table is just outdated. So I wonder how generate my own. I found this one on GitHub: https://github.com/vitkl/orthologsBioMART.

But anyhow: How did you generate your table? Can you tell us?

Thanks, yours, Chris.

Great tool! Annotating T cells is really hard and this tool is really helpful especially because it also provides a reference atlas. However, I am missing a function that allows a comparison between custom labels of the query dataset and the labels of the reference dataset. Although plot.projection shows you where your cells are located, it does not allow a direct comparison between custom labels of the query dataset and the labels of the reference atlas.

A very easy solution would be a heatmap showing reference annotation as columns and your labels as rows. If your labels are stored in [email protected]$cluster:

table(query.projected$cluster, query.projected$functional.cluster) |>
    pheatmap::pheatmap(scale = "row")