MetaTreeProfiler Tutorial

Introduction

MetaTreeProfiler is command-line tool for profiling metadata table into phylogenetic tree with descriptive analysis and output visualization

Installation

MetaTreeProfiler requires to install ete4 toolkit

# install ete4 dependencies Cython
conda install -c anaconda cython
pip install Flask
pip install Flask-RESTful Flask-HTTPAuth Flask-Compress Flask-Cors
conda install six numpy scipy

# install ete4
git clone https://github.com/etetoolkit/ete.git
cd ete/
git branch checkout ete4
pip install -e .

Install MetaTreeProfiler

# install selenium via pip 
pip install selenium
# or conda
conda install -c conda-forge selenium 

# install MetaTreeProfiler
git clone https://github.com/dengzq1234/MetaTreeDrawer
cd MetaTreeDrawer/
# add treeprofiler to path
export PATH=$PATH:$(pwd)

Input files

MetaTreeProfiler takes following file types as input

Input	Filetype
Tree	Newick
Metadata	TSV

Basic usage

MetaTreeProfiler has two main subcommand:

annotate
plot

The first one annotate is used to annotate your input tree and corresponding metadata, MetaTreeProfiler will map all the metadata into corresponding tree node. In this step, annotated tree will be generated in newick and ete format

treeprofiler.py annotate --tree tree.nw --metadata metadata.tsv --outdir ./

The second subcommand plot is used to visualize tree with associated metadata. By default, treeprofiler will launch an interactive session at localhost for user to explore input tree.

treeprofiler.py plot --tree tree_annotated.nw --tree_type newick

or

treeprofiler.py plot --tree tree_annotated.ete --tree_type ete

Using MetaTreeProfiler

In this Tutorial we will use MetaTreeProfiler and demostrate basic usage with data in examples/

cd examples/
ls 
basic_example1.nw   basic_example1.tsv
gtdb_example1.nw    gtdb_example1.tsv        
progenome3.nw   progenome3.tsv 
spongilla_example.nw  spongilla_example.tsv

`annotate`, Mapping metadata into tree

usage: treeprofiler.py annotate [-h] [-t TREE] [--annotated_tree] [--tree_type TREE_TYPE]
                                [--prop2type PROP2TYPE] [--rank_limit RANK_LIMIT]
                                [--pruned_by PRUNED_BY] [-d METADATA] [--no_colnames]
                                [--text_prop TEXT_PROP] [--num_prop NUM_PROP]
                                [--bool_prop BOOL_PROP] [--text_prop_idx TEXT_PROP_IDX]
                                [--num_prop_idx NUM_PROP_IDX]
                                [--bool_prop_idx BOOL_PROP_IDX] [--taxatree TAXATREE]
                                [--taxadb TAXADB] [--taxon_column TAXON_COLUMN]
                                [--taxon_delimiter TAXON_DELIMITER]
                                [--taxa_field TAXA_FIELD] [--taxonomic_profile]
                                [--num_stat NUM_STAT] [--counter_stat COUNTER_STAT]
                                [--ete4out] [-o OUTDIR] [--outtsv OUTTSV]

annotate tree

optional arguments:
  -h, --help            show this help message and exit

SOURCE TREE INPUT:
  Source tree input parameters

  -t TREE, --tree TREE  Input tree, .nw file, customized tree input
  --annotated_tree      input tree already annotated by treeprofiler
  --tree_type TREE_TYPE
                        statistic calculation to perform for numerical data in internal
                        nodes, [newick, ete]
  --prop2type PROP2TYPE
                        config tsv file where determine the datatype of target properties,
                        if your input tree type is .ete, it's note necessary

Pruning parameters:
  Auto pruning parameters

  --rank_limit RANK_LIMIT
                        TAXONOMIC_LEVEL prune annotate tree by rank limit
  --pruned_by PRUNED_BY
                        target tree pruned by customized conditions

METADATA TABLE parameters:
  Input parameters of METADATA

  -d METADATA, --metadata METADATA
                        <metadata.csv> .csv, .tsv. mandatory input
  --no_colnames         metadata table doesn't contain columns name
  --text_prop TEXT_PROP
                        <col1,col2> names, column index or index range of columns which
                        need to be read as categorical data
  --num_prop NUM_PROP   <col1,col2> names, column index or index range of columns which
                        need to be read as numerical data
  --bool_prop BOOL_PROP
                        <col1,col2> names, column index or index range of columns which
                        need to be read as boolean data
  --text_prop_idx TEXT_PROP_IDX
                        1,2,3 or [1-5] index of columns which need to be read as
                        categorical data
  --num_prop_idx NUM_PROP_IDX
                        1,2,3 or [1-5] index columns which need to be read as numerical
                        data
  --bool_prop_idx BOOL_PROP_IDX
                        1,2,3 or [1-5] index columns which need to be read as boolean data
  --taxatree TAXATREE   <kingdom|phylum|class|order|family|genus|species|subspecies>
                        reference tree from taxonomic database
  --taxadb TAXADB       <NCBI|GTDB> for taxonomic profiling or fetch taxatree default
                        [GTDB]
  --taxon_column TAXON_COLUMN
                        <col1> name of columns which need to be read as taxon data
  --taxon_delimiter TAXON_DELIMITER
                        delimiter of taxa columns. default [;]
  --taxa_field TAXA_FIELD
                        field of taxa name after delimiter. default 0

Annotation arguments:
  Annotation parameters

  --taxonomic_profile   Determine if you need taxonomic annotation on tree
  --num_stat NUM_STAT   statistic calculation to perform for numerical data in internal
                        nodes, [all, sum, avg, max, min, std]
  --counter_stat COUNTER_STAT
                        statistic calculation to perform for categorical data in internal
                        nodes, raw count or in percentage [raw, relative]

OUTPUT options:

  --ete4out             export intermediate tree in ete4
  -o OUTDIR, --outdir OUTDIR
                        output annotated tree
  --outtsv OUTTSV       output annotated tsv file

Simple mapping metadata into leaf nodes

treeprofiler will start local server which metadata will be mapped to corresponding leaf nodes

treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --outdir ./examples/basic_example1/

Annotated tree will be generated in newick and ete format, alongside with a config file where describes datatype of each properties

Mapping metadata into tree and profile tree internal nodes annotations and analysis

At the above example, we only mapped metadata to leaf nodes, in this example, we will also profile internal nodes annotation and analysis of their children nodes.

Mapping Categorical data

For categorical dataset, each internal node will count the selected feature of its children nodes as counter, as shown as <feature_name>_counter in internal node. To label categorical feature metadata, using following arguments

# label categorical data by column name(s) in metadata (for multiple columns, seperate by ","), using --text_prop <header>
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --text_prop random_type --outdir ./examples/basic_example1/

# label categorical data by column index, using --text_prop_idx <idx>
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --text_prop_idx 6 --outdir ./examples/basic_example1/

# label column index by range, "[star_idx-end_idx]"
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --text_prop_idx [1-6] --outdir ./examples/basic_example1/

Categorical data will be process as counter in each internal node. Users can choose either counter is raw or relative count by using --counter_stat

internal_node properties	statistic method
`<feature name>`_counter	raw, relative

# raw count, example internal_node shown as: ```random_type_counter: medium--3||high--2```
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --text_prop random_type --counter_stat raw --outdir ./examples/basic_example1/

# relative count, example internal_node shown as: ```random_type_counter: medium--0.60||high--0.40```
 internal_node example shown as, random_type_counter: medium--3||high--2
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --text_prop random_type --counter_stat relative --outdir ./examples/basic_example1/

Mapping Boolean data

For Boolean dataset, each internal node will count the selected feature(s) of its children nodes as counter as categorical, as shown as _counter of suffix feature name(s) of internal node. To label Boolean feature metadata, using following arguments

# label boolean data by column name(s) in metadata (for multiple columns, seperate by ","), using --bool_prop <header>
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --bool_prop bool_type,bool_type2 --outdir ./examples/basic_example1/

# label boolean data by column index, using --bool_prop_idx <idx>
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --bool_prop_idx 7,8 --outdir ./examples/basic_example1/

# label column index by range, "[star_idx-end_idx]"
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --bool_prop_idx [7-8] --outdir ./examples/basic_example1/

Boolean counter stats follows rule as categorical data

Mapping Numerical data

For numerical dataset, each internal node will perform folwing descriptive statistic analysis of all of its children node of selected feature(s)

internal_node properties	statistic method
`<feature name>`_avg	average
`<feature name>`_sum	sum
`<feature name>`_max	maximum
`<feature name>`_min	minimum
`<feature name>`_std	standard deviation

To label numerical data

# label numerical data by column name(s) in metadata (for multiple columns, seperate by ","), using --num_prop <header>
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --num_prop sample1,sample2,sample3,sample4,sample5 --outdir ./examples/basic_example1/

# label numerical data by column index, using --num_prop_idx <idx>
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --num_prop_idx 1,2,3,4,5 --outdir ./examples/basic_example1/ 

# label column index by range, "[star_idx-end_idx]"
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --num_prop_idx [1-5]  --outdir ./examples/basic_example1/

By default, numerical feature will be calculated all the descriptive statistic, but users can choose specific one to be calculated by using --num_stat [all, sum, avg, max, min, std]

--num_stat NUM_STAT statistic calculation to perform for numerical data in internal nodes, [all, sum, avg, max, min, std]

# by default
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --num_prop sample1 --num_stat all --outdir ./examples/basic_example1/

# only average calculation
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --num_prop sample1 --num_stat avg --outdir ./examples/basic_example1/

Mapping metadata without column names

if metadata doesn't contain column names, please add --no_colnames as flag. MetaTreeProfiler will automatically assign feature name by index order

Taxonomic profiling

If input metadada containcs taxon data, MetaTreeProfiler allows users to process taxonomic annotation with either GTDB or NCBI database.

--taxadb, NCBI or GTDB, choose the Taxonomic Database for annotation
--taxon_column, choose the column in metadata which representa taxon
--taxonomic_profile, activate taxonomic annotation
--taxon_delimiter, delimiter of taxa columns. default .
--taxa_field, field of taxa name after delimiter. default 0

Basic usage on GTDB

Here we demonstrate with examples/gtdb_example1/gtdb_example1.nw and examples/gtdb_example1/gtdb_example1.tsv

# in case of gtdb_example1.tsv
treeprofiler.py annotate --tree examples/gtdb_example1/gtdb_example1.nw --metadata examples/gtdb_example1/gtdb_example1.tsv --taxonomic_profile --taxon_column 0 --taxadb GTDB --outdir ./examples/gtdb_example1/

Basic usage on NCBI

For instance of examples/spongilla_example/spongilla_example.nw and examples/spongilla_example/spongilla_example.tsv, it contains accession ID such as 83887.comp22273_c0_seq2_m.43352, hence using --

treeprofiler.py annotate --tree examples/spongilla_example/spongilla_example.nw --metadata examples/spongilla_example/spongilla_example.tsv --taxonomic_profile --taxon_column name --taxon_delimiter .  --taxa_field 0 --taxadb NCBI --outdir ./examples/

Annotate tree format

treeprofiler annotate subcommand will generate the following output file

<input_tree> + _annotated.nw, newick format with annotated tree
<input_tree> + _annotated.ete, ete format with annotated tree
<input_tree> + _annotated_prop2type.txt, config file where store the datatype of each annotated properties

In the following plot step, users can use either .nw or .ete by putting --tree_type [newick, ete] flag to identify. The difference between .nw and .ete format is

newick file is more universal and be able to used in different other phylogenetic software although associated data of tree nodes will be considered as plain text, so if you use newick format, alongside with the prop2type config file which was generated before by adding --prop2type <prop2type_file>
ete format is a novel format developed to solve the situation we encounter in the previous step, annotated tree can be recover easily with all the annotated data without changing the data type. Besides, the ete format optimized the tree file size after mapped with its associated data. Hence it's very handy for programers in their own script. At this moment we can only view the ete format in treeprofiler, but we will make the ete format more universal to other phylogenetic software.

`plot`, visualizing annotated tree with layouts

MetaTreeProfiler provides a several of layout options for visualize features in metadata along with tree, depends on their datatype

usage: treeprofiler.py plot [-h] [-t TREE] [--annotated_tree] [--tree_type TREE_TYPE]
                            [--prop2type PROP2TYPE] [--rank_limit RANK_LIMIT]
                            [--pruned_by PRUNED_BY]
                            [--internal_plot_measure INTERNAL_PLOT_MEASURE]
                            [--collapsed_by COLLAPSED_BY]
                            [--highlighted_by HIGHLIGHTED_BY] [--drawer DRAWER]
                            [--collapse_level COLLAPSE_LEVEL] [--ultrametric]
                            [--binary_layout BINARYLAYOUT]
                            [--revbinary_layout REVBINARYLAYOUT]
                            [--colorbranch_layout COLORBRANCHLAYOUT]
                            [--label_layout LABELLAYOUT]
                            [--rectangular_layout RECTANGULARLAYOUT]
                            [--heatmap_layout HEATMAPLAYOUT]
                            [--barplot_layout BARPLOTLAYOUT] [--taxon_layout]
                            [--interactive] [--port PORT] [--plot PLOT] [--out_colordict]

annotate plot

optional arguments:
  -h, --help            show this help message and exit

SOURCE TREE INPUT:
  Source tree input parameters

  -t TREE, --tree TREE  Input tree, .nw file, customized tree input
  --annotated_tree      input tree already annotated by treeprofiler
  --tree_type TREE_TYPE
                        statistic calculation to perform for numerical data in internal
                        nodes, [newick, ete]
  --prop2type PROP2TYPE
                        config tsv file where determine the datatype of target properties,
                        if your input tree type is .ete, it's note necessary

Pruning parameters:
  Auto pruning parameters

  --rank_limit RANK_LIMIT
                        TAXONOMIC_LEVEL prune annotate tree by rank limit
  --pruned_by PRUNED_BY
                        target tree pruned by customized conditions

Conditional display arguments:
  Conditional display parameters

  --internal_plot_measure INTERNAL_PLOT_MEASURE
                        statistic measures to be shown in numerical layout for internal
                        nodes, [default: avg]
  --collapsed_by COLLAPSED_BY
                        target tree collapsed by customized conditions
  --highlighted_by HIGHLIGHTED_BY
                        target tree highlighted by customized conditions

Basic treelayout arguments:
  treelayout parameters

  --drawer DRAWER       Circular or Rectangular
  --collapse_level COLLAPSE_LEVEL
                        default collapse level, default is 10
  --ultrametric         ultrametric tree

Properties' layout arguments:
  Prop layout parameters

  --binary_layout BINARYLAYOUT
                        <col1,col2> names, column index or index range of columns which
                        need to be plot as binary_layout
  --revbinary_layout REVBINARYLAYOUT
                        <col1,col2> names, column index or index range of columns which
                        need to be plot as revbinary_layout
  --colorbranch_layout COLORBRANCHLAYOUT
                        <col1,col2> names, column index or index range of columns which
                        need to be plot as Textlayouts
  --label_layout LABELLAYOUT
                        <col1,col2> names, column index or index range of columns which
                        need to be plot as label_layout
  --rectangular_layout RECTANGULARLAYOUT
                        <col1,col2> names, column index or index range of columns which
                        need to be plot as rectangular_layout
  --heatmap_layout HEATMAPLAYOUT
                        <col1,col2> names, column index or index range of columns which
                        need to be read as heatmap_layout
  --barplot_layout BARPLOTLAYOUT
                        <col1,col2> names, column index or index range of columns which
                        need to be read as barplot_layouts
  --taxon_layout         activate taxon_layout

Output arguments:
  Output parameters

  --interactive         run interactive session
  --port PORT           run interactive session on custom port
  --plot PLOT           output as pdf
  --out_colordict       print color dictionary of each property

Layouts for categorical data

Users can add the following flag to activate layouts for categorical data

--colorbranch_layout COLORBRANCHLAYOUT
                            <col1,col2> names, column index or index range of columns which need to be plot as Textlayouts
--label_layout LABELLAYOUT
                        <col1,col2> names, column index or index range of columns which need to be plot as label_layout
--rectangular_layout RECTANGULARLAYOUT
                        <col1,col2> names, column index or index range of columns which need to be plot as rectangular_layout

example

## annotate tree first
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --text_prop random_type --outdir ./examples/basic_example1/
## target column "random_type" in examples/basic_example1/basic_example1.tsv
# List random_type feature as text in aligned panel using label_layout
treeprofiler.py plot --tree examples/basic_example1/basic_example1_annotated.nw --label_layout random_type 

# Label random_type feature on branch with different colors in aligned panel  using --colorbranch_layout
treeprofiler.py plot --tree examples/basic_example1/basic_example1_annotated.nw  --colorbranch_layout random_type 

# Label random_type feature with retangular block in aligned panel using --rectangular_layout
treeprofiler.py plot --tree examples/basic_example1/basic_example1_annotated.nw  --rectangular_layout random_type

Layouts for boolean data

Users can add the following flag to activate layouts for Boolean data

--binary_layout BINARYLAYOUT
                        <col1,col2> names, column index or index range of columns which need to be plot as binary_layout, label shown only positive value
--revbinary_layout REVBINARYLAYOUT
                        <col1,col2> names, column index or index range of columns which need to be plot as revbinary_layout, label shown only negative value

## annotate tree first
# multiple columns seperated by ','
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --bool_prop bool_type,bool_type2 --outdir ./examples/basic_example1/

## target column "bool_type", "bool_type2" in examples/basic_example1/basic_example1.tsv
# List postive bool_type feature in aligned panel using binary_layout
treeprofiler.py plot --tree examples/basic_example1/basic_example1_annotated.nw  --binary_layout bool_type

# List negative bool_type feature in aligned panel using binary_layout
treeprofiler.py plot --tree examples/basic_example1/basic_example1_annotated.nw  --revbinary_layout bool_type2

# multiple columns seperated by ','
treeprofiler.py plot --tree examples/basic_example1/basic_example1_annotated.nw  --binary_layout bool_type,bool_type2

Layouts for Numerical data

Users can add the following flag to activate layouts for Numerical data

--heatmap_layout HEATMAPLAYOUT
                        <col1,col2> names, column index or index range of columns which need to be read as heatmap_layout
--barplot_layout BARPLOTLAYOUT
                        <col1,col2> names, column index or index range of columns which need to be read as barplot_layouts

## annotate tree first
# multiple columns seperated by ','
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --num_prop_idx [1-5] --outdir ./examples/basic_example1/

## target column 'sample[1-5]' feature in examples/basic_example1/basic_example1.tsv
# visualize sample1 feature in Barplot
treeprofiler.py plot --tree examples/basic_example1/basic_example1_annotated.nw  --barplot_layout sample1,sample2,sample3,sample4,sample5

# visualize sample1-sample5 in Heatmap
#treeprofiler.py plot --tree examples/basic_example1/basic_example1.nw --metadata #examples/basic_example1/basic_example1.tsv --heatmap_layout [1-5]

Visualizing annotated internal nodes

If internal nodes are annotated, MetaTreeProfiler is also able to visualize annotated features automatically when layouts are activated

Internal nodes of categorical and boolean data

As internal nodes of categorical and boolean data are annotated as counter, hence when activating layouts of categorical or boolean data, it generate pipechart of counter summary at the top of each internal node

Internal nodes of numerical data

Internal nodes of numerical data are process descriptive statistic analysis by default, hence when users collapse any branch, barplot_layout or heatmap_layout will demonstrate representative value, avg by default. representative value can be changed by using --internal_plot_measure

example

# select max instead of avg as internal node ploting representative
treeprofiler.py plot --tree examples/basic_example1/basic_example1_annotated.nw  --heatmap_layout sample1,sample2,sample3,sample4,sample5 --internal_plot_measure max

Layouts for Taxonomic data

Activate Taxonomic layout using --taxon_layout

## Annotate
# GTDB
treeprofiler.py annotate --tree examples/gtdb_example1/gtdb_example1/gtdb_example1.nw --metadata examples/gtdb_example1/gtdb_example1/gtdb_example1.tsv --taxonomic_profile --taxon_column 0 --taxadb GTDB --outdir ./examples/gtdb_example1/

# NCBI
treeprofiler.py annotate --tree examples/spongilla_example/spongilla_example/spongilla_example.nw --metadata examples/spongilla_example/spongilla_example/spongilla_example.tsv --taxonomic_profile --taxon_column name --taxon_delimiter .  --taxa_field 0 --taxadb NCBI 
--outdir ./examples/spongilla_example/


## Visualize 
treeprofiler.py plot --tree examples/spongilla_example/spongilla_example/gtdb_example1_annotated.ete --tree_type ete --taxon_layout

Conditional query in annotated tree

MetaTreeProfiler allows users to perform conditional process based on different circumstances

Conditional pruning, conditional pruning works both annotate and plot subcommand
- --pruned_by, prune the annotated tree by conditions, and remove the branches or clades which don't fit the condition.
- --rank_limit, prune the taxonomic annotated tree based on rank of classification.
Conditional collapsing, conditional collapsing works in plot subcommand, allow users to collapsed tree internal nodes to clade under customized conditions
- --collapsed_by, collapse tree branches whose nodes if the conditions, mainly on internal nodes
Conditional highlight, conditional highlight works in plot subcommand, allow users to highlight tree nodes under customized conditions
- --highlighted_by, select tree nodes which fit the conditions

Query Syntax

Basic Query

All the conditional query shared the same syntax, a standard query consists the following

--pruned_by|collapsed_by|highlighted_by "<left_value> <operator> <right_value>"

left value, the property of leaf node or internal node
operators
- =
- !=
- >
- >=
- <
- <=
- contains
right value, custom value for the condition

Example

# annotate all metadata to tree  
treeprofiler.py annotate --tree examples/basic_example1/basic_example1.nw --metadata examples/basic_example1/basic_example1.tsv --num_prop_idx [1-5] --text_prop random_type --bool_prop bool_type,bool_type2 --counter_stat relative --outdir examples/basic_example1/ 
# Conditional pruning, prune leaf node whose name contain "FALPE"
treeprofiler.py plot --tree examples/basic_example1/basic_example1_annotated.ete --tree_type ete --pruned_by "name contains FALPE"

# Conditional highlight
# select tree node whose name contains `FALPE` character
treeprofiler.py plot --tree examples/basic_example1/basic_example1_annotated.ete --tree_type ete --highlighted_by "name contains FALPE"

# select tree node whose sample1 feature > 0.50
treeprofiler.py plot --tree examples/basic_example1/basic_example1_annotated.ete --tree_type ete --highlighted_by "sample1 > 0.50"

Query in internal nodes

Query in internal nodes' properties is also available, in this case, left_value of query will be the internal node property, remember to add the proper suffixes such as _avg, _max,etc, for the numerical data or _counter for categorical and boolean data.

Example

# select tree internal node where sample1_avg feature < 0.50
treeprofiler.py plot --tree examples/basic_example1/basic_example1_annotated.ete --tree_type ete --heatmap_layout sample1 --collapsed_by "sample1_avg < 0.50"

Syntax for internal node counter data

# collapse tree internal nodes, where `high` relative counter > 0.35 in random_type_counter property
treeprofiler.py plot --tree examples/basic_example1/basic_example1_annotated.ete --tree_type ete  --collapsed_by "random_type_counter:high > 0.35"

AND and OR conditions

The syntax for the AND condition and OR condition in MetaTreeProfiler is:

AND condition will be under one argument, syntax seperated by ,, such as

# select tree  node where sample1 feature > 0.50 AND sample2 < 0.2
treeprofiler.py plot --tree examples/basic_example1/basic_example1_annotated.ete --tree_type ete --heatmap_layout sample1,sample2,sample3,sample4,sample5 --highlighted_by "sample1>0.50,sample2<0.2"

OR condition will be used more than one arguments

# select tree node where sample1 feature > 0.50 OR sample2 < 0.2
treeprofiler.py plot --tree examples/basic_example1/basic_example1_annotated.ete --tree_type ete --heatmap_layout sample1,sample2,sample3,sample4,sample5 --highlighted_by "sample1>0.50" --highlighted_by "sample2<0.2"

conditional limit based on taxonomic level

Prune taxonomic annotated tree based on following taxonomic rank level, kingdom, phylum, class, order, family, genus, species, subspecies

# Case in GTDB
# prune tree in annotation, rank limit to family level in GTDB database
treeprofiler.py annotate --tree examples/gtdb_example1/gtdb_example1.nw --metadata examples/gtdb_example1/gtdb_example1.tsv --taxonomic_profile --taxadb GTDB --outdir ./examples/gtdb_example1/

# prune tree in visualization, rank limit to family level
treeprofiler.py plot --tree examples/gtdb_example1/gtdb_example1_annotated.ete --tree_type ete --rank_limit family --taxon_layout  


# Case in NCBI
treeprofiler.py annotate --tree examples/spongilla_example/spongilla_example.nw --metadata examples/spongilla_example/spongilla_example.tsv --taxonomic_profile --taxon_column name --taxon_delimiter .  --taxa_field 0 --taxadb NCBI --outdir ./examples/spongilla_example/

# prune tree in visualization, rank limit to phylum level
treeprofiler.py plot --tree examples/spongilla_example/spongilla_example_annotated.ete --tree_type ete --rank_limit phylum --taxon_layout

Explore progenome data

We store progenome v3 data in examples/ directory for exploration,

A glance of metadata

head -2 examples/progenome3.tsv
name    GC      GCA     aquatic_habitat host_associated size    soil_habitat    GCF
2486577.SAMN10347832.GCA_004210275.1    40.8    GCA_004210275.1 0       1       1375759 0       RS_GCF_004210275.1
2759495.SAMN15595193.GCA_014116815.1    34.3    GCA_014116815.1                 1928597         GB_GCA_014116815.1

Here we will conduct the profiling with two command line

treeprofiler.py annotate --tree examples/progenome3/progenome3.nw --metadata examples/progenome3/progenome3.tsv --taxonomic_profile --taxadb NCBI --taxon_delimiter . --taxa_field 0 --num_prop GC,size --bool_prop aquatic_habitat,host_associated,soil_habitat 
--outdir examples/progenome3/

treeprofiler.py plot --tree examples/progenome3/progenome3_annotated.ete --tree_type ete
--barplot_layout GC,size --taxon_layout --binary_layout aquatic_habitat,host_associated,soil_habitat

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MetaTreeProfiler Tutorial

MetaTreeProfiler Tutorial

Table of Contents

Introduction

Installation

Input files

Basic usage

Using MetaTreeProfiler

`annotate`, Mapping metadata into tree

Simple mapping metadata into leaf nodes

Mapping metadata into tree and profile tree internal nodes annotations and analysis

Mapping Categorical data

Mapping Boolean data

Mapping Numerical data

Mapping metadata without column names

Taxonomic profiling

Basic usage on GTDB

Basic usage on NCBI

Annotate tree format

`plot`, visualizing annotated tree with layouts

Layouts for categorical data

Layouts for boolean data

Layouts for Numerical data

Visualizing annotated internal nodes

Internal nodes of categorical and boolean data

Internal nodes of numerical data

Layouts for Taxonomic data

Conditional query in annotated tree

Query Syntax

Basic Query

Query in internal nodes

AND and OR conditions

conditional limit based on taxonomic level

Explore progenome data

Clone this wiki locally

MetaTreeProfiler Tutorial

MetaTreeProfiler Tutorial

Table of Contents

Introduction

Installation

Input files

Basic usage

Using MetaTreeProfiler

annotate, Mapping metadata into tree

Simple mapping metadata into leaf nodes

Mapping metadata into tree and profile tree internal nodes annotations and analysis

Mapping Categorical data

Mapping Boolean data

Mapping Numerical data

Mapping metadata without column names

Taxonomic profiling

Basic usage on GTDB

Basic usage on NCBI

Annotate tree format

plot, visualizing annotated tree with layouts

Layouts for categorical data

Layouts for boolean data

Layouts for Numerical data

Visualizing annotated internal nodes

Internal nodes of categorical and boolean data

Internal nodes of numerical data

Layouts for Taxonomic data

Conditional query in annotated tree

Query Syntax

Basic Query

Query in internal nodes

AND and OR conditions

conditional limit based on taxonomic level

Explore progenome data

Clone this wiki locally

`annotate`, Mapping metadata into tree

`plot`, visualizing annotated tree with layouts