rajlabmssm / echofinemap Goto Github PK
View Code? Open in Web Editor NEWechoverse module: Statistical and functional fine-mapping functions.
echoverse module: Statistical and functional fine-mapping functions.
The full error message happens when trying to execute gcta64 bin within the conda environment
+++ Multi-finemap:: COJO +++
[1] "+ COJO:: conditional analysis -- Conditioning on: rs4721411"
sh: 1: /home/acarrasco/.conda/envs/echoR/lib/R/library/echolocatoR/tools/gcta_1.92.1beta5_mac/bin/gcta64: Exec format error
[1] "+ COJO:: Processing results..."
I believe the way to download gcta64 assumes you are running COJO on a MAC by looking at the path to the exec. I am running echolocatoR on Linux and it fails when trying to use the COJO from gcta64
I am not sure where I can finf this bug reallyu. It is easy to solve locally ( Just copying the gcta64 bin for Linux within .bin/ conda env but maybe nice to automate it on echolocatoR. As if, specifying the OS where echolocatoR is used
finemapper is a function provided by PolyFun wrap together the full fine-mapping pipeline. Need to test how this compares to running each step individually (current implementation).
I'm working on getting some additional functionality from PolyFun working in the echofinemap::POLYFUN
wrapper.
The issue I'm running into is that rpy2
doesn't' seem to be able to find the required R packages, which are installed both on my desktop and within a dedicated conda env called "echoR_mini". In fact, I'm calling python from within the "echoR_mini" conda env.
/Users/schilder/Library/Caches/org.R-project.R/R/basilisk/1.9.11/echoconda/0.99.8/echoR_mini/bin/python /Library/Frameworks/R.framework/Versions/4.2/Resources/library/echofinemap/tools/polyfun/polyfun.py
--compute-h2-L2
--output-prefix /var/folders/zq/h7mtybc533b1qzkys_ttgpth0000gn/T//RtmpQlGIFj/PolyFun/output/dataset1/dataset1
--sumstats /var/folders/zq/h7mtybc533b1qzkys_ttgpth0000gn/T//RtmpQlGIFj/PolyFun/file123db57c7004fsumstats.munged.parquet
--ref-ld-chr /Library/Frameworks/R.framework/Versions/4.2/Resources/library/echofinemap/tools/polyfun/example_data/annotations.
--w-ld-chr /Library/Frameworks/R.framework/Versions/4.2/Resources/library/echofinemap/tools/polyfun/example_data/weights.
--num-bins 20
--allow-missing
********************************************************************
* PolyFun (POLYgenic FUNctionally-informed fine-mapping)
* Version 1.0.0
* (C) 2019-2021 Omer Weissbrod
*********************************************************************
[INFO] Reading summary statistics from /var/folders/zq/h7mtybc533b1qzkys_ttgpth0000gn/T//RtmpQlGIFj/PolyFun/file123db57c7004fsumstats.munged.parquet ...
[INFO] Read summary statistics for 182450 SNPs.
[INFO] Reading reference panel LD Score from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/echofinemap/tools/polyfun/example_data/annotations.[1-22] ...
100%|██████████| 22/22 [00:00<00:00, 40.86it/s]
[INFO] Read reference panel LD Scores for 182454 SNPs.
[INFO] Reading regression weight LD Score from /Library/Frameworks/R.framework/Versions/4.2/Resources/library/echofinemap/tools/polyfun/example_data/weights.[1-22] ...
100%|██████████| 22/22 [00:00<00:00, 43.36it/s]
[INFO] Read regression weight LD Scores for 182454 SNPs.
[INFO] After merging with reference panel LD, 182450 SNPs remain.
[INFO] After merging with regression SNP LD, 182450 SNPs remain.
[WARNING] number of SNPs is smaller than 200k; this is almost always bad.
[INFO] Removed 6 SNPs with chi^2 > 383.29 (182444 SNPs remain)
[INFO] iterating over chromosomes to compute XTX, XTy...
100%|██████████| 22/22 [00:00<00:00, 161.02it/s]
[INFO] Evaluating Ridge lambdas...
100%|██████████| 100/100 [00:00<00:00, 201.49it/s]
[INFO] Selected ridge lambda: 1.7475e-04 (43/100) score: 8.7191e-02 score lstsq: 8.7190e-02
100%|██████████| 2/2 [00:00<00:00, 201.82it/s]
[INFO] Estimating annotation coefficients for each chromosomes set
[INFO] Computing per-SNP h^2 for each chromosome...
100%|██████████| 22/22 [00:00<00:00, 103.64it/s]
[INFO] Saving constrained SNP variances to disk
100%|██████████| 22/22 [00:02<00:00, 10.57it/s]
[INFO] Saving SNP variances to disk
100%|██████████| 22/22 [00:02<00:00, 10.81it/s]
During startup - Warning messages:
1: package "methods" in options("defaultPackages") was not found
2: package ‘utils’ in options("defaultPackages") was not found
3: package ‘grDevices’ in options("defaultPackages") was not found
4: package ‘graphics’ in options("defaultPackages") was not found
5: package ‘stats’ in options("defaultPackages") was not found
6: package ‘methods’ in options("defaultPackages") was not found
[INFO] Clustering SNPs into bins using the R Ckmeans.1d.dp package
[INFO] cffi mode is CFFI_MODE.ANY
[INFO] R home found: /Library/Frameworks/R.framework/Resources
[DEBUG] Looking for LD_LIBRARY_PATH with: /Library/Frameworks/R.framework/Resources/bin/Rscript -e cat(Sys.getenv("LD_LIBRARY_PATH"))
[INFO] R library path:
[INFO] LD_LIBRARY_PATH:
[DEBUG] cffi mode is InterfaceType.API
[INFO] Default options to initialize R: rpy2, --quiet, --no-save
[WARNING] R[write to console]: Error in library.dynam(lib, package, package.lib) :
shared object ‘methods.dylib’ not found
[ERROR] Could not load the R package Ckmeans.1d.dp. Either install it or rerun PolyFun with --skip-Ckmedian
[ERROR]
Traceback (most recent call last):
File "/Library/Frameworks/R.framework/Versions/4.2/Resources/library/echofinemap/tools/polyfun/polyfun.py", line 848, in <module>
polyfun_obj.polyfun_main(args)
File "/Library/Frameworks/R.framework/Versions/4.2/Resources/library/echofinemap/tools/polyfun/polyfun.py", line 772, in polyfun_main
self.polyfun_h2_L2(args)
File "/Library/Frameworks/R.framework/Versions/4.2/Resources/library/echofinemap/tools/polyfun/polyfun.py", line 605, in polyfun_h2_L2
self.partition_snps_to_bins(args, use_ridge=True)
File "/Library/Frameworks/R.framework/Versions/4.2/Resources/library/echofinemap/tools/polyfun/polyfun.py", line 516, in partition_snps_to_bins
self.df_bins = self.partition_snps_Ckmedian(args, use_ridge=use_ridge)
File "/Library/Frameworks/R.framework/Versions/4.2/Resources/library/echofinemap/tools/polyfun/polyfun.py", line 432, in partition_snps_Ckmedian
import rpy2.robjects.numpy2ri as numpy2ri
File "/Users/schilder/Library/Caches/org.R-project.R/R/basilisk/1.9.11/echoconda/0.99.8/echoR_mini/lib/python3.9/site-packages/rpy2/robjects/__init__.py", line 18, in <module>
from rpy2.robjects.robject import RObjectMixin, RObject
File "/Users/schilder/Library/Caches/org.R-project.R/R/basilisk/1.9.11/echoconda/0.99.8/echoR_mini/lib/python3.9/site-packages/rpy2/robjects/robject.py", line 86, in <module>
class RObjectMixin(abc.ABC):
File "/Users/schilder/Library/Caches/org.R-project.R/R/basilisk/1.9.11/echoconda/0.99.8/echoR_mini/lib/python3.9/site-packages/rpy2/robjects/robject.py", line 98, in RObjectMixin
__show = _get_exported_value('methods', 'show')
File "/Users/schilder/Library/Caches/org.R-project.R/R/basilisk/1.9.11/echoconda/0.99.8/echoR_mini/lib/python3.9/site-packages/rpy2/rinterface_lib/conversion.py", line 45, in _
cdata = function(*args, **kwargs)
File "/Users/schilder/Library/Caches/org.R-project.R/R/basilisk/1.9.11/echoconda/0.99.8/echoR_mini/lib/python3.9/site-packages/rpy2/rinterface.py", line 810, in __call__
raise embedded.RRuntimeError(_rinterface._geterrmessage())
rpy2.rinterface_lib.embedded.RRuntimeError: Error in library.dynam(lib, package, package.lib) :
shared object ‘methods.dylib’ not found
conda list
# packages in environment at /Users/schilder/Library/Caches/org.R-project.R/R/basilisk/1.9.11/echoconda/0.99.8/echoR_mini:
#
# Name Version Build Channel
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aws-c-cal 0.5.11 hd2e2f4b_0 conda-forge
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tk 8.6.12 h5dbffcc_0 conda-forge
tktable 2.10 h49f0cf7_3 conda-forge
tomli 2.0.1 pyhd8ed1ab_0 conda-forge
toolz 0.12.0 pyhd8ed1ab_0 conda-forge
tornado 6.1 py39h63b48b0_3 conda-forge
tqdm 4.64.1 pyhd8ed1ab_0 conda-forge
typing_extensions 4.4.0 pyha770c72_0 conda-forge
tzdata 2022e h191b570_0 conda-forge
tzlocal 4.2 py39h6e9494a_2 conda-forge
urllib3 1.26.11 pyhd8ed1ab_0 conda-forge
wget 1.20.3 h52ee1ee_1 conda-forge
wheel 0.37.1 pyhd8ed1ab_0 conda-forge
wrapt 1.14.1 py39ha30fb19_1 conda-forge
xarray 2022.10.0 pyhd8ed1ab_0 conda-forge
xorg-libxau 1.0.9 h35c211d_0 conda-forge
xorg-libxdmcp 1.1.3 h35c211d_0 conda-forge
xz 5.2.6 h775f41a_0 conda-forge
yaml 0.2.5 h0d85af4_2 conda-forge
zict 2.2.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.13 hfd90126_4 conda-forge
zstandard 0.18.0 py39ha30fb19_1 conda-forge
zstd 1.5.2 hfa58983_4 conda-forge
rpy2
versionrpy2 3.5.1 py39r42h7cc1f47_1 conda-forge
You can use the following yaml to create the env I'm using:
echoR_mini.yml.txt
I just got this running
Note that:
columnsnames = echodata::construct_colmap(munged= FALSE,
CHR = "CHR", POS = "POS",
SNP = "SNP", P = "P",
Effect = "BETA", StdErr = "SE",
A1 = "A1", A2 = "A2", Freq = "FREQ",
N = "N")
#N_cases = NULL, N_controls = NULL,
#proportion_cases = NULL,
#MAF = "calculate",
#tstat = NULL)
# Pass the sample size as "N" column
# compute_n will do all what is in the docu f N does not exist
finemap_loci(# GENERAL ARGUMENTS
topSNPs = topSNPs,
results_dir = fullRS_path,
loci = topSNPs$Locus,
dataset_name = "LID_COX",
dataset_type = "GWAS",
force_new_subset = TRUE,
force_new_LD = FALSE,
force_new_finemap = TRUE,
remove_tmps = FALSE,
finemap_methods = c("ABF","FINEMAP","SUSIE", "POLYFUN_SUSIE"),
# Munge full sumstats first
munged = FALSE,
colmap = columnsnames,
# SUMMARY STATS ARGUMENTS
fullSS_path = newSS_name_colmap,
fullSS_genome_build = "hg19",
query_by ="tabix",
#compute_n = 3500,
bp_distance = 10000,#500000*2,
min_MAF = 0.001,
trim_gene_limits = FALSE,
case_control = FALSE,
# FINE-MAPPING ARGUMENTS
## General
n_causal = 5,
credset_thresh = .95,
consensus_thresh = 2,
# LD ARGUMENTS
LD_reference = "1KGphase3",#"UKB",
superpopulation = "EUR",
download_method = "axel",
LD_genome_build = "hg19",
leadSNP_LD_block = FALSE,
#### PLotting args ####
plot_types = c("simple"),
show_plot = TRUE,
zoom = "1x",
tx_biotypes = NULL,
nott_epigenome = FALSE,
nott_show_placseq = FALSE,
nott_binwidth = 200,
nott_bigwig_dir = NULL,
xgr_libnames = NULL,
roadmap = FALSE,
roadmap_query = NULL,
#### General args ####
seed = 2022,
nThread = 20,
verbose = TRUE
)
PolyFun submodule already installed.
┌─────────────────────────────────────────────────┐
│ │
│ )))> 🦇 RP11-240A16.1 [locus 1 / 3] 🦇 <((( │
│ │
└─────────────────────────────────────────────────┘
──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── 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: 'SNP'
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 ^'SNP' .../QC_SNPs_COLMAP.txt; grep
-v ^'SNP' .../QC_SNPs_COLMAP.txt | sort
-k2,2n
-k3,3n ) > .../file2fb2fcecd3b_sorted.tsv
Constructing outputs
Using existing bgzipped file: /home/rstudio/echolocatoR/echolocatoR_LID/QC_SNPs_COLMAP.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: 4:32425284-32445284
Adding 'query' column to results.
Retrieved data with 76 rows
Saving query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/RP11-240A16.1/RP11-240A16.1_LID_COX_subset.tsv.gz
+ Query: 76 SNPs x 10 columns.
Standardizing summary statistics subset.
Standardizing main column names.
++ Preparing A1,A1 cols
++ Preparing MAF,Freq cols.
++ Could not infer MAF.
++ 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: 76 SNPs x 12 columns.
++ Saving standardized query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/RP11-240A16.1/RP11-240A16.1_LID_COX_subset.tsv.gz
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 2 ▶▶▶ Extract Linkage Disequilibrium 🔗 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
LD_reference identified as: 1kg.
Previously computed LD_matrix detected. Importing: /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/RP11-240A16.1/LD/RP11-240A16.1.1KGphase3_LD.RDS
LD_reference identified as: r.
Converting obj to sparseMatrix.
+ FILTER:: Filtering by LD features.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 3 ▶▶▶ Filter SNPs 🚰 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
FILTER:: Filtering by SNP features.
+ FILTER:: Post-filtered data: 76 x 12
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 76 SNPs.
+ dat = 76 SNPs.
+ 76 SNPs in common.
Converting obj to sparseMatrix.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 4 ▶▶▶ Fine-map 🔊 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Gathering method sources.
Gathering method citations.
Preparing sample size column (N).
Using existing 'N' column.
Gathering method sources.
Gathering method citations.
Gathering method sources.
Gathering method citations.
ABF
🚫 Missing required column(s) for ABF [skipping]: MAF, proportion_cases
FINEMAP
✅ All required columns present.
⚠ Missing optional column(s) for FINEMAP: MAF
SUSIE
✅ All required columns present.
✅ All optional columns present.
POLYFUN_SUSIE
✅ All required columns present.
⚠ Missing optional column(s) for POLYFUN_SUSIE: MAF
++ Fine-mapping using 3 tool(s): FINEMAP, SUSIE, POLYFUN_SUSIE
+++ Multi-finemap:: FINEMAP +++
Preparing sample size column (N).
Using existing 'N' column.
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 76 SNPs.
+ dat = 76 SNPs.
+ 76 SNPs in common.
Converting obj to sparseMatrix.
Constructing master file.
Optional MAF col missing. Replacing with all '.1's
Constructing data.z file.
Constructing data.ld file.
FINEMAP path: /home/rstudio/.cache/R/echofinemap/FINEMAP/finemap_v1.4.1_x86_64/finemap_v1.4.1_x86_64
Inferred FINEMAP version: 1.4.1
Running FINEMAP.
cd .../RP11-240A16.1 &&
.../finemap_v1.4.1_x86_64
--sss
--in-files .../master
--log
--n-threads 20
--n-causal-snps 5
|--------------------------------------|
| Welcome to FINEMAP v1.4.1 |
| |
| (c) 2015-2022 University of Helsinki |
| |
| Help : |
| - ./finemap --help |
| - www.finemap.me |
| - www.christianbenner.com |
| |
| Contact : |
| - [email protected] |
| - [email protected] |
|--------------------------------------|
--------
SETTINGS
--------
- dataset : all
- corr-config : 0.95
- n-causal-snps : 5
- n-configs-top : 50000
- n-conv-sss : 100
- n-iter : 100000
- n-threads : 20
- prior-k0 : 0
- prior-std : 0.05
- prob-conv-sss-tol : 0.001
- prob-cred-set : 0.95
------------
FINE-MAPPING (1/1)
------------
- GWAS summary stats : FINEMAP/data.z
- SNP correlations : FINEMAP/data.ld
- Causal SNP stats : FINEMAP/data.snp
- Causal configurations : FINEMAP/data.config
- Credible sets : FINEMAP/data.cred
- Log file : FINEMAP/data.log_sss
- Reading input : done!
- Updated prior SD of effect sizes : 0.05 0.0528 0.0558 0.0589
- Number of GWAS samples : 2687
- Number of SNPs : 76
- Prior-Pr(# of causal SNPs is k) :
(0 -> 0)
1 -> 0.584
2 -> 0.292
3 -> 0.096
4 -> 0.0234
5 -> 0.00449
- 1800 configurations evaluated (0.122/100%) : converged after 122 iterations
- Computing causal SNP statistics : done!
- Regional SNP heritability : 0.0276 (SD: 0.00441 ; 95% CI: [0.0196,0.0371])
- Log10-BF of >= one causal SNP : 24.4
- Post-expected # of causal SNPs : 4.74
- Post-Pr(# of causal SNPs is k) :
(0 -> 0)
1 -> 9.4e-21
2 -> 2.73e-11
3 -> 1.41e-07
4 -> 0.265
5 -> 0.735
- Writing output : done!
- Run time : 0 hours, 0 minutes, 0 seconds
2 data.cred* file(s) found in the same subfolder.
Selected file based on postPr_k: data.cred5
Importing conditional probabilities (.cred file).
No configurations were causal at PP>=0.95.
Importing marginal probabilities (.snp file).
Importing configuration probabilities (.config file).
FINEMAP was unable to identify any credible sets at PP>=0.95.
++ Credible Set SNPs identified = 0
++ Merging FINEMAP results with multi-finemap data.
+++ Multi-finemap:: SUSIE +++
Loading required namespace: Rfast
Failed with error: 'there is no package called 'Rfast''
Preparing sample size column (N).
Using existing 'N' column.
+ SUSIE:: sample_size=2,687
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 76 SNPs.
+ dat = 76 SNPs.
+ 76 SNPs in common.
Converting obj to sparseMatrix.
+ SUSIE:: Using `susie_rss()` from susieR v0.12.27
+ SUSIE:: Extracting Credible Sets.
++ Credible Set SNPs identified = 2
++ Merging SUSIE results with multi-finemap data.
+++ Multi-finemap:: POLYFUN_SUSIE +++
PolyFun submodule already installed.
PolyFun:: Fine-mapping with method=SUSIE
PolyFun:: Using priors from mode=precomputed
Unable to find conda binary. Is Anaconda installed?Locus RP11-240A16.1 complete in: 0.33 min
┌─────────────────────────────────────────┐
│ │
│ )))> 🦇 XYLT1 [locus 2 / 3] 🦇 <((( │
│ │
└─────────────────────────────────────────┘
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── 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: 'SNP'
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 ^'SNP' .../QC_SNPs_COLMAP.txt; grep
-v ^'SNP' .../QC_SNPs_COLMAP.txt | sort
-k2,2n
-k3,3n ) > .../file2fb33669f7f_sorted.tsv
Constructing outputs
Using existing bgzipped file: /home/rstudio/echolocatoR/echolocatoR_LID/QC_SNPs_COLMAP.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: 16:17034975-17054975
Adding 'query' column to results.
Retrieved data with 80 rows
Saving query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/XYLT1/XYLT1_LID_COX_subset.tsv.gz
+ Query: 80 SNPs x 10 columns.
Standardizing summary statistics subset.
Standardizing main column names.
++ Preparing A1,A1 cols
++ Preparing MAF,Freq cols.
++ Could not infer MAF.
++ 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: 80 SNPs x 12 columns.
++ Saving standardized query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/XYLT1/XYLT1_LID_COX_subset.tsv.gz
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 2 ▶▶▶ Extract Linkage Disequilibrium 🔗 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
LD_reference identified as: 1kg.
Previously computed LD_matrix detected. Importing: /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/XYLT1/LD/XYLT1.1KGphase3_LD.RDS
LD_reference identified as: r.
Converting obj to sparseMatrix.
+ FILTER:: Filtering by LD features.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 3 ▶▶▶ Filter SNPs 🚰 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
FILTER:: Filtering by SNP features.
+ FILTER:: Post-filtered data: 78 x 12
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 78 SNPs.
+ dat = 78 SNPs.
+ 78 SNPs in common.
Converting obj to sparseMatrix.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 4 ▶▶▶ Fine-map 🔊 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Gathering method sources.
Gathering method citations.
Preparing sample size column (N).
Using existing 'N' column.
Gathering method sources.
Gathering method citations.
Gathering method sources.
Gathering method citations.
ABF
🚫 Missing required column(s) for ABF [skipping]: MAF, proportion_cases
FINEMAP
✅ All required columns present.
⚠ Missing optional column(s) for FINEMAP: MAF
SUSIE
✅ All required columns present.
✅ All optional columns present.
POLYFUN_SUSIE
✅ All required columns present.
⚠ Missing optional column(s) for POLYFUN_SUSIE: MAF
++ Fine-mapping using 3 tool(s): FINEMAP, SUSIE, POLYFUN_SUSIE
+++ Multi-finemap:: FINEMAP +++
Preparing sample size column (N).
Using existing 'N' column.
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 78 SNPs.
+ dat = 78 SNPs.
+ 78 SNPs in common.
Converting obj to sparseMatrix.
Constructing master file.
Optional MAF col missing. Replacing with all '.1's
Constructing data.z file.
Constructing data.ld file.
FINEMAP path: /home/rstudio/.cache/R/echofinemap/FINEMAP/finemap_v1.4.1_x86_64/finemap_v1.4.1_x86_64
Inferred FINEMAP version: 1.4.1
Running FINEMAP.
cd .../XYLT1 &&
.../finemap_v1.4.1_x86_64
--sss
--in-files .../master
--log
--n-threads 20
--n-causal-snps 5
|--------------------------------------|
| Welcome to FINEMAP v1.4.1 |
| |
| (c) 2015-2022 University of Helsinki |
| |
| Help : |
| - ./finemap --help |
| - www.finemap.me |
| - www.christianbenner.com |
| |
| Contact : |
| - [email protected] |
| - [email protected] |
|--------------------------------------|
--------
SETTINGS
--------
- dataset : all
- corr-config : 0.95
- n-causal-snps : 5
- n-configs-top : 50000
- n-conv-sss : 100
- n-iter : 100000
- n-threads : 20
- prior-k0 : 0
- prior-std : 0.05
- prob-conv-sss-tol : 0.001
- prob-cred-set : 0.95
------------
FINE-MAPPING (1/1)
------------
- GWAS summary stats : FINEMAP/data.z
- SNP correlations : FINEMAP/data.ld
- Causal SNP stats : FINEMAP/data.snp
- Causal configurations : FINEMAP/data.config
- Credible sets : FINEMAP/data.cred
- Log file : FINEMAP/data.log_sss
- Reading input : done!
- Updated prior SD of effect sizes : 0.05 0.0522 0.0545 0.0568
- Number of GWAS samples : 2687
- Number of SNPs : 78
- Prior-Pr(# of causal SNPs is k) :
(0 -> 0)
1 -> 0.584
2 -> 0.292
3 -> 0.0961
4 -> 0.0234
5 -> 0.0045
- 1077 configurations evaluated (0.198/100%) : converged after 198 iterations
- Computing causal SNP statistics : done!
- Regional SNP heritability : 0.0119 (SD: 0.00385 ; 95% CI: [0.00536,0.0204])
- Log10-BF of >= one causal SNP : 4.46
- Post-expected # of causal SNPs : 1.96
- Post-Pr(# of causal SNPs is k) :
(0 -> 0)
1 -> 0.245
2 -> 0.548
3 -> 0.204
4 -> 0.00238
5 -> 0
- Writing output : done!
- Run time : 0 hours, 0 minutes, 0 seconds
3 data.cred* file(s) found in the same subfolder.
Selected file based on postPr_k: data.cred2
Importing conditional probabilities (.cred file).
No configurations were causal at PP>=0.95.
Importing marginal probabilities (.snp file).
Importing configuration probabilities (.config file).
FINEMAP was unable to identify any credible sets at PP>=0.95.
++ Credible Set SNPs identified = 0
++ Merging FINEMAP results with multi-finemap data.
+++ Multi-finemap:: SUSIE +++
Loading required namespace: Rfast
Failed with error: 'there is no package called 'Rfast''
In addition: Warning messages:
1: In SUSIE(dat = dat, dataset_type = dataset_type, LD_matrix = LD_matrix, :
Install Rfast to speed up susieR even further:
install.packages('Rfast')
2: In susie_suff_stat(XtX = XtX, Xty = Xty, n = n, yty = (n - 1) * :
IBSS algorithm did not converge in 100 iterations!
Please check consistency between summary statistics and LD matrix.
See https://stephenslab.github.io/susieR/articles/susierss_diagnostic.html
Preparing sample size column (N).
Using existing 'N' column.
+ SUSIE:: sample_size=2,687
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 78 SNPs.
+ dat = 78 SNPs.
+ 78 SNPs in common.
Converting obj to sparseMatrix.
+ SUSIE:: Using `susie_rss()` from susieR v0.12.27
+ SUSIE:: Extracting Credible Sets.
++ Credible Set SNPs identified = 1
++ Merging SUSIE results with multi-finemap data.
+++ Multi-finemap:: POLYFUN_SUSIE +++
PolyFun submodule already installed.
PolyFun:: Fine-mapping with method=SUSIE
PolyFun:: Using priors from mode=precomputed
Unable to find conda binary. Is Anaconda installed?Locus XYLT1 complete in: 0.32 min
┌────────────────────────────────────────┐
│ │
│ )))> 🦇 LRP8 [locus 3 / 3] 🦇 <((( │
│ │
└────────────────────────────────────────┘
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── 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: 'SNP'
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 ^'SNP' .../QC_SNPs_COLMAP.txt; grep
-v ^'SNP' .../QC_SNPs_COLMAP.txt | sort
-k2,2n
-k3,3n ) > .../file2fb4113b218_sorted.tsv
Constructing outputs
Using existing bgzipped file: /home/rstudio/echolocatoR/echolocatoR_LID/QC_SNPs_COLMAP.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: 1:53768300-53788300
Adding 'query' column to results.
Retrieved data with 52 rows
Saving query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/LRP8/LRP8_LID_COX_subset.tsv.gz
+ Query: 52 SNPs x 10 columns.
Standardizing summary statistics subset.
Standardizing main column names.
++ Preparing A1,A1 cols
++ Preparing MAF,Freq cols.
++ Could not infer MAF.
++ 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: 52 SNPs x 12 columns.
++ Saving standardized query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/LRP8/LRP8_LID_COX_subset.tsv.gz
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 2 ▶▶▶ Extract Linkage Disequilibrium 🔗 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
LD_reference identified as: 1kg.
Previously computed LD_matrix detected. Importing: /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/LRP8/LD/LRP8.1KGphase3_LD.RDS
LD_reference identified as: r.
Converting obj to sparseMatrix.
+ FILTER:: Filtering by LD features.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 3 ▶▶▶ Filter SNPs 🚰 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
FILTER:: Filtering by SNP features.
+ FILTER:: Post-filtered data: 51 x 12
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 51 SNPs.
+ dat = 51 SNPs.
+ 51 SNPs in common.
Converting obj to sparseMatrix.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 4 ▶▶▶ Fine-map 🔊 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Gathering method sources.
Gathering method citations.
Preparing sample size column (N).
Using existing 'N' column.
Gathering method sources.
Gathering method citations.
Gathering method sources.
Gathering method citations.
ABF
🚫 Missing required column(s) for ABF [skipping]: MAF, proportion_cases
FINEMAP
✅ All required columns present.
⚠ Missing optional column(s) for FINEMAP: MAF
SUSIE
✅ All required columns present.
✅ All optional columns present.
POLYFUN_SUSIE
✅ All required columns present.
⚠ Missing optional column(s) for POLYFUN_SUSIE: MAF
++ Fine-mapping using 3 tool(s): FINEMAP, SUSIE, POLYFUN_SUSIE
+++ Multi-finemap:: FINEMAP +++
Preparing sample size column (N).
Using existing 'N' column.
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 51 SNPs.
+ dat = 51 SNPs.
+ 51 SNPs in common.
Converting obj to sparseMatrix.
Constructing master file.
Optional MAF col missing. Replacing with all '.1's
Constructing data.z file.
Constructing data.ld file.
FINEMAP path: /home/rstudio/.cache/R/echofinemap/FINEMAP/finemap_v1.4.1_x86_64/finemap_v1.4.1_x86_64
Inferred FINEMAP version: 1.4.1
Running FINEMAP.
cd .../LRP8 &&
.../finemap_v1.4.1_x86_64
--sss
--in-files .../master
--log
--n-threads 20
--n-causal-snps 5
|--------------------------------------|
| Welcome to FINEMAP v1.4.1 |
| |
| (c) 2015-2022 University of Helsinki |
| |
| Help : |
| - ./finemap --help |
| - www.finemap.me |
| - www.christianbenner.com |
| |
| Contact : |
| - [email protected] |
| - [email protected] |
|--------------------------------------|
--------
SETTINGS
--------
- dataset : all
- corr-config : 0.95
- n-causal-snps : 5
- n-configs-top : 50000
- n-conv-sss : 100
- n-iter : 100000
- n-threads : 20
- prior-k0 : 0
- prior-std : 0.05
- prob-conv-sss-tol : 0.001
- prob-cred-set : 0.95
------------
FINE-MAPPING (1/1)
------------
- GWAS summary stats : FINEMAP/data.z
- SNP correlations : FINEMAP/data.ld
- Causal SNP stats : FINEMAP/data.snp
- Causal configurations : FINEMAP/data.config
- Credible sets : FINEMAP/data.cred
- Log file : FINEMAP/data.log_sss
- Reading input : done!
- Updated prior SD of effect sizes : 0.05 0.0517 0.0535 0.0554
- Number of GWAS samples : 2687
- Number of SNPs : 51
- Prior-Pr(# of causal SNPs is k) :
(0 -> 0)
1 -> 0.585
2 -> 0.292
3 -> 0.0955
4 -> 0.0229
5 -> 0.00431
- 1081 configurations evaluated (0.123/100%) : converged after 123 iterations
- Computing causal SNP statistics : done!
- Regional SNP heritability : 0.0259 (SD: 0.00368 ; 95% CI: [0.0188,0.0334])
- Log10-BF of >= one causal SNP : 24.9
- Post-expected # of causal SNPs : 5
- Post-Pr(# of causal SNPs is k) :
(0 -> 0)
1 -> 5.84e-22
2 -> 1.71e-17
3 -> 1.74e-11
4 -> 4.56e-06
5 -> 1
- Writing output : done!
- Run time : 0 hours, 0 minutes, 0 seconds
1 data.cred* file(s) found in the same subfolder.
Selected file based on postPr_k: data.cred5
Importing conditional probabilities (.cred file).
No configurations were causal at PP>=0.95.
Importing marginal probabilities (.snp file).
Importing configuration probabilities (.config file).
FINEMAP was unable to identify any credible sets at PP>=0.95.
++ Credible Set SNPs identified = 0
++ Merging FINEMAP results with multi-finemap data.
+++ Multi-finemap:: SUSIE +++
Loading required namespace: Rfast
Failed with error: 'there is no package called 'Rfast''
In addition: Warning message:
In SUSIE(dat = dat, dataset_type = dataset_type, LD_matrix = LD_matrix, :
Install Rfast to speed up susieR even further:
install.packages('Rfast')
Preparing sample size column (N).
Using existing 'N' column.
+ SUSIE:: sample_size=2,687
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 51 SNPs.
+ dat = 51 SNPs.
+ 51 SNPs in common.
Converting obj to sparseMatrix.
+ SUSIE:: Using `susie_rss()` from susieR v0.12.27
+ SUSIE:: Extracting Credible Sets.
++ Credible Set SNPs identified = 3
++ Merging SUSIE results with multi-finemap data.
+++ Multi-finemap:: POLYFUN_SUSIE +++
PolyFun submodule already installed.
PolyFun:: Fine-mapping with method=SUSIE
PolyFun:: Using priors from mode=precomputed
Unable to find conda binary. Is Anaconda installed?Locus LRP8 complete in: 0.33 min
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 6 ▶▶▶ Postprocess data 🎁 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Returning results as nested list.
All loci done in: 0.97 min
$`RP11-240A16.1`
NULL
$XYLT1
NULL
$LRP8
NULL
$merged_dat
Null data.table (0 rows and 0 cols)
Warning message:
In SUSIE(dat = dat, dataset_type = dataset_type, LD_matrix = LD_matrix, :
Install Rfast to speed up susieR even further:
install.packages('Rfast')
> sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C 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] SNPlocs.Hsapiens.dbSNP155.GRCh37_0.99.22 SNPlocs.Hsapiens.dbSNP144.GRCh37_0.99.20 BSgenome_1.65.2
[4] rtracklayer_1.57.0 Biostrings_2.65.3 XVector_0.37.1
[7] GenomicRanges_1.49.1 GenomeInfoDb_1.33.5 IRanges_2.31.2
[10] S4Vectors_0.35.3 BiocGenerics_0.43.1 forcats_0.5.2
[13] stringr_1.4.1 dplyr_1.0.10 purrr_0.3.4
[16] readr_2.1.2 tidyr_1.2.0 tibble_3.1.8
[19] ggplot2_3.3.6 tidyverse_1.3.2 data.table_1.14.2
[22] echolocatoR_2.0.1
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.26 R.utils_2.12.0 tidyselect_1.1.2 RSQLite_2.2.16
[6] AnnotationDbi_1.59.1 htmlwidgets_1.5.4 grid_4.2.0 BiocParallel_1.31.12 XGR_1.1.8
[11] munsell_0.5.0 codetools_0.2-18 interp_1.1-3 DT_0.24 withr_2.5.0
[16] colorspace_2.0-3 OrganismDbi_1.39.1 Biobase_2.57.1 filelock_1.0.2 knitr_1.40
[21] supraHex_1.35.0 rstudioapi_0.14 DescTools_0.99.46 MatrixGenerics_1.9.1 GenomeInfoDbData_1.2.8
[26] mixsqp_0.3-43 bit64_4.0.5 echoconda_0.99.7 basilisk_1.9.2 vctrs_0.4.1
[31] generics_0.1.3 xfun_0.32 biovizBase_1.45.0 BiocFileCache_2.5.0 R6_2.5.1
[36] AnnotationFilter_1.21.0 bitops_1.0-7 cachem_1.0.6 reshape_0.8.9 DelayedArray_0.23.1
[41] assertthat_0.2.1 BiocIO_1.7.1 scales_1.2.1 googlesheets4_1.0.1 nnet_7.3-17
[46] rootSolve_1.8.2.3 gtable_0.3.1 lmom_2.9 ggbio_1.45.0 ensembldb_2.21.4
[51] rlang_1.0.5 MungeSumstats_1.5.13 echodata_0.99.14 splines_4.2.0 lazyeval_0.2.2
[56] gargle_1.2.0 dichromat_2.0-0.1 hexbin_1.28.2 broom_1.0.1 checkmate_2.1.0
[61] modelr_0.1.9 BiocManager_1.30.18 yaml_2.3.5 reshape2_1.4.4 snpStats_1.47.1
[66] backports_1.4.1 GenomicFeatures_1.49.6 ggnetwork_0.5.10 Hmisc_4.7-1 RBGL_1.73.0
[71] tools_4.2.0 echoplot_0.99.5 ellipsis_0.3.2 catalogueR_1.0.0 RColorBrewer_1.1-3
[76] proxy_0.4-27 coloc_5.1.0 Rcpp_1.0.9 plyr_1.8.7 base64enc_0.1-3
[81] progress_1.2.2 zlibbioc_1.43.0 RCurl_1.98-1.8 basilisk.utils_1.9.2 prettyunits_1.1.1
[86] rpart_4.1.16 deldir_1.0-6 viridis_0.6.2 haven_2.5.1 cluster_2.1.3
[91] SummarizedExperiment_1.27.2 ggrepel_0.9.1 fs_1.5.2 crul_1.2.0 magrittr_2.0.3
[96] echotabix_0.99.8 dnet_1.1.7 openxlsx_4.2.5 reprex_2.0.2 googledrive_2.0.0
[101] mvtnorm_1.1-3 ProtGenerics_1.29.0 matrixStats_0.62.0 hms_1.1.2 patchwork_1.1.2
[106] XML_3.99-0.10 jpeg_0.1-9 readxl_1.4.1 gridExtra_2.3 compiler_4.2.0
[111] biomaRt_2.53.2 crayon_1.5.1 R.oo_1.25.0 htmltools_0.5.3 echoannot_0.99.7
[116] tzdb_0.3.0 Formula_1.2-4 expm_0.999-6 Exact_3.1 lubridate_1.8.0
[121] DBI_1.1.3 dbplyr_2.2.1 MASS_7.3-58.1 rappdirs_0.3.3 boot_1.3-28
[126] Matrix_1.4-1 piggyback_0.1.3 cli_3.3.0 R.methodsS3_1.8.2 echofinemap_0.99.3
[131] parallel_4.2.0 igraph_1.3.4 pkgconfig_2.0.3 GenomicAlignments_1.33.1 dir.expiry_1.5.0
[136] RCircos_1.2.2 foreign_0.8-82 osfr_0.2.8 xml2_1.3.3 rvest_1.0.3
[141] echoLD_0.99.7 VariantAnnotation_1.43.3 digest_0.6.29 graph_1.75.0 httpcode_0.3.0
[146] cellranger_1.1.0 htmlTable_2.4.1 gld_2.6.5 restfulr_0.0.15 curl_4.3.2
[151] Rsamtools_2.13.4 rjson_0.2.21 lifecycle_1.0.1 nlme_3.1-159 jsonlite_1.8.0
[156] viridisLite_0.4.1 fansi_1.0.3 downloadR_0.99.4 pillar_1.8.1 susieR_0.12.27
[161] lattice_0.20-45 GGally_2.1.2 googleAuthR_2.0.0 KEGGREST_1.37.3 fastmap_1.1.0
[166] httr_1.4.4 survival_3.3-1 glue_1.6.2 zip_2.2.0 png_0.1-7
[171] bit_4.0.4 Rgraphviz_2.41.1 class_7.3-20 stringi_1.7.8 blob_1.2.3
[176] latticeExtra_0.6-30 memoise_2.0.1 irlba_2.3.5 e1071_1.7-11 ape_5.6-2
Originally posted by @AMCalejandro in RajLabMSSM/echolocatoR#114 (comment)
The polyfun repo is not getting included in "inst/tools" when installing echofinemap
.
This is because the submodule is being interpreted as an empty folder by R:
https://stackoverflow.com/questions/50474445/how-could-i-release-an-r-package-on-github-using-github-submodules
Suggested solution with packrat
:
https://blog.methodsconsultants.com/posts/using-packrat-with-git-submodules/
Discussion on devtools
:
r-lib/devtools#1163
r-lib/devtools#1222
git2r
package:
https://github.com/ropensci/git2r
This function seems promising:
r-lib/devtools#751 (comment)
FINEMAP won't run on a Mac unless Zstandard is installed (error message: dyld: Library not loaded: /usr/local/lib/libzstd.1.dylib
), see: http://www.christianbenner.com Mac OSX users: If you see dyld: Library not loaded: /usr/local/lib/libzstd.1.dylib, install Zstandard.
.
The Zstandard lib (https://facebook.github.io/zstd/) can be installed in macOS using brew install zstd
. Please consider adding this library to the install routine!
When installing echolocatoR, you'll probably see a long list of warnings like this:
...
...
Warning in utils::tar(filepath, pkgname, compression = compression, compression_level = 9L, :
storing paths of more than 100 bytes is not portable:
‘echolocatoR/inst/tools/PAINTOR_V3.0/eigen/unsupported/Eigen/src/SparseExtra/BlockOfDynamicSparseMatrix.h’
Warning in utils::tar(filepath, pkgname, compression = compression, compression_level = 9L, :
storing paths of more than 100 bytes is not portable:
‘echolocatoR/inst/tools/goshifter/test_data/bc.H3K4me1_vHMEC_strat_Myoepithelial_Cells.nperm1000.enrich’
This is coming from the fact that R doesn't like the folder naming scheme of some of the submodules echolocatoR depends on, specifically:
The only way I've come across to address this would be to fork my own version of these repos and rewrite the code so it uses a different directory structure. But this runs a very high risk of messing up these software entirely. So until I can figure out a smarter solution, we'll have to bear with loads of warning messages 😞
Test for differences between susie_suff_stat()
vs. susie_rss()
vs. susie_bhat()
.
Tweakable parameters may differ between these.
Some fine-mapping tools are designed only for case-control studies (e.g GWAS) and can produce biased results if applied with quantitative traits (e.g. height) without further adjustments.
Currently, only ABF()
takes the case_control
argument. But this should be applied to all other methods where this distinction can be applied. For methods that are unable to handle quantitative traits in an unbiased way, a warning message should be produced letting users know about this. This checking can occur within the check_required_cols()
function, which will need an additional argument: case_control
While exploring the results in the echolocatoR Shiny app, I noticed that FINEMAP never returns more than 1 credible set per locus. This was confusing since SuSiE often returns more than 1 credible set.
I started diving into the code, and I think the issue is that it only looks for data.cred
, when FINEMAP returns data.cred#
where #
is the number of credible sets. PolyFun obtains the credible sets by searching backwards from the maximum number of causal SNPs until it finds a .cred
file (source):
#add causal set info
df_finemap['CREDIBLE_SET'] = 0
cred_file = None
for m in range(num_causal_snps, 0, -1):
if os.path.exists(cred_filename+str(m)):
cred_file = cred_filename+str(m)
break
if cred_file is None:
raise IOError('cred file not found')
df_cred = pd.read_table(cred_file, sep=' ', usecols=(lambda c: c.startswith('cred')), comment='#')
df_finemap.set_index('SNP', inplace=True, drop=False)
for c_i, c in enumerate(df_cred.columns):
df_finemap.loc[df_cred[c].dropna(), 'CREDIBLE_SET'] = c_i+1
df_finemap.reset_index(inplace=True, drop=True)
echolocatoR only checks for data.cred
:
And then when it is extracting the PIPs from the .snp
file, it assigns any SNP that meets the PIP threshold to CS 1.
I attempted to put together a minimal, reproducible example to demonstrate this behavior. However, using the latest version on master, FINEMAP is currently returning NA
for both the .CS
and the .PP
columns.
library(echolocatoR)
stopifnot(packageVersion("echolocatoR") == "0.1.2")
data("Nalls_top_SNPs")
top_SNPs <- import_topSNPs(
topSS = Nalls_top_SNPs,
position_col = "BP",
pval_col = "P, all studies",
effect_col = "Beta, all studies",
gene_col = "Nearest Gene",
locus_col = "Nearest Gene",
remove_variants = "rs34637584"
)
fullSS_path <- example_fullSS()
Nalls23andMe_2019.results <- finemap_loci(
top_SNPs = top_SNPs,
results_dir = file.path(getwd(), "results"),
loci = "BST1",
dataset_name = "Nalls23andMe_2019",
remove_tmps = FALSE,
fullSS_path = fullSS_path,
query_by = "tabix",
snp_col = "RSID",
pval_col = "p",
effect_col = "beta",
stderr_col = "se",
freq_col = "freq",
MAF_col = "calculate",
bp_distance = 10000,
min_MAF = 0.001,
finemap_methods = c("FINEMAP", "SUSIE"),
LD_reference = "UKB",
download_method = "axel",
plot.types = c()
)
Nalls23andMe_2019.results[SUSIE.CS > 0, list(SNP, FINEMAP.CS, FINEMAP.PP, SUSIE.CS, SUSIE.PP)]
readLines("results/GWAS/Nalls23andMe_2019/BST1/FINEMAP/data.snp", n = 3)
readLines("results/GWAS/Nalls23andMe_2019/BST1/FINEMAP/data.cred5")
sessionInfo()
> source("reprex.R", echo = TRUE)
> library(echolocatoR)
Registered S3 method overwritten by 'GGally':
method from
+.gg ggplot2
Possible Ensembl SSL connectivity problems detected.
Please see the 'Connection Troubleshooting' section of the biomaRt vignette
vignette('accessing_ensembl', package = 'biomaRt')Error in curl::curl_fetch_memory(url, handle = handle) :
SSL certificate problem: self signed certificate in certificate chain
Bioconductor version 3.12 (BiocManager 1.30.12), ?BiocManager::install for help
> stopifnot(packageVersion("echolocatoR") == "0.1.2")
> data("Nalls_top_SNPs")
> top_SNPs <- import_topSNPs(
+ topSS = Nalls_top_SNPs,
+ position_col = "BP",
+ pval_col = "P, all studies",
+ effect_col = "Beta, all studie ..." ... [TRUNCATED]
[1] "+ Assigning gene_col and locus_col independently"
> fullSS_path <- example_fullSS()
> Nalls23andMe_2019.results <- finemap_loci(
+ top_SNPs = top_SNPs,
+ results_dir = file.path(getwd(), "results"),
+ loci = "BST1",
+ dataset_ .... [TRUNCATED]
[1] "+ CONDA:: Activating conda env 'echoR'"
[1] "Checking for tabix installation..."
[1] "Checking for bcftools installation..."
) ) ) ))))))}}}}}}}} {{{{{{{{{(((((( ( ( (
BST1 (1 / 1)
) ) ) ))))))}}}}}}}} {{{{{{{{{(((((( ( ( (
[1] "+ Extracting relevant variants from fullSS..."
[1] "+ Query Method: tabix"
[1] "+ QUERY: Chromosome = 4 ; Min position = 15727348 ; Max position = 15747348"
[1] "TABIX:: Converting full summary stats file to tabix format for fast querying..."
[1] "+ CONDA:: Identified bgzip executable in echoR env."
[1] "( grep 'CHR' ./Nalls23andMe_2019.fullSS_subset.tsv; grep -v ^'CHR' ./Nalls23andMe_2019.fullSS_subset.tsv | sort -k1,1 -k2,2n ) | ~/mambaforge/envs/echoR/bin/bgzip -f > ~/echolocatoR/results/GWAS/Nalls23andMe_2019/Nalls23andMe_2019.fullSS_subset.tsv.gz"
[1] "+ CONDA:: Identified tabix executable in echoR env."
[1] "TABIX:: Indexing"
[1] "~/mambaforge/envs/echoR/bin/tabix -f -S 1 -s 1 -b 2 -e 2 ~/echolocatoR/results/GWAS/Nalls23andMe_2019/Nalls23andMe_2019.fullSS_subset.tsv.gz"
[1] "Determining chrom type from file header"
[1] "Chromosome format = 1"
[1] "+ CONDA:: Identified tabix executable in echoR env."
[1] "TABIX:: Extracting subset of sum stats"
[1] "+ TABIX:: ~/mambaforge/envs/echoR/bin/tabix -h ~/echolocatoR/results/GWAS/Nalls23andMe_2019/Nalls23andMe_2019.fullSS_subset.tsv.gz 4:15727348-15747348"
[1] "+ TABIX:: Returning 115 x 11 data.table"
[1] "++ Saving query ==> ~/echolocatoR/results/GWAS/Nalls23andMe_2019/BST1/BST1_Nalls23andMe_2019_subset.tsv.gz"
[1] "LD:: Standardizing summary statistics subset."
[1] "++ Preparing Gene col"
[1] "++ Preparing A1,A1 cols"
[1] "++ Preparing MAF,Freq cols"
[1] "++ Inferring MAF from frequency column..."
[1] "++ Preparing N_cases,N_controls cols"
[1] "++ Preparing `proportion_cases` col"
[1] "++ Calculating `proportion_cases`."
[1] "++ Preparing N col"
[1] "+ Preparing sample_size (N) column"
[1] "++ Computing effective sample size."
[1] "++ Preparing t-stat col"
[1] "+ Calculating t-statistic from Effect and StdErr..."
[1] "++ Assigning lead SNP"
[1] "++ Ensuring Effect, StdErr, P are numeric"
[1] "++ Ensuring 1 SNP per row"
[1] "++ Removing extra whitespace"
[1] "++ Saving subset ==> ~/echolocatoR/results/GWAS/Nalls23andMe_2019/BST1/BST1_Nalls23andMe_2019_subset.tsv.gz"
[1] "+ Extraction completed in 6.72 seconds"
[1] "+ 115 SNPs x 16 columns"
[1] "LD:: Using UK Biobank LD reference panel."
[1] "+ UKB LD file name: chr4_15000001_18000001"
[1] "+ LD:: Downloading full .gz/.npz UKB files and saving to disk."
[1] "+ CONDA:: Identified axel executable in echoR env."
[1] "+ CONDA:: Identified axel executable in echoR env."
[1] "+ LD:: load_ld() python function input: ~/echolocatoR/results/GWAS/Nalls23andMe_2019/BST1/LD/chr4_15000001_18000001"
[1] "+ LD:: Reading LD matrix into memory. This could take some time..."
~/echolocatoR/results/GWAS/Nalls23andMe_2019/BST1/LD/chr4_15000001_18000001.gz
~/echolocatoR/results/GWAS/Nalls23andMe_2019/BST1/LD/chr4_15000001_18000001.npz
Processed URL: ~/echolocatoR/results/GWAS/Nalls23andMe_2019/BST1/LD/chr4_15000001_18000001
Some other message at the end
[1] "+ Full UKB LD matrix: 20815 x 20815"
[1] "+ Full UKB LD SNP data.table: 20815 x 5"
[1] "+ LD:: Saving LD matrix ==> ~/echolocatoR/results/GWAS/Nalls23andMe_2019/BST1/LD/BST1.UKB_LD.RDS"
[1] "115 x 115 LD_matrix (sparse)"
[1] "+ FILTER:: Filtering by LD features."
[1] "FILTER:: Filtering by SNP features."
[1] "+ FILTER:: Removing SNPs with MAF < 0.001"
[1] "+ FILTER:: Post-filtered data: 115 x 16"
vvvvv-- FINEMAP --vvvvv
✅ All required columns present.
✅ All suggested columns present.
vvvvv-- SUSIE --vvvvv
✅ All required columns present.
✅ All suggested columns present.
[1] "++ Fine-mapping using multiple tools: FINEMAP, SUSIE"
+++ Multi-finemap:: FINEMAP +++
[1] "++ FINEMAP:: Constructing master file."
[1] "++ FINEMAP:: Constructing data.z file."
[1] "++ FINEMAP:: Constructing data.ld file."
[1] "+ Using FINEMAP v1.4"
|--------------------------------------|
| Welcome to FINEMAP v1.4 |
| |
| (c) 2015-2020 University of Helsinki |
| |
| Help : |
| - ./finemap --help |
| - www.finemap.me |
| - www.christianbenner.com |
| |
| Contact : |
| - christian.benner@helsinki.fi |
| - matti.pirinen@helsinki.fi |
|--------------------------------------|
--------
SETTINGS
--------
- dataset : all
- corr-config : 0.95
- n-causal-snps : 5
- n-configs-top : 50000
- n-conv-sss : 100
- n-iter : 100000
- n-threads : 1
- prior-k0 : 0
- prior-std : 0.05
- prob-conv-sss-tol : 0.001
- prob-cred-set : 0.95
------------
FINE-MAPPING (1/1)
------------
- GWAS summary stats : FINEMAP/data.z
- SNP correlations : FINEMAP/data.ld
- Causal SNP stats : FINEMAP/data.snp
- Causal configurations : FINEMAP/data.config
- Credible sets : FINEMAP/data.cred
- Log file : FINEMAP/data.log_sss
- Reading input : done!
- Number of GWAS samples : 216621
- Number of SNPs : 115
- Prior-Pr(# of causal SNPs is k) :
(0 -> 0)
1 -> 0.584
2 -> 0.292
3 -> 0.0964
4 -> 0.0237
5 -> 0.00461
- 1616 configurations evaluated (0.104/100%) : converged after 104 iterations
- Computing causal SNP statistics : done!
- Regional SNP heritability : 0.00763 (SD: 0.000422 ; 95% CI: [0.00679,0.0085])
- Log10-BF of >= one causal SNP : 2.28e+04
- Post-expected # of causal SNPs : 5
- Post-Pr(# of causal SNPs is k) :
(0 -> 0)
1 -> 0
2 -> 0
3 -> 0
4 -> 2.87e-262
5 -> 1
- Computing credible sets : done!
- Writing output : done!
- Run time : 0 hours, 0 minutes, 0 seconds
[1] ".cred not detected. Using .snp instead."
[1] "+ FINEMAP:: Importing prob (.snp)..."
Error in `[.data.table`(x, r, vars, with = FALSE) :
column(s) not found: prob
[1] "++ Credible Set SNPs identified = 0"
[1] "++ Merging FINEMAP results with multi-finemap data."
+++ Multi-finemap:: SUSIE +++
[1] "+ Preparing sample_size (N) column"
[1] "+ `N` column already present in data."
[1] "+ SUSIE:: sample_size= 216621"
[1] "+ SUSIE:: max_causal = 5"
[1] "+ Subsetting LD matrix and finemap_dat to common SNPs..."
[1] "+ LD:: Removing unnamed rows/cols"
[1] "+ LD:: Replacing NAs with 0"
[1] "+ LD_matrix = 115 SNPs."
[1] "+ finemap_dat = 115 SNPs."
[1] "+ 115 SNPs in common."
[1] "+ SUSIE:: Using `susie_suff_stat()` from susieR v0.10.1"
[1] "+ SUSIE:: Extracting Credible Sets..."
[1] "++ Credible Set SNPs identified = 3"
[1] "++ Merging SUSIE results with multi-finemap data."
[1] "+ Identifying Consensus SNPs..."
[1] "++ support_thresh = 2"
[1] "++ top_CS_only=FALSE"
[1] "+ Calculating mean Posterior Probability (mean.PP)..."
[1] "+ Replacing PP==NA with 0"
[1] "++ 2 fine-mapping methods used."
[1] "++ 3 Credible Set SNPs identified."
[1] "++ 0 Consensus SNPs identified."
[1] "+ Fine-mapping with ' FINEMAP, SUSIE ' completed:"
Time difference of 3.1 secs
Fine-mapping complete in:
Time difference of 1.7 mins
[1] "+ Identifying Consensus SNPs..."
[1] "++ support_thresh = 2"
[1] "++ top_CS_only=FALSE"
[1] "+ Calculating mean Posterior Probability (mean.PP)..."
[1] "+ Replacing PP==NA with 0"
[1] "++ 2 fine-mapping methods used."
[1] "++ 3 Credible Set SNPs identified."
[1] "++ 0 Consensus SNPs identified."
> Nalls23andMe_2019.results[SUSIE.CS > 0, list(SNP, FINEMAP.CS, FINEMAP.PP, SUSIE.CS, SUSIE.PP)]
SNP FINEMAP.CS FINEMAP.PP SUSIE.CS SUSIE.PP
1: rs34559912 NA NA 3 1.0000000
2: rs4389574 NA NA 1 1.0000000
3: rs6852450 NA NA 2 0.9999992
> readLines("results/GWAS/Nalls23andMe_2019/BST1/FINEMAP/data.snp", n = 3)
[1] "index rsid chromosome position allele1 allele2 maf beta se z prob log10bf mean sd mean_incl sd_incl"
[2] "1 rs6828144 4 15727389 T C 0.0263 0.1765 0.0309 5.71197 1 11.8522 1.40649 0.0355444 1.40649 0.0355444"
[3] "111 rs11947310 4 15744576 A C 0.1909 0.0823 0.012 6.85833 1 11.8522 1.40649 0.0355444 1.40649 0.0355444"
> readLines("results/GWAS/Nalls23andMe_2019/BST1/FINEMAP/data.cred5")
[1] "# Post-Pr(# of causal SNPs is 5) = 1"
[2] "#log10bf 22448.9 NA 22432.1 NA 20843.6 NA 556.379 NA 264.033 NA"
[3] "#min(|ld|) 1 NA 1 NA 1 NA 1 NA 1 NA"
[4] "#mean(|ld|) 1 NA 1 NA 1 NA 1 NA 1 NA"
[5] "#median(|ld|) 1 NA 1 NA 1 NA 1 NA 1 NA"
[6] "index cred1 prob1 cred2 prob2 cred3 prob3 cred4 prob4 cred5 prob5"
[7] "1 rs11947310 1 rs11933202 1 rs1807250 1 rs6828144 1 rs73123615 0.999999"
> sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: ~/mambaforge/envs/echoR/lib/libopenblasp-r0.3.12.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] echolocatoR_0.1.2
loaded via a namespace (and not attached):
[1] colorspace_2.0-0 ellipsis_0.3.1
[3] class_7.3-18 biovizBase_1.38.0
[5] htmlTable_2.1.0 XVector_0.30.0
[7] GenomicRanges_1.42.0 base64enc_0.1-3
[9] gld_2.6.2 dichromat_2.0-0
[11] rstudioapi_0.13 proxy_0.4-25
[13] DT_0.17 bit64_4.0.5
[15] AnnotationDbi_1.52.0 fansi_0.4.2
[17] mvtnorm_1.1-1 xml2_1.3.2
[19] R.methodsS3_1.8.1 splines_4.0.3
[21] ggbio_1.38.0 cachem_1.0.4
[23] rootSolve_1.8.2.1 knitr_1.31
[25] jsonlite_1.7.2 Formula_1.2-4
[27] Rsamtools_2.6.0 dbplyr_2.1.1
[29] cluster_2.1.1 R.oo_1.24.0
[31] png_0.1-7 graph_1.68.0
[33] BiocManager_1.30.12 compiler_4.0.3
[35] httr_1.4.2 backports_1.2.1
[37] assertthat_0.2.1 Matrix_1.3-2
[39] fastmap_1.1.0 lazyeval_0.2.2
[41] htmltools_0.5.1.1 prettyunits_1.1.1
[43] tools_4.0.3 gtable_0.3.0
[45] glue_1.4.2 lmom_2.8
[47] GenomeInfoDbData_1.2.4 reshape2_1.4.4
[49] dplyr_1.0.5 rappdirs_0.3.3
[51] Rcpp_1.0.6 Biobase_2.50.0
[53] vctrs_0.3.7 Biostrings_2.58.0
[55] rtracklayer_1.50.0 crosstalk_1.1.1
[57] xfun_0.20 stringr_1.4.0
[59] lifecycle_1.0.0 ensembldb_2.14.0
[61] XML_3.99-0.6 zlibbioc_1.36.0
[63] MASS_7.3-53.1 scales_1.1.1
[65] BSgenome_1.58.0 VariantAnnotation_1.36.0
[67] ProtGenerics_1.22.0 hms_1.0.0
[69] MatrixGenerics_1.2.1 RBGL_1.66.0
[71] parallel_4.0.3 SummarizedExperiment_1.20.0
[73] expm_0.999-6 susieR_0.10.1
[75] AnnotationFilter_1.14.0 RColorBrewer_1.1-2
[77] curl_4.3 Exact_2.1
[79] reticulate_1.18 memoise_2.0.0
[81] gridExtra_2.3 ggplot2_3.3.3
[83] biomaRt_2.46.3 rpart_4.1-15
[85] reshape_0.8.8 latticeExtra_0.6-29
[87] stringi_1.5.3 RSQLite_2.2.5
[89] S4Vectors_0.28.1 e1071_1.7-6
[91] checkmate_2.0.0 GenomicFeatures_1.42.2
[93] BiocGenerics_0.36.0 boot_1.3-27
[95] BiocParallel_1.24.1 GenomeInfoDb_1.26.4
[97] rlang_0.4.10 pkgconfig_2.0.3
[99] matrixStats_0.58.0 bitops_1.0-6
[101] lattice_0.20-41 purrr_0.3.4
[103] GenomicAlignments_1.26.0 htmlwidgets_1.5.3
[105] bit_4.0.4 tidyselect_1.1.0
[107] GGally_2.1.1 plyr_1.8.6
[109] magrittr_2.0.1 R6_2.5.0
[111] IRanges_2.24.1 DescTools_0.99.40
[113] generics_0.1.0 Hmisc_4.5-0
[115] DelayedArray_0.16.3 DBI_1.1.1
[117] pillar_1.5.1 foreign_0.8-81
[119] survival_3.2-10 RCurl_1.98-1.3
[121] nnet_7.3-15 tibble_3.1.0
[123] crayon_1.4.1 utf8_1.2.1
[125] OrganismDbi_1.32.0 BiocFileCache_1.14.0
[127] jpeg_0.1-8.1 progress_1.2.2
[129] grid_4.0.3 data.table_1.14.0
[131] blob_1.2.1 digest_0.6.27
[133] R.utils_2.10.1 openssl_1.4.3
[135] stats4_4.0.3 munsell_0.5.0
[137] askpass_1.1
Warning message:
In susie_func(bhat = subset_DT$Effect, shat = subset_DT$StdErr, :
IBSS algorithm did not converge in 100 iterations!
annot
Allow users to specify custom annotations when conducting functional fine-mapping.
Originally posted here:
gkichaev/PAINTOR_V3.0#59
Can't seem to compile PAINTOR (at least on my Mac).
PolyFun requires some R packages. Include these as suggests or depends:
https://github.com/omerwe/polyfun/blob/master/polyfun.yml
Hi,
I got the following error when trying to run COJO
+++ Multi-finemap:: COJO +++
[1] "+ COJO:: conditional analysis -- Conditioning on: rs4721411"
sh: 1: /home/acarrasco/.conda/envs/echoR/lib/R/library/echolocatoR/tools/gcta_1.92.1beta5_mac/bin/gcta64: Exec format error
[1] "+ COJO:: Processing results..."
Error in data.table::fread(file.path(cojo_dir, "cojo.jma.cojo")) :
File '/mnt/rreal/RDS/RDS/acarrasco/ANALYSES_WORKSPACE/EARLY_PD/POST_GWAS/ECHOLOCATOR/RESULTS_23.2.2022/mixedmodels_GWAS/axial_TryingCOJO/MAD1L1/COJO/cojo.jma.cojo' does not exist or is non-readable. getwd()=='/mnt/rreal/RDS/RDS/acarrasco/ANALYSES_WORKSPACE/EARLY_PD/POST_GWAS/ECHOLOCATOR/SCRIPTS'
I was looking at the code but I am not sure in which step "cojo.jma.cojo" file is generated. Does it come from running COJO?
In that case, it may be solved with #11
Add the following info for each fine-mapping method:
Hi I am unable to properly run finemap_loci with a quantitative GWAS.
Few things to highlight so far.
columnsnames = echodata::construct_colmap(munged= FALSE,
CHR = "CHR", POS = "POS",
SNP = "SNP", P = "P",
Effect = "BETA", StdErr = "SE",
A1 = "A1", A2 = "A2",
N_cases = "N_CAS", MAF = "FREQ",
tstat = NULL, N_controls = NULL,
proportion_cases = NULL)
finemap_loci(# GENERAL ARGUMENTS
topSNPs = topSNPs,
results_dir = fullRS_path,
loci = topSNPs$Locus,
dataset_name = "LID_COX",
dataset_type = "GWAS",
force_new_subset = TRUE,
force_new_LD = FALSE,
force_new_finemap = TRUE,
remove_tmps = FALSE,
finemap_methods = c("ABF","FINEMAP","SUSIE", "POLYFUN_SUSIE"),
# Munge full sumstats first
munged = FALSE,
colmap = columnsnames,
# SUMMARY STATS ARGUMENTS
fullSS_path = newSS_name,
fullSS_genome_build = "hg19",
query_by ="tabix",
bp_distance = 10000,#500000*2,
min_MAF = 0.001,
trim_gene_limits = FALSE,
case_control = FALSE,
# FINE-MAPPING ARGUMENTS
## General
n_causal = 5,
credset_thresh = .95,
consensus_thresh = 2,
# LD ARGUMENTS
LD_reference = "1KGphase3",#"UKB",
superpopulation = "EUR",
download_method = "axel",
LD_genome_build = "hg19",
leadSNP_LD_block = FALSE,
#### PLotting args ####
plot_types = c("simple"),
show_plot = TRUE,
zoom = "1x",
tx_biotypes = NULL,
nott_epigenome = FALSE,
nott_show_placseq = FALSE,
nott_binwidth = 200,
nott_bigwig_dir = NULL,
xgr_libnames = NULL,
roadmap = FALSE,
roadmap_query = NULL,
#### General args ####
seed = 2022,
nThread = 20,
verbose = TRUE
)
PolyFun submodule already installed.
┌─────────────────────────────────────────────────┐
│ │
│ )))> 🦇 RP11-240A16.1 [locus 1 / 3] 🦇 <((( │
│ │
└─────────────────────────────────────────────────┘
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── 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' .../QC_V2.txt; grep
-v ^'CHR' .../QC_V2.txt | sort
-k1,1n
-k2,2n ) > .../file2efc11009c2a_sorted.tsv
Constructing outputs
Using existing bgzipped file: /home/rstudio/echolocatoR/echolocatoR_LID/QC_V2.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: 4:32425284-32445284
Adding 'query' column to results.
Retrieved data with 76 rows
Saving query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/RP11-240A16.1/RP11-240A16.1_LID_COX_subset.tsv.gz
+ Query: 76 SNPs x 10 columns.
Standardizing summary statistics subset.
Standardizing main column names.
++ Preparing A1,A1 cols
++ Preparing MAF,Freq cols.
++ Could not infer MAF.
++ Preparing N_cases,N_controls cols.
++ Preparing proportion_cases col.
++ proportion_cases not included in data subset.
Preparing sample size column (N).
WARNING: Neff column could not be calculated as the columns N_CAS & N_CON were not found in the datset
+ Mapping colnames from MungeSumstats ==> echolocatoR
+ 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: 76 SNPs x 12 columns.
++ Saving standardized query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/RP11-240A16.1/RP11-240A16.1_LID_COX_subset.tsv.gz
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 2 ▶▶▶ Extract Linkage Disequilibrium 🔗 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
LD_reference identified as: 1kg.
Previously computed LD_matrix detected. Importing: /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/RP11-240A16.1/LD/RP11-240A16.1.1KGphase3_LD.RDS
LD_reference identified as: r.
Converting obj to sparseMatrix.
+ FILTER:: Filtering by LD features.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 3 ▶▶▶ Filter SNPs 🚰 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
FILTER:: Filtering by SNP features.
+ FILTER:: Post-filtered data: 76 x 12
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 76 SNPs.
+ dat = 76 SNPs.
+ 76 SNPs in common.
Converting obj to sparseMatrix.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 4 ▶▶▶ Fine-map 🔊 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Gathering method sources.
Gathering method citations.
Preparing sample size column (N).
WARNING: Neff column could not be calculated as the columns N_CAS & N_CON were not found in the datset
+ Mapping colnames from MungeSumstats ==> echolocatoR
Gathering method sources.
Gathering method citations.
Gathering method sources.
Gathering method citations.
ABF
🚫 Missing required column(s) for ABF [skipping]: N, MAF, proportion_cases
FINEMAP
✅ All required columns present.
⚠ Missing optional column(s) for FINEMAP: MAF, N
SUSIE
✅ All required columns present.
⚠ Missing optional column(s) for SUSIE: N
POLYFUN_SUSIE
✅ All required columns present.
⚠ Missing optional column(s) for POLYFUN_SUSIE: MAF, N
++ Fine-mapping using 3 tool(s): FINEMAP, SUSIE, POLYFUN_SUSIE
+++ Multi-finemap:: FINEMAP +++
Preparing sample size column (N).
WARNING: Neff column could not be calculated as the columns N_CAS & N_CON were not found in the datset
+ Mapping colnames from MungeSumstats ==> echolocatoR
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 76 SNPs.
+ dat = 76 SNPs.
+ 76 SNPs in common.
Converting obj to sparseMatrix.
Constructing master file.
Optional MAF col missing. Replacing with all '.1's
Constructing data.z file.
Constructing data.ld file.
FINEMAP path: /home/rstudio/.cache/R/echofinemap/FINEMAP/finemap_v1.4.1_x86_64/finemap_v1.4.1_x86_64
Inferred FINEMAP version: 1.4.1
Running FINEMAP.
cd .../RP11-240A16.1 &&
.../finemap_v1.4.1_x86_64
--sss
--in-files .../master
--log
--n-threads 20
--n-causal-snps 5
Error : Master file '/home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/RP11-240A16.1/FINEMAP/master' is missing an entry in line 2 column 'n_samples'!
|--------------------------------------|
| Welcome to FINEMAP v1.4.1 |
| |
| (c) 2015-2022 University of Helsinki |
| |
| Help : |
| - ./finemap --help |
| - www.finemap.me |
| - www.christianbenner.com |
| |
| Contact : |
| - [email protected] |
| - [email protected] |
|--------------------------------------|
--------
SETTINGS
--------
- dataset : all
- corr-config : 0.95
- n-causal-snps : 5
- n-configs-top : 50000
- n-conv-sss : 100
- n-iter : 100000
- n-threads : 20
- prior-k0 : 0
- prior-std : 0.05
- prob-conv-sss-tol : 0.001
- prob-cred-set : 0.95
+++ Multi-finemap:: SUSIE +++
Loading required namespace: Rfast
Failed with error: 'there is no package called 'Rfast''
Preparing sample size column (N).
WARNING: Neff column could not be calculated as the columns N_CAS & N_CON were not found in the datset
+ Mapping colnames from MungeSumstats ==> echolocatoR
sample_size=NULL: must be valid integer.Locus RP11-240A16.1 complete in: 0.26 min
┌─────────────────────────────────────────┐
│ │
│ )))> 🦇 XYLT1 [locus 2 / 3] 🦇 <((( │
│ │
└─────────────────────────────────────────┘
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── 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' .../QC_V2.txt; grep
-v ^'CHR' .../QC_V2.txt | sort
-k1,1n
-k2,2n ) > .../file2efc3ee606a8_sorted.tsv
Constructing outputs
Using existing bgzipped file: /home/rstudio/echolocatoR/echolocatoR_LID/QC_V2.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: 16:17034975-17054975
Adding 'query' column to results.
Retrieved data with 82 rows
Saving query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/XYLT1/XYLT1_LID_COX_subset.tsv.gz
+ Query: 82 SNPs x 10 columns.
Standardizing summary statistics subset.
Standardizing main column names.
++ Preparing A1,A1 cols
++ Preparing MAF,Freq cols.
++ Could not infer MAF.
++ Preparing N_cases,N_controls cols.
++ Preparing proportion_cases col.
++ proportion_cases not included in data subset.
Preparing sample size column (N).
WARNING: Neff column could not be calculated as the columns N_CAS & N_CON were not found in the datset
+ Mapping colnames from MungeSumstats ==> echolocatoR
+ 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: 80 SNPs x 12 columns.
++ Saving standardized query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/XYLT1/XYLT1_LID_COX_subset.tsv.gz
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 2 ▶▶▶ Extract Linkage Disequilibrium 🔗 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
LD_reference identified as: 1kg.
Previously computed LD_matrix detected. Importing: /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/XYLT1/LD/XYLT1.1KGphase3_LD.RDS
LD_reference identified as: r.
Converting obj to sparseMatrix.
+ FILTER:: Filtering by LD features.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 3 ▶▶▶ Filter SNPs 🚰 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
FILTER:: Filtering by SNP features.
+ FILTER:: Post-filtered data: 79 x 12
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 79 SNPs.
+ dat = 79 SNPs.
+ 79 SNPs in common.
Converting obj to sparseMatrix.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 4 ▶▶▶ Fine-map 🔊 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Gathering method sources.
Gathering method citations.
Preparing sample size column (N).
WARNING: Neff column could not be calculated as the columns N_CAS & N_CON were not found in the datset
+ Mapping colnames from MungeSumstats ==> echolocatoR
Gathering method sources.
Gathering method citations.
Gathering method sources.
Gathering method citations.
ABF
🚫 Missing required column(s) for ABF [skipping]: N, MAF, proportion_cases
FINEMAP
✅ All required columns present.
⚠ Missing optional column(s) for FINEMAP: MAF, N
SUSIE
✅ All required columns present.
⚠ Missing optional column(s) for SUSIE: N
POLYFUN_SUSIE
✅ All required columns present.
⚠ Missing optional column(s) for POLYFUN_SUSIE: MAF, N
++ Fine-mapping using 3 tool(s): FINEMAP, SUSIE, POLYFUN_SUSIE
+++ Multi-finemap:: FINEMAP +++
Preparing sample size column (N).
WARNING: Neff column could not be calculated as the columns N_CAS & N_CON were not found in the datset
+ Mapping colnames from MungeSumstats ==> echolocatoR
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 79 SNPs.
+ dat = 79 SNPs.
+ 79 SNPs in common.
Converting obj to sparseMatrix.
Constructing master file.
Optional MAF col missing. Replacing with all '.1's
Constructing data.z file.
Constructing data.ld file.
FINEMAP path: /home/rstudio/.cache/R/echofinemap/FINEMAP/finemap_v1.4.1_x86_64/finemap_v1.4.1_x86_64
Inferred FINEMAP version: 1.4.1
Running FINEMAP.
cd .../XYLT1 &&
.../finemap_v1.4.1_x86_64
--sss
--in-files .../master
--log
--n-threads 20
--n-causal-snps 5
Error : Master file '/home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/XYLT1/FINEMAP/master' is missing an entry in line 2 column 'n_samples'!
|--------------------------------------|
| Welcome to FINEMAP v1.4.1 |
| |
| (c) 2015-2022 University of Helsinki |
| |
| Help : |
| - ./finemap --help |
| - www.finemap.me |
| - www.christianbenner.com |
| |
| Contact : |
| - [email protected] |
| - [email protected] |
|--------------------------------------|
--------
SETTINGS
--------
- dataset : all
- corr-config : 0.95
- n-causal-snps : 5
- n-configs-top : 50000
- n-conv-sss : 100
- n-iter : 100000
- n-threads : 20
- prior-k0 : 0
- prior-std : 0.05
- prob-conv-sss-tol : 0.001
- prob-cred-set : 0.95
+++ Multi-finemap:: SUSIE +++
Loading required namespace: Rfast
Failed with error: 'there is no package called 'Rfast''
In addition: Warning message:
In SUSIE(dat = dat, dataset_type = dataset_type, LD_matrix = LD_matrix, :
Install Rfast to speed up susieR even further:
install.packages('Rfast')
Preparing sample size column (N).
WARNING: Neff column could not be calculated as the columns N_CAS & N_CON were not found in the datset
+ Mapping colnames from MungeSumstats ==> echolocatoR
sample_size=NULL: must be valid integer.Locus XYLT1 complete in: 0.3 min
┌────────────────────────────────────────┐
│ │
│ )))> 🦇 LRP8 [locus 3 / 3] 🦇 <((( │
│ │
└────────────────────────────────────────┘
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── 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' .../QC_V2.txt; grep
-v ^'CHR' .../QC_V2.txt | sort
-k1,1n
-k2,2n ) > .../file2efc33368771_sorted.tsv
Constructing outputs
Using existing bgzipped file: /home/rstudio/echolocatoR/echolocatoR_LID/QC_V2.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: 1:53768300-53788300
Adding 'query' column to results.
Retrieved data with 52 rows
Saving query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/LRP8/LRP8_LID_COX_subset.tsv.gz
+ Query: 52 SNPs x 10 columns.
Standardizing summary statistics subset.
Standardizing main column names.
++ Preparing A1,A1 cols
++ Preparing MAF,Freq cols.
++ Could not infer MAF.
++ Preparing N_cases,N_controls cols.
++ Preparing proportion_cases col.
++ proportion_cases not included in data subset.
Preparing sample size column (N).
WARNING: Neff column could not be calculated as the columns N_CAS & N_CON were not found in the datset
+ Mapping colnames from MungeSumstats ==> echolocatoR
+ 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: 52 SNPs x 12 columns.
++ Saving standardized query ==> /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/LRP8/LRP8_LID_COX_subset.tsv.gz
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 2 ▶▶▶ Extract Linkage Disequilibrium 🔗 ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
LD_reference identified as: 1kg.
Previously computed LD_matrix detected. Importing: /home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/LRP8/LD/LRP8.1KGphase3_LD.RDS
LD_reference identified as: r.
Converting obj to sparseMatrix.
+ FILTER:: Filtering by LD features.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 3 ▶▶▶ Filter SNPs 🚰 ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
FILTER:: Filtering by SNP features.
+ FILTER:: Post-filtered data: 51 x 12
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 51 SNPs.
+ dat = 51 SNPs.
+ 51 SNPs in common.
Converting obj to sparseMatrix.
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 4 ▶▶▶ Fine-map 🔊 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Gathering method sources.
Gathering method citations.
Preparing sample size column (N).
WARNING: Neff column could not be calculated as the columns N_CAS & N_CON were not found in the datset
+ Mapping colnames from MungeSumstats ==> echolocatoR
Gathering method sources.
Gathering method citations.
Gathering method sources.
Gathering method citations.
ABF
🚫 Missing required column(s) for ABF [skipping]: N, MAF, proportion_cases
FINEMAP
✅ All required columns present.
⚠ Missing optional column(s) for FINEMAP: MAF, N
SUSIE
✅ All required columns present.
⚠ Missing optional column(s) for SUSIE: N
POLYFUN_SUSIE
✅ All required columns present.
⚠ Missing optional column(s) for POLYFUN_SUSIE: MAF, N
++ Fine-mapping using 3 tool(s): FINEMAP, SUSIE, POLYFUN_SUSIE
+++ Multi-finemap:: FINEMAP +++
Preparing sample size column (N).
WARNING: Neff column could not be calculated as the columns N_CAS & N_CON were not found in the datset
+ Mapping colnames from MungeSumstats ==> echolocatoR
+ Subsetting LD matrix and dat to common SNPs...
Removing unnamed rows/cols
Replacing NAs with 0
+ LD_matrix = 51 SNPs.
+ dat = 51 SNPs.
+ 51 SNPs in common.
Converting obj to sparseMatrix.
Constructing master file.
Optional MAF col missing. Replacing with all '.1's
Constructing data.z file.
Constructing data.ld file.
FINEMAP path: /home/rstudio/.cache/R/echofinemap/FINEMAP/finemap_v1.4.1_x86_64/finemap_v1.4.1_x86_64
Inferred FINEMAP version: 1.4.1
Running FINEMAP.
cd .../LRP8 &&
.../finemap_v1.4.1_x86_64
--sss
--in-files .../master
--log
--n-threads 20
--n-causal-snps 5
Error : Master file '/home/rstudio/echolocatoR/echolocatoR_LID/RESULTS/GWAS/LID_COX/LRP8/FINEMAP/master' is missing an entry in line 2 column 'n_samples'!
|--------------------------------------|
| Welcome to FINEMAP v1.4.1 |
| |
| (c) 2015-2022 University of Helsinki |
| |
| Help : |
| - ./finemap --help |
| - www.finemap.me |
| - www.christianbenner.com |
| |
| Contact : |
| - [email protected] |
| - [email protected] |
|--------------------------------------|
--------
SETTINGS
--------
- dataset : all
- corr-config : 0.95
- n-causal-snps : 5
- n-configs-top : 50000
- n-conv-sss : 100
- n-iter : 100000
- n-threads : 20
- prior-k0 : 0
- prior-std : 0.05
- prob-conv-sss-tol : 0.001
- prob-cred-set : 0.95
+++ Multi-finemap:: SUSIE +++
Loading required namespace: Rfast
Failed with error: 'there is no package called 'Rfast''
In addition: Warning message:
In SUSIE(dat = dat, dataset_type = dataset_type, LD_matrix = LD_matrix, :
Install Rfast to speed up susieR even further:
install.packages('Rfast')
Preparing sample size column (N).
WARNING: Neff column could not be calculated as the columns N_CAS & N_CON were not found in the datset
+ Mapping colnames from MungeSumstats ==> echolocatoR
sample_size=NULL: must be valid integer.Locus LRP8 complete in: 0.26 min
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
── Step 6 ▶▶▶ Postprocess data 🎁 ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Returning results as nested list.
All loci done in: 0.81 min
$`RP11-240A16.1`
NULL
$XYLT1
NULL
$LRP8
NULL
$merged_dat
Null data.table (0 rows and 0 cols)
Warning message:
In SUSIE(dat = dat, dataset_type = dataset_type, LD_matrix = LD_matrix, :
Install Rfast to speed up susieR even further:
install.packages('Rfast')
> head(data_2)
CHR BP SNP A1 A2 FREQ BETA SE P N_CAS
1: 1 731718 rs58276399 t c 0.8837 -0.1775 0.1583 0.2621 1297
2: 1 731718 rs142557973 t c 0.8837 -0.1775 0.1583 0.2621 1297
3: 1 734349 rs141242758 t c 0.8843 -0.1577 0.1593 0.3223 1297
4: 1 753541 rs2073813 a g 0.1257 0.0721 0.1177 0.5399 2687
5: 1 766007 rs61768174 a c 0.9005 -0.2559 0.1642 0.1190 1297
6: 1 769223 rs60320384 c g 0.8749 -0.0772 0.1178 0.5124 2687
> head(topSNPs)
# A tibble: 3 × 7
Locus Gene CHR POS SNP P BETA
<chr> <chr> <fct> <int> <chr> <dbl> <dbl>
1 RP11-240A16.1 RP11-240A16.1 4 32435284 rs189093213 0.00000000167 1.12
2 XYLT1 XYLT1 16 17044975 rs180924818 0.00000000626 -1.14
3 LRP8 LRP8 1 53778300 rs72673189 0.0000000153 1.02
> sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C 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] SNPlocs.Hsapiens.dbSNP144.GRCh37_0.99.20 BSgenome_1.65.2 rtracklayer_1.57.0
[4] Biostrings_2.65.3 XVector_0.37.1 GenomicRanges_1.49.1
[7] GenomeInfoDb_1.33.5 IRanges_2.31.2 S4Vectors_0.35.3
[10] BiocGenerics_0.43.1 forcats_0.5.2 stringr_1.4.1
[13] dplyr_1.0.10 purrr_0.3.4 readr_2.1.2
[16] tidyr_1.2.0 tibble_3.1.8 ggplot2_3.3.6
[19] tidyverse_1.3.2 data.table_1.14.2 echolocatoR_2.0.1
loaded via a namespace (and not attached):
[1] Hmisc_4.7-1 class_7.3-20 ps_1.7.1
[4] Rsamtools_2.13.4 rprojroot_2.0.3 echotabix_0.99.8
[7] crayon_1.5.1 MASS_7.3-58.1 nlme_3.1-159
[10] backports_1.4.1 reprex_2.0.2 basilisk_1.9.2
[13] rlang_1.0.5 readxl_1.4.1 irlba_2.3.5
[16] nloptr_2.0.3 callr_3.7.2 limma_3.53.6
[19] filelock_1.0.2 proto_1.0.0 BiocParallel_1.31.12
[22] rjson_0.2.21 bit64_4.0.5 glue_1.6.2
[25] mixsqp_0.3-43 parallel_4.2.0 processx_3.7.0
[28] AnnotationDbi_1.59.1 HGNChelper_0.8.1 haven_2.5.1
[31] tidyselect_1.1.2 SummarizedExperiment_1.27.2 coloc_5.1.0
[34] usethis_2.1.6 XML_3.99-0.10 ggpubr_0.4.0
[37] GenomicAlignments_1.33.1 catalogueR_1.0.0 echoplot_0.99.5
[40] chron_2.3-57 xtable_1.8-4 ggnetwork_0.5.10
[43] magrittr_2.0.3 evaluate_0.16 cli_3.3.0
[46] zlibbioc_1.43.0 rstudioapi_0.14 miniUI_0.1.1.1
[49] rpart_4.1.16 echoannot_0.99.7 ensembldb_2.21.4
[52] treeio_1.21.2 shiny_1.7.2 xfun_0.32
[55] BSgenome.Hsapiens.1000genomes.hs37d5_0.99.1 pkgbuild_1.3.1 cluster_2.1.3
[58] echoconda_0.99.7 KEGGREST_1.37.3 interactiveDisplayBase_1.35.0
[61] expm_0.999-6 ggrepel_0.9.1 SNPlocs.Hsapiens.dbSNP155.GRCh37_0.99.22
[64] biovizBase_1.45.0 ape_5.6-2 echodata_0.99.12
[67] png_0.1-7 reshape_0.8.9 withr_2.5.0
[70] bitops_1.0-7 RBGL_1.73.0 plyr_1.8.7
[73] cellranger_1.1.0 AnnotationFilter_1.21.0 e1071_1.7-11
[76] pillar_1.8.1 cachem_1.0.6 GenomicFeatures_1.49.6
[79] fs_1.5.2 googleAuthR_2.0.0 echoLD_0.99.7
[82] osfr_0.2.8 snpStats_1.47.1 vctrs_0.4.1
[85] ellipsis_0.3.2 generics_0.1.3 gsubfn_0.7
[88] devtools_2.4.4 tools_4.2.0 foreign_0.8-82
[91] munsell_0.5.0 susieR_0.12.27 proxy_0.4-27
[94] DelayedArray_0.23.1 abind_1.4-5 fastmap_1.1.0
[97] compiler_4.2.0 pkgload_1.3.0 httpuv_1.6.5
[100] ExperimentHub_2.5.0 sessioninfo_1.2.2 ewceData_1.5.0
[103] plotly_4.10.0 DescTools_0.99.46 GenomeInfoDbData_1.2.8
[106] gridExtra_2.3 lattice_0.20-45 dir.expiry_1.5.0
[109] deldir_1.0-6 utf8_1.2.2 later_1.3.0
[112] BiocFileCache_2.5.0 jsonlite_1.8.0 GGally_2.1.2
[115] scales_1.2.1 gld_2.6.5 graph_1.75.0
[118] tidytree_0.4.0 carData_3.0-5 lazyeval_0.2.2
[121] promises_1.2.0.1 car_3.1-0 RCircos_1.2.2
[124] latticeExtra_0.6-30 R.utils_2.12.0 reticulate_1.26
[127] checkmate_2.1.0 rmarkdown_2.16 openxlsx_4.2.5
[130] dichromat_2.0-0.1 Biobase_2.57.1 igraph_1.3.4
[133] survival_3.3-1 yaml_2.3.5 htmltools_0.5.3
[136] memoise_2.0.1 VariantAnnotation_1.43.3 profvis_0.3.7
[139] BiocIO_1.7.1 supraHex_1.35.0 viridisLite_0.4.1
[142] digest_0.6.29 assertthat_0.2.1 mime_0.12
[145] piggyback_0.1.3 rappdirs_0.3.3 dnet_1.1.7
[148] downloadR_0.99.4 RSQLite_2.2.16 sqldf_0.4-11
[151] yulab.utils_0.0.5 Exact_3.1 remotes_2.4.2
[154] orthogene_1.3.2 urlchecker_1.0.1 blob_1.2.3
[157] R.oo_1.25.0 splines_4.2.0 Formula_1.2-4
[160] googledrive_2.0.0 AnnotationHub_3.5.0 OrganismDbi_1.39.1
[163] ProtGenerics_1.29.0 RCurl_1.98-1.8 broom_1.0.1
[166] hms_1.1.2 gprofiler2_0.2.1 modelr_0.1.9
[169] colorspace_2.0-3 base64enc_0.1-3 BiocManager_1.30.18
[172] aplot_0.1.6 echofinemap_0.99.3 nnet_7.3-17
[175] Rcpp_1.0.9 mvtnorm_1.1-3 fansi_1.0.3
[178] tzdb_0.3.0 brio_1.1.3 R6_2.5.1
[181] grid_4.2.0 crul_1.2.0 lifecycle_1.0.1
[184] rootSolve_1.8.2.3 zip_2.2.0 MungeSumstats_1.5.13
[187] ggsignif_0.6.3 curl_4.3.2 googlesheets4_1.0.1
[190] minqa_1.2.4 testthat_3.1.4 XGR_1.1.8
[193] Matrix_1.4-1 desc_1.4.1 ggbio_1.45.0
[196] RColorBrewer_1.1-3 htmlwidgets_1.5.4 biomaRt_2.53.2
[199] gridGraphics_0.5-1 MAGMA.Celltyping_2.0.7 rvest_1.0.3
[202] lmom_2.9 htmlTable_2.4.1 patchwork_1.1.2
[205] codetools_0.2-18 matrixStats_0.62.0 lubridate_1.8.0
[208] EWCE_1.5.7 prettyunits_1.1.1 SingleCellExperiment_1.19.0
[211] dbplyr_2.2.1 basilisk.utils_1.9.2 R.methodsS3_1.8.2
[214] gtable_0.3.1 DBI_1.1.3 ggfun_0.0.7
[217] httr_1.4.4 stringi_1.7.8 progress_1.2.2
[220] reshape2_1.4.4 viridis_0.6.2 hexbin_1.28.2
[223] Rgraphviz_2.41.1 ggtree_3.5.3 DT_0.24
[226] xml2_1.3.3 ggdendro_0.1.23 boot_1.3-28
[229] lme4_1.1-30 restfulr_0.0.15 RNOmni_1.0.1
[232] interp_1.1-3 ggplotify_0.1.0 homologene_1.4.68.19.3.27
[235] BiocVersion_3.16.0 bit_4.0.4 jpeg_0.1-9
[238] MatrixGenerics_1.9.1 babelgene_22.3 pkgconfig_2.0.3
[241] gargle_1.2.0 rstatix_0.7.0 knitr_1.40
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