Body Location Tutorial - Revision history

Dparks: /* A longer Rpy2 script */

2012-03-26T17:23:50Z

A longer Rpy2 script

Dparks: /* A longer Rpy2 script */

2012-03-26T17:09:45Z

A longer Rpy2 script

Dparks: /* A simple example */

2012-03-26T17:09:03Z

A simple example

Dparks: /* A simple example */

2012-03-26T17:06:02Z

A simple example

Dparks: /* A simple example */

2012-03-26T16:50:49Z

A simple example

Dparks: /* Running R commands directly in the Python console */

2012-03-26T16:50:04Z

Running R commands directly in the Python console

Dparks: /* Running R commands directly in the Python console */

2012-03-26T16:49:36Z

Running R commands directly in the Python console

Dparks: /* Linear regression */

2012-03-26T16:48:45Z

Linear regression

Dparks: /* Linear regression */

2012-03-26T16:46:50Z

Linear regression

Dparks: /* Linear regression */

2012-03-26T16:44:13Z

Linear regression

@@ Line 118: / Line 118: @@
 ###    columnnToUse: a column heading from the sequence file
 ###    labelsToUse: a list object containing at least two different labels from the selected column.
 ###
-### Example usage: Heatmap.RunHeatmap("Phylum",["Bacteroides","Firmicutes"])
+### Example usage: Heatmap.RunHeatmap("Phylum",["Bacteroides","Firmicutes"],"Count")
-def RunHeatmap(columnToUse,labelsToUse):
+def RunHeatmap(columnToUse,labelsToUse,countField):
 	r = robjects.r
 </pre>
@@ Line 144: / Line 144: @@
 	### Iterate through the sequence data and build the dictionary of frequencies
 	for loc in locs:
 		seqCounts[loc] = {}
 		allCounts[loc] = 0
@@ Line 152: / Line 151: @@
 		### Some sequences may be disabled, ignore them
 		seqLayers = loc.GetAllActiveSequenceLayers()
-		sc = len(seqLayers)
-		allCounts[loc] = sc
+		for seqLayer in seqLayers:
+			data = seqLayer.GetController().GetData()
-		for j in xrange(0, sc):
+			thisLabel = data[columnToUse]
-				data = seqLayers[j].GetController().GetData()
+			seqCount = float(data[countField])
-				thisLabel = data[columnToUse]
+			if thisLabel in labelsToUse:
-				if thisLabel in labelsToUse:
+				seqCounts[loc][thisLabel] += seqCount
-					seqCounts[loc][thisLabel] += 1
+			allCounts[loc] += seqCount
 </pre>
-A couple of important things are going on here: first, we initialize the counts of all interesting labels to zero. The call to loc.GetAllActiveSequenceLayers() returns a list of all sequences associated with the current site. We then iterate through each sequence, incrementing the global count, AND the specific count if it has one of the labels we're looking for.
+A couple of important things are going on here: first, we initialize the counts of all interesting labels to zero. The call to loc.GetAllActiveSequenceLayers() returns a list of all active sequences associated with the current site. We then iterate through each sequence, incrementing the global count, and the location specific count if it has one of the labels we're looking for.
-The astute reader will notice that we haven't ''done'' anything with Rpy2 yet, but that is about to change. To plot a sensible heatmap we need three items: the data matrix, and the labels for the rows and columns. Let's build those label lists now:
+The astute reader will notice that we haven't ''done'' anything with RPy2 yet, but that is about to change. To plot a sensible heatmap we need three items: the data matrix, and the labels for the rows and columns. Let's build those label lists now:
 <pre>
@@ Line 175: / Line 174: @@
 </pre>
-This is a good opportunity to emphasize the syntax differences between Rpy2 version 2.03 (the version used by GenGIS) and versions 2.1 and higher. These lists are built differently, and the example above is based on the  [[http://rpy.sourceforge.net/rpy2/doc-2.0/html/introduction.html#examples Version 2.08 documentation]].
+This is a good opportunity to emphasize the syntax differences between RPy2 version 2.03 (the version used by GenGIS) and versions 2.1 and higher. These lists are built differently, and the example above is based on the  [[http://rpy.sourceforge.net/rpy2/doc-2.0/html/introduction.html#examples Version 2.08 documentation]].
-The row and column labels must be string vectors invoked using R's c(1, 2, 3, ...) syntax: the above rpy2 calls are equivalent to this. The Python list comprehension in 'xVec' collects the value of the "Organ" column from the Location object. This corresponds to the appropriate column in the location file.
+The row and column labels must be string vectors invoked using R's c(1, 2, 3, ...) syntax: the above RPy2 calls are equivalent to this. The Python list comprehension in 'xVec' collects the value of the "Organ" column from the Location object. This corresponds to the appropriate column in the location file.
 Now, let's build the normalized data matrix:
@@ Line 211: / Line 210: @@
 As an example of usage, invoking RunHeatmap with the following command:
-Heatmap.RunHeatmap("Phylum",["Bacteroidetes","Firmicutes","Proteobacteria"])
+Heatmap.RunHeatmap("Phylum",["Bacteroidetes","Firmicutes","Proteobacteria"], "Count")
 should produce the following image.

@@ Line 88: / Line 88: @@
 ==A longer Rpy2 script==
-If we want to do fancier things using R, then it makes sense to write a script and execute that script within the GenGIS console. Here is an example of how to generate heatmaps of your data using a Python script. The example here is the creatively named "heatmap.py".
+If we want to do fancier things using R, then it makes sense to write a script and execute that script within the GenGIS console. Here is an example of how to generate heatmaps of your data using a Python script. The example here is the creatively named "Heatmap.py".
 First, you need to tell the GenGIS Python interpreter where to find the script. If the script is not in one of the default locations, here is one way to help GenGIS find it:

@@ Line 80: / Line 80: @@
 </pre>
-With this accomplished, we can use the R "barplot" function to generate a bar plot that should be shown in a new window.
+With this accomplished, we can use the R "barplot" function to generate a bar plot:
 <pre>

@@ Line 60: / Line 60: @@
 </pre>
-For the bar plot, we would like to pair up the name of each body site with the number of sequences. In both cases we will use list comprehensions. The 'counts' list is filled using a list comprehension that counts the number of active sequence layers (= the number of active sequences) from each location. 'names' is filled by grabbing the name from the "Organ" field.
+For the bar plot, we would like to pair up the name of each body site with the number of sequences. The 'counts' list contains the number of active sequences from each location. 'names' is filled by grabbing the name from the "Organ" field.
 <pre>
-counts = [len(locX.GetAllActiveSequenceLayers()) for locX in locs]
+counts = []
 names = [locX.GetController().GetData()["Organ"] for locX in locs]
 </pre>

@@ Line 46: / Line 46: @@
 ==A simple example==
-Simple analyses can be done directly by typing or pasting commands into the Python console. A good way to start is by importing the required Rpy2 libraries, and setting the variable 'r' to allow simpler calls to R functions.
+Simple analyses can be done directly by typing or pasting commands into the Python console. A good way to start is by importing the required RPy2 libraries, and setting the variable 'r' to allow simpler calls to R functions.
 <pre>

@@ Line 42: / Line 42: @@
 =Running R commands directly in the Python console=
-The inclusion of the [http://rpy.sourceforge.net/rpy2.html RPy2] libraries allows R commands to be embedded in a Python script. This allows you to perform statistical analyses interactively in the Python console, and to create external scripts that can be invoked with a single command to the console.
+The inclusion of the [http://rpy.sourceforge.net/rpy2.html RPy2] libraries allows R commands to be embedded in a Python script. This allows you to perform statistical analyses interactively in the GenGIS Python console, and to create external scripts that can be invoked with a single command to the console.
 ==A simple example==

@@ Line 34: / Line 34: @@
 The results of the analysis are shown in the lower left-hand corner, and the plot shows a weak negative trend with an unimpressive p-value of 0.189. Interestingly, one of the sites appear to be enriched in Clostridia relative to the others: which one is it?
-Results from this plugin are propagated to the "Viewport" based on the "Viewport display" options. The default action is to plot regression residuals on the map, but we can change this to show either of the variables we just examined. Select "x data" in the Plot Type drop-down menu, and click Calculate again. The regression doesn't change, but now we can switch over to the "Viewport" window to see the association of ''Clostridium'' frequencies with different body sites. The highest frequencies in this pincushion example are seen in the knees and the feet, although the right ear is also somewhat enriched in Clostridia, possibly due to a very high frequency in one of the right ear samples that is summarized here.
+Results from this plugin are propagated to the "Viewport" based on the "Viewport display" options. The default action is to plot regression residuals on the map, but we can change this to show either of the variables we just examined. Select "x data" in the Plot Type drop-down menu, and click Calculate again. The regression doesn't change, but now we can switch over to the "Viewport" window to see the association of ''Clostridium'' frequencies with different body sites. The highest frequencies in this pincushion example are seen in the left elbow, although the right ear is also somewhat enriched in Clostridia, possibly due to a very high frequency in one of the right ear samples that is summarized here.
 [[Image:LR_viewport.png|thumb|center|600px|Figure 3. ''Clostridium'' frequencies plotted on the human body sites.]]

@@ Line 36: / Line 36: @@
 Results from this plugin are propagated to the "Viewport" based on the "Viewport display" options. The default action is to plot regression residuals on the map, but we can change this to show either of the variables we just examined. Select "x data" in the Plot Type drop-down menu, and click Calculate again. The regression doesn't change, but now we can switch over to the "Viewport" window to see the association of ''Clostridium'' frequencies with different body sites. The highest frequencies in this pincushion example are seen in the knees and the feet, although the right ear is also somewhat enriched in Clostridia, possibly due to a very high frequency in one of the right ear samples that is summarized here.
-[[Image:LR_res.png|thumb|center|600px|Figure 3. ''Clostridium'' frequencies plotted on the human body sites.]]
+[[Image:LR_viewport.png|thumb|center|600px|Figure 3. ''Clostridium'' frequencies plotted on the human body sites.]]
 The Mantel test plugin is structured in a very similar way to the linear regression plugin, but it operates on pairwise distances between sites rather than directly on the values associated with the sites. The exact same procedure can be followed as above, with the additional opportunity to include geographic distances (which are undefined in this case) or Euclidean distances between sites as a variable. The Mantel test is widely used in ecological studies, see for instance Hughes Martiny et al. (2006).

@@ Line 32: / Line 32: @@
 [[Image:LR.png|thumb|center|800px|Figure 2. Linear regression plugin window.]]
-The results of the analysis are shown in the lower left-hand corner, and the plot shows a weak negative trend with an unimpressive p-value of 0.189. Two sites appear to be enriched in Clostridia relative to the others: which ones are they?
+The results of the analysis are shown in the lower left-hand corner, and the plot shows a weak negative trend with an unimpressive p-value of 0.189. Interestingly, one of the sites appear to be enriched in Clostridia relative to the others: which one is it?
-Results from this plugin are propagated to this window via the "Viewport Display" options. The default action is to plot regression residuals on the map, but we can change this to show either of the variables we just examined. Select "x data" in the Plot Type drop-down menu, and click Calculate again. The regression doesn't change, but now we can switch over to the map window to see the association of ''Clostridium'' frequencies with different body sites. The highest frequencies in this pincushion example are seen in the knees and the feet, although the right ear is also somewhat enriched in Clostridia, possibly due to a very high frequency in one of the right ear samples that is summarized here.
+Results from this plugin are propagated to the "Viewport" based on the "Viewport display" options. The default action is to plot regression residuals on the map, but we can change this to show either of the variables we just examined. Select "x data" in the Plot Type drop-down menu, and click Calculate again. The regression doesn't change, but now we can switch over to the "Viewport" window to see the association of ''Clostridium'' frequencies with different body sites. The highest frequencies in this pincushion example are seen in the knees and the feet, although the right ear is also somewhat enriched in Clostridia, possibly due to a very high frequency in one of the right ear samples that is summarized here.
 [[Image:LR_res.png|thumb|center|600px|Figure 3. ''Clostridium'' frequencies plotted on the human body sites.]]