plot_Fig_PC3E_GS869.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-12T15:09:51.795254Z",
     "start_time": "2022-05-12T15:09:51.789326Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
      "  %reload_ext rpy2.ipython\n"
     ]
    }
   ],
   "source": [
    "%load_ext rpy2.ipython\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unzip all zipped files in folder `./PC3E-GS689/rmats/post`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Panel a: pie chart for all events\n",
    "- folders contains the input file: `./PC3E-GS689/rmats_filtering/`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-12T15:09:55.959261Z",
     "start_time": "2022-05-12T15:09:51.807039Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "nSE <- system(\"cat ./PC3E-GS689/rmats_filtering/filtered_SE.MATS.JC.txt | wc -l \", intern = TRUE)\n",
    "nA5SS <- system(\"cat ./PC3E-GS689/rmats_filtering/filtered_A5SS.MATS.JC.txt | wc -l \", intern = TRUE)\n",
    "nA3SS <- system(\"cat ./PC3E-GS689/rmats_filtering/filtered_A3SS.MATS.JC.txt | wc -l \", intern = TRUE)\n",
    "nMXE <- system(\"cat ./PC3E-GS689/rmats_filtering/filtered_MXE.MATS.JC.txt | wc -l \", intern = TRUE)\n",
    "nRI <- system(\"cat ./PC3E-GS689/rmats_filtering/filtered_RI.MATS.JC.txt | wc -l \", intern = TRUE)\n",
    "\n",
    "Prop <- c(SE = as.integer(nSE)-1, A5SS = as.integer(nA5SS)-1, A3SS = as.integer(nA3SS)-1, MXE = as.integer(nMXE)-1, RI = as.integer(nRI)-1)\n",
    "labels <- paste0(names(Prop), ' ', format(Prop, big.mark = ','))\n",
    "\n",
    "# pdf('plot/plot_pie_filtered.pdf', width = 4, height = 4)\n",
    "pie(Prop , labels = labels, clockwise = T, angle = 0)\n",
    "# legend(\"topright\", c(\"SE\",\"A5SS\",\"A3SS\",\"MXE\",\"RI\"), fill = rainbow(5))\n",
    "# dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Panel b: pie chart for significant events\n",
    "- folders contains the input file: `./PC3E-GS689/rmats_filtering/`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-12T15:10:11.142063Z",
     "start_time": "2022-05-12T15:10:10.557418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "nSE <- system(\"cat ./PC3E-GS689/rmats_filtering/up_SE.MATS.JC.txt ./PC3E-GS689/rmats_filtering/dn_SE.MATS.JC.txt | wc -l \", intern = TRUE)\n",
    "nA5SS <- system(\"cat ./PC3E-GS689/rmats_filtering/up_A5SS.MATS.JC.txt ./PC3E-GS689/rmats_filtering/dn_A5SS.MATS.JC.txt | wc -l \", intern = TRUE)\n",
    "nA3SS <- system(\"cat ./PC3E-GS689/rmats_filtering/up_A3SS.MATS.JC.txt ./PC3E-GS689/rmats_filtering/dn_A3SS.MATS.JC.txt | wc -l \", intern = TRUE)\n",
    "nMXE <- system(\"cat ./PC3E-GS689/rmats_filtering/up_MXE.MATS.JC.txt ./PC3E-GS689/rmats_filtering/dn_MXE.MATS.JC.txt | wc -l \", intern = TRUE)\n",
    "nRI <- system(\"cat ./PC3E-GS689/rmats_filtering/up_RI.MATS.JC.txt ./PC3E-GS689/rmats_filtering/dn_RI.MATS.JC.txt | wc -l \", intern = TRUE)\n",
    "\n",
    "Prop <- c(SE = as.integer(nSE)-2, A5SS = as.integer(nA5SS)-2, A3SS = as.integer(nA3SS)-2, MXE = as.integer(nMXE)-2, RI = as.integer(nRI)-2)\n",
    "labels <- paste0(names(Prop), ' ', format(Prop, big.mark = ','))\n",
    "\n",
    "# pdf('plot/plot_pie_significant.pdf', width = 4, height = 4)\n",
    "pie(Prop , labels = labels, clockwise = T, angle = 0)\n",
    "# dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Panel c: Volcano Plot for SE events\n",
    "- folders contains the input file: `./PC3E-GS689/rmats_filtering/`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-12T15:10:30.332731Z",
     "start_time": "2022-05-12T15:10:17.452785Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "library(ggplot2)\n",
    "library(ggrepel)\n",
    "\n",
    "# load data\n",
    "mydf = read.table('./PC3E-GS689/rmats_filtering/filtered_SE.MATS.JC.txt', header = T, row.names = 1, sep = '\\t', stringsAsFactors = FALSE, check.names = FALSE)\n",
    "up = read.table('./PC3E-GS689/rmats_filtering/up_SE.MATS.JC.txt', header = T, row.names = 1, sep = '\\t', stringsAsFactors = FALSE, check.names = FALSE)\n",
    "dn = read.table('./PC3E-GS689/rmats_filtering/dn_SE.MATS.JC.txt', header = T, row.names = 1, sep = '\\t', stringsAsFactors = FALSE, check.names = FALSE)\n",
    "\n",
    "mydf$col = 'Background'\n",
    "mydf$col[mydf$ID %in% up$ID] = 'High in GS689'\n",
    "mydf$col[mydf$ID %in% dn$ID] = 'High in PC3E'\n",
    "mydf$col = factor(mydf$col, levels = c('Background', 'High in GS689', 'High in PC3E'))\n",
    "\n",
    "xlab = 'delta PSI (GS689-PC3E)'\n",
    "\n",
    "# process data\n",
    "mydf$logFDR = -log10(mydf$FDR); \n",
    "mydf[which(is.infinite(mydf$logFDR) | mydf$logFDR > 15), 'logFDR'] = 15\n",
    "\n",
    "# plot\n",
    "p <- ggplot(mydf) + \n",
    "    geom_point(aes(x = -IncLevelDifference, y = logFDR, col = col)) +\n",
    "    geom_hline(yintercept = 2, linetype=\"dotdash\", color = \"black\", size = 1) +\n",
    "    geom_vline(xintercept = -0.05, linetype=\"dotdash\", color = \"black\", size = 1) +\n",
    "    geom_vline(xintercept = 0.05, linetype=\"dotdash\", color = \"black\", size = 1) +\n",
    "    scale_color_manual(name = NULL,\n",
    "                       labels = c('Not significant', \n",
    "                                  paste0(levels(mydf$col)[2], ' (n=', format(table(mydf$col)[levels(mydf$col)[2]], big.mark = ','), ')'), \n",
    "                                  paste0(levels(mydf$col)[3], ' (n=', format(table(mydf$col)[levels(mydf$col)[3]], big.mark = ','), ')')),\n",
    "                       values = c('darkgrey', '#CA0020', '#0571B0'), \n",
    "                       guide = 'legend') +\n",
    "    coord_cartesian(ylim = c(0, 15), expand = T) +\n",
    "    labs(x = xlab, y = \"-log10(FDR)\") +\n",
    "    scale_y_continuous(breaks = c(0, 5, 10, 15), labels=c('0', '5', '10', expression(phantom(y) >=15))) +\n",
    "    theme_classic(base_size = 15) + \n",
    "    theme(aspect.ratio = 1,\n",
    "          panel.border = element_rect(colour = \"black\", fill=NA, size=1),\n",
    "          axis.line = element_blank(),\n",
    "          axis.text = element_text(color = 'black'),\n",
    "          legend.text = element_text(size = 15, color = 'black'))\n",
    "\n",
    "# pdf('plot/plot_valcano.pdf', width = 6, height = 3.5)\n",
    "p\n",
    "# dev.off()\n",
    "# ggsave('plot/plot_valcano.pdf', plot = p, width = 6, height = 3.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Panel d and e: Sashimi plot\n",
    "- Software used [rmats2sashimiplot software](https://github.com/Xinglab/rmats2sashimiplot)\n",
    "- Input files:\n",
    "    - `./PC3E-GS689/plot_rmats2sashimi/sashimi_events_example.txt`: contains the events that will be plotted\n",
    "    - `./PC3E-GS689/bam/`: contains the bam files (truncated to only include reads mapped to USO1 and MAST3 gene for illustration purpose)\n",
    "    - `./PC3E-GS689/plot_rmats2sashimi/sashimi_groupInfo.txt`: contains the grouping information of the bam files\n",
    "- Output file\n",
    "    - `./PC3E-GS689/plot_rmats2sashimi/output_example/Sashimi_plot`: location where the output PDF files will be located\n",
    "- Command used\n",
    "    - `./PC3E-GS689/plot_rmats2sashimi/run_example.sh`: contains the command used to generate the sashimi plots.\n",
    "    - it is also shown in the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-12T15:12:55.090138Z",
     "start_time": "2022-05-12T15:12:13.989594Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<module 'misopy' from '/mnt/isilon/xing_lab/wangy14/Software/Anaconda2-2019.03-Linux-x86_64/lib/python2.7/site-packages/rmats2sashimiplot-2.0.2-py2.7.egg/MISO/misopy/__init__.pyc'>\n",
      "Indexing GFF...\n",
      "/mnt/isilon/xing_lab/wangy14/project/annotation_rmats-turbo/nature_protocols/PC3E-GS689/plot_rmats2sashimi/output_example/Sashimi_index_USO1_1/tmp.gff3 appears to already be indexed. Aborting.\n",
      "chr4:76714843:76715054:+@chr4:76716489:76716509:+@chr4:76720775:76720885:+ chr4:76714843:76715054:+@chr4:76716489:76716509:+@chr4:76720775:76720885:+\n",
      "Reading settings from: /mnt/isilon/xing_lab/wangy14/project/annotation_rmats-turbo/nature_protocols/PC3E-GS689/plot_rmats2sashimi/output_example/Sashimi_index_USO1_1/sashimi_plot_settings.txt\n",
      "Parsing data:bam_prefix\n",
      "Parsing data:miso_prefix\n",
      "Parsing data:bam_files\n",
      "Parsing data:miso_files\n",
      "Parsing plotting:group_info\n",
      "Parsing plotting:font_size\n",
      "Parsing plotting:reverse_minus\n",
      "Parsing plotting:show_xlabel\n",
      "Parsing plotting:nyticks\n",
      "Parsing plotting:min_counts\n",
      "Parsing plotting:text_background\n",
      "Parsing plotting:logged\n",
      "Parsing plotting:plot_label\n",
      "Parsing plotting:show_ylabel\n",
      "Parsing plotting:fig_height\n",
      "Parsing plotting:nxticks\n",
      "Parsing plotting:plot_title\n",
      "Parsing plotting:exon_scale\n",
      "Parsing plotting:fig_width\n",
      "Parsing plotting:intron_scale\n",
      "Parsing plotting:resolution\n",
      "Parsing plotting:bar_posteriors\n",
      "Parsing plotting:number_junctions\n",
      "Parsing plotting:show_posteriors\n",
      "Parsing plotting:colors\n",
      "Parsing plotting:sample_labels\n",
      "Reading dimensions from settings...\n",
      " - Height: 7.00\n",
      " - Width: 8.00\n",
      "Plotting read densities and MISO estimates along event...\n",
      "  - Event: chr4:76714843:76715054:+@chr4:76716489:76716509:+@chr4:76720775:76720885:+\n",
      "Setting up plot using dimensions:  [8.0, 7.0]\n",
      "Using intron scale  5.0\n",
      "Using exon scale  1.0\n",
      "chr4:76714843:76715054:+@chr4:76716489:76716509:+@chr4:76720775:76720885:+ chr4:76714843:76715054:+@chr4:76716489:76716509:+@chr4:76720775:76720885:+\n",
      "\u001b[0;31;m\n",
      "The number of custom colors is 6 which doesn't match the group number 2. The program uses the rainbow color as default.\n",
      "\u001b[0m\n",
      "Reading sample label: USO1 PC3E IncLevel: 0.91\n",
      "Skipping read with CIGAR 8M2892N67M1D26M\n",
      "Skipping read with CIGAR 78M1D23M\n",
      "Skipping read with CIGAR 40M1D61M\n",
      "Skipping read with CIGAR 28M1D73M\n",
      "Skipping read with CIGAR 36M1D65M\n",
      "Skipping read with CIGAR 36M1D65M\n",
      "Skipping read with CIGAR 17M1D84M\n",
      "Skipping read with CIGAR 36M1D64M1434N1M\n",
      "Skipping read with CIGAR 36M1D64M5720N1M\n",
      "Skipping read with CIGAR 14M5720N63M1D24M\n",
      "Skipping read with CIGAR 14M5720N63M1D24M\n",
      "Skipping read with CIGAR 29M5D72M\n",
      "Skipping read with CIGAR 27M5D74M\n",
      "Skipping read with CIGAR 27M5D74M\n",
      "Skipping read with CIGAR 23M5D78M\n",
      "Skipping read with CIGAR 23M5D78M\n",
      "Skipping read with CIGAR 14M5D87M\n",
      "Skipping read with CIGAR 14M5D87M\n",
      "Skipping read with CIGAR 39M1I61M\n",
      "Skipping read with CIGAR 76M1D25M\n",
      "Skipping read with CIGAR 47M1D54M\n",
      "Skipping read with CIGAR 35M1D66M\n",
      "Skipping read with CIGAR 28M1D73M\n",
      "Skipping read with CIGAR 23M4265N16M1I61M\n",
      "Skipping read with CIGAR 68M1I32M\n",
      "Skipping read with CIGAR 79M1I21M\n",
      "Skipping read with CIGAR 68M1I32M\n",
      "Skipping read with CIGAR 79M1I21M\n",
      "Skipping read with CIGAR 55M618N17M2D29M\n",
      "Skipping read with CIGAR 78M2D23M\n",
      "Skipping read with CIGAR 78M2D23M\n",
      "Skipping read with CIGAR 31M1D70M\n",
      "Skipping read with CIGAR 11M2D3M5720N87M\n",
      "Skipping read with CIGAR 33M5D68M\n",
      "Skipping read with CIGAR 12M5D89M\n",
      "Skipping read with CIGAR 16M2D85M\n",
      "Reading sample label: USO1 GS689 IncLevel: 0.05\n",
      "Skipping read with CIGAR 72M1D29M\n",
      "Skipping read with CIGAR 62M1D39M\n",
      "Skipping read with CIGAR 57M5D44M\n",
      "Skipping read with CIGAR 14M5D87M\n",
      "Skipping read with CIGAR 26M1D75M\n",
      "Skipping read with CIGAR 52M1D49M\n",
      "Skipping read with CIGAR 78M5D23M\n",
      "Skipping read with CIGAR 59M1I41M\n",
      "Skipping read with CIGAR 59M1I41M\n",
      "Skipping read with CIGAR 58M2892N18M1I24M\n",
      "Skipping read with CIGAR 45M2892N18M1I37M\n",
      "Skipping read with CIGAR 70M1D31M\n",
      "Skipping read with CIGAR 47M5D54M\n",
      "Skipping read with CIGAR 16M5D85M\n",
      "Skipping read with CIGAR 37M5720N40M1D24M\n",
      "Saving plot to: /mnt/isilon/xing_lab/wangy14/project/annotation_rmats-turbo/nature_protocols/PC3E-GS689/plot_rmats2sashimi/output_example/Sashimi_plot/chr4:76714843:76715054:+@chr4:76716489:76716509:+@chr4:76720775:76720885:+.pdf\n",
      "<module 'misopy' from '/mnt/isilon/xing_lab/wangy14/Software/Anaconda2-2019.03-Linux-x86_64/lib/python2.7/site-packages/rmats2sashimiplot-2.0.2-py2.7.egg/MISO/misopy/__init__.pyc'>\n",
      "Indexing GFF...\n",
      "/mnt/isilon/xing_lab/wangy14/project/annotation_rmats-turbo/nature_protocols/PC3E-GS689/plot_rmats2sashimi/output_example/Sashimi_index_MAST3_2/tmp.gff3 appears to already be indexed. Aborting.\n",
      "chr19:18232660:18232732:+@chr19:18232917:18232937:+@chr19:18233483:18233561:+ chr19:18232660:18232732:+@chr19:18232917:18232937:+@chr19:18233483:18233561:+\n",
      "Reading settings from: /mnt/isilon/xing_lab/wangy14/project/annotation_rmats-turbo/nature_protocols/PC3E-GS689/plot_rmats2sashimi/output_example/Sashimi_index_MAST3_2/sashimi_plot_settings.txt\n",
      "Parsing data:bam_prefix\n",
      "Parsing data:miso_prefix\n",
      "Parsing data:bam_files\n",
      "Parsing data:miso_files\n",
      "Parsing plotting:group_info\n",
      "Parsing plotting:font_size\n",
      "Parsing plotting:reverse_minus\n",
      "Parsing plotting:show_xlabel\n",
      "Parsing plotting:nyticks\n",
      "Parsing plotting:min_counts\n",
      "Parsing plotting:text_background\n",
      "Parsing plotting:logged\n",
      "Parsing plotting:plot_label\n",
      "Parsing plotting:show_ylabel\n",
      "Parsing plotting:fig_height\n",
      "Parsing plotting:nxticks\n",
      "Parsing plotting:plot_title\n",
      "Parsing plotting:exon_scale\n",
      "Parsing plotting:fig_width\n",
      "Parsing plotting:intron_scale\n",
      "Parsing plotting:resolution\n",
      "Parsing plotting:bar_posteriors\n",
      "Parsing plotting:number_junctions\n",
      "Parsing plotting:show_posteriors\n",
      "Parsing plotting:colors\n",
      "Parsing plotting:sample_labels\n",
      "Reading dimensions from settings...\n",
      " - Height: 7.00\n",
      " - Width: 8.00\n",
      "Plotting read densities and MISO estimates along event...\n",
      "  - Event: chr19:18232660:18232732:+@chr19:18232917:18232937:+@chr19:18233483:18233561:+\n",
      "Setting up plot using dimensions:  [8.0, 7.0]\n",
      "Using intron scale  5.0\n",
      "Using exon scale  1.0\n",
      "chr19:18232660:18232732:+@chr19:18232917:18232937:+@chr19:18233483:18233561:+ chr19:18232660:18232732:+@chr19:18232917:18232937:+@chr19:18233483:18233561:+\n",
      "\u001b[0;31;m\n",
      "The number of custom colors is 6 which doesn't match the group number 2. The program uses the rainbow color as default.\n",
      "\u001b[0m\n",
      "Reading sample label: MAST3 PC3E IncLevel: 0.09\n",
      "Skipping read with CIGAR 26M1I74M\n",
      "Skipping read with CIGAR 80M1I20M\n",
      "Skipping read with CIGAR 2M3430N68M2D19M76N12M\n",
      "Skipping read with CIGAR 2M3430N68M2D19M76N12M\n",
      "Reading sample label: MAST3 GS689 IncLevel: 0.41\n",
      "Skipping read with CIGAR 80M1I20M\n",
      "Skipping read with CIGAR 79M1I21M\n",
      "Skipping read with CIGAR 79M1I21M\n",
      "Skipping read with CIGAR 31M1I69M\n",
      "Saving plot to: /mnt/isilon/xing_lab/wangy14/project/annotation_rmats-turbo/nature_protocols/PC3E-GS689/plot_rmats2sashimi/output_example/Sashimi_plot/chr19:18232660:18232732:+@chr19:18232917:18232937:+@chr19:18233483:18233561:+.pdf\n",
      "'../bam/PC3E_rep1/Aligned.sortedByCoord.out.bam' is indexed already: '../bam/PC3E_rep1/Aligned.sortedByCoord.out.bam.bai'\n",
      "'../bam/PC3E_rep2/Aligned.sortedByCoord.out.bam' is indexed already: '../bam/PC3E_rep2/Aligned.sortedByCoord.out.bam.bai'\n",
      "'../bam/PC3E_rep3/Aligned.sortedByCoord.out.bam' is indexed already: '../bam/PC3E_rep3/Aligned.sortedByCoord.out.bam.bai'\n",
      "'../bam/GS689_rep1/Aligned.sortedByCoord.out.bam' is indexed already: '../bam/GS689_rep1/Aligned.sortedByCoord.out.bam.bai'\n",
      "'../bam/GS689_rep2/Aligned.sortedByCoord.out.bam' is indexed already: '../bam/GS689_rep2/Aligned.sortedByCoord.out.bam.bai'\n",
      "'../bam/GS689_rep3/Aligned.sortedByCoord.out.bam' is indexed already: '../bam/GS689_rep3/Aligned.sortedByCoord.out.bam.bai'\n",
      "/mnt/isilon/xing_lab/wangy14/Software/Anaconda2-2019.03-Linux-x86_64/bin/python /mnt/isilon/xing_lab/wangy14/Software/Anaconda2-2019.03-Linux-x86_64/lib/python2.7/site-packages/rmats2sashimiplot-2.0.2-py2.7.egg/MISO/misopy/sashimi_plot/sashimi_plot.py --plot-event \"chr4:76714843:76715054:+@chr4:76716489:76716509:+@chr4:76720775:76720885:+\" /mnt/isilon/xing_lab/wangy14/project/annotation_rmats-turbo/nature_protocols/PC3E-GS689/plot_rmats2sashimi/output_example/Sashimi_index_USO1_1 /mnt/isilon/xing_lab/wangy14/project/annotation_rmats-turbo/nature_protocols/PC3E-GS689/plot_rmats2sashimi/output_example/Sashimi_index_USO1_1/sashimi_plot_settings.txt --output-dir /mnt/isilon/xing_lab/wangy14/project/annotation_rmats-turbo/nature_protocols/PC3E-GS689/plot_rmats2sashimi/output_example/Sashimi_plot\n",
      "/mnt/isilon/xing_lab/wangy14/Software/Anaconda2-2019.03-Linux-x86_64/bin/python /mnt/isilon/xing_lab/wangy14/Software/Anaconda2-2019.03-Linux-x86_64/lib/python2.7/site-packages/rmats2sashimiplot-2.0.2-py2.7.egg/MISO/misopy/sashimi_plot/sashimi_plot.py --plot-event \"chr19:18232660:18232732:+@chr19:18232917:18232937:+@chr19:18233483:18233561:+\" /mnt/isilon/xing_lab/wangy14/project/annotation_rmats-turbo/nature_protocols/PC3E-GS689/plot_rmats2sashimi/output_example/Sashimi_index_MAST3_2 /mnt/isilon/xing_lab/wangy14/project/annotation_rmats-turbo/nature_protocols/PC3E-GS689/plot_rmats2sashimi/output_example/Sashimi_index_MAST3_2/sashimi_plot_settings.txt --output-dir /mnt/isilon/xing_lab/wangy14/project/annotation_rmats-turbo/nature_protocols/PC3E-GS689/plot_rmats2sashimi/output_example/Sashimi_plot\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/isilon/xing_lab/wangy14/Software/Anaconda2-2019.03-Linux-x86_64/lib/python2.7/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: The mpl_toolkits.axes_grid module was deprecated in version 2.1. Use mpl_toolkits.axes_grid1 and mpl_toolkits.axisartist provies the same functionality instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n",
      "/mnt/isilon/xing_lab/wangy14/Software/Anaconda2-2019.03-Linux-x86_64/lib/python2.7/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: The mpl_toolkits.axes_grid module was deprecated in version 2.1. Use mpl_toolkits.axes_grid1 and mpl_toolkits.axisartist provies the same functionality instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cd PC3E-GS689/plot_rmats2sashimi\n",
    "rmats2sashimiplot  \\\n",
    "        --b1 ../bam/PC3E_rep1/Aligned.sortedByCoord.out.bam,../bam/PC3E_rep2/Aligned.sortedByCoord.out.bam,../bam/PC3E_rep3/Aligned.sortedByCoord.out.bam \\\n",
    "        --b2 ../bam/GS689_rep1/Aligned.sortedByCoord.out.bam,../bam/GS689_rep2/Aligned.sortedByCoord.out.bam,../bam/GS689_rep3/Aligned.sortedByCoord.out.bam \\\n",
    "        -t SE \\\n",
    "        -e sashimi_events_example.txt \\\n",
    "        --l1 PC3E_rep --l2 GS689_rep \\\n",
    "        --exon_s 1 --intron_s 5 \\\n",
    "        -o ./output_example \\\n",
    "        --group-info sashimi_groupInfo.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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