diff --git a/README.md b/README.md index 4ac4fc21d..68c7b5128 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,23 @@ Developed for reproducible & reusable molecular QTL analyses for the NIH/NIA Alzheimer's Disease Sequencing Project (ADSP) Functional Genomics xQTL (FunGen-xQTL) Project. +## What the protocol covers + +| Stage | What it does | +|---|---| +| **Molecular phenotypes** | Quantify expression, splicing, methylation and polyadenylation from bulk RNA-seq, single-nuclei or array data | +| **Data preprocessing** | Genotype QC and formatting, phenotype imputation and formatting, covariate and hidden-factor estimation | +| **QTL association testing** | cis and trans scans with TensorQTL, quantile-regression QTL, hierarchical multiple testing | +| **Multivariate modelling** | MASH, to borrow information across tissues and cell types | +| **Fine-mapping and TWAS** | SuSiE, mvSuSiE, fSuSiE and SuSiE-RSS, from individual-level data or summary statistics, with TWAS weights | +| **GWAS integration** | Colocalization (SuSiE-enloc, ColocBoost), TWAS and cTWAS, INTACT | +| **Enrichment and validation** | Pathway analysis, GREGOR, stratified LD score regression | + +Not every project needs every stage. The +[xQTL Analysis Workflow Builder](https://statfungen.github.io/xqtl-protocol/xqtl_protocol_workflow_builder.html) +asks what you measured and what you want to find out, then shows the modules on +that route with the commands to run them. + ## Getting started | I want to... | Go to | diff --git a/code/SoS/xqtl_protocol_demo.ipynb b/code/SoS/xqtl_protocol_demo.ipynb index 126fc2015..c829cedb1 100644 --- a/code/SoS/xqtl_protocol_demo.ipynb +++ b/code/SoS/xqtl_protocol_demo.ipynb @@ -232,7 +232,42 @@ "\n", "The demo on this page runs on a desktop workstation. Production analyses typically run on an HPC cluster, and SoS supports this natively via [SoS Remote Tasks](https://vatlab.github.io/sos-docs/doc/user_guide/task_statement.html) on [configured host computers](https://vatlab.github.io/sos-docs/doc/user_guide/host_setup.html).\n", "\n", - "We provide a [toy example for running SoS pipelines on a typical HPC cluster environment](https://github.com/statfungen/xqtl-protocol/blob/main/code/misc/Job_Example.ipynb) — first-time users are encouraged to work through it before launching real jobs. It covers the host and task configuration you'll reuse for every subsequent pipeline, and it's schedule-agnostic (SLURM, LSF, SGE, PBS/Torque all work).\n" + "We provide a [toy example for running SoS pipelines on a typical HPC cluster environment](https://github.com/statfungen/xqtl-protocol/blob/main/code/misc/Job_Example.ipynb) — first-time users are encouraged to work through it before launching real jobs. It covers the host and task configuration you'll reuse for every subsequent pipeline, and it's schedule-agnostic (SLURM, LSF, SGE, PBS/Torque all work).\n", + "\n", + "---\n", + "\n", + "## Using your own data\n", + "\n", + "The examples above run against files committed in `tests/fixtures/`. To run the same\n", + "pipeline on your own study, replace those paths with your own -- the parameters do\n", + "not change.\n", + "\n", + "Most modules expect some combination of:\n", + "\n", + "| Parameter | What it wants |\n", + "|---|---|\n", + "| `--genoFile` | Genotypes: PLINK `.bed/.bim/.fam`, or a list of per-chromosome files |\n", + "| `--phenoFile` | Molecular phenotype matrix: bgzipped BED, `#chr start end ID` then one column per sample |\n", + "| `--covFile` | Covariates, gzipped |\n", + "| `--region-list` | Regions to analyse, or a region list produced by an earlier step |\n", + "| `--cwd` | Where outputs are written |\n", + "\n", + "Sample identifiers must match across the genotype, phenotype and covariate files.\n", + "Most \"no overlapping samples\" errors come from a mismatch here rather than from a\n", + "problem with the data itself.\n", + "\n", + "To see the parameters a module accepts:\n", + "\n", + "```bash\n", + "sos run pipeline/.ipynb -h\n", + "```\n", + "\n", + "And for a set of arguments known to work, look at that module's test -- each one\n", + "carries a parameter set that CI runs:\n", + "\n", + "```bash\n", + "grep -rl 'run_sos' tests/notebooks --include='*.py'\n", + "```\n" ] } ], diff --git a/code/SoS/xqtl_protocol_workflow_builder.html b/code/SoS/xqtl_protocol_workflow_builder.html index 0e8766d09..92bf0e149 100644 --- a/code/SoS/xqtl_protocol_workflow_builder.html +++ b/code/SoS/xqtl_protocol_workflow_builder.html @@ -640,7 +640,7 @@

Your analysis route

return h; } let curBtn=null; -const 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\"mash::mash::var10\", \"mash::mash::var11\", \"mash::mash::var12\", \"mash::mash::var13\", \"mash::mash::var14\", \"mash::mash::var15\", \"mash::mash::var16\", \"mash::mash::var17\"))) ..."]},"tests/fixtures/mash_posterior/fine_mapping.rds":{"kind":"rds","lines":["Object: data.frame [17 x 3]"," variants cs_order pip"," 1 mash::mash::var1 1 0.60"," 2 mash::mash::var2 1 0.40"," 3 mash::mash::var3 0 0.02"]},"tests/fixtures/mash/expected/mash_input.qss.rds":{"kind":"rds","lines":["Object: list [length 10]"," Components: strong.b, strong.s, random.b, random.s, null.b, null.s, random.z, null.z, ...","$strong.b: matrix/array [2 x 2]"," Mic_De_Jager_eQTL"," protocol_example::mash::chr22:15528675:A:G_region1.qss 3.0964631"," protocol_example::mash::chr22:15528699:A:G_region1.qss 0.3039547"," Ast_De_Jager_eQTL"," protocol_example::mash::chr22:15528675:A:G_region1.qss 0.2011453"," protocol_example::mash::chr22:15528699:A:G_region1.qss 4.5483141","","$strong.s: matrix/array [2 x 2]"," 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protocol_example::mash::chr22:15528227:A:G_region1.qss 1"," Ast_De_Jager_eQTL"," protocol_example::mash::chr22:15528319:A:G_region1.qss 1"," protocol_example::mash::chr22:15529124:A:G_region1.qss 1"," protocol_example::mash::chr22:15528227:A:G_region1.qss 1","","$null.b: matrix/array [15 x 2]"," Mic_De_Jager_eQTL"," protocol_example::mash::chr22:15528612:A:G_region1.qss 1.66040624"," protocol_example::mash::chr22:15528787:A:G_region1.qss -0.01514105"," protocol_example::mash::chr22:15529068:A:G_region1.qss 1.63336444"," Ast_De_Jager_eQTL"," protocol_example::mash::chr22:15528612:A:G_region1.qss -1.0569069"," protocol_example::mash::chr22:15528787:A:G_region1.qss -0.9860239"," protocol_example::mash::chr22:15529068:A:G_region1.qss 0.1641178","","$null.s: matrix/array [15 x 2]"," Mic_De_Jager_eQTL"," protocol_example::mash::chr22:15528612:A:G_region1.qss 1"," protocol_example::mash::chr22:15528787:A:G_region1.qss 1"," protocol_example::mash::chr22:15529068:A:G_region1.qss 1"," Ast_De_Jager_eQTL"," protocol_example::mash::chr22:15528612:A:G_region1.qss 1"," protocol_example::mash::chr22:15528787:A:G_region1.qss 1"," protocol_example::mash::chr22:15529068:A:G_region1.qss 1"]},"tests/fixtures/mash/expected/mash_input.fmr.rds":{"kind":"rds","lines":["Object: list [length 10]"," Components: strong.b, strong.s, random.b, random.s, null.b, null.s, random.z, null.z, ...","$strong.b: matrix/array [1 x 2]"," Mic_De_Jager_eQTL Ast_De_Jager_eQTL"," [1,] 2.70564 0.5429115","","$strong.s: matrix/array [1 x 2]"," Mic_De_Jager_eQTL Ast_De_Jager_eQTL"," [1,] 1 1","","$random.b: matrix/array [15 x 2]"," Mic_De_Jager_eQTL"," chr22:15528319:A:G_protocol_example.QtlFineMappingResult 0.69777934"," chr22:15529124:A:G_protocol_example.QtlFineMappingResult 1.32852955"," chr22:15528227:A:G_protocol_example.QtlFineMappingResult -0.05627064"," Ast_De_Jager_eQTL"," chr22:15528319:A:G_protocol_example.QtlFineMappingResult -0.1991450"," chr22:15529124:A:G_protocol_example.QtlFineMappingResult -0.2385934"," chr22:15528227:A:G_protocol_example.QtlFineMappingResult 0.9808774","","$random.s: matrix/array [15 x 2]"," Mic_De_Jager_eQTL"," chr22:15528319:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15529124:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15528227:A:G_protocol_example.QtlFineMappingResult 1"," Ast_De_Jager_eQTL"," chr22:15528319:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15529124:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15528227:A:G_protocol_example.QtlFineMappingResult 1","","$null.b: matrix/array [15 x 2]"," Mic_De_Jager_eQTL"," chr22:15528612:A:G_protocol_example.QtlFineMappingResult 1.66040624"," chr22:15528787:A:G_protocol_example.QtlFineMappingResult -0.01514105"," chr22:15529068:A:G_protocol_example.QtlFineMappingResult 1.63336444"," Ast_De_Jager_eQTL"," chr22:15528612:A:G_protocol_example.QtlFineMappingResult -1.0569069"," chr22:15528787:A:G_protocol_example.QtlFineMappingResult -0.9860239"," chr22:15529068:A:G_protocol_example.QtlFineMappingResult 0.1641178","","$null.s: matrix/array [15 x 2]"," Mic_De_Jager_eQTL"," chr22:15528612:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15528787:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15529068:A:G_protocol_example.QtlFineMappingResult 1"," Ast_De_Jager_eQTL"," chr22:15528612:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15528787:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15529068:A:G_protocol_example.QtlFineMappingResult 1"]},"tests/fixtures/mash/expected/mash_input.indep.rds":{"kind":"rds","lines":["Object: list [length 10]"," Components: strong.b, strong.s, random.b, random.s, null.b, null.s, random.z, null.z, ...","$strong.b: matrix/array [1 x 2]"," Mic_De_Jager_eQTL Ast_De_Jager_eQTL"," [1,] 2.70564 0.5429115","","$strong.s: matrix/array [1 x 2]"," Mic_De_Jager_eQTL Ast_De_Jager_eQTL"," [1,] 1 1","","$random.b: matrix/array [10 x 2]"," Mic_De_Jager_eQTL"," chr22:15528791:A:G_protocol_example.QtlFineMappingResult 0.8741938"," chr22:15528516:A:G_protocol_example.QtlFineMappingResult -2.8592052"," chr22:15528699:A:G_protocol_example.QtlFineMappingResult 0.3039547"," Ast_De_Jager_eQTL"," chr22:15528791:A:G_protocol_example.QtlFineMappingResult -0.0510958"," chr22:15528516:A:G_protocol_example.QtlFineMappingResult -1.2528124"," chr22:15528699:A:G_protocol_example.QtlFineMappingResult 4.5483141","","$random.s: matrix/array [10 x 2]"," Mic_De_Jager_eQTL"," chr22:15528791:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15528516:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15528699:A:G_protocol_example.QtlFineMappingResult 1"," Ast_De_Jager_eQTL"," chr22:15528791:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15528516:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15528699:A:G_protocol_example.QtlFineMappingResult 1","","$null.b: matrix/array [9 x 2]"," Mic_De_Jager_eQTL"," chr22:15528467:A:G_protocol_example.QtlFineMappingResult 0.57965162"," chr22:15528227:A:G_protocol_example.QtlFineMappingResult -0.05627064"," chr22:15528746:A:G_protocol_example.QtlFineMappingResult 1.10137261"," Ast_De_Jager_eQTL"," chr22:15528467:A:G_protocol_example.QtlFineMappingResult 1.3747398"," chr22:15528227:A:G_protocol_example.QtlFineMappingResult 0.9808774"," chr22:15528746:A:G_protocol_example.QtlFineMappingResult 0.6216373","","$null.s: matrix/array [9 x 2]"," Mic_De_Jager_eQTL"," chr22:15528467:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15528227:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15528746:A:G_protocol_example.QtlFineMappingResult 1"," Ast_De_Jager_eQTL"," chr22:15528467:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15528227:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15528746:A:G_protocol_example.QtlFineMappingResult 1"]},"tests/fixtures/mash/expected/mixture_prior.EE.prior.rds":{"kind":"rds","lines":["Object: list [length 3]"," Components: U, w, loglik","$U: list [length 32]"," Components: identity, ALL, Ast, End, Exc, Inh, Mic, OPC, ...","$w: numeric [length 33]"," Values: c(null = 0.350889466152308, identity = 0, ALL = 0, Ast = 0, End = 0.0626903097191792, Exc = 0) ...","$loglik: NULL [length 0]"]},"tests/fixtures/mash/expected/vhat.identity.EE.rds":{"kind":"rds","lines":["Object: matrix/array [8 x 8]"," [,1] [,2] [,3] [,4] [,5] [,6]"," [1,] 1 0 0 0 0 0"," [2,] 0 1 0 0 0 0"," [3,] 0 0 1 0 0 0"]},"tests/fixtures/mash/expected/vhat.simple.EE.rds":{"kind":"rds","lines":["Object: matrix/array [8 x 8]"," ALL Ast End Exc Inh Mic"," ALL 1.00000000 0.01420407 -0.06297940 0.65424529 0.01352651 0.2149795"," Ast 0.01420407 1.00000000 0.05708375 0.01527867 0.19794257 -0.1509936"," End -0.06297940 0.05708375 1.00000000 -0.21535872 -0.02215867 -0.3384365"]},"tests/fixtures/mash/expected/vhat.corshrink.EE.rds":{"kind":"rds","lines":["Object: matrix/array [8 x 8]"," ALL Ast End Exc Inh"," ALL 1.0000000000 0.000987488 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0]"]},"tests/fixtures/mash/expected/cov.canonical.EE.rds":{"kind":"rds","lines":["Object: list [length 13]"," Components: identity, ALL, Ast, End, Exc, Inh, Mic, OPC, ...","$identity: matrix/array [8 x 8]"," [,1] [,2] [,3] [,4] [,5] [,6]"," [1,] 1 0 0 0 0 0"," [2,] 0 1 0 0 0 0"," [3,] 0 0 1 0 0 0","","$ALL: matrix/array [8 x 8]"," [,1] [,2] [,3] [,4] [,5] [,6]"," [1,] 1 0 0 0 0 0"," [2,] 0 0 0 0 0 0"," [3,] 0 0 0 0 0 0","","$Ast: matrix/array [8 x 8]"," [,1] [,2] [,3] [,4] [,5] [,6]"," [1,] 0 0 0 0 0 0"," [2,] 0 1 0 0 0 0"," [3,] 0 0 0 0 0 0","","$End: matrix/array [8 x 8]"," [,1] [,2] [,3] [,4] [,5] [,6]"," [1,] 0 0 0 0 0 0"," [2,] 0 0 0 0 0 0"," [3,] 0 0 1 0 0 0","","$Exc: matrix/array [8 x 8]"," [,1] [,2] [,3] [,4] [,5] [,6]"," [1,] 0 0 0 0 0 0"," [2,] 0 0 0 0 0 0"," [3,] 0 0 0 0 0 0","","$Inh: matrix/array [8 x 8]"," [,1] [,2] [,3] [,4] [,5] [,6]"," [1,] 0 0 0 0 0 0"," [2,] 0 0 0 0 0 0"," [3,] 0 0 0 0 0 0"]},"tests/fixtures/mash/expected/cov.pca.EE.rds":{"kind":"rds","lines":["Object: list [length 4]"," Components: PCA_1, PCA_2, PCA_3, tPCA","$PCA_1: matrix/array [8 x 8]"," ALL Ast End Exc Inh Mic"," ALL 0.006180290 0.008174521 0.008739213 -0.007784481 0.003495496 0.02409971"," Ast 0.008174521 0.010812242 0.011559147 -0.010296345 0.004623408 0.03187610"," End 0.008739213 0.011559147 0.012357649 -0.011007613 0.004942791 0.03407809","","$PCA_2: matrix/array [8 x 8]"," ALL Ast End Exc Inh Mic"," ALL 0.02563814 0.03520979 0.1427297 0.03855221 0.004478845 0.01598269"," Ast 0.03520979 0.04835487 0.1960158 0.05294513 0.006150959 0.02194960"," End 0.14272971 0.19601585 0.7945883 0.21462338 0.024934105 0.08897698","","$PCA_3: matrix/array [8 x 8]"," ALL Ast End Exc Inh Mic"," ALL 0.17897232 -0.020181322 -0.15323506 0.17052991 0.071118778 0.12859069"," Ast -0.02018132 0.002275691 0.01727913 -0.01922934 -0.008019513 -0.01450018"," End -0.15323506 0.017279130 0.13119896 -0.14600671 -0.060891483 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0.00655251076072851, 0.00755478832393643, 0.00636790267383328, 0.00598101961309056, 0.00508689343582039, 0.000304269733382108, 0.00787846287951198, 0.00908708912978019, 0.00598101961309056, 0.0085694021122118, 0.00611110457710638, 0.00027499145557414, 0.00669526284240114, 0.00771975591706234, 0.00508689343582039, 0.00611110457710638, 0.00659351995538288, 0.000267097385890687, 0.0063762465394884, 0.00735109333332331, 0.00484581768852353, 0.00582039621050936, 0.00495074086866018, 4.46556985217172e-05, -0.00261310530646169, -0.00303720863701038, -0.00194729814154248, -0.00237128478695276, -0.00199294456741381 ), dim = c(6L, 8L, 17L), dimnames = list(c(\"ALL\", \"Ast\", \"End\", \"Exc\", \"Inh\", \"Mic\"), c(\"ALL\", \"Ast\", \"End\", \"Exc\", \"Inh\", \"Mic\", \"OPC\", \"Oli\"), c(\"mash::mash::var1\", \"mash::mash::var2\", \"mash::mash::var3\", \"mash::mash::var4\", \"mash::mash::var5\", \"mash::mash::var6\", \"mash::mash::var7\", \"mash::mash::var8\", \"mash::mash::var9\", \"mash::mash::var10\", \"mash::mash::var11\", \"mash::mash::var12\", \"mash::mash::var13\", \"mash::mash::var14\", \"mash::mash::var15\", \"mash::mash::var16\", \"mash::mash::var17\"))) ..."]},"tests/fixtures/mash_posterior/fine_mapping.rds":{"kind":"rds","lines":["Object: data.frame [17 x 3]"," variants cs_order pip"," 1 mash::mash::var1 1 0.60"," 2 mash::mash::var2 1 0.40"," 3 mash::mash::var3 0 0.02"]},"tests/fixtures/mash/expected/mash_input.qss.rds":{"kind":"rds","lines":["Object: list [length 10]"," Components: strong.b, strong.s, random.b, random.s, null.b, null.s, random.z, null.z, ...","$strong.b: matrix/array [2 x 2]"," Mic_De_Jager_eQTL"," protocol_example::mash::chr22:15528675:A:G_region1.qss 3.0964631"," protocol_example::mash::chr22:15528699:A:G_region1.qss 0.3039547"," Ast_De_Jager_eQTL"," protocol_example::mash::chr22:15528675:A:G_region1.qss 0.2011453"," protocol_example::mash::chr22:15528699:A:G_region1.qss 4.5483141","","$strong.s: matrix/array [2 x 2]"," Mic_De_Jager_eQTL"," protocol_example::mash::chr22:15528675:A:G_region1.qss 1"," protocol_example::mash::chr22:15528699:A:G_region1.qss 1"," Ast_De_Jager_eQTL"," protocol_example::mash::chr22:15528675:A:G_region1.qss 1"," protocol_example::mash::chr22:15528699:A:G_region1.qss 1","","$random.b: matrix/array [15 x 2]"," Mic_De_Jager_eQTL"," protocol_example::mash::chr22:15528319:A:G_region1.qss 0.69777934"," protocol_example::mash::chr22:15529124:A:G_region1.qss 1.32852955"," protocol_example::mash::chr22:15528227:A:G_region1.qss -0.05627064"," Ast_De_Jager_eQTL"," protocol_example::mash::chr22:15528319:A:G_region1.qss -0.1991450"," protocol_example::mash::chr22:15529124:A:G_region1.qss -0.2385934"," protocol_example::mash::chr22:15528227:A:G_region1.qss 0.9808774","","$random.s: matrix/array [15 x 2]"," Mic_De_Jager_eQTL"," protocol_example::mash::chr22:15528319:A:G_region1.qss 1"," protocol_example::mash::chr22:15529124:A:G_region1.qss 1"," protocol_example::mash::chr22:15528227:A:G_region1.qss 1"," Ast_De_Jager_eQTL"," protocol_example::mash::chr22:15528319:A:G_region1.qss 1"," protocol_example::mash::chr22:15529124:A:G_region1.qss 1"," protocol_example::mash::chr22:15528227:A:G_region1.qss 1","","$null.b: matrix/array [15 x 2]"," Mic_De_Jager_eQTL"," protocol_example::mash::chr22:15528612:A:G_region1.qss 1.66040624"," protocol_example::mash::chr22:15528787:A:G_region1.qss -0.01514105"," protocol_example::mash::chr22:15529068:A:G_region1.qss 1.63336444"," Ast_De_Jager_eQTL"," protocol_example::mash::chr22:15528612:A:G_region1.qss -1.0569069"," protocol_example::mash::chr22:15528787:A:G_region1.qss -0.9860239"," protocol_example::mash::chr22:15529068:A:G_region1.qss 0.1641178","","$null.s: matrix/array [15 x 2]"," Mic_De_Jager_eQTL"," protocol_example::mash::chr22:15528612:A:G_region1.qss 1"," protocol_example::mash::chr22:15528787:A:G_region1.qss 1"," protocol_example::mash::chr22:15529068:A:G_region1.qss 1"," Ast_De_Jager_eQTL"," protocol_example::mash::chr22:15528612:A:G_region1.qss 1"," protocol_example::mash::chr22:15528787:A:G_region1.qss 1"," protocol_example::mash::chr22:15529068:A:G_region1.qss 1"]},"tests/fixtures/mash/expected/mash_input.fmr.rds":{"kind":"rds","lines":["Object: list [length 10]"," Components: strong.b, strong.s, random.b, random.s, null.b, null.s, random.z, null.z, ...","$strong.b: matrix/array [1 x 2]"," Mic_De_Jager_eQTL Ast_De_Jager_eQTL"," [1,] 2.70564 0.5429115","","$strong.s: matrix/array [1 x 2]"," Mic_De_Jager_eQTL Ast_De_Jager_eQTL"," [1,] 1 1","","$random.b: matrix/array [15 x 2]"," Mic_De_Jager_eQTL"," chr22:15528319:A:G_protocol_example.QtlFineMappingResult 0.69777934"," chr22:15529124:A:G_protocol_example.QtlFineMappingResult 1.32852955"," chr22:15528227:A:G_protocol_example.QtlFineMappingResult -0.05627064"," Ast_De_Jager_eQTL"," chr22:15528319:A:G_protocol_example.QtlFineMappingResult -0.1991450"," chr22:15529124:A:G_protocol_example.QtlFineMappingResult -0.2385934"," chr22:15528227:A:G_protocol_example.QtlFineMappingResult 0.9808774","","$random.s: matrix/array [15 x 2]"," Mic_De_Jager_eQTL"," chr22:15528319:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15529124:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15528227:A:G_protocol_example.QtlFineMappingResult 1"," Ast_De_Jager_eQTL"," chr22:15528319:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15529124:A:G_protocol_example.QtlFineMappingResult 1"," chr22:15528227:A:G_protocol_example.QtlFineMappingResult 1","","$null.b: matrix/array [15 x 2]"," Mic_De_Jager_eQTL"," chr22:15528612:A:G_protocol_example.QtlFineMappingResult 1.66040624"," chr22:15528787:A:G_protocol_example.QtlFineMappingResult -0.01514105"," chr22:15529068:A:G_protocol_example.QtlFineMappingResult 1.63336444"," Ast_De_Jager_eQTL"," chr22:15528612:A:G_protocol_example.QtlFineMappingResult -1.0569069"," 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use."]],"methylation_calling":[["DNA methylation","Addition of a methyl group to DNA, commonly measured at CpG sites as an epigenetic regulatory mark."],["Beta value","The estimated fraction of methylated signal at a CpG probe."]],"apa_calling":[["Alternative polyadenylation","Use of different transcript cleavage and polyadenylation sites, which changes the RNA 3-prime end."],["Polyadenylation site","The transcript position at which RNA is cleaved before addition of the poly(A) tail."]],"apa_impute":[["Imputation","Estimation of missing molecular measurements from patterns observed across features and samples."],["Missingness","The pattern and proportion of unavailable measurements in a molecular phenotype matrix."]],"VCF_QC":[["Minor allele frequency (MAF)","The frequency of the less common allele at a variant in the analyzed sample."],["Hardy-Weinberg equilibrium","The expected genotype-frequency relationship under random mating, used as one signal of genotype quality."]],"genotype_formatting":[["Allele harmonization","Alignment of variant identifiers, reference alleles, alternate alleles, and strand orientation across datasets."],["Dosage","The expected number of alternate alleles carried by an individual, often ranging continuously from zero to two after imputation."]],"GWAS_QC":[["Genome-wide association study (GWAS)","A scan for genetic variants associated with a complex trait or disease."],["Genomic inflation","Systematic excess of association signal that can reflect confounding, relatedness, or polygenicity."]],"PCA":[["Population structure","Systematic genetic differences among ancestry groups or subpopulations."],["Genotype principal component","A major axis of genetic variation used to adjust association analyses for population structure."]],"gene_annotation":[["Transcription start site (TSS)","The genomic position where transcription of a gene begins."],["Gene model","The annotated genomic structure of a gene, including its exons, transcripts, and strand."]],"phenotype_imputation":[["Phenotype imputation","Estimation of missing molecular phenotype values using information shared across samples or features."],["Limit of detection","The smallest abundance that an assay can distinguish reliably from background."]],"phenotype_formatting":[["Molecular phenotype","A quantitative molecular trait such as gene expression, splicing, methylation, or protein abundance."],["Genomic interval","A chromosome, start, and end coordinate used to locate a molecular feature."]],"covariate_formatting":[["Covariate","A measured variable included in a model to account for known biological or technical variation."],["Design matrix","A numeric representation of model covariates across samples."]],"covariate_hidden_factor":[["Hidden factor","An unmeasured source of variation, such as cell composition, technical batch, or RNA quality, inferred from the molecular phenotype matrix."],["Confounding","Distortion of a genetic association by a variable related to both the tested genotype and molecular phenotype."]],"TensorQTL":[["xQTL","A genetic variant associated with variation in a molecular phenotype such as expression, splicing, methylation, or protein abundance."],["cis association","An association between a variant and a nearby molecular feature within a defined genomic window."],["False discovery rate (FDR)","The expected proportion of false positives among results declared significant."]],"qr_and_twas":[["Quantile regression","A model that estimates genetic effects at selected points of a phenotype distribution rather than only its mean."],["TWAS weight","An estimated genetic effect used to predict a molecular trait from local variants."]],"qtl_association_postprocessing":[["Lead variant","The variant with the strongest association signal for a molecular feature or region."],["Allelic effect","The direction and magnitude of phenotype change associated with an allele."]],"METAL":[["Meta-analysis","Statistical combination of association evidence across cohorts while allowing each cohort to retain its own participants."],["Heterogeneity","Variation in estimated genetic effects across cohorts or studies."]],"mash_preprocessing":[["Effect-size matrix","A matrix of association estimates arranged across variants or genes and biological conditions."],["Standard error","The estimated uncertainty of an effect-size estimate."]],"mixture_prior":[["Covariance prior","A learned representation of how genetic effects tend to be shared across tissues, cell types, or molecular traits."],["Residual correlation","Correlation among association estimates that remains after accounting for true shared effects."]],"mash_fit":[["Empirical Bayes","A framework that estimates a prior distribution from the observed data and uses it to update noisy effects."],["Shrinkage","Pulling uncertain effect estimates toward patterns supported by the full dataset."],["Local false sign rate","The posterior probability that the reported direction of an effect is wrong."]],"mash_posterior":[["Posterior distribution","The updated probability distribution of an effect after combining the observed data with the fitted prior."],["Posterior contrast","A probabilistic comparison of effects between biological conditions."]],"mnm_regression":[["Fine-mapping","Prioritization of variants that may causally explain an association signal."],["Posterior inclusion probability (PIP)","The posterior probability that a variant contributes to the genetic signal in the fitted model."],["Credible set","A group of variants that jointly contains a causal regulatory variant with a stated posterior probability under the fitted model."]],"rss_analysis":[["Fine-mapping","Prioritization of variants that may causally explain an association signal."],["Posterior inclusion probability (PIP)","The posterior probability that a variant contributes to the genetic signal in the fitted model."],["Credible set","A group of variants that jointly contains a causal regulatory variant with a stated posterior probability under the fitted model."]],"SuSiE_enloc":[["Colocalization","Evidence that molecular-trait and complex-trait associations in a region are explained by the same underlying genetic signal."],["Regional enrichment","Increased probability that a trait-associated region also contains a molecular QTL signal."]],"twas_ctwas":[["Transcriptome-wide association study (TWAS)","A test relating genetically predicted molecular phenotypes to a complex trait."],["Mediated association","A trait association consistent with a genetic effect acting through a measured molecular phenotype."]],"colocboost":[["Colocalization","Evidence that molecular-trait and complex-trait associations in a region are explained by the same underlying genetic signal."],["Multiple causal signals","More than one distinct causal association pattern within the same genomic region."]],"intact":[["Colocalization","Evidence that molecular-trait and complex-trait associations in a region are explained by the same underlying genetic signal."],["Cross-tissue evidence","Association information combined across tissues or molecular contexts."]],"watershed":[["Variant-to-gene prioritization","Ranking variants by evidence that they regulate a particular gene and contribute to disease risk."],["Functional annotation","Biological information about a variant or genomic region used to interpret its potential mechanism."]],"eoo_enrichment":[["Enrichment","An excess of overlap between two sets of genomic signals relative to an appropriate null expectation."],["Observed-to-expected ratio","The observed overlap divided by the overlap expected under a null model."]],"gsea":[["Gene set enrichment analysis (GSEA)","A test for coordinated concentration of association evidence within a predefined group of genes."],["Gene set","A collection of genes sharing a pathway, function, annotation, or experimental signature."]],"gregor":[["Regulatory enrichment","Overrepresentation of associated variants in regulatory annotations compared with matched control variants."],["Matched control variant","A background variant selected to resemble an associated variant in properties such as allele frequency and LD."]],"sldsc_enrichment":[["Stratified LD score regression (S-LDSC)","A method that partitions SNP heritability across genomic annotations using GWAS summary statistics and LD."],["SNP heritability","The proportion of trait variation attributable to the additive effects of measured or tagged variants."]],"ems_training":[["Expression modifier score (EMS)","A learned score estimating the probability that a variant has a regulatory effect on a gene."],["Training label","An observed outcome used to teach a predictive model which genomic patterns distinguish regulatory variants."]],"ems_prediction":[["Expression modifier score (EMS)","A learned score estimating the probability that a variant has a regulatory effect on a gene."],["Calibration","Agreement between predicted probabilities and the observed frequency of the corresponding outcome."]]}; function open(btn){curBtn=btn; const nb=btn.closest('.mw').dataset.nb; cur=nb; initM(nb); diff --git a/code/SoS/xqtl_protocol_workflow_builder.ipynb b/code/SoS/xqtl_protocol_workflow_builder.ipynb index 5e2ac084f..872075adf 100644 --- a/code/SoS/xqtl_protocol_workflow_builder.ipynb +++ b/code/SoS/xqtl_protocol_workflow_builder.ipynb @@ -3,7 +3,754 @@ { "cell_type": "markdown", "metadata": {}, - "source": "# xQTL Analysis Workflow Builder\n\nNot set up yet? Start with [Environment Setup](https://statfungen.github.io/xqtl-protocol/xqtl_protocol_demo.html) to install the software stack and run a first pipeline on the example data shipped in this repository. Each module below links to its own page, where the inputs, expected outputs and full command interface are documented.\n\n\n````{raw} html\n
\n\n\n
\n
\n

xQTL Analysis Workflow Builder

\n

xQTL studies ask how inherited genetic variation changes a molecular phenotype—such as gene expression, splicing, methylation or polyadenylation—and whether that regulatory effect helps explain variation in a complex trait. The map follows that scientific argument from a measured molecular phenotype to a localized regulatory signal and, when appropriate, to evidence connecting the signal with disease biology. Genetic regulation is often tissue- and cell-context-specific, so the relevant path depends on what was measured and where it was measured (GTEx Consortium, 2020).

\n
\n
\n

A practical map of the analysis

\n

From biological samples to interpretable xQTL discoveries

\n

The pipeline turns a broad biological question—does genetic variation alter a molecular process?—into progressively more specific evidence. Not every project needs every stage: the route should match the molecular phenotype, study design and claim being tested.

\n
    \n
  1. 1Define the molecular phenotype

    Turn the assay into biologically interpretable features—expression levels, splice usage, methylation states or polyadenylation usage—in the tissue or cell population relevant to the question. Quality control protects the biological comparison from sample swaps, outliers and poorly measured features.

  2. \n
  3. 2Separate inherited regulation from other variation

    Individuals differ because of ancestry, environment, cell composition and technical effects as well as genotype. Accounting for these sources of variation makes the allelic effect easier to interpret as genetic regulation of the molecular phenotype.

  4. \n
  5. 3Map and localize regulatory effects

    Association testing identifies loci where genotype tracks the molecular phenotype. Multi-context analysis asks whether an effect is shared across tissues or cell types; fine-mapping narrows correlated variants to credible sets that quantify uncertainty rather than declaring a single causal variant (Wang et al., 2020).

  6. \n
  7. 4Relate regulation to complex traits

    Colocalization evaluates whether molecular and trait associations are consistent with a shared causal signal (Giambartolomei et al., 2014), while TWAS tests whether genetically predicted molecular levels are associated with the trait (Gusev et al., 2016). Together with functional enrichment, these analyses prioritize mechanisms but do not by themselves prove mediation or causality.

  8. \n
\n

How to use this page: describe your data and goal, follow the highlighted route, open a module to inspect its real fixture files, and run the displayed sos run command.

\n
\n
\n
\n

Tell us about your data and goal

\n
\n
\n \n \n \n
\n

\n
\n\n

Inputs

Reference Data

Quantification

Molecular Phenotype Quantification

Bulk RNA-seq
Single-nuclei / pseudobulk
Alternative splicing
DNA methylation
Polyadenylation

Pre-processing

Data Pre-processing

Genotype
Phenotype
Covariate

Discovery

QTL Association Testing

Multivariate modelling

Cross-cohort Meta-analysis

Multivariate Mixture (MASH)

Regression

High-dimensional Regression

Individual level
Summary statistics level

Integration

GWAS Integration

Rare-variant xQTL

Interpretation

Enrichment & Validation

xQTL Modifier Score

\n
\n\n\n\n
\n\n\n````\n" + "source": [ + "# xQTL Analysis Workflow Builder\n", + "\n", + "Not set up yet? Start with [Environment Setup](https://statfungen.github.io/xqtl-protocol/xqtl_protocol_demo.html) to install the software stack and run a first pipeline on the example data shipped in this repository. Each module below links to its own page, where the inputs, expected outputs and full command interface are documented.\n", + "\n", + "\n", + "````{raw} html\n", + "
\n", + "\n", + "\n", + "
\n", + "
\n", + "

xQTL Analysis Workflow Builder

\n", + "

xQTL studies ask how inherited genetic variation changes a molecular phenotype—such as gene expression, splicing, methylation or polyadenylation—and whether that regulatory effect helps explain variation in a complex trait. The map follows that scientific argument from a measured molecular phenotype to a localized regulatory signal and, when appropriate, to evidence connecting the signal with disease biology. Genetic regulation is often tissue- and cell-context-specific, so the relevant path depends on what was measured and where it was measured (GTEx Consortium, 2020).

\n", + "
\n", + "
\n", + "

A practical map of the analysis

\n", + "

From biological samples to interpretable xQTL discoveries

\n", + "

The pipeline turns a broad biological question—does genetic variation alter a molecular process?—into progressively more specific evidence. Not every project needs every stage: the route should match the molecular phenotype, study design and claim being tested.

\n", + "
    \n", + "
  1. 1Define the molecular phenotype

    Turn the assay into biologically interpretable features—expression levels, splice usage, methylation states or polyadenylation usage—in the tissue or cell population relevant to the question. Quality control protects the biological comparison from sample swaps, outliers and poorly measured features.

  2. \n", + "
  3. 2Separate inherited regulation from other variation

    Individuals differ because of ancestry, environment, cell composition and technical effects as well as genotype. Accounting for these sources of variation makes the allelic effect easier to interpret as genetic regulation of the molecular phenotype.

  4. \n", + "
  5. 3Map and localize regulatory effects

    Association testing identifies loci where genotype tracks the molecular phenotype. Multi-context analysis asks whether an effect is shared across tissues or cell types; fine-mapping narrows correlated variants to credible sets that quantify uncertainty rather than declaring a single causal variant (Wang et al., 2020).

  6. \n", + "
  7. 4Relate regulation to complex traits

    Colocalization evaluates whether molecular and trait associations are consistent with a shared causal signal (Giambartolomei et al., 2014), while TWAS tests whether genetically predicted molecular levels are associated with the trait (Gusev et al., 2016). Together with functional enrichment, these analyses prioritize mechanisms but do not by themselves prove mediation or causality.

  8. \n", + "
\n", + "

How to use this page: describe your data and goal, follow the highlighted route, open a module to inspect its real fixture files, and run the displayed sos run command.

\n", + "
\n", + "
\n", + "
\n", + "

Tell us about your data and goal

\n", + "
\n", + "
\n", + " \n", + " \n", + " \n", + "
\n", + "

\n", + "
\n", + "\n", + "

Inputs

Reference Data

Quantification

Molecular Phenotype Quantification

Bulk RNA-seq
Single-nuclei / pseudobulk
Alternative splicing
DNA methylation
Polyadenylation

Pre-processing

Data Pre-processing

Genotype
Phenotype
Covariate

Discovery

QTL Association Testing

Multivariate modelling

Cross-cohort Meta-analysis

Multivariate Mixture (MASH)

Regression

High-dimensional Regression

Individual level
Summary statistics level

Integration

GWAS Integration

Rare-variant xQTL

Interpretation

Enrichment & Validation

xQTL Modifier Score

\n", + "
\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + "\n", + "````\n" + ] } ], "metadata": { diff --git a/tests/fixtures/mnm_regression/expected/README.md b/tests/fixtures/mnm_regression/expected/README.md new file mode 100644 index 000000000..f0bd01a13 --- /dev/null +++ b/tests/fixtures/mnm_regression/expected/README.md @@ -0,0 +1,38 @@ +# mnm_regression expected outputs + +| file | produced by | +|---|---| +| `univariate_bvsr.rds` | `susie_twas` — per-context SuSiE fine-mapping (`method = "susie"`) | +| `univariate_twas_weights.rds` | `susie_twas` — TWAS weights from the same run | +| `protocol_example.ENSG00000283047.multicontext_bvsr.rds` | `mnm` — joint multi-context fit (`method = "mvsusie"`, carries a `jointContexts` column) | + +## Regenerating the multicontext result + +`mnm` needs the dataset built first, so run both steps together. Note two things the +module documentation currently gets wrong: the parameter is `--prior`, not +`--mixture-prior`, and `--transpose-covariates True` is required — without it the run +fails with "No shared samples between phenotype and covariate file", which looks like +a data problem but is an orientation one. + +```bash +sos run pipeline/mnm_regression.ipynb qtl_dataset_construct+mnm \ + --name protocol_example --cwd \ + --genoFile tests/fixtures/qtl_mini/protocol_example.genotype.chr22.bed \ + --phenoFile tests/fixtures/qtl_mini/protocol_example.pheno_manifest_context.tsv \ + --covFile tests/fixtures/qtl_mini/example_covariates.tsv \ + --transpose-covariates True \ + --customized-association-windows tests/fixtures/qtl_mini/association_windows.bed \ + --region-name ENSG00000283047 --save-data --no-skip-twas-weights \ + --phenotype-names mv_pheno \ + --prior tests/fixtures/mash/expected/mixture_prior.EE.prior.rds \ + --ld-reference-meta-file tests/fixtures/ld_reference/ld_meta_file.tsv \ + --modular_script_dir code/script -j1 +``` + +The mixture prior is a choice: `tests/fixtures/mash/expected/` also holds +`prior.cov_ed.EE.rds`. This fixture was generated with `mixture_prior.EE.prior.rds`. +If that is not the intended prior for the toy data, regenerate and replace. + +No test currently drives the `mnm` step — `test_mnm_regression` runs +`qtl_dataset_construct+susie_twas` only — so this file is documentation of the +expected shape rather than an asserted comparison. diff --git a/tests/fixtures/mnm_regression/expected/protocol_example.ENSG00000283047.multicontext_bvsr.rds b/tests/fixtures/mnm_regression/expected/protocol_example.ENSG00000283047.multicontext_bvsr.rds new file mode 100644 index 000000000..65db97ce2 Binary files /dev/null and b/tests/fixtures/mnm_regression/expected/protocol_example.ENSG00000283047.multicontext_bvsr.rds differ