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author george-weingart
date Tue, 13 May 2014 21:58:57 -0400
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1 <tool id="micropita" name="Run" version="1.0.1">
2 <code file="micropita_format_input_selector.py"/>
3 <description>micropita</description>
4 <command interpreter="python">micropita_prepare.py
5 --lastmeta $cls_x
6 -m $cond.method_sel
7 -n $selected_samples
8 --input $inp_data
9 --output $out_file1
10 --stratify_value $cls_s
11
12 #if $cond.method_sel == "features":
13 --feature_method $cond.feature_method
14 --targets $cond.cls_f
15 #end if
16 #if $cond.method_sel == "distinct" or $cond.method_sel == "discriminant" :
17 --label_value $cond.cls_L
18 #end if
19
20 </command>
21 <inputs>
22 <param format="micropita" name="inp_data" type="data" label="Input file"/>
23
24 <param name="cls_x" type="select" label="Last metadata row (Use 'Label' for demo data)" multiple="False" size ="70" dynamic_options="get_cols(inp_data,'0')"/>
25
26
27 <param name="cond" type="data_column" data_ref="inp_data" accept_default="true" />
28 <conditional name="cond" type="data_column" data_ref="inp_data" accept_default="true">
29 <param name="method_sel" type="select" data_ref="inp_data" label="Select method">
30 <option value="representative" selected="True">Representative</option>
31 <option value="diverse">Diverse</option>
32 <option value="extreme" >Extreme</option>
33 <option value="features" >Features</option>
34 <option value="distinct" >Distinct (Supervised)</option>
35 <option value="discriminant" >Discriminant (Supervised)</option>
36 </param>
37 <when value="representative">
38 </when>
39 <when value="diverse">
40 </when>
41 <when value="extreme">
42 </when>
43 <when value="features">
44 <param name="cls_f" type="select" label="Targeted feature(s)" multiple="True" size ="70" dynamic_options="get_cols_features(inp_data,'0',cls_x)"/>
45 <param name="feature_method" type="select" format="text">
46 <label>Selection type</label>
47 <option value="rank">Rank</option>
48 <option value="abundance">Abundance</option>
49 </param>
50 </when>
51 <when value="distinct">
52 <param name="cls_L" type="select" label="Label (Use 'Group' for demo data)" multiple="False" size ="70" dynamic_options="get_cols_add_line(inp_data,'0',cls_x)"/>
53 </when>
54
55 <when value="discriminant">
56 <param name="cls_L" type="select" label="Label (Use 'Group' for demo data)" multiple="False" size ="70" dynamic_options="get_cols_add_line(inp_data,'0',cls_x)"/>
57 </when>
58
59 </conditional>
60
61
62 <param name="cls_s" type="select" label="Stratify by (optional) (Use 'StratifyLabel' for demo data)" multiple="False" size ="70" dynamic_options="get_cols_add_line(inp_data,'0',cls_x)"/>
63
64 <param name="selected_samples" type="integer" size="4" value="10" label="Number of samples to select"/>
65
66
67
68 </inputs>
69
70 <outputs>
71 <data format="text" name="out_file1" />
72 </outputs>
73 <requirements>
74 <requirement type="set_environment">micropita_SCRIPT_PATH</requirement>
75 </requirements>
76 <help>
77
78
79 microbiome: Picking Interesting Taxonomic Abundance
80 ---------------------------------------------------
81
82
83
84 microPITA is a computational tool enabling sample selection in tiered studies. Using tiered-study designs can more efficiently allocate resources, reducing study costs, and maximizing the use of samples. From a survey study, selection of samples can be performed to target various microbial communities including:
85
86 1. Samples with the most diverse community (maximum diversity);
87 2. Samples dominated by specific microbes (targeted feature);
88 3. Samples with microbial communities representative of the survey (representative dissimilarity);
89 4. Samples with the most extreme microbial communities in the survey (most dissimilar);
90 5. Given a phenotype (like disease state), samples at the border of phenotypes (discriminant) or samples typical of each phenotype (distinct).
91
92 Additionally, methods can leverage clinical metadata by stratifying samples into groups in which samples are subsequently selected. This enables the use of microPITA in cohort studies.
93
94
95 .. image:: https://bytebucket.org/biobakery/galaxy_micropita/wiki/HMPStool10PCoA.png
96 :height: 500
97 :width: 600
98
99 MicroPITA unsupervised method selection in the HMP 16S Gut Microbiome. Selection of 10 samples using targeted feature targeting *Bacteroides* (blue), maximum diversity (orange), representative dissimilarity (purple), and most dissimilar (pink) using Principle Covariance Analysis (PCoA) for ordination. Targeted feature selects samples dominated by *Bacteroides* (upper left) while maximum diversity select more diverse samples away from *Bacteroides* dominant samples. Representative selection selects samples covering the range of samples in the PCoA plot focusing on the higher density central region while maximum dissimilarity selects samples at the periphery of the plot.
100
101
102 Intructions to run:
103 -------------------
104
105 Before running microPita, you must upload your data using Glaxay's **Get Data - Upload File**
106 Please make sure that you choose **File Format Micropita**
107 An example can be found at https://bytebucket.org/biobakery/micropita/wiki/micropita_sample_PCL.txt
108
109 Required inputs
110 ---------------
111
112 microPITA requires an input pcl file of metadata and microbial community measurements. Although some defaults can be changed, microPITA expects a PCL file as an input file. A PCL file is a text delimited file similar to an excel spread sheet with the following characteristics.
113
114 1. **Rows** represent metadata and features (bugs), **columns** represent samples.
115 2. The **first row** by default should be the sample ids.
116 3. Metadata rows should be next.
117 4. Lastly, rows containing features (bugs) measurements (like abundance) should be after metadata rows.
118 5. The **first column** should contain the ID describing the column. For metadata this may be, for example, "Age" for a row containing the age of the patients donating the samples. For measurements, this should be the feature name (bug name).
119 6. The file is expected to be TAB delimited.
120 7. If a consensus lineage or hierarchy of taxonomy is contained in the feature name, the default delimiter between clades is the pipe ("|").
121
122 **Note** MAC users, please save file as windows formatted text.
123
124 .. image:: https://bytebucket.org/biobakery/galaxy_micropita/wiki/pcl_diagram.png
125 :height: 500
126 :width: 600
127
128 Outputs
129 -------
130
131 The Run MicroPITA module will create one output text file. The output will consist of one line starting with a key word for the selection method and then followed by selected samples delimited by tabs. An example of 6 samples selected by the representative:
132
133 representative sample_1 sample_2 sample_3 sample_4 sample_5 sample_6
134
135
136
137
138 Run microPITA
139 -------------
140
141 A brief description of the Run micropita module.
142
143 **Input file:**
144 This should be populated by the Load microPITA module.
145
146 **Last metadata row:**
147 The row on the input pcl file that is the last metadata. All microbial measurements should follow this row.
148
149 **Select method:**
150 Select which method to use for sample selection. Selection methods include:
151
152 1. Representative. Samples with microbial communities representative of the survey (representative dissimilarity);
153 2. Diverse. Samples with the most diverse community (maximum diversity);
154 3. Extreme. Samples with the most extreme microbial communities in the survey (most dissimilar);
155 4. Features. Samples dominated by specific microbes (targeted feature);
156 5. Distinct. Given a phenotype (like disease state), samples typical of each phenotype (Distinct).
157 6. Discriminant. Given a phenotype (like disease state), samples at the border of phenotypes (Discriminant).
158
159 **Targeted feature(s):** (visible with Features method selection only)
160 Select 1 or more features to target in sample selection.
161
162 **Selection type:** (visible with Features method selection only)
163 Rank or Abundance.
164
165 1. Rank indicates selecting samples that have the highest rank of the Targeted features(s), this tends to select sample in which these feature dominant the sample.
166 2. Abundance indicates selecting samples that have the highest average abundance of the Targeted features(s), this selects samples where features are most abundant but not necessarily dominant in the community.
167
168 **Label:** (visible with supervised method selection only)
169 The row which contains the label used to classify the samples from supervised methods.
170
171 **Stratify by (optional):**
172 The row which contains the groupings the samples will first be placed in before running the selection method on each group. If no grouping is selected, selection methods will be performed on the data set as a whole.
173
174 **Number of samples to select:**
175 The number of samples to select. If samples are stratified, this is per stratification (or group). If supervised methods are used, this is the number of samples selected per classification group (as defined by the label).
176
177 For more information please visit http://huttenhower.sph.harvard.edu/micropita
178
179
180 Acknowledgments
181 ---------------
182 Special thanks to Eric Franzosa for developing the above PCL figure!
183
184 Citation and Contacts
185 ---------------------
186
187 For more information please visit http://huttenhower.sph.harvard.edu/micropita
188 When using MicroPITA please cite:
189 Tickle T, Segata N, Waldron L, Weingart G, Huttenhower C. Two-stage microbial community experimental design. (Under review)
190
191 Please feel free to contact us at ttickle@hsph.harvard.edu for any questions or comments!
192
193
194 </help>
195 </tool>