Error in silhouetteRankTest "/par." file not found

I'm attempting to produce Giotto results for the Spatial LIBD data using the SEDR Analyses

The issue seems to be that the par files are not getting created via the do_gpd.R script called within the python stack. I'm not sure why this is happening. Thoughts?

Output

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paths <- .libPaths() paths <- c("/geode2/home/u100/whitealj/BigRed3/r_4_0_4_library/",paths) .libPaths(paths)

library(Giotto) library(Seurat) Registered S3 method overwritten by 'spatstat.geom': method from print.boxx cli Attaching SeuratObject library(ggplot2) library(patchwork) options(bitmapType = 'cairo')

args <- commandArgs(trailingOnly = TRUE) sample.name <- args[1] n_cluster <- args[2]

1. Load Data

data_path = file.path('../data/DLPFC/', sample.name) dir.output = file.path('../output/DLPFC/', sample.name, 'Giotto')

if(!dir.exists(file.path(dir.output))){

dir.create(file.path(dir.output), recursive = TRUE)

}

expr_data_path=fs::path(data_path, "filtered_feature_bc_matrix.h5") raw_matrix=get10Xmatrix_h5(path_to_data=expr_data_path)$Gene Expression spatial_locations=data.table::fread(fs::path(data_path, "spatial", "tissue_positions_list.csv")) spatial_locations = spatial_locations[match(colnames(raw_matrix), V1)] colnames(spatial_locations) = c('barcode', 'in_tissue', 'array_row', 'array_col', 'col_pxl', 'row_pxl') myinst=createGiottoInstructions(save_plot=T, show_plot=F, save_dir=dir.output)

no external python path was provided, but a giotto python environment was found and will be used sh: /geode2/home/u100/whitealj/BigRed3/.local/share/r-miniconda/envs/giotto_env/lib/libtinfo.so.6: no version information available (required by /lib64/libreadline.so.7)

2. Create Giotto object & process data.

visium_brain <- createGiottoObject(raw_exprs = raw_matrix,

spatial_locs = spatial_locations[,.(row_pxl,-col_pxl)],

instructions = myinst,

cell_metadata = spatial_locations[,.(in_tissue, array_row, array_col)]) Consider to install these (optional) packages to run all possible Giotto commands for spatial analyses: scran MAST smfishHmrf trendsceek SPARK multinet RTriangle FactoMiner Giotto does not automatically install all these packages as they are not absolutely required and this reduces the number of dependenciessh: /geode2/home/u100/whitealj/BigRed3/.local/share/r-miniconda/envs/giotto_env/lib/libtinfo.so.6: no version information available (required by /lib64/libreadline.so.7) Warning messages: 1: In createGiottoObject(raw_exprs = raw_matrix, spatial_locs = spatial_locations[, : module: igraph was not found with python path: /geode2/home/u100/whitealj/BigRed3/.local/share/r-miniconda/envs/giotto_env/bin/python

2: In createGiottoObject(raw_exprs = raw_matrix, spatial_locs = spatial_locations[, : module: leidenalg was not found with python path: /geode2/home/u100/whitealj/BigRed3/.local/share/r-miniconda/envs/giotto_env/bin/python

3: In createGiottoObject(raw_exprs = raw_matrix, spatial_locs = spatial_locations[, : module: community was not found with python path: /geode2/home/u100/whitealj/BigRed3/.local/share/r-miniconda/envs/giotto_env/bin/python

4: In createGiottoObject(raw_exprs = raw_matrix, spatial_locs = spatial_locations[, : module: networkx was not found with python path: /geode2/home/u100/whitealj/BigRed3/.local/share/r-miniconda/envs/giotto_env/bin/python

metadata = pDataDT(visium_brain) in_tissue_barcodes = metadata[in_tissue == 1]$cell_ID visium_brain = subsetGiotto(visium_brain, cell_ids = in_tissue_barcodes)

filter genes and cells

visium_brain <- filterGiotto(gobject = visium_brain,

expression_threshold = 1,

gene_det_in_min_cells = 50,

min_det_genes_per_cell = 500,

expression_values = c('raw'),

verbose = T) Number of cells removed: 2 out of 4226 Number of genes removed: 23711 out of 33538
normalize

visium_brain <- normalizeGiotto(gobject = visium_brain, scalefactor = 6000, verbose = T)

first scale genes and then cells

add gene & cell statistics

visium_brain <- addStatistics(gobject = visium_brain)

3. Dimensional reduction

HVG

visium_brain <- calculateHVG(gobject = visium_brain) return_plot = TRUE and return_gobject = TRUE

      plot will not be returned to object, but can still be saved with save_plot = TRUE or manually

PCA

select genes based on HVG and gene statistics, both found in gene metadata

gene_metadata = fDataDT(visium_brain) featgenes = gene_metadata[hvg == 'yes' & perc_cells > 3 & mean_expr_det > 0.4]$gene_ID

run PCA on expression values (default)

visium_brain <- runPCA(gobject = visium_brain,

genes_to_use = featgenes,

scale_unit = F,

center=T,

method="factominer") a custom vector of genes will be used to subset the matrix

UMAP and tSNE

visium_brain <- runUMAP(visium_brain, dimensions_to_use = 1:20) visium_brain <- runtSNE(visium_brain, dimensions_to_use = 1:20)

create spatial network

visium_brain <- createSpatialNetwork(gobject=visium_brain,

method='kNN',

k=5,

maximum_distance_knn=400,

name='spatial_network')

silhouette

spatial_genes=silhouetteRankTest(visium_brain,

overwrite_input_bin=F,

output=file.path(dir.output, "sil.result"),

matrix_type="dissim",

num_core=20,

verbose=T,

expression_values="norm",

query_sizes=10) sh: /geode2/home/u100/whitealj/BigRed3/.local/share/r-miniconda/envs/giotto_env/lib/libtinfo.so.6: no version information available (required by /lib64/libreadline.so.7) Reading gene expression... Reading cell coordinates... Calculate all pairwise Euclidean distance between cells using their physical coordinates For rbp_p 0.95: Start ranking forward... Done 0 of 4224 Done 1000 of 4224 Done 2000 of 4224 Done 3000 of 4224 Done 4000 of 4224 Start ranking backward... Done 0 of 4224 Done 1000 of 4224 Done 2000 of 4224 Done 3000 of 4224 Done 4000 of 4224 Calculate mutual rank... Calculate exponential transform... For rbp_p 0.99: Start ranking forward... Done 0 of 4224 Done 1000 of 4224 Done 2000 of 4224 Done 3000 of 4224 Done 4000 of 4224 Start ranking backward... Done 0 of 4224 Done 1000 of 4224 Done 2000 of 4224 Done 3000 of 4224 Done 4000 of 4224 Calculate mutual rank... Calculate exponential transform... Start calculating silhouette rank, saving logs to log directory (check progress here)... sh: /usr/bin/parallel: No such file or directory cat: write error: Broken pipe Start randomization, saving logs to log directory (check progress here)... sh: /usr/bin/parallel: No such file or directory cat: write error: Broken pipe Start computing P-values... Entering silhouette.exact.rbp.0.95.top.0.005.txt... Setting query size 3 instead of 10 Error in py_call_impl(callable, dots$args, dots$keywords) : FileNotFoundError: [Errno 2] No such file or directory: '../output/DLPFC//151507/Giotto/sil.result/silhouetteRank_output//result_5000_0.95_0.005/par.43'

Detailed traceback: File "", line 100, in silhouette_rank File "/geode2/home/u100/whitealj/BigRed3/.local/share/r-miniconda/envs/giotto_env/lib/python3.6/site-packages/silhouetteRank/use_previous_cluster.py", line 140, in do_one f = open("%s/par.%d" % (outdir,target)) Calls: silhouetteRankTest -> silhouette_rank -> py_call_impl Execution halted`

Session Info R version 4.0.4 (2021-02-15) Platform: x86_64-pc-linux-gnu (64-bit) Running under: SUSE Linux Enterprise Server 15 SP2

Matrix products: default BLAS/LAPACK: /geode2/soft/hps/cle6/intel/19.5/compilers_and_libraries_2019.5.281/linux/mkl/lib/intel64_lin/libmkl_rt.so

locale: [1] C

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

other attached packages: [1] eva_0.2.6 patchwork_1.1.1 ggplot2_3.3.5 SeuratObject_4.0.4 [5] Seurat_4.1.0 Giotto_1.1.0

drieslab / Giotto

Error in silhouetteRankTest "/par." file not found #188

1. Load Data

2. Create Giotto object & process data.

filter genes and cells

gene_det_in_min_cells = 50,

min_det_genes_per_cell = 500,

normalize

add gene & cell statistics

3. Dimensional reduction

HVG

PCA

select genes based on HVG and gene statistics, both found in gene metadata

run PCA on expression values (default)

UMAP and tSNE

create spatial network

silhouette