{
"choices":{
"statistics":[
{"value":"anova", "label":"Anova"},
{"value":"kruskal", "label":"Kruskal-Wallis rank sum test"},
{"value":"wilcox", "label":"Wilcoxon test"},
{"value":"ttest", "label":"T-test"},
{"value":"friedman", "label":"Friedman test"}
],
"binning":[
{"value":"sum", "label":"Sum values within categories"},
{"value":"mean", "label":"Average values within categories"}
],
"transformation":[
{"value":"log2cpm", "label":"log2 of count per million"},
{"value":"log2", "label":"Log2"},
{"value":"percent", "label":"Percent"},
{"value":"none", "label":"No transformation"}
],
"diversity":[
{"value":"shannon", "label":"Shannon Diversity Index", "pkg":"vegan"},
{"value":"simpson", "label":"Simpson Diversity Index", "pkg":"vegan"},
{"value":"invsimpson", "label":"inverse Simpson Diversity Index", "pkg":"vegan"},
{"value":"richness", "label":"Richness", "pkg":""},
{"value":"Gini", "label":"Gini coefficient", "pkg":"ineq"},
{"value":"chao1", "label":"chao1 estimator", "pkg":"fossil"},
{"value":"RS", "label":"Ricci-Schutz coefficient", "pkg":"ineq"},
{"value":"Atkinson", "label":"Atkinson measure", "pkg":"ineq"},
{"value":"Theil", "label":"Theil entropy measure", "pkg":"ineq"},
{"value":"Kolm", "label":"Kolm measure", "pkg":"ineq"},
{"value":"var", "label":"Coefficient of variation", "pkg":"ineq"}
],
"correlation":[
{"value":"spearman", "label":"Spearman Correlation Coefficient", "pkg":""},
{"value":"pearson", "label":"Pearson Correlation Coefficient", "pkg":""}
],
"distance":[
{"value":"bray", "label":"Bray-Curtis Distance", "pkg":"vegan"},
{"value":"jaccard", "label":"Jaccard Index", "pkg":"vegan"},
{"value":"spearman", "label":"Spearman Correlation Coefficient", "pkg":""},
{"value":"pearson", "label":"Pearson Correlation Coefficient", "pkg":""},
{"value":"canberra", "label":"Canberra Distance", "pkg":"vegan"},
{"value":"kulczynski", "label":"Kulczynski Similarity Measure", "pkg":"vegan"},
{"value":"gower", "label":"Gower Similarity Coefficient", "pkg":"vegan"},
{"value":"morisita", "label":"Morisita Overlap Index", "pkg":"vegan"},
{"value":"horn", "label":"Horn-Morisita Index", "pkg":"vegan"},
{"value":"mountford", "label":"Mountford Index", "pkg":"vegan"},
{"value":"raup", "label":"Raup-Crick dissimilarity", "pkg":"vegan"},
{"value":"binomial", "label":"Binomial distance", "pkg":"vegan"},
{"value":"chao", "label":"Chao Index", "pkg":"vegan"},
{"value":"euclidean", "label":"Euclidean Distance", "pkg":"vegan"},
{"value":"manhattan", "label":"Manhattan Distance", "pkg":"vegan"}
],
"clustering":[
{"value":"none",
"label":"",
"pkg":""},
{"value":"pam",
"label":"k-medoids",
"pkg":"fpc"},
{"value":"pam-bray",
"label":"k-medoids on Bray-Curtis dissimilarity",
"pkg":"fpc"},
{"value":"pam-jaccard",
"label":"k-medoids on Jaccard distance",
"pkg":"fpc"},
{"value":"kmeans",
"label":"k-means",
"pkg":"fpc"}
],
"graph_clustering":[
{"value":"none", "label":"", "pkg":""},
{"value":"fastgreedy", "label":"Greedy optimization of modularity algorithm", "pkg":"igraph"},
{"value":"louvain", "label":"Multi-level optimization of modularity algorithm", "pkg":"igraph"},
{"value":"walktrap", "label":"Short random walks algorithm", "pkg":"igraph"},
{"value":"labelpropagation", "label":"Label propagation method of Raghavan et al.", "pkg":"igraph"}
]
},
"fields":{
"Inputs":[
{"id":"name",
"scope":"form_only",
"help":"This will be the name of the new job. Choose something relevent and avoid special characters. Ex: Exp01_Seq01.",
"type":"text",
"optional":false,
"placeholder":"New job name"
},
{"id":"description",
"scope":"form_only",
"help":"This will be the description of the new job. Give enough details for your analysis to be understood by someone else (if you want to share).",
"type":"textarea",
"optional":true,
"placeholder":"Some details"
},
{"id":"primary_dataset",
"help":"Data file. Format must be tab-delimited text with one column per sample and one row per OTU/gene. Last column must be 'taxonomy' and contain ';'-delimited categories. No row or column names duplicate are allowed. First row must include samples names. Comments can be included at the beginning of the file and start with '#'. [File format]",
"type":"file",
"optional":false
},
{"id":"category_column",
"help":"This must be the name of the column in your input file that contains the category (i.e. taxonomy, pathways, etc.).",
"type":"select",
"optional":false,
"depends_on":"primary_dataset"
},
{"id":"map",
"help":"Mapping file. It describes the experimental design. Columns represent factors (ex: treatment,sex,etc.) and rows indicates samples. Format must be tab-delimited text. First row must contain column names. At least one column must contain the sample names,matching the first row of data file (Input). [File format]",
"type":"file",
"optional":false
},
{"id":"secondary_dataset",
"help":"Optional metadata file. It can include related data from a different type of analysis that was performed on the same samples. Format must be the same as data file (Input) but without any category column and without any comment. Column names must match column names in data file (Input) in spelling and order. [File format]",
"type":"file"
}
],
"Pre-processing":[
{"id":"abundance_threshold_type",
"type":"threshold_type",
"default":"percent",
"bypass":1
},
{"id":"abundance_threshold",
"help":"Abundance threshold for filtering.",
"type":"threshold",
"default_type":"percent",
"default_integer":"10",
"default_percent":"0.03",
"placeholder_integer":"0",
"placeholder_percent":"0-100"
},
{"id":"presence_threshold_type",
"type":"threshold_type",
"default":"int",
"bypass":1
},
{"id":"presence_threshold",
"help":"Presence threshold for filtering.",
"type":"threshold",
"default_type":"integer",
"default_integer":"2",
"default_percent":"10",
"placeholder_integer":">0",
"placeholder_percent":"0-100"
},
{"id":"bin_levels",
"label":"Binning levels",
"help":"Category binning levels.",
"type":"select",
"multiple":true
},
{"id":"bin_fun",
"label":"Category binning function",
"help":"Function to apply for category binning.",
"type":"select",
"values":"binning",
"default":"sum"
}
],
"Transformations":[
{"id":"prim_rarefaction",
"label":"Rarefaction (Primary dataset)",
"help":"Sub-sampling (random drawings without replacement).",
"type":"check_box",
"db_field":"prim_rarefaction",
"trigger":"drop_down",
"default":true
},
{"id":"prim_sampling_depth",
"label":"Sampling depth",
"help":"Number of elements (counts) to draw in each sample. Must be a positive integer. The value cannot exceed the minimum count per sample in the data set (it will be corrected automatically if needed).",
"belongs_to":"prim_rarefaction",
"type":"integer",
"default":"500000",
"placeholder":">0"
},
{"id":"prim_nsampling",
"label":"N samplings",
"help":"Number of repeated rarefactions to perform (the result will be an average of these rarefactions).",
"belongs_to":"prim_rarefaction",
"type":"integer",
"default":"3",
"placeholder":">0"
},
{"id":"prim_trans_method",
"label":"Transformation (Primary dataset)",
"help":"Choose a data transformation method.",
"type":"select",
"values":"transformation",
"default":"log2cpm"
},
{"id":"prim_batch_effect_suppression",
"label":"Batch effect suppression (Primary dataset)",
"help":"Batch effect(s) can be suppressed to facilitate statistical tests. Example: In an experiment where 20 samples from experimental group A and 20 samples from experimental group B where distributed into two batches for processing,the batch-effect can be supressed. Make sure it is consitent with Model,in the Experimental design section.",
"type":"select",
"multiple":true
},
{"id":"sec_rarefaction",
"label":"Rarefaction (Secondary dataset)",
"help":"Sub-sampling (random drawings without replacement).",
"type":"check_box",
"db_field":"sec_rarefaction",
"trigger":"drop_down",
"default":true
},
{"id":"sec_sampling_depth",
"label":"Sampling depth",
"help":"Number of elements (counts) to draw in each sample. Must be a positive integer. The value cannot exceed the minimum count per sample in the data set (it will be corrected automatically if needed).",
"belongs_to":"sec_rarefaction",
"type":"integer",
"default":"500000",
"placeholder":">0"
},
{"id":"sec_nsampling",
"label":"N samplings",
"help":"Number of repeated rarefactions to perform (the result will be an average of these rarefactions).",
"belongs_to":"sec_rarefaction",
"type":"integer",
"default":"3",
"placeholder":">0"
},
{"id":"sec_trans_method",
"label":"Transformation (Secondary dataset)",
"help":"Choose a data transformation method.",
"type":"select",
"values":"transformation",
"default":"log2cpm"
},
{"id":"sec_batch_effect_suppression",
"label":"Batch effect suppression (Secondary dataset)",
"help":"Batch effect(s) can be suppressed to facilitate statistical tests. Example: In an experiment where 20 samples from experimental group A and 20 samples from experimental group B where distributed into two batches for processing,the batch-effect can be supressed. Make sure it is consitent with Model,in the Experimental design section.",
"type":"select",
"multiple":true
}
],
"Analysis":[
{"id":"model_type",
"label":"Experimental design",
"help":"Choose a basic or advanced model.",
"type":"model_type",
"default":"basic"
},
{"id":"basic_model",
"label":"Model",
"help":"This will define the statistical model to use for the analysis. An Anova will be used to compare groups.",
"type":"select",
"belongs_to":"model_type_basic"
},
{"id":"advanced_stats",
"label":"Statistics",
"help":"Define which statistics will be used to compare groups. You need to make sure that the syntax of the Model formula fits the statistics requirements.",
"type":"select",
"belongs_to":"model_type_advanced",
"values":"statistics",
"default":"anova"
},
{"id":"advanced_model",
"label":"Model",
"help":"This will define the statistical model to use for the analysis. It should be a formula with terms refering to columns in the mapping file. Simple example: In an experiment where column A defines the subjects genders and column B defines subjects diets,a model testing both the effect of gender and diet and their interactions could be writen A*B. Example with nested variables: In an experiment where column A defines individuals and column B defines replicated sampling within each individuals,a model testing the effect of B could be writen A/B.",
"type":"text",
"placeholder":"ex:ExperimentalGroup*Gender",
"belongs_to":"model_type_advanced"
},
{"id":"proportions",
"scope":"form_only",
"label":"Proportions",
"help":"Display proportions in a stacked bar-chart.",
"type":"check_box",
"db_field":"analysis",
"default":true
},
{"id":"diversity",
"scope":"form_only",
"help":"This will assess the diversity within each sample. It will generate rarefaction curves for each selected diversity metric that will allow you to compare diversity between samples and between the groups defined in your model.",
"type":"check_box",
"color":"tomato",
"db_field":"analysis",
"trigger":"drop_down",
"default":false
},
{"id":"diversity_metric",
"label":"Metric",
"belongs_to":"diversity",
"help":"Choose metric(s).",
"type":"select",
"multiple":true,
"values":"diversity",
"default":"richness"
},
{"id":"compare_diversity",
"label":"Compare groups",
"belongs_to":"diversity",
"help":"Compare experimental groups using model and statistical test?",
"type":"bool",
"default":false
},
{"id":"adonis",
"scope":"form_only",
"label":"perMANOVA",
"help":"This will perform a Permutational Multivariate Analysis of Variance using a distance matrix (Adonis method). It will show how your model explains differences based on a similarity matrix that is computed using the chosen similarity metric.",
"type":"check_box",
"db_field":"analysis",
"trigger":"drop_down",
"default":false
},
{"id":"adonis_distfun",
"label":"Distance metric",
"belongs_to":"adonis",
"help":"Distance metric.",
"type":"select",
"multiple":true,
"values":"distance"
},
{"id":"adonis_model",
"label":"Model",
"belongs_to":"adonis",
"help":"Model for Adonis.",
"type":"text",
"placeholder":"ex:ExperimentalGroup*Gender"
},
{"id":"adonis_strata",
"label":"Strata",
"belongs_to":"adonis",
"help":"Adonis strata parameter.",
"type":"text",
"placeholder":"ex:Subject"
},
{"id":"pca",
"scope":"form_only",
"label":"PCA",
"help":"Principal component analysis (PCA).",
"type":"check_box",
"db_field":"analysis",
"default":false
},
{"id":"pcoa",
"scope":"form_only",
"label":"PCoA",
"help":"Principal coordinate analysis (PCoA).",
"type":"check_box",
"db_field":"analysis",
"trigger":"drop_down",
"default":true
},
{"id":"pcoa_distfun",
"label":"Distance metric",
"belongs_to":"pcoa",
"help":"Distance metric.",
"type":"select",
"multiple":true,
"values":"distance"
},
{"id":"heatmap",
"scope":"form_only",
"label":"Heatmap",
"help":"Abundance heatmap.",
"type":"check_box",
"db_field":"analysis",
"default":false
},
{"id":"change",
"scope":"form_only",
"label":"Changes",
"help":"Differential analysis including fold-change and visualization with MA.",
"type":"check_box",
"db_field":"analysis",
"default":false
},
{"id":"correlation_network",
"scope":"form_only",
"label":"Correlation network",
"help":"Correlation network.",
"type":"check_box",
"db_field":"analysis",
"trigger":"drop_down",
"default":false
},
{"id":"correlation_network_fun",
"label":"Correlation method",
"belongs_to":"correlation_network",
"help":"Correlation method.",
"type":"select",
"values":"correlation"
},
{"id":"clustering",
"scope":"form_only",
"label":"Clustering",
"help":"Perform categorical clustering on the primary dataset.",
"type":"check_box",
"db_field":"analysis",
"trigger":"drop_down",
"default":false
},
{"id":"clustering_fun",
"label":"Algorithm",
"belongs_to":"clustering",
"help":"Choose clustering algorithm(s).",
"type":"select",
"multiple":true,
"values":"clustering"
},
{"id":"similarity_network",
"scope":"form_only",
"label":"Similarity network",
"help":"Similarity network, graph-based clustering and similarity network fusion (SNF).",
"type":"check_box",
"db_field":"analysis",
"trigger":"drop_down",
"default":false
},
{"id":"similarity_network_fun1",
"label":"Metric for primary dataset",
"belongs_to":"similarity_network",
"help":"Similarity metric for primary dataset.",
"type":"select",
"values":"distance"
},
{"id":"similarity_network_fun2",
"label":"Metric for secondary dataset",
"belongs_to":"similarity_network",
"help":"Similarity metric for secondary dataset.",
"type":"select",
"values":"distance"
},
{"id":"similarity_network_clust",
"label":"Clustering algorithm",
"belongs_to":"similarity_network",
"help":"Graph-based clustering algorithm.",
"type":"select",
"values":"graph_clustering",
"default":"walktrap"
}
]
}
}