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View Code? Open in Web Editor NEWechoverse module: Statistical and functional fine-mapping functions.
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xiechengyong123echofinemap's Issues
PolyFun can't find R dependencies
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.
Command
/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
Output
********************************************************************
* 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
_r-mutex 1.0.1 anacondar_1 conda-forge
arrow-cpp 7.0.1 py39h04a14be_6_cpu conda-forge
attrs 22.1.0 pyh71513ae_1 conda-forge
aws-c-cal 0.5.11 hd2e2f4b_0 conda-forge
aws-c-common 0.6.2 h0d85af4_0 conda-forge
aws-c-event-stream 0.2.7 hb9330a7_13 conda-forge
aws-c-io 0.10.5 h35aa462_0 conda-forge
aws-checksums 0.1.11 h0010a65_7 conda-forge
aws-sdk-cpp 1.8.186 h7c85d8e_4 conda-forge
axel 2.17.11 h815e4d9_0 conda-forge
bitarray 2.6.0 py39ha30fb19_1 conda-forge
bokeh 2.4.3 pyhd8ed1ab_3 conda-forge
brotlipy 0.7.0 py39ha30fb19_1005 conda-forge
bwidget 1.9.14 h694c41f_1 conda-forge
bzip2 1.0.8 h0d85af4_4 conda-forge
c-ares 1.18.1 h0d85af4_0 conda-forge
ca-certificates 2022.9.24 h033912b_0 conda-forge
cairo 1.16.0 h904041c_1014 conda-forge
cctools_osx-64 973.0.1 h2b95895_10 conda-forge
certifi 2022.9.24 pyhd8ed1ab_0 conda-forge
cffi 1.15.1 py39h131948b_2 conda-forge
charset-normalizer 2.1.1 pyhd8ed1ab_0 conda-forge
clang 14.0.4 h694c41f_0 conda-forge
clang-14 14.0.4 default_h55ffa42_0 conda-forge
clang_osx-64 14.0.4 h3a95cd4_2 conda-forge
clangxx 14.0.4 default_h55ffa42_0 conda-forge
clangxx_osx-64 14.0.4 he1dbc44_2 conda-forge
click 8.1.3 unix_pyhd8ed1ab_2 conda-forge
cloudpickle 2.2.0 pyhd8ed1ab_0 conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
compiler-rt 14.0.4 h7fcd477_0 conda-forge
compiler-rt_osx-64 14.0.4 h6df654d_0 conda-forge
cramjam 2.6.0 py39hd4bc93a_0 conda-forge
cryptography 38.0.2 py39h7eb6a14_2 conda-forge
curl 7.86.0 h57eb407_0 conda-forge
cytoolz 0.12.0 py39ha30fb19_1 conda-forge
dask 2022.10.1 pyhd8ed1ab_0 conda-forge
dask-core 2022.10.1 pyhd8ed1ab_0 conda-forge
deprecated 1.2.13 pyh6c4a22f_0 conda-forge
distributed 2022.10.1 pyhd8ed1ab_0 conda-forge
exceptiongroup 1.0.0 pyhd8ed1ab_0 conda-forge
expat 2.5.0 hf0c8a7f_0 conda-forge
fastparquet 0.8.3 py39h7cc1f47_1 conda-forge
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fonts-conda-ecosystem 1 0 conda-forge
fonts-conda-forge 1 0 conda-forge
freetype 2.12.1 h3f81eb7_0 conda-forge
fribidi 1.0.10 hbcb3906_0 conda-forge
fsspec 2022.10.0 pyhd8ed1ab_0 conda-forge
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gflags 2.2.2 hb1e8313_1004 conda-forge
gfortran_impl_osx-64 11.3.0 h4c39eb8_25 conda-forge
gfortran_osx-64 11.3.0 h18f7dce_0 conda-forge
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gmp 6.2.1 h2e338ed_0 conda-forge
graphite2 1.3.13 h2e338ed_1001 conda-forge
grpc-cpp 1.47.1 h834a566_7 conda-forge
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gzip 1.12 h5eb16cf_0 conda-forge
harfbuzz 5.3.0 h08f8713_0 conda-forge
heapdict 1.0.1 py_0 conda-forge
htslib 1.16 h567f53e_0 bioconda
icu 70.1 h96cf925_0 conda-forge
idna 3.4 pyhd8ed1ab_0 conda-forge
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isl 0.22.1 hb1e8313_2 conda-forge
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ld64_osx-64 609 h1e06c2b_10 conda-forge
lerc 4.0.0 hb486fe8_0 conda-forge
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libblas 3.9.0 16_osx64_openblas conda-forge
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libcblas 3.9.0 16_osx64_openblas conda-forge
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libcrc32c 1.1.2 he49afe7_0 conda-forge
libcurl 7.86.0 h57eb407_0 conda-forge
libcxx 14.0.6 hccf4f1f_0 conda-forge
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libxml2 2.10.3 hb9e07b5_0 conda-forge
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setuptools 65.5.0 pyhd8ed1ab_0 conda-forge
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six 1.16.0 pyh6c4a22f_0 conda-forge
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sqlite 3.39.4 h9ae0607_0 conda-forge
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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 version
rpy2 3.5.1 py39r42h7cc1f47_1 conda-forge
Conda env yaml
You can use the following yaml to create the env I'm using:
echoR_mini.yml.txt
`required_cols`: Add more metadata
Add the following info for each fine-mapping method:
- GitHub repo link
- Citation link
- Version
- Executable path
Add polyfun R dependencies
PolyFun requires some R packages. Include these as suggests or depends:
https://github.com/omerwe/polyfun/blob/master/polyfun.yml
test POLYFUN.finemapper() function
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).
Document each FINEMAP output column
Finemap missing dependency in macOS
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!
Reimplement PAINTOR
- Now working with echoverse.
- Generalized to any data input type (GWAS/QTL/TWAS)
to avoid hard-coded args. - Streamlined functions and reduced clutter.
- Automated installation of PAINTOR. #12
- Added unit tests.
- Get/query full PAINTOR annotations RajLabMSSM/echoannot#10
- User-supplied annotations via
annot - xgr/roadmap annotations.
ABF fails to run for a qt trait
I just got this running
Note that:
- ABF does not get to if proportion_cases is missinng
- polyfun_susie is missing the environment.
Code
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
)
Output
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')
Session Info
> 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)
Allow custom annotations
Allow users to specify custom annotations when conducting functional fine-mapping.
- PAINTOR
- PolyFun
Error when running COJO - cojo.jma.cojo not found
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
Warning in utils::tar(filepath, pkgname, compression = compression, compression_level = 9L, : storing paths of more than 100 bytes is not portable:
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 😞
Sources
- Description of a similar problem with packrat here.
- Medium post on the general challenges of using git submodules.
Integrate additional fine-mapping tools
Fine-mapping
- ABF
- SUSIE
- FINEMAP
- PolyFun-SUSIE
- PolyFun-FINEMAP
- PAINTOR (statistical finemapping)
- PAINTOR_annot (finemap with annotations as priors)
- PAINTOR_multi (jointly finemap between multiple ZSCORES)
- PAINTOR_multiancestry (finemap different multiple LD refs across multiple ZSCORES)
- ANNORE
- JLIM
- CAFEH
- INFIMA
- SparsePro
Colocalization/genetic correlation
Preprocessing/outlier detection
- COJO_stepwise
- COJO_conditional
- COJO_joint
- SLALOM
Evaluate differences in susie implementations
Test for differences between susie_suff_stat() vs. susie_rss() vs. susie_bhat().
Tweakable parameters may differ between these.
Error using gcta64 within echoR conda environment. -> gcta64: Exec format error
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
FINEMAP credible sets not included in the results
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.
See here for minimal, reproducible example
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()
See here for the results
> 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$StdErr, :
IBSS algorithm did not converge in 100 iterations!finemap_loci failing
1. Bug description
Hi I am unable to properly run finemap_loci with a quantitative GWAS.
Few things to highlight so far.
- The sample size column to do finemapping for a quant GWAS is not properly found even though specifying case_control = FALSE.
- Need to have a look at all of this. However, the standardised query seems wrong. Too few SNPs... I manueally filtered SNPs +-1Mb from lead variant. I had over 80K SNPs in eac loci.
- Rfast missing in MAGMA.Celltyping
2. Reproducible example
Code
columnsnames = echodata::construct_colmap(munged= FALSE,
CHR = "CHR", POS = "POS",
SNP = "SNP", P = "P",
Effect = "BETA", StdErr = "SE",
A1 = "A1", A2 = "A2",
N_cases = "N_CAS", MAF = "FREQ",
tstat = NULL, N_controls = NULL,
proportion_cases = NULL)
finemap_loci(# GENERAL ARGUMENTS
topSNPs = topSNPs,
results_dir = fullRS_path,
loci = topSNPs$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
)
Console output
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')
Data
> 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
3. Session info
> 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
PAINTOR: installation issues
Originally posted here:
gkichaev/PAINTOR_V3.0#59
Can't seem to compile PAINTOR (at least on my Mac).
Make sure polyfun submodule gets installed
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)
Check whether fine-mapping method is compatible for quantitative vs. case-control sumstats
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
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