Qiime脚本：详解添加 Alpha 多样性数据到元数据的工具

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Qiime脚本命令大全是一份全面指南，专注于Qiime软件包中的Python脚本使用方法。Qiime是一个广泛用于微生物组学分析的开源工具集，特别在生物信息学领域中发挥着重要作用。本文档的核心关注点在于"add_alpha_to_mapping_file.py"脚本，该脚本主要用于将alpha多样性数据添加到元数据映射文件中，以便与其他Qiime脚本，例如"make_3d_plots.py"协同工作。这个功能对于整合复杂的数据分析流程至关重要。 "add_alpha_to_mapping_file.py"命令的主要作用是为每个alpha多样性指标在原始数据中增加三个新的列：第一列保留原始值，第二列是对原始值的规范化处理，第三列则是根据规范化值对样本进行分类，这有助于更好地理解和可视化数据分布。用户必须提供两个关键输入参数： 1. `-i` 或 `--alpha_fps`：这是必需的参数，指定包含一个或多个alpha多样性指标的数据，通常由`alpha_diversity.py`的输出或`collate_alpha.py`（当使用合并在深度选项下）的输出组成。这个参数可以接受单个文件路径，也可以是一个逗号分隔的文件列表，表示合并在深度下的结果。 2. `-m` 或 `--mapping_fp`：另一个必需参数，这是要修改的元数据映射文件，脚本会在这个文件中添加新计算出的alpha多样性数据。此外，还有几个可选参数供用户自定义输出和行为： - `-o` 或 `--output_mapping_fp`：允许用户指定输出修改后的元数据文件路径，默认为`mapping_file_with_alpha.txt`。 - `-b` 或 `--number_of_bins`：用户可以选择将数据分为多少个区间（默认为4），这将决定分类标签的数量。 - `-x` 或 `--missing_value_name`：用于指定当数据缺失时的前缀名称，这在处理缺失值时提供了灵活性。通过正确使用这些参数，用户能够增强其元数据文件的丰富度，并将其与后续的分析步骤无缝对接，从而获得更深入的微生物组学研究洞察。学习和掌握这些脚本命令对于有效利用Qiime进行定量和定性的微生物组分析至关重要。

be provided. Unlike the RDP Classifier, sequences in the training set may be

assigned at any level of the taxonomy.

To make taxonomic classifications of the representative sequences, where the

results are output to default directory “mothur_assigned_taxonomy”, you can

run the following command:

assign_taxonomy.py -i mothur_repr_set_seqs.fasta -m mothur -r

mothur_ref_seq_set.fna -t mothur_id_to_taxonomy.txt

beta_diversity.py – Calculate beta

diversity (pairwise sample dissimilarity)

on one or many otu tables

Description:

The input for this script is the OTU table containing the number of sequences

observed in each OTU (rows) for each sample (columns). For more

information pertaining to the OTU table refer to the documentation for

make_otu_table. If the user would like phylogenetic beta diversity metrics

using UniFrac, a phylogenetic tree must also be passed as input

(see make_phylogeny.py). The output of this script is a distance matrix

containing a dissimilarity value for each pairwise comparison.

A number of metrics are currently supported, including unweighted and

weighted UniFrac (pass the -s option to see available metrics). In general,

because unifrac uses phylogenetic information, one of the unifrac metrics is

recommended, as results can be vastly more useful (Hamady & Knight, 2009).

Quantitative measures (e.g. weighted unifrac) are ideally suited to revealing

community differences that are due to changes in relative taxon abundance

(e.g., when a particular set of taxa flourish because a limiting nutrient source

becomes abundant). Qualitative measures (e.g. unweighted unifrac) are most

informative when communities differ primarily by what can live in them (e.g., at

high temperatures), in part because abundance information can obscure

significant patterns of variation in which taxa are present (Lozupone et al.,

2007). Most qualitative measures are referred to here e.g. “binary_jaccard”.

Typically both weighted and unweighted unifrac are used.

Usage: beta_diversity.py [options]

Input Arguments:

[OPTIONAL]

-i, --input_path

Input OTU table in biom format or input directory containing OTU tables in

biom format for batch processing.

-r, --rows

Compute for only these rows of the distance matrix. User should pass a list of

sample names (e.g. “s1,s3”) [default: None; full n x n matrix is generated]

-o, --output_dir

Output directory. One will be created if it doesn’t exist.

-m, --metrics

Beta-diversity metric(s) to use. A comma-separated list should be provided

when multiple metrics are specified. [default:

unweighted_unifrac,weighted_unifrac]

-s, --show_metrics

Show the available beta-diversity metrics and exit. Metrics starting with

“binary...” specifies that a metric is qualitative, and considers only the presence

or absence of each taxon [default: False]

-t, --tree_path

Input newick tree filepath, which is required when phylogenetic metrics are

specified. [default: None]

-f, --full_tree

By default, tips not corresponding to OTUs in the OTU table are removed from

the tree for diversity calculations. Pass to skip this step if you’re already

passing a minimal tree. Beware with “full_tree” metrics, as extra tips in the tree

change the result

Output:

Each file in the input directory should be an otu table, and the output

of beta_diversity.py is a folder containing text files, each a distance matrix

between samples corresponding to an input otu table.

Single File Beta Diversity (non-phylogenetic):

To perform beta diversity (using e.g. euclidean distance) on a single OTU table,

where the results are output to beta_div/, use the following command:

beta_diversity.py -i otu_table.biom -m euclidean -o beta_div

Single File Beta Diversity (phylogenetic):

In the case that you would like to perform beta diversity using a phylogenetic

metric (e.g. weighted_unifrac), you can use the following command:

beta_diversity.py -i otu_table.biom -m weighted_unifrac -o beta_div/ -t

rep_set.tre

Multiple File (batch) Beta Diversity (phylogenetic):

To perform beta diversity on multiple OTU tables (e.g., resulting files

from multiple_rarefactions.py), specify an input directory (e.g. otu_tables/) as

shown by the following command:

beta_diversity.py -i otu_tables/ -m weighted_unifrac -o beta_div/ -t

rep_set.tre

beta_diversity_metrics – List of available

metrics

Non-phylogenetic beta diversity metrics. These are count based metrics which

is based on the OTU table:

• abund_jaccard: abundance weighted Jaccard distance

• binary_dist_chisq: Binary chi-square distance

• binary_dist_chord: Binary chord distance

• binary_dist_euclidean: Binary euclidean distance

• binary_dist_hamming: Binary Hamming distance (binary

Manhattan distance)

• binary_dist_jaccard: Binary Jaccard distance (binary Soergel

distance)

• binary_dist_lennon: Binary Lennon distance

• binary_dist_ochiai: Binary Ochiai distance

• binary_otu_gain: Binary distance similar to Unifrac G

• binary_dist_pearson: Binary Pearson distance

• binary_dist_sorensen_dice: Binary Sörensen-Dice distance

(binary Bray-Curtis distance or binary Whittaker distance)

• dist_bray_curtis: Bray-Curtis distance (normalized Manhattan

distance)

• dist_canberra: Canberra distance

• dist_chisq: Chi-square distance

• dist_chord: Chord distance

• dist_euclidean: Euclidean distance

• dist_gower: Gower distance

• dist_hellinger: Hellinger distance

• dist_kulczynski: Kulczynski distance

• dist_manhattan: Manhattan distance

• dist_morisita_horn: Morisita-Horn distance

• dist_pearson: Pearson distance

• dist_soergel: Soergel distance

• dist_spearman_approx: Spearman rank distance

• dist_specprof: Species profile distance

Phylogenetic beta diversity metrics. These metrics are based on UniFrac,

which takes into account the evolutionary relationship between sequences:

• dist_unifrac_G: The G metric calculates the fraction branch length

in the sample i + sample j tree that is exclusive to sample i and it

is asymmetric.

• dist_unifrac_G_full_tree: The full_tree version calculates the

fraction of branch length in the full tree that is exclusive to

sample i and it is asymmetric.

• dist_unweighted_unifrac: This is the standard unweighted UniFrac,

which is used to assess ‘who’s there’ without taking in account

the relative abundance of identical sequences.

• dist_unweighted_unifrac_full_tree: Typically, when computing the

dissimilarity between two samples, unifrac considers only the

parts of the phylogenetic tree contained by otus in either sample.

The full_tree modification considers the entire supplied tree.

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