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Integrate basilisk
Seems quite useful:
https://bioconductor.org/packages/release/bioc/html/basilisk.html
GHA Windows: conda dep conflicts
echoR can't build bc of some dependency conflicts.
https://github.com/RajLabMSSM/echoconda/runs/8194389596?check_suite_focus=true#step:21:647
GHA Linux: `sh: 5: /github/home/.cache/R/basilisk/1.9.2/echoconda/0.99.7/echoR_mini/etc/conda/activate.d/activate-r-base.sh: [[: not found`
Failing on GHA Linux currently:
https://github.com/RajLabMSSM/echoconda/runs/8194389372?check_suite_focus=true#step:23:43
sh: 5: /github/home/.cache/R/basilisk/1.9.2/echoconda/0.99.7/echoR_mini/etc/conda/activate.d/activate-r-base.sh: [[: not found
Integrate new yaml export/import tools
`~/.conda` violates CRAN rules
~/.conda is apparently created at some point when installing/running echoconda, but I don't know if this is from my script or something that reticulate does. This violate CRAN's rule of not leaving behind any files in root (but allowing them to be created in the first place?).
If it does come from reticulate, trying to figure out how they have reticulate on CRAN to begin with.
My first thought was to delete ~/.conda at the end of the each function call, but this doesn't seem like a good idea since it exists for a reason.
Next, looking for a way to specify .conda to be created somewhere other than root and still work with reticulate.
Some info on the function of .conda folder.
conda/conda#5388
PolyFun SusiE fails to find echor_mini
just got this running
Note that
Polyfun susie does not get to run as it is missing the environment
Also note the software is installed
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", MAF = "calculate",)
# 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
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