From 7f2d0a40d63aeb4a34850de1ae0a2fa96afb8083 Mon Sep 17 00:00:00 2001 From: Julian Bender Date: Wed, 14 Feb 2024 11:29:51 +0100 Subject: [PATCH] Delete .ipynb_checkpoints directory --- .ipynb_checkpoints/01_ap-ms-checkpoint.ipynb | 2546 ------------------ 1 file changed, 2546 deletions(-) delete mode 100644 .ipynb_checkpoints/01_ap-ms-checkpoint.ipynb diff --git a/.ipynb_checkpoints/01_ap-ms-checkpoint.ipynb b/.ipynb_checkpoints/01_ap-ms-checkpoint.ipynb deleted file mode 100644 index 11f39b9..0000000 --- a/.ipynb_checkpoints/01_ap-ms-checkpoint.ipynb +++ /dev/null @@ -1,2546 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "36be508e-7c87-4980-83f0-9bd4028ff7cf", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Loading required package: rrcovNA\n", - "Loading required package: rrcov\n", - "Loading required package: robustbase\n", - "Scalable Robust Estimators with High Breakdown Point (version 1.7-2)\n", - "\n", - "Scalable Robust Estimators with High Breakdown Point for\n", - "Incomplete Data (version 0.4-15)\n", - "\n", - "Loading required package: tidyverse\n", - "── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──\n", - "✔ ggplot2 3.4.0 ✔ purrr 1.0.1\n", - "✔ tibble 3.1.8 ✔ dplyr 1.1.0\n", - "✔ tidyr 1.3.0 ✔ stringr 1.5.0\n", - "✔ readr 2.1.3 ✔ forcats 1.0.0\n", - "── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n", - "✖ dplyr::filter() masks stats::filter()\n", - "✖ dplyr::lag() masks stats::lag()\n", - "Loading required package: BiocManager\n", - "Bioconductor version '3.16' is out-of-date; the current release version '3.18'\n", - " is available with R version '4.3'; see https://bioconductor.org/install\n", - "Loading required package: devtools\n", - "Loading required package: usethis\n", - "\n", - "Attaching package: ‘devtools’\n", - "\n", - "The following object is masked from ‘package:BiocManager’:\n", - "\n", - " install\n", - "\n", - "Loading required package: limma\n", - "Loading required package: vsn\n", - "Loading required package: Biobase\n", - "Loading required package: BiocGenerics\n", - "\n", - "Attaching package: ‘BiocGenerics’\n", - "\n", - "The following object is masked from ‘package:limma’:\n", - "\n", - " plotMA\n", - "\n", - "The following objects are masked from ‘package:dplyr’:\n", - "\n", - " combine, intersect, setdiff, union\n", - "\n", - "The following objects are masked from ‘package:stats’:\n", - "\n", - " IQR, mad, sd, var, xtabs\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " anyDuplicated, aperm, append, as.data.frame, basename, cbind,\n", - " colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,\n", - " get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,\n", - " match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,\n", - " Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,\n", - " table, tapply, union, unique, unsplit, which.max, which.min\n", - "\n", - "Welcome to Bioconductor\n", - "\n", - " Vignettes contain introductory material; view with\n", - " 'browseVignettes()'. 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To cite Bioconductor, see\n", - " 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n", - "\n", - "\n", - "Attaching package: ‘Biobase’\n", - "\n", - "The following object is masked from ‘package:robustbase’:\n", - "\n", - " rowMedians\n", - "\n", - "Loading required package: RankProd\n", - "Loading required package: Rmpfr\n", - "Loading required package: gmp\n", - "\n", - "Attaching package: ‘gmp’\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " %*%, apply, crossprod, matrix, tcrossprod\n", - "\n", - "C code of R package 'Rmpfr': GMP using 64 bits per limb\n", - "\n", - "\n", - "Attaching package: ‘Rmpfr’\n", - "\n", - "The following object is masked from ‘package:gmp’:\n", - "\n", - " outer\n", - "\n", - "The following objects are masked from ‘package:BiocGenerics’:\n", - "\n", - " cbind, pmax, pmin, rbind\n", - "\n", - "The following objects are masked from ‘package:stats’:\n", - "\n", - " dbinom, dgamma, dnbinom, dnorm, dpois, dt, pnorm\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " cbind, pmax, pmin, rbind\n", - "\n", - "Loading required package: pcaMethods\n", - "\n", - "Attaching package: ‘pcaMethods’\n", - "\n", - "The following object is masked from ‘package:stats’:\n", - "\n", - " loadings\n", - "\n", - "Loading required package: impute\n", - "Loading required package: SummarizedExperiment\n", - "Loading required package: MatrixGenerics\n", - "Loading required package: matrixStats\n", - "\n", - "Attaching package: ‘matrixStats’\n", - "\n", - "The following objects are masked from ‘package:Biobase’:\n", - "\n", - " anyMissing, rowMedians\n", - "\n", - "The following object is masked from ‘package:dplyr’:\n", - "\n", - " count\n", - "\n", - "The following objects are masked from ‘package:robustbase’:\n", - "\n", - " colMedians, rowMedians\n", - "\n", - "\n", - "Attaching package: ‘MatrixGenerics’\n", - "\n", - "The following objects are masked from ‘package:matrixStats’:\n", - "\n", - 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"Loading required package: imputation\n", - "Loading required package: DMwR\n", - "Loading required package: lattice\n", - "Loading required package: grid\n", - "Registered S3 method overwritten by 'quantmod':\n", - " method from\n", - " as.zoo.data.frame zoo \n", - "Loading required package: DIMAR\n", - "Loading required package: rrcovNA\n", - "Loading required package: rrcov\n", - "Loading required package: robustbase\n", - "Scalable Robust Estimators with High Breakdown Point (version 1.7-2)\n", - "\n", - "Scalable Robust Estimators with High Breakdown Point for\n", - "Incomplete Data (version 0.4-15)\n", - "\n", - "Loading required package: tidyverse\n", - "── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──\n", - "✔ ggplot2 3.4.0 ✔ purrr 1.0.1\n", - "✔ tibble 3.1.8 ✔ dplyr 1.1.0\n", - "✔ tidyr 1.3.0 ✔ stringr 1.5.0\n", - "✔ readr 2.1.3 ✔ forcats 1.0.0\n", - "── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n", - "✖ dplyr::filter() masks stats::filter()\n", - 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To cite Bioconductor, see\n", - " 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n", - "\n", - "\n", - "Attaching package: ‘Biobase’\n", - "\n", - "The following object is masked from ‘package:robustbase’:\n", - "\n", - " rowMedians\n", - "\n", - "Loading required package: RankProd\n", - "Loading required package: Rmpfr\n", - "Loading required package: gmp\n", - "\n", - "Attaching package: ‘gmp’\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " %*%, apply, crossprod, matrix, tcrossprod\n", - "\n", - "C code of R package 'Rmpfr': GMP using 64 bits per limb\n", - "\n", - "\n", - "Attaching package: ‘Rmpfr’\n", - "\n", - "The following object is masked from ‘package:gmp’:\n", - "\n", - " outer\n", - "\n", - "The following objects are masked from ‘package:BiocGenerics’:\n", - "\n", - " cbind, pmax, pmin, rbind\n", - "\n", - "The following objects are masked from ‘package:stats’:\n", - "\n", - " dbinom, dgamma, dnbinom, dnorm, dpois, dt, pnorm\n", - 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"Loading required package: imputation\n", - "Loading required package: DMwR\n", - "Loading required package: lattice\n", - "Loading required package: grid\n", - "Registered S3 method overwritten by 'quantmod':\n", - " method from\n", - " as.zoo.data.frame zoo \n", - "Loading required package: DIMAR\n", - "Loading required package: rrcovNA\n", - "Loading required package: rrcov\n", - "Loading required package: robustbase\n", - "Scalable Robust Estimators with High Breakdown Point (version 1.7-2)\n", - "\n", - "Scalable Robust Estimators with High Breakdown Point for\n", - "Incomplete Data (version 0.4-15)\n", - "\n", - "Loading required package: tidyverse\n", - "── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──\n", - "✔ ggplot2 3.4.0 ✔ purrr 1.0.1\n", - "✔ tibble 3.1.8 ✔ dplyr 1.1.0\n", - "✔ tidyr 1.3.0 ✔ stringr 1.5.0\n", - "✔ readr 2.1.3 ✔ forcats 1.0.0\n", - "── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n", - "✖ dplyr::filter() masks stats::filter()\n", - "✖ dplyr::lag() masks stats::lag()\n", - "Loading required package: BiocManager\n", - "Bioconductor version '3.16' is out-of-date; the current release version '3.18'\n", - " is available with R version '4.3'; see https://bioconductor.org/install\n", - "Loading required package: devtools\n", - "Loading required package: usethis\n", - "\n", - "Attaching package: ‘devtools’\n", - "\n", - "The following object is masked from ‘package:BiocManager’:\n", - "\n", - " install\n", - "\n", - "Loading required package: limma\n", - "Loading required package: vsn\n", - "Loading required package: Biobase\n", - "Loading required package: BiocGenerics\n", - "\n", - "Attaching package: ‘BiocGenerics’\n", - "\n", - "The following object is masked from ‘package:limma’:\n", - "\n", - " plotMA\n", - "\n", - "The following objects are masked from ‘package:dplyr’:\n", - "\n", - " combine, intersect, setdiff, union\n", - "\n", - "The following objects are masked from ‘package:stats’:\n", - "\n", - " IQR, mad, sd, var, xtabs\n", - 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To cite Bioconductor, see\n", - " 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n", - "\n", - "\n", - "Attaching package: ‘Biobase’\n", - "\n", - "The following object is masked from ‘package:robustbase’:\n", - "\n", - " rowMedians\n", - "\n", - "Loading required package: RankProd\n", - "Loading required package: Rmpfr\n", - "Loading required package: gmp\n", - "\n", - "Attaching package: ‘gmp’\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " %*%, apply, crossprod, matrix, tcrossprod\n", - "\n", - "C code of R package 'Rmpfr': GMP using 64 bits per limb\n", - "\n", - "\n", - "Attaching package: ‘Rmpfr’\n", - "\n", - "The following object is masked from ‘package:gmp’:\n", - "\n", - " outer\n", - "\n", - "The following objects are masked from ‘package:BiocGenerics’:\n", - "\n", - " cbind, pmax, pmin, rbind\n", - "\n", - "The following objects are masked from ‘package:stats’:\n", - "\n", - " dbinom, dgamma, dnbinom, dnorm, dpois, dt, pnorm\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " cbind, pmax, pmin, rbind\n", - "\n", - "Loading required package: pcaMethods\n", - "\n", - "Attaching package: ‘pcaMethods’\n", - "\n", - "The following object is masked from ‘package:stats’:\n", - "\n", - " loadings\n", - "\n", - "Loading required package: impute\n", - "Loading required package: SummarizedExperiment\n", - "Loading required package: MatrixGenerics\n", - "Loading required package: matrixStats\n", - "\n", - "Attaching package: ‘matrixStats’\n", - "\n", - "The following objects are masked from ‘package:Biobase’:\n", - "\n", - " anyMissing, rowMedians\n", - "\n", - "The following object is masked from ‘package:dplyr’:\n", - "\n", - " count\n", - "\n", - "The following objects are masked from ‘package:robustbase’:\n", - "\n", - " colMedians, rowMedians\n", - "\n", - "\n", - "Attaching package: ‘MatrixGenerics’\n", - "\n", - "The following objects are masked from ‘package:matrixStats’:\n", - "\n", - 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"Loading required package: imputation\n", - "Loading required package: DMwR\n", - "Loading required package: lattice\n", - "Loading required package: grid\n", - "Registered S3 method overwritten by 'quantmod':\n", - " method from\n", - " as.zoo.data.frame zoo \n", - "Loading required package: DIMAR\n", - "Loading required package: rrcovNA\n", - "Loading required package: rrcov\n", - "Loading required package: robustbase\n", - "Scalable Robust Estimators with High Breakdown Point (version 1.7-2)\n", - "\n", - "Scalable Robust Estimators with High Breakdown Point for\n", - "Incomplete Data (version 0.4-15)\n", - "\n", - "Loading required package: tidyverse\n", - "── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──\n", - "✔ ggplot2 3.4.0 ✔ purrr 1.0.1\n", - "✔ tibble 3.1.8 ✔ dplyr 1.1.0\n", - "✔ tidyr 1.3.0 ✔ stringr 1.5.0\n", - "✔ readr 2.1.3 ✔ forcats 1.0.0\n", - "── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n", - "✖ dplyr::filter() masks stats::filter()\n", - "✖ dplyr::lag() masks stats::lag()\n", - "Loading required package: BiocManager\n", - "Bioconductor version '3.16' is out-of-date; the current release version '3.18'\n", - " is available with R version '4.3'; see https://bioconductor.org/install\n", - "Loading required package: devtools\n", - "Loading required package: usethis\n", - "\n", - "Attaching package: ‘devtools’\n", - "\n", - "The following object is masked from ‘package:BiocManager’:\n", - "\n", - " install\n", - "\n", - "Loading required package: limma\n", - "Loading required package: vsn\n", - "Loading required package: Biobase\n", - "Loading required package: BiocGenerics\n", - "\n", - "Attaching package: ‘BiocGenerics’\n", - "\n", - "The following object is masked from ‘package:limma’:\n", - "\n", - " plotMA\n", - "\n", - "The following objects are masked from ‘package:dplyr’:\n", - "\n", - " combine, intersect, setdiff, union\n", - "\n", - "The following objects are masked from ‘package:stats’:\n", - "\n", - " IQR, mad, sd, var, xtabs\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " anyDuplicated, aperm, append, as.data.frame, basename, cbind,\n", - " colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,\n", - " get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,\n", - " match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,\n", - " Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,\n", - " table, tapply, union, unique, unsplit, which.max, which.min\n", - "\n", - "Welcome to Bioconductor\n", - "\n", - " Vignettes contain introductory material; view with\n", - " 'browseVignettes()'. To cite Bioconductor, see\n", - " 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n", - "\n", - "\n", - "Attaching package: ‘Biobase’\n", - "\n", - "The following object is masked from ‘package:robustbase’:\n", - "\n", - " rowMedians\n", - "\n", - "Loading required package: RankProd\n", - "Loading required package: Rmpfr\n", - "Loading required package: gmp\n", - "\n", - "Attaching package: ‘gmp’\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " %*%, apply, crossprod, matrix, tcrossprod\n", - "\n", - "C code of R package 'Rmpfr': GMP using 64 bits per limb\n", - "\n", - "\n", - "Attaching package: ‘Rmpfr’\n", - "\n", - "The following object is masked from ‘package:gmp’:\n", - "\n", - " outer\n", - "\n", - "The following objects are masked from ‘package:BiocGenerics’:\n", - "\n", - " cbind, pmax, pmin, rbind\n", - "\n", - "The following objects are masked from ‘package:stats’:\n", - "\n", - " dbinom, dgamma, dnbinom, dnorm, dpois, dt, pnorm\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " cbind, pmax, pmin, rbind\n", - "\n", - "Loading required package: pcaMethods\n", - "\n", - "Attaching package: ‘pcaMethods’\n", - "\n", - "The following object is masked from ‘package:stats’:\n", - "\n", - " loadings\n", - "\n", - "Loading required package: impute\n", - "Loading required package: SummarizedExperiment\n", - "Loading required package: MatrixGenerics\n", - "Loading required package: matrixStats\n", - "\n", - "Attaching package: ‘matrixStats’\n", - "\n", - "The following objects are masked from ‘package:Biobase’:\n", - "\n", - " anyMissing, rowMedians\n", - "\n", - "The following object is masked from ‘package:dplyr’:\n", - "\n", - " count\n", - "\n", - "The following objects are masked from ‘package:robustbase’:\n", - "\n", - " colMedians, rowMedians\n", - "\n", - "\n", - "Attaching package: ‘MatrixGenerics’\n", - "\n", - "The following objects are masked from ‘package:matrixStats’:\n", - "\n", - 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"Loading required package: imputation\n", - "Loading required package: DMwR\n", - "Loading required package: lattice\n", - "Loading required package: grid\n", - "Registered S3 method overwritten by 'quantmod':\n", - " method from\n", - " as.zoo.data.frame zoo \n", - "Loading required package: DIMAR\n", - "Loading required package: rrcovNA\n", - "Loading required package: rrcov\n", - "Loading required package: robustbase\n", - "Scalable Robust Estimators with High Breakdown Point (version 1.7-2)\n", - "\n", - "Scalable Robust Estimators with High Breakdown Point for\n", - "Incomplete Data (version 0.4-15)\n", - "\n", - "Loading required package: tidyverse\n", - "── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──\n", - "✔ ggplot2 3.4.0 ✔ purrr 1.0.1\n", - "✔ tibble 3.1.8 ✔ dplyr 1.1.0\n", - "✔ tidyr 1.3.0 ✔ stringr 1.5.0\n", - "✔ readr 2.1.3 ✔ forcats 1.0.0\n", - "── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n", - "✖ dplyr::filter() masks stats::filter()\n", - "✖ dplyr::lag() masks stats::lag()\n", - "Loading required package: BiocManager\n", - "Bioconductor version '3.16' is out-of-date; the current release version '3.18'\n", - " is available with R version '4.3'; see https://bioconductor.org/install\n", - "Loading required package: devtools\n", - "Loading required package: usethis\n", - "\n", - "Attaching package: ‘devtools’\n", - "\n", - "The following object is masked from ‘package:BiocManager’:\n", - "\n", - " install\n", - "\n", - "Loading required package: limma\n", - "Loading required package: vsn\n", - "Loading required package: Biobase\n", - "Loading required package: BiocGenerics\n", - "\n", - "Attaching package: ‘BiocGenerics’\n", - "\n", - "The following object is masked from ‘package:limma’:\n", - "\n", - " plotMA\n", - "\n", - "The following objects are masked from ‘package:dplyr’:\n", - "\n", - " combine, intersect, setdiff, union\n", - "\n", - "The following objects are masked from ‘package:stats’:\n", - "\n", - " IQR, mad, sd, var, xtabs\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " anyDuplicated, aperm, append, as.data.frame, basename, cbind,\n", - " colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,\n", - " get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,\n", - " match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,\n", - " Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,\n", - " table, tapply, union, unique, unsplit, which.max, which.min\n", - "\n", - "Welcome to Bioconductor\n", - "\n", - " Vignettes contain introductory material; view with\n", - " 'browseVignettes()'. To cite Bioconductor, see\n", - " 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n", - "\n", - "\n", - "Attaching package: ‘Biobase’\n", - "\n", - "The following object is masked from ‘package:robustbase’:\n", - "\n", - " rowMedians\n", - "\n", - "Loading required package: RankProd\n", - "Loading required package: Rmpfr\n", - "Loading required package: gmp\n", - "\n", - "Attaching package: ‘gmp’\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " %*%, apply, crossprod, matrix, tcrossprod\n", - "\n", - "C code of R package 'Rmpfr': GMP using 64 bits per limb\n", - "\n", - "\n", - "Attaching package: ‘Rmpfr’\n", - "\n", - "The following object is masked from ‘package:gmp’:\n", - "\n", - " outer\n", - "\n", - "The following objects are masked from ‘package:BiocGenerics’:\n", - "\n", - " cbind, pmax, pmin, rbind\n", - "\n", - "The following objects are masked from ‘package:stats’:\n", - "\n", - " dbinom, dgamma, dnbinom, dnorm, dpois, dt, pnorm\n", - 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"\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " anyDuplicated, aperm, append, as.data.frame, basename, cbind,\n", - " colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,\n", - " get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,\n", - " match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,\n", - " Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,\n", - " table, tapply, union, unique, unsplit, which.max, which.min\n", - "\n", - "Welcome to Bioconductor\n", - "\n", - " Vignettes contain introductory material; view with\n", - " 'browseVignettes()'. To cite Bioconductor, see\n", - " 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n", - "\n", - "\n", - "Attaching package: ‘Biobase’\n", - "\n", - "The following object is masked from ‘package:robustbase’:\n", - "\n", - " rowMedians\n", - "\n", - "Loading required package: RankProd\n", - "Loading required package: Rmpfr\n", - "Loading required package: gmp\n", - "\n", - "Attaching package: ‘gmp’\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " %*%, apply, crossprod, matrix, tcrossprod\n", - "\n", - "C code of R package 'Rmpfr': GMP using 64 bits per limb\n", - "\n", - "\n", - "Attaching package: ‘Rmpfr’\n", - "\n", - "The following object is masked from ‘package:gmp’:\n", - "\n", - " outer\n", - "\n", - "The following objects are masked from ‘package:BiocGenerics’:\n", - "\n", - " cbind, pmax, pmin, rbind\n", - "\n", - "The following objects are masked from ‘package:stats’:\n", - "\n", - " dbinom, dgamma, dnbinom, dnorm, dpois, dt, pnorm\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " cbind, pmax, pmin, rbind\n", - "\n", - "Loading required package: pcaMethods\n", - "\n", - "Attaching package: ‘pcaMethods’\n", - "\n", - "The following object is masked from ‘package:stats’:\n", - "\n", - " loadings\n", - "\n", - "Loading required package: impute\n", - "Loading required package: SummarizedExperiment\n", - "Loading required package: MatrixGenerics\n", - "Loading required package: matrixStats\n", - "\n", - "Attaching package: ‘matrixStats’\n", - "\n", - "The following objects are masked from ‘package:Biobase’:\n", - "\n", - " anyMissing, rowMedians\n", - "\n", - "The following object is masked from ‘package:dplyr’:\n", - "\n", - " count\n", - "\n", - "The following objects are masked from ‘package:robustbase’:\n", - "\n", - " colMedians, rowMedians\n", - "\n", - "\n", - "Attaching package: ‘MatrixGenerics’\n", - "\n", - "The following objects are masked from 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rowWeightedMedians,\n", - " rowWeightedSds, rowWeightedVars\n", - "\n", - "The following object is masked from ‘package:Biobase’:\n", - "\n", - " rowMedians\n", - "\n", - "The following objects are masked from ‘package:robustbase’:\n", - "\n", - " colMedians, rowMedians\n", - "\n", - "Loading required package: GenomicRanges\n", - "Loading required package: stats4\n", - "Loading required package: S4Vectors\n", - "\n", - "Attaching package: ‘S4Vectors’\n", - "\n", - "The following objects are masked from ‘package:dplyr’:\n", - "\n", - " first, rename\n", - "\n", - "The following object is masked from ‘package:tidyr’:\n", - "\n", - " expand\n", - "\n", - "The following objects are masked from ‘package:base’:\n", - "\n", - " expand.grid, I, unname\n", - "\n", - "Loading required package: IRanges\n", - "\n", - "Attaching package: ‘IRanges’\n", - "\n", - "The following objects are masked from ‘package:dplyr’:\n", - "\n", - " collapse, desc, slice\n", - "\n", - "The following object is masked from ‘package:purrr’:\n", - "\n", - " reduce\n", - "\n", - "Loading required package: GenomeInfoDb\n", - "Loading required package: imputation\n", - "Loading required package: DMwR\n", - "Loading required package: lattice\n", - "Loading required package: grid\n", - "Registered S3 method overwritten by 'quantmod':\n", - " method from\n", - " as.zoo.data.frame zoo \n", - "Loading required package: DIMAR\n" - ] - } - ], - "source": [ - "import re\n", - "\n", - "import matplotlib.pyplot as plt\n", - "from autoprot_dev import analysis as ana\n", - "from autoprot_dev import preprocessing as pp\n", - "from autoprot_dev import visualization as vis" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "f0d36ffa-e8f2-4e9f-ae9b-2a21532386ae", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2307 rows before filter operation.\n", - "2307 rows after filter operation.\n" - ] - } - ], - "source": [ - "pg = pp.read_csv(\"../MitoCop_q-AP-MS_proteinGroups.txt\")\n", - "\n", - "pg = pp.cleaning(pg, file=\"proteinGroups.txt\")\n", - "\n", - "pg[\"Gene names first\"] = pg[\"Gene names\"].str.split(\";\").str[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "71ee3863-ec3a-4a71-b74a-8e5d2c058673", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Ratio H/L TMEM256_1',\n", - " 'Ratio H/L TMEM256_2',\n", - " 'Ratio H/L TMEM256_3',\n", - " 'Ratio H/L TMEM256_4']" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ratio_cols = pg.filter(regex=\"Ratio H/L TMEM\").columns.tolist()\n", - "ratio_cols" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "9775584f-6b06-46d8-89d4-e2f85aa39f1d", - "metadata": {}, - "outputs": [], - "source": [ - "pg, log_ratio_cols = pp.log(pg, ratio_cols, invert=(1, 1, -1, -1), return_cols=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "2d6da775-b7e7-4e9c-a5f8-17ed7076fb10", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(nrows=1, ncols=4, figsize=(14, 3), sharey=True, sharex=True)\n", - "\n", - "for ax, ratio in zip(axs, log_ratio_cols):\n", - " pg.plot(\n", - " kind=\"scatter\",\n", - " ax=ax,\n", - " x=ratio,\n", - " y=\"Intensity\",\n", - " color=\"grey\",\n", - " logy=True,\n", - " )\n", - " pg[pg[\"Gene names\"].str.contains(\"TMEM256\", na=False)].plot(\n", - " kind=\"scatter\", ax=ax, x=ratio, y=\"Intensity\", color=\"red\", logy=True\n", - " )\n", - "\n", - " pg[pg[\"Gene names\"].str.contains(\"TIMM50\", na=False)].plot(\n", - " kind=\"scatter\", ax=ax, x=ratio, y=\"Intensity\", color=\"blue\", logy=True\n", - " )\n", - "\n", - " ymin, ymax = ax.get_ylim()\n", - " ax.vlines(0, ymin, ymax)\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(\"apms_fig1.pdf\")" - ] - }, - { - "cell_type": "markdown", - "id": "72a2508e-b5a6-4736-ae6e-3d8dcf9f5143", - "metadata": {}, - "source": [ - "## Normalisation\n", - "### Median normalisation" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "0f47d3b6-75ee-4017-926d-2e0e396db03e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['log2_Ratio H/L TMEM256_1_mnorm',\n", - " 'log2_Ratio H/L TMEM256_2_mnorm',\n", - " 'log2_Ratio L/H TMEM256_3_mnorm',\n", - " 'log2_Ratio L/H TMEM256_4_mnorm']" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# median normalisation\n", - "mnorm_cols = []\n", - "for col in log_ratio_cols:\n", - " median_shift = pg[col].median() - pg[log_ratio_cols].median().median()\n", - " new_col = col + \"_mnorm\"\n", - " mnorm_cols.append(new_col)\n", - " pg[new_col] = pg[col] - median_shift\n", - "\n", - "mnorm_cols" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "42e56862-b6ab-4721-a15f-05fa2b5e699d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(7, 3), sharex=True, sharey=True)\n", - "\n", - "pg[log_ratio_cols].plot(kind=\"kde\", ax=axs[0])\n", - "pg[mnorm_cols].plot(kind=\"kde\", ax=axs[1])\n", - "\n", - "# shorten labels\n", - "for ax in axs:\n", - " handles, labels = ax.get_legend_handles_labels()\n", - " labels = [x.split(\" \")[-1] for x in labels]\n", - "\n", - " ax.legend(handles, labels)\n", - " ax.set_xlabel(\"Log2 ratio TMEM256-FLAG/control\")\n", - " ax.set_xlim((-5, 5))\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(\"apms_fig2.pdf\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "5b7aaeef-46b2-4b5e-9f13-845a8182779e", - "metadata": {}, - "outputs": [], - "source": [ - "# LOESS normalisation\n", - "pg, loess_cols = pp.cyclic_loess(pg, mnorm_cols, return_cols=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "8b8301ad-28b1-489e-8b2c-fd72a6ecfc9e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(7, 3), sharex=True, sharey=True)\n", - "pg[mnorm_cols].plot(kind=\"kde\", ax=axs[0])\n", - "pg[loess_cols].plot(kind=\"kde\", ax=axs[1])\n", - "\n", - "# shorten labels\n", - "for ax in axs:\n", - " handles, labels = ax.get_legend_handles_labels()\n", - " labels = [re.split(\" |\\.\", x)[-1] for x in labels]\n", - "\n", - " ax.legend(handles, labels)\n", - " ax.set_xlabel(\"Log10 ratio TMEM256-FLAG/control\")\n", - " ax.set_xlim((-5, 5))\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig(\"apms_fig3.pdf\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "61092645-f31a-4348-8b43-bec58e0e63b0", - "metadata": {}, - "source": [ - "## Statistical analysis\n", - "### Linear models\n", - "In contrast to conventional t-tests, linear models take global patterns into account and can include information for example about label-switches." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8815059-ea59-437c-9f30-3b5fdb6bfbcd", - "metadata": {}, - "outputs": [], - "source": [ - "pg = ana.limma(pg, loess_cols, cond=\"_limma\")" - ] - }, - { - "cell_type": "markdown", - "id": "db1e6dc8-d2c4-4858-b98c-42a327f833bc", - "metadata": {}, - "source": [ - "## Visualisation\n", - "### Volcano plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6b689511-00fb-4658-99d1-60c15c607527", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))\n", - "\n", - "oxphos = [\"COX6\", \"COX5B\", \"ATP5\", \"COX4\", \"SCO2\", \"MT-CO2\", \"COA3\", \"NDUF\"]\n", - "highlight_oxphos = pg[pg[\"Gene names\"].str.contains(\"|\".join(oxphos), na=False)].index\n", - "\n", - "protein_import = [\"TIMM\"]\n", - "highlight_import = pg[\n", - " pg[\"Gene names\"].str.contains(\"|\".join(protein_import), na=False)\n", - "].index\n", - "\n", - "kwargs_highlight = [{\"color\": \"orange\"}, {\"color\": \"blue\"}]\n", - "\n", - "vis.volcano(\n", - " pg,\n", - " log_fc_colname=\"logFC_limma\",\n", - " p_colname=\"adj.P.Val_limma\",\n", - " log_fc_thresh=2,\n", - " annotate=None,\n", - " kwargs_both_sig={\"color\": \"lightgrey\"},\n", - " kwargs_p_sig={\"color\": \"lightgrey\"},\n", - " kwargs_log_fc_sig={\"color\": \"lightgrey\"},\n", - " show_legend=False,\n", - " highlight=[highlight_oxphos, highlight_import],\n", - " kwargs_highlight=kwargs_highlight,\n", - " ax=axs[0],\n", - " show_caption=False,\n", - " title=\"TMEM256-FLAG\",\n", - ")\n", - "\n", - "zoom = pg[(pg[\"logFC_limma\"] > 0) & (pg[\"adj.P.Val_limma\"] < 0.05)]\n", - "\n", - "highlight_oxphos = zoom[\n", - " zoom[\"Gene names\"].str.contains(\"|\".join(oxphos), na=False)\n", - "].index\n", - "\n", - "highlight_import = zoom[\n", - " zoom[\"Gene names\"].str.contains(\"|\".join(protein_import), na=False)\n", - "].index\n", - "\n", - "\n", - "vis.volcano(\n", - " zoom,\n", - " log_fc_colname=\"logFC_limma\",\n", - " p_colname=\"adj.P.Val_limma\",\n", - " log_fc_thresh=2,\n", - " annotate=\"highlight\",\n", - " kwargs_both_sig={\"color\": \"lightgrey\"},\n", - " kwargs_p_sig={\"color\": \"lightgrey\"},\n", - " kwargs_log_fc_sig={\"color\": \"lightgrey\"},\n", - " show_legend=False,\n", - " highlight=[highlight_oxphos, highlight_import],\n", - " kwargs_highlight=kwargs_highlight,\n", - " ax=axs[1],\n", - " show_caption=False,\n", - " annotate_colname=\"Gene names first\",\n", - " title=\"TMEM256-FLAG (zoom)\",\n", - ")\n", - "\n", - "axs[1].set_xlim(0, 5)\n", - "\n", - "plt.tight_layout()\n", - "\n", - "plt.savefig(\"apms_fig4.pdf\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "275c04e3-a4af-4f2f-a6d9-88897616fe4a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3 (autoprot)", - "language": "python", - "name": "conda-env-autoprot-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}