update software/paper links in docs

biona001 · Mar 4, 2024 · 943ed9e · 943ed9e
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diff --git a/docs/src/man/download.ipynb b/docs/src/man/download.ipynb
@@ -10,9 +10,9 @@
     "\n",
     "## Software\n",
     "\n",
-    "| Operating System | v0.1.0 (Pre-release, 19 Feb, 2024)      |\n",
+    "| Operating System | v0.1.1 (March 4th, 2024)      |\n",
     "| :---             |       :----:               |\n",
-    "| Linux 64-bit     | [Download](https://github.com/biona001/GhostKnockoffGWAS/releases/tag/v0.1.0)       |\n",
+    "| Linux 64-bit     | [Download](https://github.com/biona001/GhostKnockoffGWAS/releases/tag/v0.1.1)       |\n",
     "\n",
     "After unzipping, the executable will be located inside `bin/GhostKnockoffGWAS`. We recommend adding the folder containing the `GhostKnockoffGWAS` executable to `PATH` for easier access."
    ]

diff --git a/docs/src/man/download.md b/docs/src/man/download.md
@@ -5,9 +5,9 @@ Here is the main downloads page. New software and pre-processed knockoff data wi
 
 ## Software
 
-| Operating System | v0.1.0 (Pre-release, 19 Feb, 2024)      |
+| Operating System | v0.1.1 (March 4th, 2024)      |
 | :---             |       :----:               |
-| Linux 64-bit     | [Download](https://github.com/biona001/GhostKnockoffGWAS/releases/tag/v0.1.0)       |
+| Linux 64-bit     | [Download](https://github.com/biona001/GhostKnockoffGWAS/releases/tag/v0.1.1)       |
 
 After unzipping, the executable will be located inside `bin/GhostKnockoffGWAS`. We recommend adding the folder containing the `GhostKnockoffGWAS` executable to `PATH` for easier access.
 

diff --git a/docs/src/man/intro.ipynb b/docs/src/man/intro.ipynb
@@ -8,11 +8,11 @@
     "\n",
     "This package conducts knockoff-based inference to perform genome-wide conditional independent tests based on GWAS summary statistics. The methodology is described in the following papers\n",
     "\n",
-    "> Chen Z, He Z, Chu BB, Gu J, Morrison T, Sabatti C, Candes C. \"Controlled Variable Selection from Summary Statistics Only? A Solution via GhostKnockoffs and Penalized Regression\", arXiv preprint arXiv:2402.12724 (2024).\n",
+    "> Chen Z, He Z, Chu BB, Gu J, Morrison T, Sabatti C, Candes C. \"Controlled Variable Selection from Summary Statistics Only? A Solution via GhostKnockoffs and Penalized Regression\", arXiv preprint arXiv:2402.12724 (2024); doi: [https://doi.org/10.48550/arXiv.2402.12724](https://doi.org/10.48550/arXiv.2402.12724)\n",
     "\n",
-    "> Chu BB, Gu J, Chen Z, Morrison T, Candes E, He Z, Sabatti C. (2023). Second-order group knockoffs with applications to GWAS. arXiv preprint arXiv:2310.15069.\n",
+    "> Chu BB, Gu J, Chen Z, Morrison T, Candes E, He Z, Sabatti C. (2023). Second-order group knockoffs with applications to GWAS. arXiv preprint arXiv:2310.15069; doi: [https://doi.org/10.48550/arXiv.2310.15069](https://doi.org/10.48550/arXiv.2310.15069)\n",
     "\n",
-    "> He Z, Chu BB, Yang J, Gu J, Chen Z, Liu L, Morrison T, Bellow M, Qi X, Hejazi N, Mathur M, Le Guen Y, Tang H, Hastie T, Ionita-laza I, Sabatti C, Candes C. \"In silico identification of putative causal genetic variants\", bioRxiv 2024. \n",
+    "> He Z, Chu BB, Yang J, Gu J, Chen Z, Liu L, Morrison T, Bellow M, Qi X, Hejazi N, Mathur M, Le Guen Y, Tang H, Hastie T, Ionita-laza I, Sabatti C, Candes C. \"In silico identification of putative causal genetic variants\", bioRxiv, 2024.02.28.582621; doi: [https://doi.org/10.1101/2024.02.28.582621](https://doi.org/10.1101/2024.02.28.582621)\n",
     "\n",
     "The main working assumption is that we do not have access to individual level genotype or phenotype data. Rather, for each SNP, we have its Z-scores with respect to some phenotype from a GWAS, and access to LD (linkage disequilibrium) data. The user is expected supply the Z-scores, while we supply the LD data in addition to some pre-computed knockoff data."
    ]

diff --git a/docs/src/man/intro.md b/docs/src/man/intro.md
@@ -3,11 +3,11 @@
 
 This package conducts knockoff-based inference to perform genome-wide conditional independent tests based on GWAS summary statistics. The methodology is described in the following papers
 
-> Chen Z, He Z, Chu BB, Gu J, Morrison T, Sabatti C, Candes C. "Controlled Variable Selection from Summary Statistics Only? A Solution via GhostKnockoffs and Penalized Regression", arXiv preprint arXiv:2402.12724 (2024).
+> Chen Z, He Z, Chu BB, Gu J, Morrison T, Sabatti C, Candes C. "Controlled Variable Selection from Summary Statistics Only? A Solution via GhostKnockoffs and Penalized Regression", arXiv preprint arXiv:2402.12724 (2024); doi: [https://doi.org/10.48550/arXiv.2402.12724](https://doi.org/10.48550/arXiv.2402.12724)
 
-> Chu BB, Gu J, Chen Z, Morrison T, Candes E, He Z, Sabatti C. (2023). Second-order group knockoffs with applications to GWAS. arXiv preprint arXiv:2310.15069.
+> Chu BB, Gu J, Chen Z, Morrison T, Candes E, He Z, Sabatti C. (2023). Second-order group knockoffs with applications to GWAS. arXiv preprint arXiv:2310.15069; doi: [https://doi.org/10.48550/arXiv.2310.15069](https://doi.org/10.48550/arXiv.2310.15069)
 
-> He Z, Chu BB, Yang J, Gu J, Chen Z, Liu L, Morrison T, Bellow M, Qi X, Hejazi N, Mathur M, Le Guen Y, Tang H, Hastie T, Ionita-laza I, Sabatti C, Candes C. "In silico identification of putative causal genetic variants", bioRxiv 2024. 
+> He Z, Chu BB, Yang J, Gu J, Chen Z, Liu L, Morrison T, Bellow M, Qi X, Hejazi N, Mathur M, Le Guen Y, Tang H, Hastie T, Ionita-laza I, Sabatti C, Candes C. "In silico identification of putative causal genetic variants", bioRxiv, 2024.02.28.582621; doi: [https://doi.org/10.1101/2024.02.28.582621](https://doi.org/10.1101/2024.02.28.582621)
 
 The main working assumption is that we do not have access to individual level genotype or phenotype data. Rather, for each SNP, we have its Z-scores with respect to some phenotype from a GWAS, and access to LD (linkage disequilibrium) data. The user is expected supply the Z-scores, while we supply the LD data in addition to some pre-computed knockoff data.