Week_5_practical.ipynb~

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    "© Copyright 2016 Dr Marta Milo and Dr Mike Croucher, University of Sheffield. \n",
    "\n",
    "\n",
    "# Week 5 Practical \n",
    "\n",
    "\n",
    "This Notebook contains practical assignments for Week 4. \n",
    "\n",
    "It contains guidance on how to implement basic *workflow* for gene expression data analysis. It constists in the following steps\n",
    "\n",
    "* **Step1**: Load packages with data from Bioconductor and/or access it from file in the data directory\n",
    "* **Step 2**: Arrange the data in an affybatch using Bioconductor commands. Annotate the PhenoData\n",
    "* **Step 3**: Analysis of gene expression data with different methods and normalisation techniques\n",
    "* **Step 4**: Diagnostics of the data with plotting techniques\n",
    "* **Step 5**: Basic use of limma and puma for Differential Expression Analysis\n",
    "* **Step 6**: Visualisation of the data with PCA\n",
    "* **Step 7**: Hierarchical clustering of DE (Differentially Expressed) genes\n",
    "* **Step 8**: Functional analysis of DE targets using PANTHER or DAVID\n",
    "* **Step 9**: Pathway analysis using PANTHER\n",
    "\n",
    "As for the other practicals, this notebook also contains practical tasks that you will have to implement yourself. In this paractical you will find small tasks within the main one that is to implement the workflow specified above. \n",
    "\n",
    "\n",
    "We will use data that is stored in the preprocessed Bioconductor packages for this task and if we will need to access data, it will be stored in your SageMathCloud folder. You are free to base your work on the examples given here but you are also welcome to use different methods if you prefer, adding and/or creating new ways of displaying the data, as long as they are justified and are coherent with the task you are carrying forward. \n",
    "\n",
    "\n",
    "Remeber to use Bioconductor website as a reference and to search for user guides: https://www.bioconductor.org/\n",
    "\n",
    "** Please be aware ** that Bioconductor has many different packages which change/update very frequently, this might couse problem with the install of them onto SageMathCloud. If you need help on packeges installation please contact Dr Marta Milo and/or Dr Mike Croucher. \n",
    "\n",
    "To complete this practical, **you will need to create a new notebook in the Week 5 folder of your SageMathCloud account** that you will call **your username_week5.ipynb**.\n",
    "\n",
    "The notebooks assessed but formative feedback will be minimal and ONLY in case of completely wrong implementation clear explanation will be given. This is because your final task will consist in a similar exercise but on real and previously unseen data. \n",
    "\n",
    "All the notebooks are meant to be used interactively.  All the code needs to be written into *code* cells -- that can be executed by an R kernel. The outputs are not present in this notebook, but the code cells are executable."
   ]
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    "**We will be plotting large amounts of data. To avoid problems with file size, you should run the following line which changes plot outputs to small .png files**\n",
    "\n",
    "```R\n",
    "options(jupyter.plot_mimetypes ='image/png')\n",
    "```"
   ]
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   "source": [
    "### Exercise\n",
    "Implement the FULL workflow as described above on the estrogen data. Below are TIPS and SUGGESTIONS for each of the above steps.\n",
    "\n",
    "\n",
    "**Step 1**:\n",
    "Load data from the data directory `data_wk5` using the command `load(estrogen_data.RDA)`. Ensure that you have loaded the data checking with `ls()`. the data summarises a time series experiement of human cells in culture growing in the presence or absence of the estrogen hormon. samples were taken after 10hrs and 48hrs, for the treated  (estrogen present) and untreated (estrogen absent).\n",
    "\n",
    "Activate the package `affy` and wait to activate puma til after processing the data with `rma`, because in some cases creates a conflict within some of `affy` commands. After processing the affybatch, activate the others relevant libraries for cmpleting this analysis when you need them. \n",
    "\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "options(jupyter.plot_mimetypes ='image/png')"
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  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": false
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   "source": [
    "load(\"estrogen_data.RDA\")\n",
    "ls()\n",
    "library(affy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2**:Annotate the `pData` with the `pData()` command.\n",
    "\n",
    "\n",
    "**Step 3**:Calculate gene expression with at least one single point statitics method and puma. \n",
    "To avoid waiting time for the calclualtion of gene expression with puma load the data from the data folder using `load(\"eset_puma.RDA\")`\n",
    "\n",
    "**Step 4**: Use the plot tools we have seen in the Week5 practical and the `MAplot()` from `limma`. Remember to exececute `library(limma)` before doing so. Explore the command with the estrogen data. Does it make sense to use an `MAplot()` directly on the eset? What bout the code below?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
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   "outputs": [
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups<-c(\"A10\",\"A10\",\"P10\",\"P10\",\"A48\",\"A48\",\"P48\",\"P48\")\n",
    "table_estrogen<-data.frame(sampleNames(eset_estrogen_puma),groups)\n",
    "group10hr<-factor(groups[1:4])\n",
    "MAplot(eset_estrogen_puma[,1:4], pairs=TRUE, groups=group10hr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use this suggestion to explore the data. \n",
    "\n",
    "\n",
    "**Step 5**: Before performing any type of DE analysis you need to combine the data as explained in the lectures. For `puma` we combine the data using an bayesian Hierarchical model, which might take some time when implemented on data with the comand `pumaCombImproved()`.\n",
    "Load the puma combined data for this exercise from the data folder using the command `load(\"eset_puma_comb.RDA\")`.\n",
    "\n",
    "\n",
    "We have seen in week 4 initial use of `limma` for DE analysis. Remember the three core steps of `limma`\n",
    "* **Step 1**: build the design contrast matrix\n",
    "* **Step 2**: fit the linear model\n",
    "* **Step 3**: calculate the p-values and FDRs with a empirical Bayes test\n",
    "we used built-in function in `puma` to create the design and constrast matrix. We can also use the following code to create thoese matrices.\n",
    "\n",
    "```R\n",
    "group<-factor(c(\"A10\",\"A10\",\"P10\",\"P10\",\"A48\",\"A48\",\"P48\",\"P48\"))\n",
    "design<-model.matrix(~0+group)\n",
    "colnames(design)<-c(\"A10\",\"P10\",\"A48\",\"P48\")\n",
    "\n",
    "constrast.matrix<-makeContrasts(A10-P10,A48-P48,levels=design)\n",
    "\n",
    "fit<-lmFit(eset_estrogen,design)\n",
    "fit2<-contrasts.fit(fit,constrast.matrix)\n",
    "fit2<-eBayes(fit2)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the top DE genes using `topTable()` as seen in week10. Explore the results. You might want to extract more genes from using topTable... look at the parameters.( n=100, for examples...)\n",
    "\n",
    "\n",
    "Rembenber the threshold for selecting FC after they passed the significance threhold is FC<-1 and FC>1. Assign the thresholds to variables in the script, this helps to modify them and keep the code correct. In this way you will change only the value of one variable and not in all instances when it is recalled.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "topDEGenes<-topTable(fit2, coef=1, adjust=\"BH\", n=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do the DE analysis with `puma` use the puma combined data stored in the expression set `eset_estrogen_comb`. Then use the command `pumaDE()`. the user guide is available to download from the website. \n",
    "\n",
    "\n",
    "Remember to activate the package `puma`\n",
    "\n",
    "Example of use extracted from the userguide is:\n",
    "\n",
    "\n",
    "To identify genes that are differentially expressed between the conditions\n",
    "use the pumaDE function. \n",
    "```R\n",
    "pumaDERes <- pumaDE(eset_estrogen_comb)\n",
    "```\n",
    "The results of this command are ranked gene lists. If we want to write out the statistics of differential expression (the PPLR values), and the fold change values, we use the command `write.reslts()`.\n",
    "```R\n",
    "write.reslts(pumaDERes, file=\"pumaDERes\")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Always make sure that the p-values are distributed as expected. Use the `hist()` to diagnose this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "par(mfrow=c(1,2))\n",
    "hist(fit2$p.value[,1], main=\"A10 vs P10\")\n",
    "hist(fit2$p.value[,2], main=\"A48 vs P48\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**BEFORE** continuing with more analysis we need to *annotate* the probeset_IDs with gene names and symbols. We can use \n",
    "```R\n",
    "library(annotate)\n",
    "library(hgu95av2.db)\n",
    "```\n",
    "to do this. `hu95av2` is the database for the annotation of the geneChip we are using. To annotate the list of selected genes you need to use the command `select()`. Below is an example of this:\n",
    "```R\n",
    "geneProbes<-as.character(rownames(topGenes))\n",
    "annotated_list<-select(hgu95av2.db, geneProbes,c(\"SYMBOL\",\"GENENAME\"))\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 6**: Pricipal component analysis. \n",
    "\n",
    "You can perform PCA in R using the command `prcomp()`. This is an example:\n",
    "```R\n",
    "pca_estrogen <- prcomp(t(exprs(eset_estrogen)))\n",
    "```\n",
    "It needs the traspose command `t()` since the input for the `prcomp()` wants the genes in the columns. You can plot it using:\n",
    "\n",
    "```R\n",
    "plot(pca_estrogen$x, xlab=\"Component 1\", ylab=\"Component 2\", pch=unclass(as.factor(pData(eset_estrogen)[,1])), col=unclass(as.factor(pData(eset_estrogen)[,2])), main=\"Standard PCA\")\n",
    "\n",
    "groups<-paste(eset_estrogen$condition,eset_estrogen$time.h, sep =\" \")\n",
    "legend(0,0,groups,pch=unclass(as.factor(pData(eset_estrogen)[,1]))\n",
    ", col=unclass(as.factor(pData(eset_estrogen)[,2])))\n",
    "```\n",
    "\n",
    "\n",
    "For probabilistic PCA you can use `pumaPCA()` as below:\n",
    "\n",
    "```R\n",
    "pumapca_estrogen<-pumaPCA(eset_estrogen_puma)\n",
    "plot(pumapca_estrogen)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 7**: Hierarchical clustering\n",
    "\n",
    "To perform this we need to activate a library called `gplots`. We will use the command `heatmap.2()`.\n",
    "We do clustering a the selected genes from our DE analysis this is to search for patterns in of differentially regulatend pathways.\n",
    "\n",
    "For example for the RMA processed we can do:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "\t<li>'AFFX-BioDn-5_at'</li>\n",
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       "\\begin{enumerate*}\n",
       "\\item 'AFFX-CreX-5_at'\n",
       "\\item 'AFFX-CreX-3_at'\n",
       "\\item 'AFFX-BioDn-5_at'\n",
       "\\item 'AFFX-BioB-M_at'\n",
       "\\item 'AFFX-BioDn-3_at'\n",
       "\\item '39581_at'\n",
       "\\item 'AFFX-BioC-3_at'\n",
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       "\\item '2004_at'\n",
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      ],
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       "1. 'AFFX-CreX-5_at'\n",
       "2. 'AFFX-CreX-3_at'\n",
       "3. 'AFFX-BioDn-5_at'\n",
       "4. 'AFFX-BioB-M_at'\n",
       "5. 'AFFX-BioDn-3_at'\n",
       "6. '39581_at'\n",
       "7. 'AFFX-BioC-3_at'\n",
       "8. '37014_at'\n",
       "9. '2004_at'\n",
       "10. 'AFFX-BioC-5_at'\n",
       "11. '34363_at'\n",
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       "39. '2010_at'\n",
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       "41. '36631_at'\n",
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       "45. '34736_at'\n",
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       "47. '37895_at'\n",
       "48. '39092_at'\n",
       "49. '960_g_at'\n",
       "50. '32448_at'\n",
       "51. '543_g_at'\n",
       "52. '39926_at'\n",
       "53. '37828_at'\n",
       "54. '37331_g_at'\n",
       "55. '37250_at'\n",
       "56. '32263_at'\n",
       "57. '1272_at'\n",
       "58. '1035_g_at'\n",
       "59. '406_at'\n",
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       "64. '37758_s_at'\n",
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       "84. '37000_at'\n",
       "85. '37009_at'\n",
       "86. '32215_i_at'\n",
       "87. '35303_at'\n",
       "88. '39330_s_at'\n",
       "89. '34728_g_at'\n",
       "90. '37362_at'\n",
       "91. '40182_s_at'\n",
       "92. '41565_at'\n",
       "93. '36432_at'\n",
       "94. '38974_at'\n",
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       "96. '35437_at'\n",
       "97. '38661_at'\n",
       "98. '38068_at'\n",
       "99. '39907_at'\n",
       "100. '41231_f_at'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] \"AFFX-CreX-5_at\"  \"AFFX-CreX-3_at\"  \"AFFX-BioDn-5_at\" \"AFFX-BioB-M_at\" \n",
       "  [5] \"AFFX-BioDn-3_at\" \"39581_at\"        \"AFFX-BioC-3_at\"  \"37014_at\"       \n",
       "  [9] \"2004_at\"         \"AFFX-BioC-5_at\"  \"34363_at\"        \"38065_at\"       \n",
       " [13] \"40071_at\"        \"33730_at\"        \"32597_at\"        \"1034_at\"        \n",
       " [17] \"41386_i_at\"      \"910_at\"          \"AFFX-BioB-3_at\"  \"35059_at\"       \n",
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       " [25] \"1197_at\"         \"38814_at\"        \"40759_at\"        \"39397_at\"       \n",
       " [29] \"35995_at\"        \"AFFX-BioB-5_at\"  \"35224_at\"        \"37026_at\"       \n",
       " [33] \"36617_at\"        \"34717_s_at\"      \"34472_at\"        \"40690_at\"       \n",
       " [37] \"39560_at\"        \"1945_at\"         \"2010_at\"         \"32239_at\"       \n",
       " [41] \"36631_at\"        \"31867_at\"        \"881_at\"          \"38368_at\"       \n",
       " [45] \"34736_at\"        \"37409_at\"        \"37895_at\"        \"39092_at\"       \n",
       " [49] \"960_g_at\"        \"32448_at\"        \"543_g_at\"        \"39926_at\"       \n",
       " [53] \"37828_at\"        \"37331_g_at\"      \"37250_at\"        \"32263_at\"       \n",
       " [57] \"1272_at\"         \"1035_g_at\"       \"406_at\"          \"40407_at\"       \n",
       " [61] \"32143_at\"        \"472_at\"          \"32750_r_at\"      \"37758_s_at\"     \n",
       " [65] \"1998_i_at\"       \"37669_s_at\"      \"36845_at\"        \"35442_at\"       \n",
       " [69] \"34112_r_at\"      \"40881_at\"        \"927_s_at\"        \"1943_at\"        \n",
       " [73] \"37405_at\"        \"40631_at\"        \"33836_at\"        \"41635_at\"       \n",
       " [77] \"471_f_at\"        \"36958_at\"        \"39154_at\"        \"36288_at\"       \n",
       " [81] \"34863_s_at\"      \"39174_at\"        \"40441_g_at\"      \"37000_at\"       \n",
       " [85] \"37009_at\"        \"32215_i_at\"      \"35303_at\"        \"39330_s_at\"     \n",
       " [89] \"34728_g_at\"      \"37362_at\"        \"40182_s_at\"      \"41565_at\"       \n",
       " [93] \"36432_at\"        \"38974_at\"        \"41342_at\"        \"35437_at\"       \n",
       " [97] \"38661_at\"        \"38068_at\"        \"39907_at\"        \"41231_f_at\"     "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rownames(topDEGenes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(gplots)\n",
    "\n",
    "# find the IDs that belong to the DE genes.\n",
    "tID<-rownames(topDEGenes)\n",
    "ind<-1\n",
    "j<-1\n",
    "for (i in 1: length(tID)) {\n",
    "\tind[j]<-which(rownames(eset_estrogen)==tID[i],arr.ind=TRUE)\n",
    "\tj<-j+1\n",
    "}\n",
    "\n",
    "# ind is the vector with all the indexes\n",
    "topExpr<-exprs(eset_estrogen)[ind,]\n",
    "heatmap.2(topExpr, col=redgreen(75), scale=\"row\",\n",
    "key=TRUE, symkey=FALSE, density.info=\"none\", trace=\"none\", cexRow=0.5, cexCol=0.8)"
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    "If you want to change rownames and column, save the list into a dataframe and chage the rownames with the gene SYMBOL.\n",
    "\n",
    "\n",
    "For **Step 7 and 8** you need to use the lists of symbols and upload them onto the web portal. Save the SYMBOL into a tab delimited file and use that into the PANTHER web portal."
   ]
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