ModuleNotFoundError: No module named 'scipy'

[manuelatan]

1. Bug description

echolocatoR fails to find module scipy. I get the error "ModuleNotFoundError: No module named 'scipy'" when extracting Linkage Disequilibrium. I am running the latest version of echolocatoR, v2.0.3. Any advice would be much appreciated!

2. Reproducible example

Code

#Map column names in summary stats
columnsnames = echodata::construct_colmap(munged= FALSE,
                                          CHR = "CHR", POS = "POS",
                                          SNP = "SNP", P = "pvalue",
                                          Effect = "beta", StdErr = "SE", 
                                          A1 = "A1", A2 = "A2", Freq = "freq",
                                          N = "N")

#Fine mapping
results <- echolocatoR::finemap_loci(
  topSNPs = topSNPs,
  loci = topSNPs$Locus,
  LD_reference = "UKB", #using UK Biobank for LD reference panel
  dataset_name = "mortality_GWAS",
  fullSS_genome_build = "hg19",
  case_control = FALSE,
  finemap_methods = c("ABF","SUSIE","FINEMAP"),
  force_new_subset = TRUE,
  force_new_LD = TRUE,
  force_new_finemap = TRUE,
  
  # SNP filters
  bp_distance = 1000000, #distance around the lead SNP to include (1Mb, +/- 500kb)
  min_MAF = 0.001, 
  
  # Munge full sumstats first
  munged = FALSE,
  fullSS_path = "/Users/manuela/Documents/Work/survival_GWAS/echolocatoR/summary_stats/mortalityGWAS_summaryStats_forMunge.txt",
  colmap = columnsnames,
  
  #Plot options
  plot_types = c("fancy"), #in addition to GWAS and fine mapping tracks, plot XGR annotation tracks - XGR, Roadmap, Nott2019
  show_plot = TRUE,
  zoom = c("1x", "4x", "10x"))

Console output

[1] "+ Assigning Gene and Locus independently."
Standardising column headers.
First line of summary statistics file: 
SNP	CHR	POS	P	Effect	StdErr	Freq	A1	A2	N	Locus	Gene	
Returning unmapped column names without making them uppercase.
+ Mapping colnames from MungeSumstats ==> echolocatoR
┌────────────────────────────────────────────┐
│                                            │
│   )))> 🦇 ANKRD55 [locus 1 / 10] 🦇 <(((   │
│                                            │
└────────────────────────────────────────────┘

──────────────────────────────────────────────────────────────────────────────────

── Step 1 ▶▶▶ Query 🔎 ───────────────────────────────────────────────────────────

──────────────────────────────────────────────────────────────────────────────────
+ Query Method: tabix
Constructing GRanges query using min/max ranges within a single chromosome.
query_dat is already a GRanges object. Returning directly.
========= echotabix::convert =========
Converting full summary stats file to tabix format for fast querying.
Inferred format: 'table'
Explicit format: 'table'
Inferring comment_char from tabular header: 'CHR'
Determining chrom type from file header.
Chromosome format: 1
Detecting column delimiter.
Identified column separator: \t
Sorting rows by coordinates via bash.
Searching for header row with grep.
( grep ^'CHR' .../mortalityGWAS_summaryStats_forMunge.txt; grep
    -v ^'CHR' .../mortalityGWAS_summaryStats_forMunge.txt | sort
    -k1,1n
    -k2,2n ) > .../file16c12683ada49_sorted.tsv
Constructing outputs
Using existing bgzipped file: /Users/manuela/Documents/Work/survival_GWAS/echolocatoR/summary_stats/mortalityGWAS_summaryStats_forMunge.txt.bgz 
Set force_new=TRUE to override this.
Tabix-indexing file using: Rsamtools
Data successfully converted to bgzip-compressed, tabix-indexed format.
========= echotabix::query =========
query_dat is already a GRanges object. Returning directly.
Inferred format: 'table'
Querying tabular tabix file using: Rsamtools.
Checking query chromosome style is correct.
Chromosome format: 1
Retrieving data.
Converting query results to data.table.
Processing query: 5:54533638-56533638
Adding 'query' column to results.
Retrieved data with 6,058 rows
Saving query ==> /var/folders/gs/pbd9rgqs6jg963j_g70phh3h0000gn/T//RtmpsoB6CR/results/GWAS/mortality_GWAS/ANKRD55/ANKRD55_mortality_GWAS_subset.tsv.gz
+ Query: 6,058 SNPs x 15 columns.
Standardizing summary statistics subset.
Standardizing main column names.
++ Preparing A1,A1 cols
++ Preparing MAF,Freq cols.
++ Inferring MAF from frequency column.
++ Removing SNPs with MAF== 0 | NULL | NA or >1.
++ Preparing N_cases,N_controls cols.
++ Preparing proportion_cases col.
++ proportion_cases not included in data subset.
Preparing sample size column (N).
Using existing 'N' column.
+ Imputing t-statistic from Effect and StdErr.
+ leadSNP missing. Assigning new one by min p-value.
++ Ensuring Effect,StdErr,P are numeric.
++ Ensuring 1 SNP per row and per genomic coordinate.
++ Removing extra whitespace
+ Standardized query: 6,058 SNPs x 18 columns.
++ Saving standardized query ==> /var/folders/gs/pbd9rgqs6jg963j_g70phh3h0000gn/T//RtmpsoB6CR/results/GWAS/mortality_GWAS/ANKRD55/ANKRD55_mortality_GWAS_subset.tsv.gz

──────────────────────────────────────────────────────────────────────────────────

── Step 2 ▶▶▶ Extract Linkage Disequilibrium 🔗 ──────────────────────────────────

──────────────────────────────────────────────────────────────────────────────────
LD_reference identified as: ukb.
Using UK Biobank LD reference panel.
+ UKB LD file name: chr5_54000001_57000001
Downloading full .gz/.npz UKB files and saving to disk.
echoconda:: conda already installed.
Retrieving conda env name from yaml: echoR_mini
echoconda:: Conda environment already exists: echoR_mini
Searching for 1 package(s) across 1 conda environment(s).
Listing all packages in environment: echoR_mini
1 unique package(s) found across 1 conda environment(s).
Downloading with axel [1 thread(s)]: https://data.broadinstitute.org/alkesgroup/UKBB_LD/chr5_54000001_57000001.gz ==> /var/folders/gs/pbd9rgqs6jg963j_g70phh3h0000gn/T//RtmpsoB6CR/results/GWAS/mortality_GWAS/ANKRD55/LD/chr5_54000001_57000001.gz
+ Overwriting pre-existing file.
axel download successful.
Time difference of 4.1 secs
echoconda:: conda already installed.
Retrieving conda env name from yaml: echoR_mini
echoconda:: Conda environment already exists: echoR_mini
Searching for 1 package(s) across 1 conda environment(s).
Listing all packages in environment: echoR_mini
1 unique package(s) found across 1 conda environment(s).
Downloading with axel [1 thread(s)]: https://data.broadinstitute.org/alkesgroup/UKBB_LD/chr5_54000001_57000001.npz ==> /var/folders/gs/pbd9rgqs6jg963j_g70phh3h0000gn/T//RtmpsoB6CR/results/GWAS/mortality_GWAS/ANKRD55/LD/chr5_54000001_57000001.npz
+ Overwriting pre-existing file.
axel download successful.
Time difference of 15.7 secs
ModuleNotFoundError: No module named 'scipy'
Locus ANKRD55 complete in: 1.28 min

3. Session info

R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.6

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

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

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

other attached packages:
 [1] MungeSumstats_1.7.10                       
 [2] forcats_0.5.2                              
 [3] stringr_1.5.0                              
 [4] dplyr_1.0.10                               
 [5] purrr_0.3.5                                
 [6] readr_2.1.3                                
 [7] tidyr_1.2.1                                
 [8] tibble_3.1.8                               
 [9] ggplot2_3.4.0                              
[10] tidyverse_1.3.2                            
[11] data.table_1.14.6                          
[12] BSgenome.Hsapiens.1000genomes.hs37d5_0.99.1
[13] SNPlocs.Hsapiens.dbSNP155.GRCh37_0.99.22   
[14] BSgenome_1.66.1                            
[15] rtracklayer_1.58.0                         
[16] Biostrings_2.66.0                          
[17] XVector_0.38.0                             
[18] GenomicRanges_1.50.1                       
[19] GenomeInfoDb_1.34.4                        
[20] IRanges_2.32.0                             
[21] S4Vectors_0.36.1                           
[22] BiocGenerics_0.44.0                        
[23] echolocatoR_2.0.3                          

loaded via a namespace (and not attached):
  [1] rappdirs_0.3.3              GGally_2.1.2               
  [3] R.methodsS3_1.8.2           echoLD_0.99.8              
  [5] bit64_4.0.5                 knitr_1.41                 
  [7] irlba_2.3.5.1               DelayedArray_0.24.0        
  [9] R.utils_2.12.2              rpart_4.1.16               
 [11] KEGGREST_1.38.0             RCurl_1.98-1.9             
 [13] AnnotationFilter_1.22.0     generics_0.1.3             
 [15] GenomicFeatures_1.50.2      RSQLite_2.2.19             
 [17] proxy_0.4-27                bit_4.0.5                  
 [19] tzdb_0.3.0                  xml2_1.3.3                 
 [21] lubridate_1.9.0             SummarizedExperiment_1.28.0
 [23] assertthat_0.2.1            viridis_0.6.2              
 [25] gargle_1.2.1                xfun_0.35                  
 [27] hms_1.1.2                   fansi_1.0.3                
 [29] restfulr_0.0.15             progress_1.2.2             
 [31] dbplyr_2.2.1                readxl_1.4.1               
 [33] Rgraphviz_2.41.1            igraph_1.3.5               
 [35] DBI_1.1.3                   htmlwidgets_1.5.4          
 [37] reshape_0.8.9               downloadR_0.99.5           
 [39] googledrive_2.0.0           ellipsis_0.3.2             
 [41] ggnewscale_0.4.8            backports_1.4.1            
 [43] biomaRt_2.54.0              deldir_1.0-6               
 [45] MatrixGenerics_1.10.0       vctrs_0.5.1                
 [47] Biobase_2.58.0              here_1.0.1                 
 [49] ensembldb_2.22.0            withr_2.5.0                
 [51] cachem_1.0.6                checkmate_2.1.0            
 [53] GenomicAlignments_1.34.0    prettyunits_1.1.1          
 [55] cluster_2.1.4               ape_5.6-2                  
 [57] dir.expiry_1.6.0            lazyeval_0.2.2             
 [59] crayon_1.5.2                basilisk.utils_1.10.0      
 [61] crul_1.3                    pkgconfig_2.0.3            
 [63] nlme_3.1-160                ProtGenerics_1.30.0        
 [65] XGR_1.1.8                   nnet_7.3-18                
 [67] pals_1.7                    rlang_1.0.6                
 [69] lifecycle_1.0.3             filelock_1.0.2             
 [71] httpcode_0.3.0              BiocFileCache_2.6.0        
 [73] modelr_0.1.9                echotabix_0.99.8           
 [75] dichromat_2.0-0.1           rprojroot_2.0.3            
 [77] cellranger_1.1.0            coloc_5.1.0.1              
 [79] matrixStats_0.63.0          graph_1.76.0               
 [81] Matrix_1.5-1                osfr_0.2.9                 
 [83] boot_1.3-28                 reprex_2.0.2               
 [85] base64enc_0.1-3             googlesheets4_1.0.1        
 [87] png_0.1-8                   viridisLite_0.4.1          
 [89] rjson_0.2.21                rootSolve_1.8.2.3          
 [91] bitops_1.0-7                R.oo_1.25.0                
 [93] ggnetwork_0.5.10            blob_1.2.3                 
 [95] mixsqp_0.3-48               echoplot_0.99.6            
 [97] dnet_1.1.7                  jpeg_0.1-10                
 [99] echodata_0.99.16            scales_1.2.1               
[101] memoise_2.0.1               magrittr_2.0.3             
[103] plyr_1.8.8                  hexbin_1.28.2              
[105] zlibbioc_1.44.0             compiler_4.2.1             
[107] echoconda_0.99.8            BiocIO_1.8.0               
[109] RColorBrewer_1.1-3          catalogueR_1.0.0           
[111] Rsamtools_2.14.0            cli_3.4.1                  
[113] echoannot_0.99.10           patchwork_1.1.2            
[115] htmlTable_2.4.1             Formula_1.2-4              
[117] MASS_7.3-58.1               tidyselect_1.2.0           
[119] stringi_1.7.8               yaml_2.3.6                 
[121] supraHex_1.35.0             latticeExtra_0.6-30        
[123] ggrepel_0.9.2               grid_4.2.1                 
[125] VariantAnnotation_1.44.0    tools_4.2.1                
[127] lmom_2.9                    timechange_0.1.1           
[129] parallel_4.2.1              rstudioapi_0.14            
[131] foreign_0.8-83              piggyback_0.1.4            
[133] gridExtra_2.3               gld_2.6.6                  
[135] digest_0.6.31               snpStats_1.48.0            
[137] BiocManager_1.30.19         Rcpp_1.0.9                 
[139] broom_1.0.1                 OrganismDbi_1.40.0         
[141] httr_1.4.4                  AnnotationDbi_1.60.0       
[143] RCircos_1.2.2               ggbio_1.46.0               
[145] biovizBase_1.46.0           colorspace_2.0-3           
[147] rvest_1.0.3                 XML_3.99-0.13              
[149] fs_1.5.2                    reticulate_1.26            
[151] splines_4.2.1               RBGL_1.74.0                
[153] expm_0.999-6                echofinemap_0.99.4         
[155] basilisk_1.10.2             Exact_3.2                  
[157] mapproj_1.2.9               jsonlite_1.8.4             
[159] susieR_0.12.27              R6_2.5.1                   
[161] Hmisc_4.7-2                 pillar_1.8.1               
[163] htmltools_0.5.4             glue_1.6.2                 
[165] fastmap_1.1.0               DT_0.26                    
[167] BiocParallel_1.32.4         class_7.3-20               
[169] codetools_0.2-18            maps_3.4.1                 
[171] mvtnorm_1.1-3               utf8_1.2.2                 
[173] lattice_0.20-45             curl_4.3.3                 
[175] DescTools_0.99.47           zip_2.2.2                  
[177] openxlsx_4.2.5.1            interp_1.1-3               
[179] survival_3.4-0              googleAuthR_2.0.0          
[181] munsell_0.5.0               e1071_1.7-12               
[183] GenomeInfoDbData_1.2.9      haven_2.5.1                
[185] reshape2_1.4.4              gtable_0.3.1