| Title: | Identification of Core Microbiome |
|---|---|
| Description: | The Core Microbiome refers to the group of microorganisms that are consistently present in a particular environment, habitat, or host species. These microorganisms play a crucial role in the functioning and stability of that ecosystem. Identifying these microorganisms can contribute to the emerging field of personalized medicine. The 'CoreMicrobiomeR' is designed to facilitate the identification, statistical testing, and visualization of this group of microorganisms.This package offers three key functions to analyze and visualize microbial community data. This package has been developed based on the research papers published by Pereira et al.(2018) <doi:10.1186/s12864-018-4637-6> and Beule L, Karlovsky P. (2020) <doi:10.7717/peerj.9593>. |
| Authors: | Sorna A M [aut], Mohammad Samir Farooqi [aut, cre], Dwijesh Chandra Mishra [aut], Krishna Kumar Chaturvedi [aut], Anu Sharma [aut], Prawin Arya [aut], Sudhir Srivastava [aut], Sharanbasappa [aut], Girish Kumar Jha [aut], Kabilan S [ctb] |
| Maintainer: | Mohammad Samir Farooqi <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2024-12-19 06:28:45 UTC |
| Source: | CRAN |
This function provides a comprehensive pipeline for processing OTU (Operational Taxonomic Unit) tables, taxonomic tables, and metadata tables. It applies various filtering methods based on user-defined parameters to select core OTUs and non-core OTUs calculates alpha and beta diversity measures. The pipeline can be customized with different normalization methods and filtering criteria. Taxa are ranked in descending according to the cumulative sum obtained. This method assigns taxa to the core if they are in the top X% of reads. Any taxa which appears before some cutoff percentage is included in the core.
CoreMicrobiome(otu_table, tax_table, metadata_table, filter_type, ..., method, beta_diversity_method, top_percentage)CoreMicrobiome(otu_table, tax_table, metadata_table, filter_type, ..., method, beta_diversity_method, top_percentage)
otu_table |
A dataframe of OTUs where the first row is the OTU ID and column names refer to sites/sample names. |
tax_table |
A dataframe of taxonomies where the first row is the OTU ID and column names refer to taxonomic classification. |
metadata_table |
A dataframe of sites/samples where the first row is the sites/sample names and column names refer to groups of samples. |
filter_type |
Filtering method type, includes "abundance_fun_filter", "occupancy_fun_filter", or "combined_filter". |
... |
Other parameters. These are ignored, except in filter_type = "abundance_fun_filter" which accepts min_count, prop, min_total_count parameter, and in filter_type = "occupancy_fun_filter" which accepts percent parameter, and also filter_type = "combined_filter" which accepts percent, min_count, prop, min_total_count parameters. |
method |
Different normalization methods, includes "rrarefy", "srs", "css", "tmm", "tmmwsp", "rle", "upperquartile" or "none". The default method is tmm. |
beta_diversity_method |
Different beta diversity methods, includes "bray", "jaccard", "mountford". The default method is bray. |
top_percentage |
Percentage used for Core OTUs identification and the default is 10 percent. |
This function gives the list which consist of following results.
'final_otu_table_bef_filter' otu_table obtained after sorting according to the provided tax_table and metadata_table
'filtered_md_table' metadata_table obtained after sorting according to the provided otu_table
'final_otu_aft_filter' otu_table obtained after filtering according to the user defined filtering method
'normalized_table' normalized_otu_table obtained after normalizing according to the user defined normalization method
'alpha_diversity' Alpha diversity measures of the samples
'beta_diversity' Beta diversity measures between the samples
'core_otus' Core OTUs obtained
'non_core_otus' Non Core OTUs obtained
'core_otus_tax' Taxonomy of the obtained Core OTUs
'core_otus_count_data' Original count data of the obtained core OTUs
'core_otus_relative_abundance' Relative abundance data of the obtained core OTUs
Pereira, M., Wallroth, M., Jonsson, V. et al. (2018). Comparison of normalization methods for the analysis of metagenomic gene abundance data. BMC Genomics 19, 274. <doi:https://doi.org/10.1186/s12864-018-4637-6>
Beule L, Karlovsky P. (2020). Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): application to microbial communities. PeerJ 8:e9593.<doi:https://doi.org/10.7717/peerj.9593>
#To run input data core_1 <- CoreMicrobiome( otu_table = demo_otu, tax_table = demo_tax, metadata_table = demo_md, filter_type = "occupancy_fun_filter", #Or "abundance_fun_filter", Or "combined_filter" percent = 0.5, method = "css", # Or "srs", "rrarefy", "tmm", "tmmwsp", "rle", "upperquartile", "none" beta_diversity_method = "jaccard", top_percentage = 10 # Adjust the percentage as needed for core/non-core OTUs ) #To view the core otus obtained core_1[["core_otus"]] #To view the taxonomy of the obtained core otus core_1[["core_otus_tax"]]#To run input data core_1 <- CoreMicrobiome( otu_table = demo_otu, tax_table = demo_tax, metadata_table = demo_md, filter_type = "occupancy_fun_filter", #Or "abundance_fun_filter", Or "combined_filter" percent = 0.5, method = "css", # Or "srs", "rrarefy", "tmm", "tmmwsp", "rle", "upperquartile", "none" beta_diversity_method = "jaccard", top_percentage = 10 # Adjust the percentage as needed for core/non-core OTUs ) #To view the core otus obtained core_1[["core_otus"]] #To view the taxonomy of the obtained core otus core_1[["core_otus_tax"]]
This dataset was given by Lundberg et al., (2012). The metadata table contains additional information about each sample (root) included in the study. It typically includes details about the experimental conditions, environmental factors, sample genotype, location, and other relevant contextual information. Metadata is crucial for linking the microbial community data to specific experimental variables and understanding how the root microbiome might vary in response to different factors. The original dataset contains 1049 samples(rows) and factors like soil type, genotype, treatment, developmental stage and replication information in columns for each particular sample.
demo_mddemo_md
An object of class tbl_df (inherits from tbl, data.frame) with 103 rows and 6 columns.
Here only the portion of the dataset is taken for running the functions. The dataset contains 103 rows and 6 columns.
This dataset was given by Lundberg et al., (2012). The OTU table is a central part of the data set. It represents the abundance or presence/absence of different microbial taxa (operational taxonomic units) in the root samples of Arabidopsis thaliana. Each column in the OTU table corresponds to a specific sample (root) from the study, and each row represents a different OTU, which could be a species or a group of closely related organisms. The table contains numerical values representing the count of each OTU in the corresponding samples. The original dataset contains 18783 rows of OTUs and 1439 samples(columns).
demo_otudemo_otu
An object of class tbl_df (inherits from tbl, data.frame) with 188 rows and 1440 columns.
Here only the portion of the dataset is taken for running the functions.
The dataset contains 188 rows and 1440 columns.
This dataset was given by Lundberg et al., (2012). The taxonomy table provides information about the taxonomic identity of the OTUs listed in the OTU table. Each row in the taxonomy table corresponds to an OTU from the OTU table, and the columns provide details about the taxonomic classification of that OTU, such as kingdom, phylum, class, order, family, genus, and species. This information allows researchers to identify the microbial species or groups that are present in the root samples. The original dataset contains 777 rows of OTUs and Phylum, Class, Order, Family in columns corresponding to particular OTU.
demo_taxdemo_tax
An object of class tbl_df (inherits from tbl, data.frame) with 188 rows and 5 columns.
Here only the portion of the dataset is taken for running the functions. The dataset contains 188 rows and 5 columns.
The grouped_bar_plots function is designed for generating grouped bar plots to visualize data. It takes a OTU table before filtering and OTU table after filtering as input containing data for multiple samples and creates a series of grouped bar plots, each representing a specific group of samples.
group_bar_plots(otu_table_bef_filtering, otu_table_aft_filtering, num_samples_per_plot)group_bar_plots(otu_table_bef_filtering, otu_table_aft_filtering, num_samples_per_plot)
otu_table_bef_filtering |
A data frame of OTUs before filtering where the first row is the OTU ID and column names refer to sites/sample names |
otu_table_aft_filtering |
A data frame of OTUs after filtering where the first row is the OTU ID and column names refer to sites/sample names |
num_samples_per_plot |
The number of samples to be displayed in each grouped bar plot. |
A list of interactive grouped bar plots, showing the change in sample size before and after filtering OTU table
#To run input data core_1 <- CoreMicrobiome( otu_table = demo_otu, tax_table = demo_tax, metadata_table = demo_md, filter_type = "occupancy_fun_filter", #Or "abundance_fun_filter", Or "combined_filter" percent = 0.5, method = "css", # Or "srs", "rrarefy", "tmm", "tmmwsp", "rle", "upperquartile", "none" beta_diversity_method = "jaccard", top_percentage = 10 # Adjust the percentage as needed for core/non-core OTUs ) #To run grouped bar plot function plot_group_bar <- group_bar_plots(core_1$final_otu_table_bef_filter, core_1$final_otu_aft_filter, 10) #To view the grouped bar plot plot_group_bar[[1]]#To run input data core_1 <- CoreMicrobiome( otu_table = demo_otu, tax_table = demo_tax, metadata_table = demo_md, filter_type = "occupancy_fun_filter", #Or "abundance_fun_filter", Or "combined_filter" percent = 0.5, method = "css", # Or "srs", "rrarefy", "tmm", "tmmwsp", "rle", "upperquartile", "none" beta_diversity_method = "jaccard", top_percentage = 10 # Adjust the percentage as needed for core/non-core OTUs ) #To run grouped bar plot function plot_group_bar <- group_bar_plots(core_1$final_otu_table_bef_filter, core_1$final_otu_aft_filter, 10) #To view the grouped bar plot plot_group_bar[[1]]
This function performs a two-sample variance test to assess the statistical significance of differences in abundance between core OTUs and non-core OTUs. It takes two data frames as input, representing the abundance of core OTUs and non-core OTUs, and returns the results of the variance test. It tells whether the identified core represents the particular environment or habitat.
significance(core_ids, non_core_ids)significance(core_ids, non_core_ids)
core_ids |
A Dataframe of core OTUs where the first row is the OTU ID and column names refer to sites/sample names |
non_core_ids |
A Dataframe of non_core OTUs where the first row is the OTU ID and column names refer to sites/sample names |
This function gives the list which consist of following results.
'statistic' Calculated F test statistic
'parameter' The numerator degrees of freedom (num df), and the denominator degrees of freedom (denom df)
'p-value' Probability value
'alternative' The alternative hypothesis for this test is that the true ratio of variances is not equal to 1. This suggests that the variances of the two data sets are different
'conf.int' 95 percent confidence interval limit for the ratio of variances
'estimate' Ratio of variances between core_data and non_core_data calculated
'method' The test performed is an F test, which compares the variances of the two data sets
'data.name' The data used for the test are core_ids and non_core_ids
#To run input data core_1 <- CoreMicrobiome( otu_table = demo_otu, tax_table = demo_tax, metadata_table = demo_md, filter_type = "occupancy_fun_filter", #Or "abundance_fun_filter", Or "combined_filter" percent = 0.5, method = "css", # Or "srs", "rrarefy", "tmm", "tmmwsp", "rle", "upperquartile", "none" beta_diversity_method = "jaccard", top_percentage = 10 # Adjust the percentage as needed for core/non-core OTUs ) #To run significance test f_test <- significance(core_1[["core_otus"]] , core_1[["non_core_otus"]] ) #To view the significance test result f_test#To run input data core_1 <- CoreMicrobiome( otu_table = demo_otu, tax_table = demo_tax, metadata_table = demo_md, filter_type = "occupancy_fun_filter", #Or "abundance_fun_filter", Or "combined_filter" percent = 0.5, method = "css", # Or "srs", "rrarefy", "tmm", "tmmwsp", "rle", "upperquartile", "none" beta_diversity_method = "jaccard", top_percentage = 10 # Adjust the percentage as needed for core/non-core OTUs ) #To run significance test f_test <- significance(core_1[["core_otus"]] , core_1[["non_core_otus"]] ) #To view the significance test result f_test
This function generates stacked bar plots for visualizing the relative abundance data of different operational taxonomic units (OTUs) in various samples.
stacked_bar_plots(data, num_samples_per_plot)stacked_bar_plots(data, num_samples_per_plot)
data |
A data frame containing the relative abundance data for the OTUs. The first column should contain the OTU IDs, and the subsequent columns should represent samples. |
num_samples_per_plot |
The number of samples to be displayed in each stacked bar plot. |
A list of interactive stacked bar plots, one for each group of samples, showing the relative abundance of OTUs in the samples.
#To run input data core_1 <- CoreMicrobiome( otu_table = demo_otu, tax_table = demo_tax, metadata_table = demo_md, filter_type = "occupancy_fun_filter", #Or "abundance_fun_filter", Or "combined_filter" percent = 0.5, method = "css", # Or "srs", "rrarefy", "tmm", "tmmwsp", "rle", "upperquartile", "none" beta_diversity_method = "jaccard", top_percentage = 10 # Adjust the percentage as needed for core/non-core OTUs ) #To run the stacked bar plots function stacked_plots <- stacked_bar_plots(core_1$core_otus_relative_abundance, 10) #To view the stacked bar plot stacked_plots[[1]]#To run input data core_1 <- CoreMicrobiome( otu_table = demo_otu, tax_table = demo_tax, metadata_table = demo_md, filter_type = "occupancy_fun_filter", #Or "abundance_fun_filter", Or "combined_filter" percent = 0.5, method = "css", # Or "srs", "rrarefy", "tmm", "tmmwsp", "rle", "upperquartile", "none" beta_diversity_method = "jaccard", top_percentage = 10 # Adjust the percentage as needed for core/non-core OTUs ) #To run the stacked bar plots function stacked_plots <- stacked_bar_plots(core_1$core_otus_relative_abundance, 10) #To view the stacked bar plot stacked_plots[[1]]
The visualize function generates interactive line plots that allow users to explore the impact of different min_count values on the number of core OTUs. Users can interact with the plots to examine the relationship between filtering criteria and core OTU identification visually.
visualize(filtered_otu, min_count_val, max_count_val, count_val_interval, prop, min_total_count, method, top_percentage)visualize(filtered_otu, min_count_val, max_count_val, count_val_interval, prop, min_total_count, method, top_percentage)
filtered_otu |
A dataframe of OTUs obtained before filtering which is retrieved from CoreMicrobiome function where the first row is the OTU ID and column names refer to sites/sample names |
min_count_val |
A numeric value of Minimum count for each OTU to be present in each to be included after the filtering |
max_count_val |
A numeric value of Maximum count for each OTU to be present in each to be included after the filtering |
count_val_interval |
Count value interval for each OTU to be present in each to be included after the filtering |
prop |
Minimum proportion of samples in which an OTU must be present |
min_total_count |
Minimum total count for each OTU to be included after the filtering |
method |
Different normalization methods, includes "rrarefy", "srs", "css", "tmm", or "none" |
top_percentage |
Percentage used for obtaining the Core OTUs |
This function gives a line plot which shows change in number of core OTUs with minimum count
#To run input data core_1 <- CoreMicrobiome( otu_table = demo_otu, tax_table = demo_tax, metadata_table = demo_md, filter_type = "occupancy_fun_filter", #Or "abundance_fun_filter", Or "combined_filter" percent = 0.5, method = "css", # Or "srs", "rrarefy", "tmm", "tmmwsp", "rle", "upperquartile", "none" beta_diversity_method = "jaccard", top_percentage = 10 # Adjust the percentage as needed for core/non-core OTUs ) #To view the line plot visualize(filtered_otu = core_1[["final_otu_table_bef_filter"]], min_count_val = 5, max_count_val = 25, count_val_interval = 5, prop = 0.1, min_total_count = 10, method = "srs", top_percentage =10)#To run input data core_1 <- CoreMicrobiome( otu_table = demo_otu, tax_table = demo_tax, metadata_table = demo_md, filter_type = "occupancy_fun_filter", #Or "abundance_fun_filter", Or "combined_filter" percent = 0.5, method = "css", # Or "srs", "rrarefy", "tmm", "tmmwsp", "rle", "upperquartile", "none" beta_diversity_method = "jaccard", top_percentage = 10 # Adjust the percentage as needed for core/non-core OTUs ) #To view the line plot visualize(filtered_otu = core_1[["final_otu_table_bef_filter"]], min_count_val = 5, max_count_val = 25, count_val_interval = 5, prop = 0.1, min_total_count = 10, method = "srs", top_percentage =10)