sinanugur / cellsnake

Cellsnake tool main repo
https://cellsnake.readthedocs.io/
MIT License
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Integration missing files #3

Open ab4cp opened 10 months ago

ab4cp commented 10 months ago

Hi, thanks so much for making this program it looks really promising. I had a couple of issues I was hoping to get help with.

My specifications are: OS is ubuntu 20.04, linux 5.4 conda version 23.9.0 mamba version 1.5.3 python version 3.10.13.final.0

I downloaded cellsnake with no issues installed the R packages and got the 'all packages are OK' message and ran cellsnake standard data on the practice fetal-brain dataset provided in the workflow tutorial. This is the path to the data files (cellsnake) unam@IP:~/Downloads/data$ ls 10X_17_028 10X_17_029

When I run 'cellsnake standard data' (cellsnake) unam@IP:~/Downloads$ cellsnake standard data

It runs and I get some of the output files but then it terminates due to this following error. Any suggestions on how I can resolve this? Thanks!

Execution halted [Fri Nov 3 15:52:34 2023] Error in rule normalization_pca_rds: jobid: 9 input: analyses/raw/percent_mt~10/resolution~0.8/10X_17_028.rds output: analyses/processed/percent_mt~10/resolution~0.8/10X_17_028.rds shell: /home/unam@IP/miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/scripts/scrna-normalization-pca.R --rds analyses/raw/percent_mt~10/resolution~0.8/10X_17_028.rds --doublet.filter --normalization.method LogNormalize --cpu 1 --scale.factor 10000 --reference BlueprintEncodeData --variable.selection.method vst --nfeature 2000 --resolution 0.8 --output.rds analyses/processed/percent_mt~10/resolution~0.8/10X_17_028.rds --umap --tsne
(one of the commands exited with non-zero exit code; note that snakemake uses bash strict mode!)

Exiting because a job execution failed. Look above for error message Complete log: .snakemake/log/2023-11-03T153327.979328.snakemake.log Traceback (most recent call last): File "/home/#####/#####/miniforge3/envs/cellsnake/bin/cellsnake", line 10, in sys.exit(main()) File "/home//#####/#####//miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/command_line.py", line 379, in main run_workflow(cli_arguments) File "/home//#####/#####//miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/command_line.py", line 351, in run_workflow subprocess.check_call(str(snakemake_argument),shell=True) File "/home//#####/#####//miniforge3/envs/cellsnake/lib/python3.9/subprocess.py", line 373, in check_call raise CalledProcessError(retcode, cmd) subprocess.CalledProcessError: Command 'snakemake --retries 5 --rerun-incomplete -k -j 1 -s /home//#####/#####//miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/Snakefile --config datafolder=data cellsnake_path=/home//#####/#####//miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/ show_labels=True marker_plots_per_cluster_n=20 min_percentage_to_plot=5 reduction=cca highly_variable_features=2000 umap_markers_plot=True test_use=wilcox resolution=0.8 singler_ref=BlueprintEncodeData celltypist_model=Immune_All_Low.pkl mapping=org.Hs.eg.db min_features=200 percent_mt=10 max_molecules=Inf min_molecules=0 normalization_method=LogNormalize scale_factor=10000 metadata_column=condition confidence=0.05 organism=hsa percent_rp=0 variable_selection_method=vst doublet_filter=True species=human microbiome_min_features=3 metadata=None microbiome_min_cells=1 max_features=Inf logfc_threshold=0.25 tsne_markers_plot=False min_cells=3 min_hit_groups=4 taxa=genus dims=30 runid=haz29875 option=standard' returned non-zero exit status 1

I then attempted to integrate the data to see if it would run but I get the following output:

(cellsnake) unam:IP:~/Downloads$ cellsnake integrate data {'10X_17_029', '10X_17_028'} Building DAG of jobs... Using shell: /usr/bin/bash Provided cores: 1 (use --cores to define parallelism) Rules claiming more threads will be scaled down. Job stats: job count


all 1 total 1

Select jobs to execute...

[Fri Nov 3 15:53:54 2023] localrule all: jobid: 0 reason: Rules with neither input nor output files are always executed. resources: tmpdir=/tmp

ab4cp commented 10 months ago

I attempted to run it again with the snakemake pathway. Not surprisingly I got the same error. Here's the script below which might be more helpful than the one I previously posted.

(scrna-workflow) unam@IP:~/scrna-workflow$ snakemake -j 10 --config datafolder=data option=minimal {'10X_17_028'} Building DAG of jobs... Using shell: /usr/bin/bash Provided cores: 10 Rules claiming more threads will be scaled down. Job stats: job count


all 1 normalization_pca_rds 1 plot_dimplots 3 plot_selected_marker_plots_separately 2 plot_singler_celltype 1 plot_singler_dimplots 3 plot_some_metrics 1 plot_some_technicals 1 singler_annotation 1 total 14

Select jobs to execute...

[Sat Nov 4 11:35:23 2023] rule normalization_pca_rds: input: analyses/raw/percent_mt~10/resolution~0.8/10X_17_028.rds output: analyses/processed/percent_mt~10/resolution~0.8/10X_17_028.rds jobid: 2 reason: Missing output files: analyses/processed/percent_mt~10/resolution~0.8/10X_17_028.rds wildcards: sample=10X_17_028, percent_mt=10, resolution=0.8 threads: 5 resources: tmpdir=/tmp, mem_mb=5000, mem_mib=4769

Loading required package: optparse Warning message: package ‘optparse’ was built under R version 4.2.3 Loading required package: SingleR Loading required package: SummarizedExperiment Loading required package: MatrixGenerics Loading required package: matrixStats

Attaching package: ‘MatrixGenerics’

The following objects are masked from ‘package:matrixStats’:

colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
colWeightedMeans, colWeightedMedians, colWeightedSds,
colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
rowWeightedSds, rowWeightedVars

Loading required package: GenomicRanges Loading required package: stats4 Loading required package: BiocGenerics

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:stats’:

IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

anyDuplicated, aperm, append, as.data.frame, basename, cbind,
colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
table, tapply, union, unique, unsplit, which.max, which.min

Loading required package: S4Vectors

Attaching package: ‘S4Vectors’

The following objects are masked from ‘package:base’:

expand.grid, I, unname

Loading required package: IRanges Loading required package: GenomeInfoDb Loading required package: Biobase Welcome to Bioconductor

Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.

Attaching package: ‘Biobase’

The following object is masked from ‘package:MatrixGenerics’:

rowMedians

The following objects are masked from ‘package:matrixStats’:

anyMissing, rowMedians

Warning messages: 1: package ‘SingleR’ was built under R version 4.2.3 2: package ‘matrixStats’ was built under R version 4.2.3 3: package ‘GenomicRanges’ was built under R version 4.2.3 4: package ‘S4Vectors’ was built under R version 4.2.3 5: package ‘IRanges’ was built under R version 4.2.3 6: package ‘GenomeInfoDb’ was built under R version 4.2.3 7: package ‘Biobase’ was built under R version 4.2.3 Loading required package: celldex

Attaching package: ‘celldex’

The following objects are masked from ‘package:SingleR’:

BlueprintEncodeData, DatabaseImmuneCellExpressionData,
HumanPrimaryCellAtlasData, ImmGenData, MonacoImmuneData,
MouseRNAseqData, NovershternHematopoieticData

Loading required package: tidyverse ── Attaching packages ─────────────────────────────────────────────── tidyverse 1.3.2 ── ✔ ggplot2 3.4.4 ✔ purrr 1.0.2 ✔ tibble 3.2.1 ✔ dplyr 1.1.3 ✔ tidyr 1.3.0 ✔ stringr 1.5.0 ✔ readr 2.1.4 ✔ forcats 1.0.0 ── Conflicts ────────────────────────────────────────────────── tidyverse_conflicts() ── ✖ dplyr::collapse() masks IRanges::collapse() ✖ dplyr::combine() masks Biobase::combine(), BiocGenerics::combine() ✖ dplyr::count() masks matrixStats::count() ✖ dplyr::desc() masks IRanges::desc() ✖ tidyr::expand() masks S4Vectors::expand() ✖ dplyr::filter() masks stats::filter() ✖ dplyr::first() masks S4Vectors::first() ✖ dplyr::lag() masks stats::lag() ✖ ggplot2::Position() masks BiocGenerics::Position(), base::Position() ✖ purrr::reduce() masks GenomicRanges::reduce(), IRanges::reduce() ✖ dplyr::rename() masks S4Vectors::rename() ✖ dplyr::slice() masks IRanges::slice() Warning messages: 1: package ‘ggplot2’ was built under R version 4.2.3 2: package ‘tibble’ was built under R version 4.2.3 3: package ‘tidyr’ was built under R version 4.2.3 4: package ‘readr’ was built under R version 4.2.3 5: package ‘purrr’ was built under R version 4.2.3 6: package ‘dplyr’ was built under R version 4.2.3 7: package ‘stringr’ was built under R version 4.2.3 8: package ‘forcats’ was built under R version 4.2.3 Loading required package: Seurat Attaching SeuratObject Seurat v4 was just loaded with SeuratObject v5; disabling v5 assays and validation routines, and ensuring assays work in strict v3/v4 compatibility mode

Attaching package: ‘Seurat’

The following object is masked from ‘package:SummarizedExperiment’:

Assays

Warning message: package ‘Seurat’ was built under R version 4.2.3 Loading required package: patchwork Warning message: package ‘patchwork’ was built under R version 4.2.3 Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'cellsnake' Warning messages: 1: In system("python -c 'import os; import cellsnake; print(os.path.dirname(cellsnake.file))'", : running command 'python -c 'import os; import cellsnake; print(os.path.dirname(cellsnake.file))'' had status 1 2: In file(filename, "r", encoding = encoding) : cannot open file '/scrna/workflow/scripts/scrna-functions.R': No such file or directory Performing log-normalization 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Calculating gene variances 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Calculating feature variances of standardized and clipped values 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Centering and scaling data matrix |======================================================================| 100% PC 1 Positive: STMN2, MYT1L, NSG2, SYT1, CNTNAP2, CCSER1, RTN1, DSCAM, LRRC7, GAD1 DCC, NRXN3, CELF4, SOX11, MAPT, PTPRD, HECW1, GAD2, RBFOX1, FAM155A PCSK1N, BCL11B, ADARB2, GRIA2, GATA3, LINC01210, SOX14, KIZ, OTX2-AS1, IGFBPL1 Negative: VIM, ZFP36L1, SPARC, HES1, CD99, TTYH1, B2M, CCN1, CREB5, PON2 HSPB1, SOX2, FOS, HES4, CLU, PTN, SLC1A3, TAGLN2, ZFP36L2, EGR1 CLIC1, RHOC, GPM6B, PLPP3, HMGB2, RCN1, SAMD4A, QKI, FABP7, PEA15 PC 2 Positive: HMGB2, NUSAP1, TOP2A, SOX2, CENPF, MKI67, SMC4, CREB5, PBK, UBE2C CDK1, BIRC5, MAD2L1, NUF2, H2AFX, PTTG1, CKS2, PIMREG, TPX2, PRC1 DIAPH3, HMGN2, KIFC1, NDC80, SGO1, PANTR1, AURKB, GTSE1, CCNA2, CKAP2L Negative: BGN, CYTOR, IGFBP7, CAVIN3, NDUFA4L2, PLXDC1, AC017002.5, COL4A1, FOXS1, TMEM204 RGS5, ITIH5, EPAS1, DLC1, OLFML3, TMEM173, VAMP5, S100A11, ITGA1, EDNRA LINC02147, COL3A1, PPP1R14A, ENG, EVA1B, PRELP, ARHGAP29, MIR4435-2HG, ADGRF5, RASL12 PC 3 Positive: BCAN, NTRK2, CFAP126, EDNRB, FIBIN, GDPD2, PIFO, SPON1, SPARCL1, NDRG2 TTYH1, FGFBP3, HEPN1, GFAP, CLU, ARMC3, LRRC4C, MGST1, FABP7, PLP1 PMP2, CST3, SULF1, RSPH1, CPXM2, GLIS3, VWA3A, HAS2, ANKFN1, VCAM1 Negative: UBE2C, MKI67, TOP2A, CENPF, NUSAP1, AURKB, BIRC5, NUF2, GTSE1, TPX2 CDK1, PIMREG, CCNA2, HMGB2, CDC20, KIFC1, PBK, ASPM, CCNB2, SGO1 NDC80, PLK1, CDCA8, CKAP2L, PTTG1, SMC4, MAD2L1, PRC1, SGO2, TACC3 PC 4 Positive: NEUROD2, NEUROD6, FRMD4B, NFIB, TBR1, NFIA, SLA, SOX5, KCNQ3, SLC24A2 CACNA1E, NRN1, PPP1R1B, PPP2R2B, CACNA2D1, MPPED1, FSTL5, SLC4A10, EPHA5, GRIA1 FAM49A, EMX1, NELL2, RBFOX3, ANKS1B, NEUROD1, UNC5D, EPHA7, ARPP21, BCL11B Negative: OTX2-AS1, OTX2, GATA3, LINC01210, GAD1, SOX14, ADARB2, LHX5-AS1, LHX5, GAD2 ASIC4, NRXN3, SV2C, KIZ, PAX7, PAX3, LHX1, TENM3, CACNA2D3, PBX3 RELN, LHX1-DT, AL033504.1, SAMD5, ZFPM2-AS1, CEP112, GATA2, TENM2, ZFHX3, ZNF385D PC_ 5 Positive: SLCO2B1, SRGN, VSIR, RGS10, INPP5D, AIF1, SPP1, TYROBP, C1QB, LAPTM5 CCL4, PDGFB, RASGRP3, C1QC, CCL3, IL1B, C1QA, ARPC1B, CCL3L1, CCL4L2 HHEX, CD68, CD74, RGS1, LY86, CXCL8, TREM2, SYNGR2, ALOX5AP, TMSB4X Negative: TENM2, GRID2, PRKG1, LRRC4C, CEP112, PLXDC1, CSMD1, MEG3, RORA, NTM NRG3, NDUFA4L2, CCDC3, EDNRA, ARL6IP1, RGS5, FAM155A, CACNA1C, DLG2, GPC5 CADM2, ZNF385D, MDGA2, LRRTM4, GRM7, DAB1, NRXN1, FOXP2, DSCAM, RMST Computing nearest neighbor graph Computing SNN Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 6809 Number of edges: 239085

Running Louvain algorithm... 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Maximum modularity in 10 random starts: 0.9127 Number of communities: 23 Elapsed time: 0 seconds Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation' This message will be shown once per session 11:35:47 UMAP embedding parameters a = 0.9922 b = 1.112 Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics' Also defined by ‘spam’ 11:35:47 Read 6809 rows and found 23 numeric columns 11:35:47 Using Annoy for neighbor search, n_neighbors = 30 Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics' Also defined by ‘spam’ 11:35:47 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| 11:35:47 Writing NN index file to temp file /tmp/RtmpDyhI2v/file38dab932e3acf7 11:35:47 Searching Annoy index using 1 thread, search_k = 3000 11:35:49 Annoy recall = 100% 11:35:50 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30 11:35:51 Initializing from normalized Laplacian + noise (using irlba) 11:35:52 Commencing optimization for 500 epochs, with 280188 positive edges Using method 'umap' 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| 11:36:00 Optimization finished Loading required package: DoubletFinder Loading required package: fields Loading required package: spam Spam version 2.10-0 (2023-10-23) is loaded. Type 'help( Spam)' or 'demo( spam)' for a short introduction and overview of this package. Help for individual functions is also obtained by adding the suffix '.spam' to the function name, e.g. 'help( chol.spam)'.

Attaching package: ‘spam’

The following object is masked from ‘package:stats4’:

mle

The following objects are masked from ‘package:base’:

backsolve, forwardsolve

Loading required package: viridisLite

Try help(fields) to get started. Loading required package: KernSmooth KernSmooth 2.23 loaded Copyright M. P. Wand 1997-2009 [1] "Creating 2270 artificial doublets..." [1] "Creating Seurat object..." [1] "Normalizing Seurat object..." Performing log-normalization 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| [1] "Finding variable genes..." Calculating gene variances 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| Calculating feature variances of standardized and clipped values 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **| [1] "Scaling data..." Centering and scaling data matrix |======================================================================| 100% [1] "Running PCA..." [1] "Calculating PC distance matrix..." [1] "Computing pANN..." [1] "Classifying doublets.." Warning messages: 1: package ‘fields’ was built under R version 4.2.3 2: package ‘spam’ was built under R version 4.2.3 3: package ‘viridisLite’ was built under R version 4.2.3 4: package ‘KernSmooth’ was built under R version 4.2.3 Error in collect(): ! Failed to collect lazy table. Caused by error in db_collect(): ! Arguments in ... must be used. ✖ Problematic argument: • ..1 = Inf ℹ Did you misspell an argument name? Backtrace: ▆

  1. ├─get(opt$reference)()
  2. │ └─celldex:::.create_se(...)
  3. │ └─celldex:::.ExperimentHub()
  4. │ └─ExperimentHub::ExperimentHub()
  5. │ └─AnnotationHub::.Hub(...)
  6. │ └─AnnotationHub:::.create_cache(...)
  7. │ └─BiocFileCache::BiocFileCache(cache = cache, ask = ask)
  8. │ └─BiocFileCache:::.sql_create_db(bfc)
  9. │ └─BiocFileCache:::.sql_validate_version(bfc)
    1. │ └─BiocFileCache:::.sql_schema_version(bfc)
    2. │ ├─base::tryCatch(...)
    3. │ │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
    4. │ └─tbl(src, "metadata") %>% collect(Inf)
    5. ├─dplyr::collect(., Inf)
    6. └─dbplyr:::collect.tbl_sql(., Inf)
    7. ├─base::tryCatch(...)
    8. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
    9. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
    10. │ └─base (local) doTryCatch(return(expr), name, parentenv, handler)
    11. └─dbplyr::db_collect(x$src$con, sql, n = n, warn_incomplete = warn_incomplete, ...)
    12. └─rlang (local) <fn>()
    13. └─rlang:::check_dots(env, error, action, call)
    14. └─rlang:::action_dots(...)
    15. ├─base (local) try_dots(...)
    16. └─rlang (local) action(...) Execution halted [Sat Nov 4 11:36:51 2023] Error in rule normalization_pca_rds: jobid: 2 input: analyses/raw/percent_mt~10/resolution~0.8/10X_17_028.rds output: analyses/processed/percent_mt~10/resolution~0.8/10X_17_028.rds shell: workflow/scripts/scrna-normalization-pca.R --rds analyses/raw/percent_mt~10/resolution~0.8/10X_17_028.rds --doublet.filter --normalization.method LogNormalize --cpu 5 --scale.factor 10000 --reference BlueprintEncodeData --variable.selection.method vst --nfeature 2000 --resolution 0.8 --output.rds analyses/processed/percent_mt~10/resolution~0.8/10X_17_028.rds --umap --tsne
      (one of the commands exited with non-zero exit code; note that snakemake uses bash strict mode!)

Shutting down, this might take some time. Exiting because a job execution failed. Look above for error message Complete log: .snakemake/log/2023-11-04T113522.930249.snakemake.log (scrna-workflow) unam@IP:~/scrna-workflow$

sinanugur commented 10 months ago

Hi, Thanks for your interest in cellsnake.

First can you share the specifications of your computer. Second can you conda list your environment?

conda list

can you also type R and send me thesessionInfo().

I suspect some packages changed in the environment due to updates which may cause some issues.

Cheers,

sinanugur commented 10 months ago

I tested myself. Just as I suspected, this is related to an updated package. I will pinpoint the problematic one and offer you a solution. Meantime, I can suggest our Docker container. You do not have to reinstall anything and it is pretty much run in the same way.

https://hub.docker.com/r/sinanugur/cellsnake

Cheers,

sinanugur commented 10 months ago

I think I found the problematic package as explained here https://stackoverflow.com/questions/77370659/error-failed-to-collect-lazy-table-caused-by-error-in-db-collect-using

In short, just run this in your environment to downgrade the problematic package:

conda install r-dbplyr=2.3.2 -c conda-forge

Then run from the start. The problem was the prepocessing never finished so you cannot integrate. Therefore:

cellsnake standard data
cellsnake integrate data

Now the conclusion of standard part may take more time since the clustering will be done. I recommend using --jobs 5 argument to use multiprocessing.

Sorry for the inconvenience. I will fix the Bioconda recipe if this creates a problem for everyone before an update of that package.

Cheers,

ab4cp commented 10 months ago

Thanks for the help. I came across that bug as well and it's all up and running now!

sinanugur commented 10 months ago

@ab4cp No worries, please share if you notice any other bugs or problems. Cellsnake is based on Seurat4 and usually requires more resources if you want to integrate more than 50,000 cells and after 100,000 cells Seurat4 is not that reliable.

Seurat5 is better however the resource requirement still grows rapidly. I will push a new version on Seurat5. Unfortunately, high-performance computers are still required and laptops/desktops make single-cell analysis difficult. Cheers,

ab4cp commented 10 months ago

@sinanugur is the seurat 5 version available? I am on linux with 32vCPU 2000RAM and 5TB of free disk space. I was going to trial cellsnake to integrate about 25 datasets the number of cells would easily be over 1M.

ab4cp commented 10 months ago

@sinanugur

On the fetal brain dataset I tried running:

cellsnake: cellsnake integrated advanced analyses_integrated/seurat/integrated.rds --resolution auto

It drops out at about 86% completion.

I'm not sure if this again is a related to an update in any packages.

ERROR MESSAGE

🔎 No available models. Downloading... Traceback (most recent call last): File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/scripts/scrna-celltypist.py", line 19, in predictions = celltypist.annotate(data, model = model, majority_voting = True) File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/celltypist/annotate.py", line 77, in annotate lr_classifier = model if isinstance(model, Model) else Model.load(model) File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/celltypist/models.py", line 90, in load if model in get_all_models(): File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/celltypist/models.py", line 359, in get_all_models download_if_required() File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/celltypist/models.py", line 372, in download_if_required download_models() File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/celltypist/models.py", line 432, in download_models models_json = get_models_index(force_update) File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/celltypist/models.py", line 394, in get_models_index return json.load(f) File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/json/init.py", line 293, in load return loads(fp.read(), File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/json/init.py", line 346, in loads return _default_decoder.decode(s) File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/json/decoder.py", line 337, in decode obj, end = self.raw_decode(s, idx=_w(s, 0).end()) File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/json/decoder.py", line 355, in raw_decode raise JSONDecodeError("Expecting value", s, err.value) from None json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0) [Sun Nov 5 21:57:49 2023]

Error in rule celltypist_celltype: jobid: 11 input: analyses_integrated/h5ad/percent_mt~auto/resolution~auto/integrated.h5ad output: analyses_integrated/celltypist/Immune_All_Low.pkl/percent_mt~auto/resolution~auto/integrated/seurat_clusters, analyses_integrated/celltypist/Immune_All_Low.pkl/percent_mt~auto/resolution~auto/integrated/seurat_clusters/predicted_labels.csv, results_integrated/integrated/percent_mt~auto/resolution~auto/celltypist/Immune_All_Low.pkl/plot_celltypist_dotplot-seurat_clusters.pdf, results_integrated/integrated/percent_mt~auto/resolution~auto/celltypist/Immune_All_Low.pkl/table_cluster_annotation_table-seurat_clusters.xlsx shell: /home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/scripts/scrna-celltypist.py analyses_integrated/h5ad/percent_mt~auto/resolution~auto/integrated.h5ad results_integrated/integrated/percent_mt~auto/resolution~auto/celltypist/Immune_All_Low.pkl/plot_celltypist_dotplot-seurat_clusters.pdf analyses_integrated/celltypist/Immune_All_Low.pkl/percent_mt~auto/resolution~auto/integrated/seurat_clusters results_integrated/integrated/percent_mt~auto/resolution~auto/celltypist/Immune_All_Low.pkl/table_cluster_annotation_table-seurat_clusters.xlsx Immune_All_Low.pkl seurat_clusters (one of the commands exited with non-zero exit code; note that snakemake uses bash strict mode!)

Removing output files of failed job celltypist_celltype since they might be corrupted: analyses_integrated/celltypist/Immune_All_Low.pkl/percent_mt~auto/resolution~auto/integrated/seurat_clusters Trying to restart job 11. Select jobs to execute...

My conda info/list and R sessionInfo() is below:

CONDA INFO

(cellsnake) acboynes@rdl-b5qh9t:~$ conda info

 active environment : cellsnake
active env location : /home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake
        shell level : 2
   user config file : /home/student.unimelb.edu.au/acboynes/.condarc

populated config files : /home/student.unimelb.edu.au/acboynes/miniforge3/.condarc /home/student.unimelb.edu.au/acboynes/.condarc conda version : 23.9.0 conda-build version : not installed python version : 3.10.13.final.0 virtual packages : archspec=1=zen2 glibc=2.31=0 linux=5.4.0=0 unix=0=0 base environment : /home/student.unimelb.edu.au/acboynes/miniforge3 (writable) conda av data dir : /home/student.unimelb.edu.au/acboynes/miniforge3/etc/conda conda av metadata url : None channel URLs : https://conda.anaconda.org/conda-forge/linux-64 https://conda.anaconda.org/conda-forge/noarch package cache : /home/student.unimelb.edu.au/acboynes/miniforge3/pkgs /home/student.unimelb.edu.au/acboynes/.conda/pkgs envs directories : /home/student.unimelb.edu.au/acboynes/miniforge3/envs /home/student.unimelb.edu.au/acboynes/.conda/envs platform : linux-64 user-agent : conda/23.9.0 requests/2.31.0 CPython/3.10.13 Linux/5.4.0-122-generic ubuntu/20.04.4 glibc/2.31 solver/libmamba conda-libmamba-solver/23.11.0 libmambapy/1.5.3 UID:GID : 1512668138:1512668138 netrc file : None offline mode : False

CONDA LIST

(cellsnake) acboynes@rdl-b5qh9t:~$ conda list

packages in environment at /home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake:

#

Name Version Build Channel

_libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_gnu conda-forge _r-mutex 1.0.1 anacondar_1 conda-forge aioeasywebdav 2.4.0 py39hf3d152e_1001 conda-forge aiohttp 3.8.6 py39hd1e30aa_1 conda-forge aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge amply 0.1.6 pyhd8ed1ab_0 conda-forge anndata 0.10.2 pyhd8ed1ab_0 conda-forge appdirs 1.4.4 pyh9f0ad1d_0 conda-forge argcomplete 3.1.4 pyhd8ed1ab_0 conda-forge arpack 3.7.0 hdefa2d7_2 conda-forge array-api-compat 1.4 pyhd8ed1ab_0 conda-forge async-timeout 4.0.3 pyhd8ed1ab_0 conda-forge attmap 0.13.2 pyhd8ed1ab_0 conda-forge attrs 23.1.0 pyh71513ae_1 conda-forge backports 1.0 pyhd8ed1ab_3 conda-forge backports.functools_lru_cache 1.6.5 pyhd8ed1ab_0 conda-forge bcrypt 4.0.1 py39h9fdd4d6_1 conda-forge bedtools 2.31.0 hf5e1c6e_3 bioconda binutils_impl_linux-64 2.40 hf600244_0 conda-forge bioconductor-annotationdbi 1.60.0 r42hdfd78af_0 bioconda bioconductor-annotationhub 3.6.0 r42hdfd78af_0 bioconda bioconductor-batchelor 1.14.0 r42hf17093f_1 bioconda bioconductor-beachmat 2.14.0 r42hf17093f_1 bioconda bioconductor-biobase 2.58.0 r42ha9d7317_1 bioconda bioconductor-biocfilecache 2.6.0 r42hdfd78af_0 bioconda bioconductor-biocgenerics 0.44.0 r42hdfd78af_0 bioconda bioconductor-biocneighbors 1.16.0 r42hf17093f_1 bioconda bioconductor-biocparallel 1.32.5 r42hf17093f_1 bioconda bioconductor-biocsingular 1.14.0 r42hf17093f_1 bioconda bioconductor-biocversion 3.16.0 r42hdfd78af_0 bioconda bioconductor-biostrings 2.66.0 r42ha9d7317_1 bioconda bioconductor-celldex 1.8.0 r42hdfd78af_0 bioconda bioconductor-clusterprofiler 4.6.0 r42hdfd78af_0 bioconda bioconductor-complexheatmap 2.14.0 r42hdfd78af_0 bioconda bioconductor-data-packages 20230718 hdfd78af_1 bioconda bioconductor-delayedarray 0.24.0 r42ha9d7317_1 bioconda bioconductor-delayedmatrixstats 1.20.0 r42hdfd78af_0 bioconda bioconductor-dose 3.24.0 r42hdfd78af_0 bioconda bioconductor-enhancedvolcano 1.16.0 r42hdfd78af_0 bioconda bioconductor-enrichplot 1.18.0 r42hdfd78af_0 bioconda bioconductor-experimenthub 2.6.0 r42hdfd78af_0 bioconda bioconductor-fgsea 1.24.0 r42hf17093f_1 bioconda bioconductor-genomeinfodb 1.34.9 r42hdfd78af_0 bioconda bioconductor-genomeinfodbdata 1.2.9 r42hdfd78af_0 bioconda bioconductor-genomicranges 1.50.0 r42ha9d7317_1 bioconda bioconductor-ggtree 3.6.0 r42hdfd78af_0 bioconda bioconductor-go.db 3.16.0 r42hdfd78af_0 bioconda bioconductor-gosemsim 2.24.0 r42hf17093f_1 bioconda bioconductor-graph 1.76.0 r42ha9d7317_1 bioconda bioconductor-hdo.db 0.99.1 r42hdfd78af_0 bioconda bioconductor-interactivedisplaybase 1.36.0 r42hdfd78af_0 bioconda bioconductor-iranges 2.32.0 r42ha9d7317_1 bioconda bioconductor-keggrest 1.38.0 r42hdfd78af_0 bioconda bioconductor-limma 3.54.0 r42ha9d7317_1 bioconda bioconductor-matrixgenerics 1.10.0 r42hdfd78af_0 bioconda bioconductor-miqc 1.6.0 r42hdfd78af_0 bioconda bioconductor-org.hs.eg.db 3.16.0 r42hdfd78af_0 bioconda bioconductor-qvalue 2.30.0 r42hdfd78af_0 bioconda bioconductor-residualmatrix 1.8.0 r42hdfd78af_0 bioconda bioconductor-rhdf5lib 1.20.0 r42ha9d7317_2 bioconda bioconductor-s4vectors 0.36.0 r42ha9d7317_1 bioconda bioconductor-scaledmatrix 1.6.0 r42hdfd78af_0 bioconda bioconductor-scater 1.26.0 r42hdfd78af_0 bioconda bioconductor-scuttle 1.8.0 r42hf17093f_1 bioconda bioconductor-singlecellexperiment 1.20.0 r42hdfd78af_0 bioconda bioconductor-singler 2.0.0 r42hf17093f_1 bioconda bioconductor-sparsematrixstats 1.10.0 r42hf17093f_1 bioconda bioconductor-summarizedexperiment 1.28.0 r42hdfd78af_0 bioconda bioconductor-topgo 2.50.0 r42hdfd78af_0 bioconda bioconductor-treeio 1.22.0 r42hdfd78af_0 bioconda bioconductor-xvector 0.38.0 r42ha9d7317_1 bioconda bioconductor-zlibbioc 1.44.0 r42ha9d7317_2 bioconda blast 2.14.1 pl5321h6f7f691_0 bioconda blosc 1.21.5 h0f2a231_0 conda-forge boost-cpp 1.78.0 h5adbc97_2 conda-forge boto3 1.28.77 pyhd8ed1ab_0 conda-forge botocore 1.31.77 pyhd8ed1ab_0 conda-forge brotli 1.1.0 hd590300_1 conda-forge brotli-bin 1.1.0 hd590300_1 conda-forge brotli-python 1.1.0 py39h3d6467e_1 conda-forge bwidget 1.9.14 ha770c72_1 conda-forge bzip2 1.0.8 h7f98852_4 conda-forge c-ares 1.21.0 hd590300_0 conda-forge ca-certificates 2023.7.22 hbcca054_0 conda-forge cached-property 1.5.2 hd8ed1ab_1 conda-forge cached_property 1.5.2 pyha770c72_1 conda-forge cachetools 5.3.2 pyhd8ed1ab_0 conda-forge cairo 1.16.0 ha61ee94_1014 conda-forge cellsnake 0.2.0.11 pyh7cba7a3_0 bioconda celltypist 1.3.0 pyhdfd78af_0 bioconda certifi 2023.7.22 pyhd8ed1ab_0 conda-forge cffi 1.16.0 py39h7a31438_0 conda-forge cfitsio 4.2.0 hd9d235c_0 conda-forge charset-normalizer 3.3.2 pyhd8ed1ab_0 conda-forge click 8.1.7 unix_pyh707e725_0 conda-forge cmake 3.25.2 h077f3f9_0 conda-forge coin-or-cbc 2.10.10 h9002f0b_0 conda-forge coin-or-cgl 0.60.7 h516709c_0 conda-forge coin-or-clp 1.17.8 h1ee7a9c_0 conda-forge coin-or-osi 0.108.8 ha2443b9_0 conda-forge coin-or-utils 2.11.9 hee58242_0 conda-forge coincbc 2.10.10 0_metapackage conda-forge colorama 0.4.6 pyhd8ed1ab_0 conda-forge configargparse 1.7 pyhd8ed1ab_0 conda-forge connection_pool 0.0.3 pyhd3deb0d_0 conda-forge contourpy 1.1.1 py39h7633fee_1 conda-forge cryptography 39.0.0 py39hd598818_0 conda-forge curl 7.87.0 h6312ad2_0 conda-forge cycler 0.12.1 pyhd8ed1ab_0 conda-forge cython 3.0.5 py39h3d6467e_0 conda-forge datrie 0.8.2 py39hd1e30aa_7 conda-forge defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge docopt 0.6.2 py_1 conda-forge docutils 0.20.1 py39hf3d152e_2 conda-forge dpath 2.1.6 pyha770c72_0 conda-forge dropbox 11.36.2 pyhd8ed1ab_0 conda-forge eido 0.2.1 pyhd8ed1ab_0 conda-forge entrez-direct 16.2 he881be0_1 bioconda et_xmlfile 1.1.0 pyhd8ed1ab_0 conda-forge exceptiongroup 1.1.3 pyhd8ed1ab_0 conda-forge expat 2.5.0 hcb278e6_1 conda-forge filechunkio 1.8 py_2 conda-forge font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge font-ttf-inconsolata 3.000 h77eed37_0 conda-forge font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge font-ttf-ubuntu 0.83 hab24e00_0 conda-forge fontconfig 2.14.2 h14ed4e7_0 conda-forge fonts-conda-ecosystem 1 0 conda-forge fonts-conda-forge 1 0 conda-forge fonttools 4.43.1 py39hd1e30aa_0 conda-forge freetype 2.12.1 h267a509_2 conda-forge freexl 1.0.6 h166bdaf_1 conda-forge fribidi 1.0.10 h36c2ea0_0 conda-forge frozenlist 1.4.0 py39hd1e30aa_1 conda-forge ftputil 5.0.4 pyhd8ed1ab_0 conda-forge fuzzywuzzy 0.18.0 pyhd8ed1ab_0 conda-forge gcc_impl_linux-64 13.2.0 h338b0a0_2 conda-forge geos 3.11.1 h27087fc_0 conda-forge geotiff 1.7.1 ha76d385_4 conda-forge gettext 0.21.1 h27087fc_0 conda-forge gfortran_impl_linux-64 13.2.0 h76e1118_2 conda-forge giflib 5.2.1 h0b41bf4_3 conda-forge gitdb 4.0.11 pyhd8ed1ab_0 conda-forge gitpython 3.1.40 pyhd8ed1ab_0 conda-forge glpk 5.0 h445213a_0 conda-forge gmp 6.2.1 h58526e2_0 conda-forge google-api-core 2.12.0 pyhd8ed1ab_0 conda-forge google-api-python-client 2.106.0 pyhd8ed1ab_0 conda-forge google-auth 2.23.4 pyhca7485f_0 conda-forge google-auth-httplib2 0.1.1 pyhd8ed1ab_0 conda-forge google-cloud-core 2.3.3 pyhd8ed1ab_0 conda-forge google-cloud-storage 2.13.0 pyhca7485f_0 conda-forge google-crc32c 1.1.2 py39h328ec2c_5 conda-forge google-resumable-media 2.6.0 pyhd8ed1ab_0 conda-forge googleapis-common-protos 1.61.0 pyhd8ed1ab_0 conda-forge graphite2 1.3.13 h58526e2_1001 conda-forge grpcio 1.46.3 py39h0f497a6_0 conda-forge gsl 2.7 he838d99_0 conda-forge gxx_impl_linux-64 13.2.0 h338b0a0_2 conda-forge h5py 3.7.0 nompi_py39h817c9c5_102 conda-forge harfbuzz 6.0.0 h8e241bc_0 conda-forge hdf4 4.2.15 h9772cbc_5 conda-forge hdf5 1.12.2 nompi_h2386368_101 conda-forge htslib 1.17 h6bc39ce_1 bioconda httplib2 0.22.0 pyhd8ed1ab_0 conda-forge humanfriendly 10.0 pyhd8ed1ab_6 conda-forge icu 70.1 h27087fc_0 conda-forge idna 3.4 pyhd8ed1ab_0 conda-forge igraph 0.10.3 hb9ddf80_0 conda-forge importlib-metadata 6.8.0 pyha770c72_0 conda-forge importlib_metadata 6.8.0 hd8ed1ab_0 conda-forge importlib_resources 6.1.0 pyhd8ed1ab_0 conda-forge iniconfig 2.0.0 pyhd8ed1ab_0 conda-forge jinja2 3.1.2 pyhd8ed1ab_1 conda-forge jmespath 1.0.1 pyhd8ed1ab_0 conda-forge joblib 1.3.2 pyhd8ed1ab_0 conda-forge jpeg 9e h0b41bf4_3 conda-forge jq 1.5 0 bioconda json-c 0.16 hc379101_0 conda-forge jsonschema 4.19.2 pyhd8ed1ab_0 conda-forge jsonschema-specifications 2023.7.1 pyhd8ed1ab_0 conda-forge jupyter_core 5.5.0 py39hf3d152e_0 conda-forge kaleido-core 0.2.1 h3644ca4_0 conda-forge kealib 1.4.15 ha7026e8_1 conda-forge kernel-headers_linux-64 2.6.32 he073ed8_16 conda-forge keyutils 1.6.1 h166bdaf_0 conda-forge kiwisolver 1.4.5 py39h7633fee_1 conda-forge kraken2 2.1.3 pl5321hdcf5f25_0 bioconda krb5 1.20.1 hf9c8cef_0 conda-forge lcms2 2.14 h6ed2654_0 conda-forge ld_impl_linux-64 2.40 h41732ed_0 conda-forge leidenalg 0.9.1 py39h227be39_0 conda-forge lerc 4.0.0 h27087fc_0 conda-forge levenshtein 0.23.0 py39h3d6467e_0 conda-forge libabseil 20230802.1 cxx17_h59595ed_0 conda-forge libaec 1.1.2 h59595ed_1 conda-forge libblas 3.9.0 16_linux64_openblas conda-forge libbrotlicommon 1.1.0 hd590300_1 conda-forge libbrotlidec 1.1.0 hd590300_1 conda-forge libbrotlienc 1.1.0 hd590300_1 conda-forge libcblas 3.9.0 16_linux64_openblas conda-forge libcrc32c 1.1.2 h9c3ff4c_0 conda-forge libcurl 7.87.0 h6312ad2_0 conda-forge libdeflate 1.13 h166bdaf_0 conda-forge libedit 3.1.20191231 he28a2e2_2 conda-forge libev 4.33 h516909a_1 conda-forge libexpat 2.5.0 hcb278e6_1 conda-forge libffi 3.4.2 h7f98852_5 conda-forge libgcc 7.2.0 h69d50b8_2 conda-forge libgcc-devel_linux-64 13.2.0 ha9c7c90_2 conda-forge libgcc-ng 13.2.0 h807b86a_2 conda-forge libgdal 3.6.0 ha189470_7 conda-forge libgfortran-ng 13.2.0 h69a702a_2 conda-forge libgfortran5 13.2.0 ha4646dd_2 conda-forge libglib 2.78.0 hebfc3b9_0 conda-forge libgomp 13.2.0 h807b86a_2 conda-forge libhwloc 2.9.1 hd6dc26d_0 conda-forge libiconv 1.17 h166bdaf_0 conda-forge libidn2 2.3.4 h166bdaf_0 conda-forge libkml 1.3.0 h01aab08_1016 conda-forge liblapack 3.9.0 16_linux64_openblas conda-forge liblapacke 3.9.0 16_linux64_openblas conda-forge libllvm11 11.1.0 he0ac6c6_5 conda-forge libnetcdf 4.8.1 nompi_h261ec11_106 conda-forge libnghttp2 1.51.0 hdcd2b5c_0 conda-forge libnsl 2.0.1 hd590300_0 conda-forge libopenblas 0.3.21 pthreads_h78a6416_3 conda-forge libpng 1.6.39 h753d276_0 conda-forge libpq 14.5 h2baec63_5 conda-forge libprotobuf 4.24.4 hf27288f_0 conda-forge librttopo 1.1.0 ha49c73b_12 conda-forge libsanitizer 13.2.0 h7e041cc_2 conda-forge libsodium 1.0.18 h36c2ea0_1 conda-forge libspatialite 5.0.1 h7c8129e_22 conda-forge libsqlite 3.44.0 h2797004_0 conda-forge libssh2 1.10.0 haa6b8db_3 conda-forge libstdcxx-devel_linux-64 13.2.0 ha9c7c90_2 conda-forge libstdcxx-ng 13.2.0 h7e041cc_2 conda-forge libtiff 4.4.0 h0e0dad5_3 conda-forge libudunits2 2.2.28 h40f5838_3 conda-forge libunistring 0.9.10 h7f98852_0 conda-forge libuuid 2.38.1 h0b41bf4_0 conda-forge libuv 1.46.0 hd590300_0 conda-forge libv8 8.9.83 h7465d70_2 conda-forge libwebp-base 1.3.2 hd590300_0 conda-forge libxcb 1.13 h7f98852_1004 conda-forge libxml2 2.10.3 hca2bb57_4 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2.2.28 h40f5838_3 conda-forge umap-learn 0.5.4 py39hf3d152e_0 conda-forge unicodedata2 15.1.0 py39hd1e30aa_0 conda-forge uriparser 0.9.7 hcb278e6_1 conda-forge uritemplate 4.1.1 pyhd8ed1ab_0 conda-forge urllib3 1.26.18 pyhd8ed1ab_0 conda-forge veracitools 0.1.3 py_0 conda-forge wcwidth 0.2.9 pyhd8ed1ab_0 conda-forge wget 1.20.3 ha56f1ee_1 conda-forge wheel 0.41.3 pyhd8ed1ab_0 conda-forge wrapt 1.15.0 py39hd1e30aa_1 conda-forge xerces-c 3.2.4 h55805fa_1 conda-forge xmltodict 0.13.0 pyhd8ed1ab_0 conda-forge xorg-kbproto 1.0.7 h7f98852_1002 conda-forge xorg-libice 1.0.10 h7f98852_0 conda-forge xorg-libsm 1.2.3 hd9c2040_1000 conda-forge xorg-libx11 1.8.4 h0b41bf4_0 conda-forge xorg-libxau 1.0.11 hd590300_0 conda-forge xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge xorg-libxext 1.3.4 h0b41bf4_2 conda-forge xorg-libxrender 0.9.10 h7f98852_1003 conda-forge xorg-libxt 1.3.0 hd590300_0 conda-forge xorg-renderproto 0.11.1 h7f98852_1002 conda-forge xorg-xextproto 7.3.0 h0b41bf4_1003 conda-forge xorg-xproto 7.0.31 h7f98852_1007 conda-forge xz 5.2.6 h166bdaf_0 conda-forge yaml 0.2.5 h7f98852_2 conda-forge yarl 1.9.2 py39hd1e30aa_1 conda-forge yq 3.2.3 pyhd8ed1ab_0 conda-forge yte 1.5.1 pyha770c72_2 conda-forge zipp 3.17.0 pyhd8ed1ab_0 conda-forge zlib 1.2.13 hd590300_5 conda-forge zstd 1.5.5 hfc55251_0 conda-forge (cellsnake) acboynes@rdl-b5qh9t:~$

R SESSIONINFO

sessionInfo() R version 4.2.2 (2022-10-31) Platform: x86_64-conda-linux-gnu (64-bit) Running under: Ubuntu 20.04.4 LTS

Matrix products: default BLAS/LAPACK: /home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/libopenblasp-r0.3.21.so

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

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

loaded via a namespace (and not attached): [1] compiler_4.2.2

sinanugur commented 10 months ago

Do you have internet connection? I think celltypist requires the models to be download.

type this and then rerun.

celltypist --update-models

I think Seurat5 wont solve your resource problem. It requires quite a large computer (we usually run on 1TB ram and 126 CPUs, CPUs is not necessary but RAM is important) and it may takes days to finish differential expression analysis. You can integrate a smaller dataset in your current setup but it may still takes hours with cellsnake. I recommend to use a PCA dims 20 and integration methodology rcpa to test on 100K cells on your computer.

For example,

cellsnake integrate data --dims 20 --reduction rcpa

ab4cp commented 10 months ago

Hi @sinanugur - this was actually a security restriction on my server sorry

ab4cp commented 10 months ago

Hi @sinanugur I may have ran into another bug which I couldn't find too much troubleshooting information on.

I'm getting a new error message with the fetal-brain dataset doubletFinder_v3. Also, getting the same error message when running some of my own datasets.

~/Downloads/fetal-brain$ cellsnake standard data --jobs 5

Loading required package: viridisLite

Try help(fields) to get started. Loading required package: KernSmooth KernSmooth 2.23 loaded Copyright M. P. Wand 1997-2009 Error in doubletFinder_v3(scrna, PCs = 1:10, pN = 0.25, pK = 0.09, nExp = nExp_poi, : no slot of name "counts" for this object of class "Assay5" In addition: Warning messages: 1: package ‘fields’ was built under R version 4.2.3 2: package ‘spam’ was built under R version 4.2.3 3: package ‘viridisLite’ was built under R version 4.2.3 4: package ‘KernSmooth’ was built under R version 4.2.3 Execution halted [Fri Nov 10 15:55:45 2023] Error in rule normalization_pca_rds: jobid: 9 input: analyses/raw/percent_mt~10/resolution~0.8/10X_17_028.rds output: analyses/processed/percent_mt~10/resolution~0.8/10X_17_028.rds shell: /home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/scripts/scrna-normalization-pca.R --rds analyses/raw/percent_mt~10/resolution~0.8/10X_17_028.rds --doublet.filter --normalization.method LogNormalize --cpu 5 --scale.factor 10000 --reference BlueprintEncodeData --variable.selection.method vst --nfeature 2000 --resolution 0.8 --output.rds analyses/processed/percent_mt~10/resolution~0.8/10X_17_028.rds --umap --tsne
(one of the commands exited with non-zero exit code; note that snakemake uses bash strict mode!)

Trying to restart job 9. Select jobs to execute...

[Fri Nov 10 15:55:45 2023] rule normalization_pca_rds: input: analyses/raw/percent_mt~10/resolution~0.8/10X_17_028.rds output: analyses/processed/percent_mt~10/resolution~0.8/10X_17_028.rds jobid: 9 reason: Missing output files: analyses/processed/percent_mt~10/resolution~0.8/10X_17_028.rds; Input files updated by another job: analyses/raw/percent_mt~10/resolution~0.8/10X_17_028.rds wildcards: sample=10X_17_028, percent_mt=10, resolution=0.8 threads: 5 resources: tmpdir=/tmp, mem_mb=15000, mem_mib=14306

sinanugur commented 10 months ago

Hi @ab4cp, Yes, it seems some new updates causing this problem. I submitted a new build and will submit a patch but before that you can fix your environment.

Here is the solution:

conda install r-seuratobject=4.1.3 r-matrix=1.5_4.1 -c conda-forge

Just to be on thre safe side, you can also check if the Seurat version messed up:

conda install r-seurat=4.3.0 -c conda-forge

Cheers,

ab4cp commented 10 months ago

@sinanugur thanks for the reply. I just updated both of those as you mentioned. I ran the fetal-brain dataset again and got the following error about cannot find seurat object

Warning message: package ‘data.table’ was built under R version 4.2.3 Error in CreateSeuratObject(counts = scrna.data, project = make.names(opt$sampleid), : could not find function "CreateSeuratObject" Execution halted [Sat Nov 11 01:20:01 2023] Error in rule create_initial_raw_rds_and_trimming: jobid: 0 input: data/10X_17_029/outs/filtered_feature_bc_matrix output: analyses/raw/percent_mt~10/resolution~0.8/10X_17_029.rds, results/10X_17_029/percent_mt~10/resolution~0.8/technicals/plot_before-qc-trimming.pdf, results/10X_17_029/percent_mt~10/resolution~0.8/technicals/plot_after-qc-trimming.pdf

RuleException: CalledProcessError in file /home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/rules/seurat.smk, line 46: Command 'set -euo pipefail; /home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/scripts/scrna-read-qc.R --data.dir data/10X_17_029/outs/filtered_feature_bc_matrix --output.rds analyses/raw/percent_mt~10/resolution~0.8/10X_17_029.rds --sampleid 10X_17_029 --percent.rp 0 --percent.mt 10 --min.features 200 --max.features inf --max.molecules inf --min.cells 3 --before.violin.plot results/10X_17_029/percent_mt~10/resolution~0.8/technicals/plot_before-qc-trimming.pdf --after.violin.plot results/10X_17_029/percent_mt~10/resolution~0.8/technicals/plot_after-qc-trimming.pdf' returned non-zero exit status 1. File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/site-packages/cellsnake/scrna/workflow/rules/seurat.smk", line 46, in __rule_create_initial_raw_rds_and_trimming File "/home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake/lib/python3.9/concurrent/futures/thread.py", line 58, in run Shutting down, this might take some time. Exiting because a job execution failed. Look above for error message Exiting because a job execution failed. Look above for error message

sinanugur commented 10 months ago

OK, now the environment is missing Seurat, did you run?

conda install r-seurat=4.3.0 -c conda-forge

Make sure the other dependencies are not updated.

ab4cp commented 10 months ago

Yeh I ran the above. Looks like the correct version of seurat is in the environment

packages in environment at /home/student.unimelb.edu.au/acboynes/miniforge3/envs/cellsnake:

#

Name Version Build Channel

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sinanugur commented 10 months ago

Hmm this environment looks really crowded, can you create a clean environment.

conda create -n cellenv cellsnake -c bioconda -c conda-forge
conda activate cellenv
#then install the new release candidate
pip install -i https://test.pypi.org/simple/ cellsnake==0.2.0.12rc1

cellsnake --install-packages
ab4cp commented 10 months ago

@sinanugur thanks for that! It's up and running smoothly. I still have security limits for celltypist (this is being sorted out). Would you be able to give me the input for celltypist so I can run that part on my local computer with python or R.

Thank you

sinanugur commented 10 months ago

Hi @ab4cp, glad that it worked. So you struggle with Cellsnake celltypist or you need to run it elsewhere for your own purpose?

First, cellsnake also uses SingleR, it is good enough for annotation. It is quite comprehensive. https://bioconductor.org/books/release/SingleRBook/

Celltypist is Python based, so cellsnake auto converts Seurat to Anndata format and so on.

For example, this script converts https://github.com/sinanugur/scrna-workflow/blob/main/workflow/scripts/scrna-convert-to-h5ad.R Seurat RDS to anndata.

this one reads anndata and use celltypist: https://github.com/sinanugur/scrna-workflow/blob/main/workflow/scripts/scrna-celltypist.py

Then this one reads the prediction results and plot them: https://github.com/sinanugur/scrna-workflow/blob/main/workflow/scripts/scrna-celltypist.R

You do not need the last one though, you can change Python code to get more plots, it also by default plot some annotation hetmaps etc.