diff --git a/docs/BIOI611_ballgown_example.ipynb b/docs/BIOI611_ballgown_example.ipynb new file mode 100644 index 0000000..530c170 --- /dev/null +++ b/docs/BIOI611_ballgown_example.ipynb @@ -0,0 +1,966 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyNhEQhkadrIADvnmQBtwLVn", + "include_colab_link": true + }, + "kernelspec": { + "name": "ir", + "display_name": "R" + }, + "language_info": { + "name": "R" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Isoform-level differential expression analysis with Ballgown.\n", + "\n", + "## Install R package" + ], + "metadata": { + "id": "RMUCWScmzm2X" + } + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "A7daQk72zNU3", + "outputId": "b6e6dc0c-c076-40f1-f940-d1aed6dae4f3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "'getOption(\"repos\")' replaces Bioconductor standard repositories, see\n", + "'help(\"repositories\", package = \"BiocManager\")' for details.\n", + "Replacement repositories:\n", + " CRAN: https://cran.rstudio.com\n", + "\n", + "Bioconductor version 3.19 (BiocManager 1.30.25), R 4.4.1 (2024-06-14)\n", + "\n", + "Warning message:\n", + "“package(s) not installed when version(s) same as or greater than current; use\n", + " `force = TRUE` to re-install: 'ballgown'”\n", + "Old packages: 'Matrix'\n", + "\n" + ] + } + ], + "source": [ + "if (!require(\"BiocManager\", quietly = TRUE))\n", + " install.packages(\"BiocManager\")\n", + "\n", + "BiocManager::install(\"ballgown\")" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Understand the folder with example data" + ], + "metadata": { + "id": "t5x16Lk3zmnM" + } + }, + { + "cell_type": "code", + "source": [ + "library(ballgown)\n", + "data_directory = system.file('extdata', package='ballgown') # automatically finds ballgown's installation directory\n", + "# examine data_directory:\n", + "data_directory" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 196 + }, + "id": "9DrZ1H-QzS8Q", + "outputId": "695d140d-e481-443f-a990-13034d8a013c" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n", + "Attaching package: ‘ballgown’\n", + "\n", + "\n", + "The following object is masked from ‘package:base’:\n", + "\n", + " structure\n", + "\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "'/usr/local/lib/R/site-library/ballgown/extdata'" + ], + "text/markdown": "'/usr/local/lib/R/site-library/ballgown/extdata'", + "text/latex": "'/usr/local/lib/R/site-library/ballgown/extdata'", + "text/plain": [ + "[1] \"/usr/local/lib/R/site-library/ballgown/extdata\"" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "list.files(data_directory)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "id": "kIQyPsB4UJ6w", + "outputId": "6acff2f6-a060-4ea1-a3e2-d9d6ff5dba99" + }, + "execution_count": 48, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "
  1. 'annot.gtf.gz'
  2. 'hg19_genes_small.gtf.gz'
  3. 'sample01'
  4. 'sample02'
  5. 'sample03'
  6. 'sample04'
  7. 'sample05'
  8. 'sample06'
  9. 'sample07'
  10. 'sample08'
  11. 'sample09'
  12. 'sample10'
  13. 'sample11'
  14. 'sample12'
  15. 'sample13'
  16. 'sample14'
  17. 'sample15'
  18. 'sample16'
  19. 'sample17'
  20. 'sample18'
  21. 'sample19'
  22. 'sample20'
  23. 'tiny.genes.results.gz'
  24. 'tiny.isoforms.results.gz'
  25. 'tiny2.genes.results.gz'
  26. 'tiny2.isoforms.results.gz'
\n" + ], + "text/markdown": "1. 'annot.gtf.gz'\n2. 'hg19_genes_small.gtf.gz'\n3. 'sample01'\n4. 'sample02'\n5. 'sample03'\n6. 'sample04'\n7. 'sample05'\n8. 'sample06'\n9. 'sample07'\n10. 'sample08'\n11. 'sample09'\n12. 'sample10'\n13. 'sample11'\n14. 'sample12'\n15. 'sample13'\n16. 'sample14'\n17. 'sample15'\n18. 'sample16'\n19. 'sample17'\n20. 'sample18'\n21. 'sample19'\n22. 'sample20'\n23. 'tiny.genes.results.gz'\n24. 'tiny.isoforms.results.gz'\n25. 'tiny2.genes.results.gz'\n26. 'tiny2.isoforms.results.gz'\n\n\n", + "text/latex": "\\begin{enumerate*}\n\\item 'annot.gtf.gz'\n\\item 'hg19\\_genes\\_small.gtf.gz'\n\\item 'sample01'\n\\item 'sample02'\n\\item 'sample03'\n\\item 'sample04'\n\\item 'sample05'\n\\item 'sample06'\n\\item 'sample07'\n\\item 'sample08'\n\\item 'sample09'\n\\item 'sample10'\n\\item 'sample11'\n\\item 'sample12'\n\\item 'sample13'\n\\item 'sample14'\n\\item 'sample15'\n\\item 'sample16'\n\\item 'sample17'\n\\item 'sample18'\n\\item 'sample19'\n\\item 'sample20'\n\\item 'tiny.genes.results.gz'\n\\item 'tiny.isoforms.results.gz'\n\\item 'tiny2.genes.results.gz'\n\\item 'tiny2.isoforms.results.gz'\n\\end{enumerate*}\n", + "text/plain": [ + " [1] \"annot.gtf.gz\" \"hg19_genes_small.gtf.gz\" \n", + " [3] \"sample01\" \"sample02\" \n", + " [5] \"sample03\" \"sample04\" \n", + " [7] \"sample05\" \"sample06\" \n", + " [9] \"sample07\" \"sample08\" \n", + "[11] \"sample09\" \"sample10\" \n", + "[13] \"sample11\" \"sample12\" \n", + "[15] \"sample13\" \"sample14\" \n", + "[17] \"sample15\" \"sample16\" \n", + "[19] \"sample17\" \"sample18\" \n", + "[21] \"sample19\" \"sample20\" \n", + "[23] \"tiny.genes.results.gz\" \"tiny.isoforms.results.gz\" \n", + "[25] \"tiny2.genes.results.gz\" \"tiny2.isoforms.results.gz\"" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# make the ballgown object:\n", + "bg = ballgown(dataDir=data_directory, samplePattern='sample', meas='all')\n", + "bg" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 394 + }, + "id": "8kZKnJx9K_fW", + "outputId": "7c12e692-5d83-4798-cead-48bd7b286c50" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Mon Oct 21 17:42:05 2024\n", + "\n", + "Mon Oct 21 17:42:05 2024: Reading linking tables\n", + "\n", + "Mon Oct 21 17:42:05 2024: Reading intron data files\n", + "\n", + "Mon Oct 21 17:42:05 2024: Merging intron data\n", + "\n", + "Mon Oct 21 17:42:05 2024: Reading exon data files\n", + "\n", + "Mon Oct 21 17:42:05 2024: Merging exon data\n", + "\n", + "Mon Oct 21 17:42:05 2024: Reading transcript data files\n", + "\n", + "Mon Oct 21 17:42:05 2024: Merging transcript data\n", + "\n", + "Wrapping up the results\n", + "\n", + "Mon Oct 21 17:42:05 2024\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "ballgown instance with 100 transcripts and 20 samples" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "fbRD0wZmLSBE" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Accessing assembly data\n", + "\n", + "A ballgown object has six slots: structure, expr, indexes, dirs, mergedDate, and meas.\n", + "\n", + "Exon, intron, and transcript structures are easily extracted from the main ballgown object:\n", + "\n" + ], + "metadata": { + "id": "LoxHcyeMLShS" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Structure" + ], + "metadata": { + "id": "8v3d-A0kUeCG" + } + }, + { + "cell_type": "code", + "source": [ + "structure(bg)$exon\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "id": "Nmmo_3JtLIzJ", + "outputId": "5108c2ca-5045-4a3c-fa90-2e4ab481f05e" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "GRanges object with 633 ranges and 2 metadata columns:\n", + " seqnames ranges strand | id transcripts\n", + " | \n", + " [1] 18 24412069-24412331 * | 12 10\n", + " [2] 22 17308271-17308950 + | 55 25\n", + " [3] 22 17309432-17310226 + | 56 25\n", + " [4] 22 18121428-18121652 + | 88 35\n", + " [5] 22 18138428-18138598 + | 89 35\n", + " ... ... ... ... . ... ...\n", + " [629] 22 51221929-51222113 - | 3777 1294\n", + " [630] 22 51221319-51221473 - | 3782 1297\n", + " [631] 22 51221929-51222162 - | 3783 1297\n", + " [632] 22 51221929-51222168 - | 3784 1301\n", + " [633] 6 31248149-31248334 * | 3794 1312\n", + " -------\n", + " seqinfo: 3 sequences from an unspecified genome; no seqlengths" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "structure(bg)$intron\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "id": "uNZtgMU4LPYU", + "outputId": "930e3312-5950-402c-fce0-7fe6269b0386" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "GRanges object with 536 ranges and 2 metadata columns:\n", + " seqnames ranges strand | id transcripts\n", + " | \n", + " [1] 22 17308951-17309431 + | 33 25\n", + " [2] 22 18121653-18138427 + | 57 35\n", + " [3] 22 18138599-18185008 + | 58 35\n", + " [4] 22 18185153-18209442 + | 59 35\n", + " [5] 22 18385514-18387397 - | 72 41\n", + " ... ... ... ... . ... ...\n", + " [532] 22 51216410-51220615 - | 2750 c(1294, 1297, 1301)\n", + " [533] 22 51220776-51221928 - | 2756 1294\n", + " [534] 22 51220780-51221318 - | 2757 1297\n", + " [535] 22 51221474-51221928 - | 2758 1297\n", + " [536] 22 51220780-51221928 - | 2759 1301\n", + " -------\n", + " seqinfo: 1 sequence from an unspecified genome; no seqlengths" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "structure(bg)$trans\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 574 + }, + "id": "YYB-5qdPLdB3", + "outputId": "44622413-2290-4a89-c773-73bbdd41e64c" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "GRangesList object of length 100:\n", + "$`10`\n", + "GRanges object with 1 range and 2 metadata columns:\n", + " seqnames ranges strand | id transcripts\n", + " | \n", + " [1] 18 24412069-24412331 * | 12 10\n", + " -------\n", + " seqinfo: 3 sequences from an unspecified genome; no seqlengths\n", + "\n", + "$`25`\n", + "GRanges object with 2 ranges and 2 metadata columns:\n", + " seqnames ranges strand | id transcripts\n", + " | \n", + " [1] 22 17308271-17308950 + | 55 25\n", + " [2] 22 17309432-17310226 + | 56 25\n", + " -------\n", + " seqinfo: 3 sequences from an unspecified genome; no seqlengths\n", + "\n", + "$`35`\n", + "GRanges object with 4 ranges and 2 metadata columns:\n", + " seqnames ranges strand | id transcripts\n", + " | \n", + " [1] 22 18121428-18121652 + | 88 35\n", + " [2] 22 18138428-18138598 + | 89 35\n", + " [3] 22 18185009-18185152 + | 90 35\n", + " [4] 22 18209443-18212080 + | 91 35\n", + " -------\n", + " seqinfo: 3 sequences from an unspecified genome; no seqlengths\n", + "\n", + "...\n", + "<97 more elements>" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### expr\n", + "\n", + "\n", + "The expr slot is a list that contains tables of expression data for the genomic features. These tables are very similar to the *_data.ctab Tablemaker output files. Ballgown implements the following syntax to access components of the expr slot:\n", + "\n", + "\n", + "```\n", + "*expr(ballgown_object_name, )\n", + "```\n", + "\n" + ], + "metadata": { + "id": "lPclFN0bLkfG" + } + }, + { + "cell_type": "markdown", + "source": [ + "where * is either e for exon, i for intron, t for transcript, or g for gene, and is an expression-measurement column name from the appropriate .ctab file. Gene-level measurements are calculated by aggregating the transcript-level measurements for that gene. All of the following are valid ways to extract expression data from the bg ballgown object:\n", + "\n" + ], + "metadata": { + "id": "UpCz1bRMLva1" + } + }, + { + "cell_type": "code", + "source": [ + "transcript_fpkm = texpr(bg, 'FPKM')\n", + "transcript_cov = texpr(bg, 'cov')\n", + "whole_tx_table = texpr(bg, 'all')\n", + "exon_mcov = eexpr(bg, 'mcov')\n", + "junction_rcount = iexpr(bg)\n", + "whole_intron_table = iexpr(bg, 'all')\n", + "gene_expression = gexpr(bg)" + ], + "metadata": { + "id": "3deNAJLpLsJj" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Indexes" + ], + "metadata": { + "id": "5POFGw2GMeHc" + } + }, + { + "cell_type": "code", + "source": [ + "pData(bg) = data.frame(id=sampleNames(bg), group=rep(c(1,0), each=10))\n" + ], + "metadata": { + "id": "mrmdJa2HMdhk" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "pData(bg)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 725 + }, + "id": "wyoZEPA5Mm0A", + "outputId": "18d383b6-86cc-4283-aaad-53ef67dd3d4d" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 20 × 2
idgroup
<chr><dbl>
sample011
sample021
sample031
sample041
sample051
sample061
sample071
sample081
sample091
sample101
sample110
sample120
sample130
sample140
sample150
sample160
sample170
sample180
sample190
sample200
\n" + ], + "text/markdown": "\nA data.frame: 20 × 2\n\n| id <chr> | group <dbl> |\n|---|---|\n| sample01 | 1 |\n| sample02 | 1 |\n| sample03 | 1 |\n| sample04 | 1 |\n| sample05 | 1 |\n| sample06 | 1 |\n| sample07 | 1 |\n| sample08 | 1 |\n| sample09 | 1 |\n| sample10 | 1 |\n| sample11 | 0 |\n| sample12 | 0 |\n| sample13 | 0 |\n| sample14 | 0 |\n| sample15 | 0 |\n| sample16 | 0 |\n| sample17 | 0 |\n| sample18 | 0 |\n| sample19 | 0 |\n| sample20 | 0 |\n\n", + "text/latex": "A data.frame: 20 × 2\n\\begin{tabular}{ll}\n id & group\\\\\n & \\\\\n\\hline\n\t sample01 & 1\\\\\n\t sample02 & 1\\\\\n\t sample03 & 1\\\\\n\t sample04 & 1\\\\\n\t sample05 & 1\\\\\n\t sample06 & 1\\\\\n\t sample07 & 1\\\\\n\t sample08 & 1\\\\\n\t sample09 & 1\\\\\n\t sample10 & 1\\\\\n\t sample11 & 0\\\\\n\t sample12 & 0\\\\\n\t sample13 & 0\\\\\n\t sample14 & 0\\\\\n\t sample15 & 0\\\\\n\t sample16 & 0\\\\\n\t sample17 & 0\\\\\n\t sample18 & 0\\\\\n\t sample19 & 0\\\\\n\t sample20 & 0\\\\\n\\end{tabular}\n", + "text/plain": [ + " id group\n", + "1 sample01 1 \n", + "2 sample02 1 \n", + "3 sample03 1 \n", + "4 sample04 1 \n", + "5 sample05 1 \n", + "6 sample06 1 \n", + "7 sample07 1 \n", + "8 sample08 1 \n", + "9 sample09 1 \n", + "10 sample10 1 \n", + "11 sample11 0 \n", + "12 sample12 0 \n", + "13 sample13 0 \n", + "14 sample14 0 \n", + "15 sample15 0 \n", + "16 sample16 0 \n", + "17 sample17 0 \n", + "18 sample18 0 \n", + "19 sample19 0 \n", + "20 sample20 0 " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Plotting transcript structures\n", + "\n", + "\n" + ], + "metadata": { + "id": "XAeCKCzoL7RC" + } + }, + { + "cell_type": "code", + "source": [ + "plotTranscripts(gene='XLOC_000454', gown=bg, samples='sample12',\n", + " meas='FPKM', colorby='transcript',\n", + " main='transcripts from gene XLOC_000454: sample 12, FPKM')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 437 + }, + "id": "z09kwBmaL-m7", + "outputId": "8f5a84da-07b6-4bb1-a88e-0a9214b123d8" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Plot with title “transcripts from gene XLOC_000454: sample 12, FPKM”" + ], + "image/png": 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+ }, + "metadata": { + "image/png": { + "width": 420, + "height": 420 + } + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "It is also possible to plot several samples at once:\n", + "\n" + ], + "metadata": { + "id": "hm8g2KdaMBr_" + } + }, + { + "cell_type": "code", + "source": [ + "plotTranscripts('XLOC_000454', bg,\n", + " samples=c('sample01', 'sample06', 'sample12', 'sample19'),\n", + " meas='FPKM', colorby='transcript')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 437 + }, + "id": "us16GFu5MCMA", + "outputId": "ec0909d1-7792-4f7a-b488-9ac54d42d68d" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Plot with title “XLOC_000454”" + ], + "image/png": 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VJkgSRJkiRJkQWSJEmSJEUWSJIkSZIUWSBJkiRJUmSBJEmSJEmRBZIkSZIkRRZI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\ndO47765SG9l9+J4q1d+I/o9NzxffL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+ }, + "metadata": { + "image/png": { + "width": 420, + "height": 420 + } + } + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "xif1Di3OMIPL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "You can also make side-by-side plots comparing mean abundances between groups (here, 0 and 1):\n", + "\n" + ], + "metadata": { + "id": "iGvAVIOSMIy1" + } + }, + { + "cell_type": "code", + "source": [ + "plotMeans('XLOC_000454', bg, groupvar='group', meas='FPKM', colorby='transcript')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 437 + }, + "id": "n3mlIzH6MJTU", + "outputId": "bb16d455-82e5-4992-ba6b-80262563d7d7" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Plot with title “XLOC_000454: 1”" + ], + "image/png": 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6iWb6X/9/gmpG+yXt8+dWpO8q36uC/l1oIDa923r6eO9v6dlaGN9RV54j6uTffv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\nV/qf+8cMYpt/69jmj9dRdm6S1XniHPPaIfRxqPMho0s8YksgHo1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+ }, + "metadata": { + "image/png": { + "width": 420, + "height": 420 + } + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Differential expression analysis\n", + "\n", + "Ballgown provides a wide selection of simple, fast statistical methods for testing whether transcripts are differentially expressed between experimental conditions or across a continuous covariate (such as time).\n", + "\n" + ], + "metadata": { + "id": "9XqwoDc_NKcS" + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "QVqRu89WNNXI" + } + }, + { + "cell_type": "code", + "source": [ + "stat_results = stattest(bg, feature='transcript',\n", + " meas='FPKM', covariate='group',\n", + " getFC=TRUE)\n" + ], + "metadata": { + "id": "eIzaXmwJNWK-" + }, + "execution_count": 21, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "results_transcripts <- data.frame(geneNames = geneNames(bg),\n", + " geneIDs = geneIDs(bg),\n", + " transcriptNames = transcriptNames(bg),\n", + " stat_results)" + ], + "metadata": { + "id": "0FYxJZpCOUMK" + }, + "execution_count": 29, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "head(results_transcripts)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 286 + }, + "id": "3CQVLRJRNXdF", + "outputId": "5009ad68-29d6-4763-a870-1a82cfb60fc7" + }, + "execution_count": 30, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 6 × 8
geneNamesgeneIDstranscriptNamesfeatureidfcpvalqval
<chr><chr><chr><chr><chr><dbl><dbl><dbl>
10XLOC_000010TCONS_00000010transcript103.1934990.013815760.10521233
25XLOC_000014TCONS_00000017transcript251.5490930.267736220.79114975
35XLOC_000017TCONS_00000020transcript354.3886260.010850700.08951825
41XLOC_000246TCONS_00000598transcript411.4405190.471080190.90253747
45XLOC_000019TCONS_00000024transcript451.7143400.084029480.48934813
67XLOC_000255TCONS_00000613transcript672.5185240.273173850.79114975
\n" + ], + "text/markdown": "\nA data.frame: 6 × 8\n\n| | geneNames <chr> | geneIDs <chr> | transcriptNames <chr> | feature <chr> | id <chr> | fc <dbl> | pval <dbl> | qval <dbl> |\n|---|---|---|---|---|---|---|---|---|\n| 10 | | XLOC_000010 | TCONS_00000010 | transcript | 10 | 3.193499 | 0.01381576 | 0.10521233 |\n| 25 | | XLOC_000014 | TCONS_00000017 | transcript | 25 | 1.549093 | 0.26773622 | 0.79114975 |\n| 35 | | XLOC_000017 | TCONS_00000020 | transcript | 35 | 4.388626 | 0.01085070 | 0.08951825 |\n| 41 | | XLOC_000246 | TCONS_00000598 | transcript | 41 | 1.440519 | 0.47108019 | 0.90253747 |\n| 45 | | XLOC_000019 | TCONS_00000024 | transcript | 45 | 1.714340 | 0.08402948 | 0.48934813 |\n| 67 | | XLOC_000255 | TCONS_00000613 | transcript | 67 | 2.518524 | 0.27317385 | 0.79114975 |\n\n", + "text/latex": "A data.frame: 6 × 8\n\\begin{tabular}{r|llllllll}\n & geneNames & geneIDs & transcriptNames & feature & id & fc & pval & qval\\\\\n & & & & & & & & \\\\\n\\hline\n\t10 & & XLOC\\_000010 & TCONS\\_00000010 & transcript & 10 & 3.193499 & 0.01381576 & 0.10521233\\\\\n\t25 & & XLOC\\_000014 & TCONS\\_00000017 & transcript & 25 & 1.549093 & 0.26773622 & 0.79114975\\\\\n\t35 & & XLOC\\_000017 & TCONS\\_00000020 & transcript & 35 & 4.388626 & 0.01085070 & 0.08951825\\\\\n\t41 & & XLOC\\_000246 & TCONS\\_00000598 & transcript & 41 & 1.440519 & 0.47108019 & 0.90253747\\\\\n\t45 & & XLOC\\_000019 & TCONS\\_00000024 & transcript & 45 & 1.714340 & 0.08402948 & 0.48934813\\\\\n\t67 & & XLOC\\_000255 & TCONS\\_00000613 & transcript & 67 & 2.518524 & 0.27317385 & 0.79114975\\\\\n\\end{tabular}\n", + "text/plain": [ + " geneNames geneIDs transcriptNames feature id fc pval \n", + "10 XLOC_000010 TCONS_00000010 transcript 10 3.193499 0.01381576\n", + "25 XLOC_000014 TCONS_00000017 transcript 25 1.549093 0.26773622\n", + "35 XLOC_000017 TCONS_00000020 transcript 35 4.388626 0.01085070\n", + "41 XLOC_000246 TCONS_00000598 transcript 41 1.440519 0.47108019\n", + "45 XLOC_000019 TCONS_00000024 transcript 45 1.714340 0.08402948\n", + "67 XLOC_000255 TCONS_00000613 transcript 67 2.518524 0.27317385\n", + " qval \n", + "10 0.10521233\n", + "25 0.79114975\n", + "35 0.08951825\n", + "41 0.90253747\n", + "45 0.48934813\n", + "67 0.79114975" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "results_transcripts <- results_transcripts[order(results_transcripts$qval), ]" + ], + "metadata": { + "id": "-VykeV1yPT1j" + }, + "execution_count": 36, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "head(results_transcripts, 10)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 411 + }, + "id": "tai6wGhAPpyx", + "outputId": "98783182-ba19-4e0e-f664-1eae9538ea11" + }, + "execution_count": 45, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 10 × 8
geneNamesgeneIDstranscriptNamesfeatureidfcpvalqval
<chr><chr><chr><chr><chr><dbl><dbl><dbl>
1225XLOC_000440TCONS_00001129transcript1225 5.674371221.035753e-050.001025395
980XLOC_000179TCONS_00000452transcript980 6.253449212.514632e-050.001244743
469XLOC_000101TCONS_00000244transcript469 119.299999382.398681e-040.007915648
695XLOC_000354TCONS_00000883transcript695 0.219509593.302059e-040.008172596
1012XLOC_000409TCONS_00001041transcript1012 0.246644341.527175e-030.030238073
123XLOC_000029TCONS_00000059transcript123 0.016033452.097875e-030.034614939
961XLOC_000176TCONS_00000435transcript961 4.960743992.736075e-030.038695918
880XLOC_000531TCONS_00001277transcript880 29.404852363.272859e-030.040501628
1063XLOC_000197TCONS_00000487transcript1063 3.129881874.555313e-030.050108442
35XLOC_000017TCONS_00000020transcript35 4.388625901.085070e-020.089518247
\n" + ], + "text/markdown": "\nA data.frame: 10 × 8\n\n| | geneNames <chr> | geneIDs <chr> | transcriptNames <chr> | feature <chr> | id <chr> | fc <dbl> | pval <dbl> | qval <dbl> |\n|---|---|---|---|---|---|---|---|---|\n| 1225 | | XLOC_000440 | TCONS_00001129 | transcript | 1225 | 5.67437122 | 1.035753e-05 | 0.001025395 |\n| 980 | | XLOC_000179 | TCONS_00000452 | transcript | 980 | 6.25344921 | 2.514632e-05 | 0.001244743 |\n| 469 | | XLOC_000101 | TCONS_00000244 | transcript | 469 | 119.29999938 | 2.398681e-04 | 0.007915648 |\n| 695 | | XLOC_000354 | TCONS_00000883 | transcript | 695 | 0.21950959 | 3.302059e-04 | 0.008172596 |\n| 1012 | | XLOC_000409 | TCONS_00001041 | transcript | 1012 | 0.24664434 | 1.527175e-03 | 0.030238073 |\n| 123 | | XLOC_000029 | TCONS_00000059 | transcript | 123 | 0.01603345 | 2.097875e-03 | 0.034614939 |\n| 961 | | XLOC_000176 | TCONS_00000435 | transcript | 961 | 4.96074399 | 2.736075e-03 | 0.038695918 |\n| 880 | | XLOC_000531 | TCONS_00001277 | transcript | 880 | 29.40485236 | 3.272859e-03 | 0.040501628 |\n| 1063 | | XLOC_000197 | TCONS_00000487 | transcript | 1063 | 3.12988187 | 4.555313e-03 | 0.050108442 |\n| 35 | | XLOC_000017 | TCONS_00000020 | transcript | 35 | 4.38862590 | 1.085070e-02 | 0.089518247 |\n\n", + "text/latex": "A data.frame: 10 × 8\n\\begin{tabular}{r|llllllll}\n & geneNames & geneIDs & transcriptNames & feature & id & fc & pval & qval\\\\\n & & & & & & & & \\\\\n\\hline\n\t1225 & & XLOC\\_000440 & TCONS\\_00001129 & transcript & 1225 & 5.67437122 & 1.035753e-05 & 0.001025395\\\\\n\t980 & & XLOC\\_000179 & TCONS\\_00000452 & transcript & 980 & 6.25344921 & 2.514632e-05 & 0.001244743\\\\\n\t469 & & XLOC\\_000101 & TCONS\\_00000244 & transcript & 469 & 119.29999938 & 2.398681e-04 & 0.007915648\\\\\n\t695 & & XLOC\\_000354 & TCONS\\_00000883 & transcript & 695 & 0.21950959 & 3.302059e-04 & 0.008172596\\\\\n\t1012 & & XLOC\\_000409 & TCONS\\_00001041 & transcript & 1012 & 0.24664434 & 1.527175e-03 & 0.030238073\\\\\n\t123 & & XLOC\\_000029 & TCONS\\_00000059 & transcript & 123 & 0.01603345 & 2.097875e-03 & 0.034614939\\\\\n\t961 & & XLOC\\_000176 & TCONS\\_00000435 & transcript & 961 & 4.96074399 & 2.736075e-03 & 0.038695918\\\\\n\t880 & & XLOC\\_000531 & TCONS\\_00001277 & transcript & 880 & 29.40485236 & 3.272859e-03 & 0.040501628\\\\\n\t1063 & & XLOC\\_000197 & TCONS\\_00000487 & transcript & 1063 & 3.12988187 & 4.555313e-03 & 0.050108442\\\\\n\t35 & & XLOC\\_000017 & TCONS\\_00000020 & transcript & 35 & 4.38862590 & 1.085070e-02 & 0.089518247\\\\\n\\end{tabular}\n", + "text/plain": [ + " geneNames geneIDs transcriptNames feature id fc \n", + "1225 XLOC_000440 TCONS_00001129 transcript 1225 5.67437122\n", + "980 XLOC_000179 TCONS_00000452 transcript 980 6.25344921\n", + "469 XLOC_000101 TCONS_00000244 transcript 469 119.29999938\n", + "695 XLOC_000354 TCONS_00000883 transcript 695 0.21950959\n", + "1012 XLOC_000409 TCONS_00001041 transcript 1012 0.24664434\n", + "123 XLOC_000029 TCONS_00000059 transcript 123 0.01603345\n", + "961 XLOC_000176 TCONS_00000435 transcript 961 4.96074399\n", + "880 XLOC_000531 TCONS_00001277 transcript 880 29.40485236\n", + "1063 XLOC_000197 TCONS_00000487 transcript 1063 3.12988187\n", + "35 XLOC_000017 TCONS_00000020 transcript 35 4.38862590\n", + " pval qval \n", + "1225 1.035753e-05 0.001025395\n", + "980 2.514632e-05 0.001244743\n", + "469 2.398681e-04 0.007915648\n", + "695 3.302059e-04 0.008172596\n", + "1012 1.527175e-03 0.030238073\n", + "123 2.097875e-03 0.034614939\n", + "961 2.736075e-03 0.038695918\n", + "880 3.272859e-03 0.040501628\n", + "1063 4.555313e-03 0.050108442\n", + "35 1.085070e-02 0.089518247" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "results_transcripts[results_transcripts$geneIDs == \"XLOC_000101\", ]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 160 + }, + "id": "o9WD8M7vRv1h", + "outputId": "4ceee569-ce6a-4924-f757-6866e7162baa" + }, + "execution_count": 46, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "
A data.frame: 2 × 8
geneNamesgeneIDstranscriptNamesfeatureidfcpvalqval
<chr><chr><chr><chr><chr><dbl><dbl><dbl>
469XLOC_000101TCONS_00000244transcript469119.2999990.00023986810.007915648
477XLOC_000101TCONS_00000252transcript477 3.1281840.45118036320.902537469
\n" + ], + "text/markdown": "\nA data.frame: 2 × 8\n\n| | geneNames <chr> | geneIDs <chr> | transcriptNames <chr> | feature <chr> | id <chr> | fc <dbl> | pval <dbl> | qval <dbl> |\n|---|---|---|---|---|---|---|---|---|\n| 469 | | XLOC_000101 | TCONS_00000244 | transcript | 469 | 119.299999 | 0.0002398681 | 0.007915648 |\n| 477 | | XLOC_000101 | TCONS_00000252 | transcript | 477 | 3.128184 | 0.4511803632 | 0.902537469 |\n\n", + "text/latex": "A data.frame: 2 × 8\n\\begin{tabular}{r|llllllll}\n & geneNames & geneIDs & transcriptNames & feature & id & fc & pval & qval\\\\\n & & & & & & & & \\\\\n\\hline\n\t469 & & XLOC\\_000101 & TCONS\\_00000244 & transcript & 469 & 119.299999 & 0.0002398681 & 0.007915648\\\\\n\t477 & & XLOC\\_000101 & TCONS\\_00000252 & transcript & 477 & 3.128184 & 0.4511803632 & 0.902537469\\\\\n\\end{tabular}\n", + "text/plain": [ + " geneNames geneIDs transcriptNames feature id fc \n", + "469 XLOC_000101 TCONS_00000244 transcript 469 119.299999\n", + "477 XLOC_000101 TCONS_00000252 transcript 477 3.128184\n", + " pval qval \n", + "469 0.0002398681 0.007915648\n", + "477 0.4511803632 0.902537469" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plotMeans('XLOC_000101', bg, groupvar='group', meas='FPKM', colorby='transcript')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 437 + }, + "id": "HBOOTG_CPAif", + "outputId": "5fa8e447-7e3d-4492-ddfc-f403c41f3490" + }, + "execution_count": 47, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Plot with title “XLOC_000101: 1”" + ], + "image/png": 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