Add section on GPL annotations to RPR-GEO2R

BIN-ALI-MSA.R

@@ -238,7 +238,7 @@ for (i in seq_along(highScoringRanges$lengths)) {
 
 # We computed a T-Coffee alignment at the EBI. msa has no native import function
 # so we need to improvise, and it's a bit of a pain to do - but a good
-# illustration of startegies to convert data into any kind of object:
+# illustration of strategies to convert data into any kind of object:
 #   -  read an .aln file
 #   -  adjust the sequence names
 #   -  convert to msaAAMultipleAlignment object
@@ -452,7 +452,7 @@ legend("bottomright",
        cex = 0.7,
        bty = "n")
 
-# Your alignment is going to be differnte from mine, due to the inclusion of
+# Your alignment is going to be different from mine, due to the inclusion of
 # MYSPE - but what I see is that MUSCLE gives the highest score overall, and
 # achieves this with fewer indels then most, and the lowest number of gaps of
 # all algorithms.

BIN-Storing_data.R

@@ -27,7 +27,7 @@
 
 
 #TOC> ==========================================================================
-#TOC> 
+#TOC>
 #TOC>   Section  Title                                             Line
 #TOC> -----------------------------------------------------------------
 #TOC>   1        A Relational Datamodel in R: review                 62
@@ -50,7 +50,7 @@
 #TOC>   3.3      Create an R script to create your own database     540
 #TOC>   3.3.1    Check and validate                                 560
 #TOC>   3.4      Task: submit for credit (part 2/2)                 601
-#TOC> 
+#TOC>
 #TOC> ==========================================================================
 
 
@@ -205,7 +205,7 @@ str(philDB)
 # go back, re-read, play with it, and ask for help. This is essential.
 
 
-# ===  1.1.1  completing the database                       
+# ===  1.1.1  completing the database
 
 
 # Next I'll add one more person, and create the other two tables:
@@ -311,7 +311,7 @@ for (ID in pID) {
 
 
 # Have a look at the structure of the yeast Mbp1 protein data:
-file.edit("./data/MBP1_SACCE.json")
+file.show("./data/MBP1_SACCE.json")
 
 # - The whole thing is an array: [ ... ]. This is not necessary for a single
 #     object, but we will have more objects in other files. And it's perfectly
@@ -369,7 +369,7 @@ dbSanitizeSequence(x)
 
 # ==   2.3  Create a protein table for our data model  =========================
 
-# ===  2.3.1  Initialize the database                       
+# ===  2.3.1  Initialize the database
 
 
 # The function dbInit contains all the code to return a list of empty
@@ -381,7 +381,7 @@ myDB <- dbInit()
 str(myDB)
 
 
-# ===  2.3.2  Add data                                      
+# ===  2.3.2  Add data
 
 
 # fromJSON() returns a dataframe that we can readily process to add data
@@ -428,7 +428,7 @@ source("./scripts/ABC-createRefDB.R")
 str(myDB)
 
 
-# ===  2.4.1  Examples of navigating the database           
+# ===  2.4.1  Examples of navigating the database
 
 
 # You can look at the contents of the tables in the usual way we access
@@ -552,7 +552,7 @@ myDB$taxonomy$species[sel]
 # in any of the JSON files. Later you will add more information ...
 
 
-# ===  3.3.1  Check and validate                            
+# ===  3.3.1  Check and validate
 
 
 # Is your protein named according to the pattern "MBP1_MYSPE"? It should be.

RPR-GEO2R.R

@@ -3,12 +3,14 @@
 # Purpose:  A Bioinformatics Course:
 #              R code accompanying the RPR_GEO2R unit.
 #
-# Version:  0.1
+# Version:  1.1
 #
-# Date:     2017  MM  DD
+# Date:     2017 09  -  2018 01
-# Author:   Boris Steipe (boris.steipe@utoronto.ca)
+# Author:   Boris Steipe <boris.steipe@utoronto.ca>
 #
 # Versions:
+#           1.1    Add section on GPL annotations
+#           1.0    Updates for BCH441 2017
 #           0.1    First code copied from 2016 material.
 #
 #
@@ -32,17 +34,18 @@
 #TOC> 
 #TOC>   Section  Title                                                Line
 #TOC> --------------------------------------------------------------------
-#TOC>   1        Preparations                                           50
+#TOC>   1        Preparations                                           53
-#TOC>   2        Loading a GEO Dataset                                  81
+#TOC>   2        Loading a GEO Dataset                                  84
-#TOC>   3        Column wise analysis - time points                    151
+#TOC>   3        Column wise analysis - time points                    154
-#TOC>   3.1      Task - Comparison of experiments                      157
+#TOC>   3.1      Task - Comparison of experiments                      160
-#TOC>   3.2      Grouped Samples                                       204
+#TOC>   3.2      Grouped Samples                                       207
-#TOC>   4        Row-wise Analysis: Expression Profiles                239
+#TOC>   4        Row-wise Analysis: Expression Profiles                242
-#TOC>   4.1      Task - Read a table of features                       274
+#TOC>   4.1      Task - Read a table of features                       277
-#TOC>   4.2      Selected Expression profiles                          322
+#TOC>   4.2      Selected Expression profiles                          325
-#TOC>   5        Differential Expression                               363
+#TOC>   5        Differential Expression                               366
-#TOC>   5.1      Final task: Gene descriptions                         487
+#TOC>   5.1      Final task: Gene descriptions                         490
-#TOC>   6        Improving on Discovery by Differential Expression     492
+#TOC>   6        Improving on Discovery by Differential Expression     495
+#TOC>   7        Annotation data                                       577
 #TOC> 
 #TOC> ==========================================================================
 
@@ -74,8 +77,8 @@ if (! require(GEOquery, quietly=TRUE)) {
 }
 # Package information:
 #  library(help = GEOquery)       # basic information
-#  browseVignettes("GEOquery")  # available vignettes
+#  browseVignettes("GEOquery")    # available vignettes
-#  data(package = "GEOquery")   # available datasets
+#  data(package = "GEOquery")      # available datasets
 
 
 # =    2  Loading a GEO Dataset  ===============================================
@@ -264,7 +267,7 @@ file.show("./data/SGD_features.README.txt")
 #     Note: the file as downloaded from SGD actually crashed RStudio due to an
 #           unbalanced quotation mark which caused R to try and read the whole
 #           of the subsequent file into a single string. This was caused by an
-#           alias gene name (B"). I have removed this abomination,
+#           alias gene name (B"). I have removed this abomination
 #           by editing the file. The version in the ./data directory can be
 #           read without issues.
 
@@ -571,5 +574,52 @@ for (i in 1:length(myBottomC)) {
 #        and explore. There is a learning curve - but the payoffs are
 #        significant.
 
+# =    7  Annotation data  =====================================================
+#
+# Loading feature data "by hand" as we've done above, is usually not necessary
+# since GEO provides rich annotations in the GPL platform files, which are
+# associated with its Gene Expression Sets files. In the code above,
+# we used getGEO("GSE3635", GSEMatrix = TRUE, getGPL = FALSE), and the GPL
+# annotations were not loaded. We could use getGPL = TRUE instead ...
+
+GSE3635annot <- getGEO("GSE3635", GSEMatrix = TRUE, getGPL = TRUE)
+GSE3635annot <- GSE3635annot[[1]]
+
+# ... and the feature data is then available in the GSE3635@featureData@data
+#     slot:
+str(GSE3635annot@featureData@data)
+GSE3635annot@featureData@data[ 1:20 , ]
+
+# ... from where you can access the columns you need, e.g. spotIDs and gene
+#     symbols:
+
+myAnnot <- GSE3635annot@featureData@data[ , c("SPOT_ID", "Gene")]
+str(myAnnot)
+
+# ... Note that this is a data frame, but - oy veh - the gene symbols are
+#     factors. Really, we need to fix this! To convert a factor into a string,
+#     we need to cast it to character.
+
+myAnnot$Gene <- as.character(myAnnot$Gene)
+
+# ... whereupon we can find things we might be looking for ...
+myAnnot[which(myAnnot$Gene == "MBP1"), ]
+
+# ... or identify rows that might give us trouble, such as probes that
+#     hybridize to more than one gene.
+
+
+# Alternatively, we could have identified the GPL file for this set:
+GSE3635@annotation   # "GPL1914"
+
+# ... and downloaded it directly from NCBI:
+GPL1914 <- getGEO("GPL1914")
+str(GPL1914)
+
+# ... from which we can get the data - which is however NOT necessarily
+# matched to the rows of our expression dataset. Note that here to: the majority
+# of data elements are factors and will likely have to be converted before
+# use.
+
 
 # [END]

RPR-SX-PDB.R

@@ -16,6 +16,7 @@
 #
 # TODO:
 #          Confirm that SS residue numbers are indices
+#          Set task seed from student number
 #
 # == DO NOT SIMPLY  source()  THIS FILE! =======================================
 #
@@ -403,7 +404,7 @@ om <- c(360 + tor$omega[tor$omega < 0],
 hist(om, xlim=c(0,360))
 abline(v=180, col="red")
 
-# Note: a cis-peptide bond will have an omega torsion angle of around 0°
+# Note: a cis-peptide bond will have an omega torsion angle around 0°
 
 
 # =    5  H-bond lengths  ======================================================