Journal: Frontiers in Microbiology

Article Title: Identification of microbial metabolic functional guilds from large genomic datasets

doi: 10.3389/fmicb.2023.1197329

Figure Lengend Snippet: Resulting clustergram plot on the presence/absence pathway data for our composite dataset (red = present, black = absent) using a cut height of 0.9 with rows (genomes) and columns (functions) clustered based on Jaccard distance.

Article Snippet: We also analyzed our composite dataset using an agglomerative hierarchical clustering method using the clustergram function from the Statistics and Machine Learning toolbox v12.1 from MATLAB R2021a (The Math Works, ).

Techniques:

Journal: Frontiers in Microbiology

Article Title: Identification of microbial metabolic functional guilds from large genomic datasets

doi: 10.3389/fmicb.2023.1197329

Figure Lengend Snippet: The distribution of guild sizes (number of functions) and the number of mapback genomes for guilds generated with clustergram at cut heights of 0.9 (blue square) and 1 (purple plus sign) as well as for AB. AB approach 1 (red circle) defined guilds using a fixed size of 5 functions while AB approach 2 (green triangle) defined guilds using a minimum mapback genome cut-off of 100. Points were jittered using the built-in position_jitter function in the ggplot2 package v3.4.2 with h = 0.1, w = 0.35 using the random seed 123.

Article Snippet: We also analyzed our composite dataset using an agglomerative hierarchical clustering method using the clustergram function from the Statistics and Machine Learning toolbox v12.1 from MATLAB R2021a (The Math Works, ).

Techniques: Generated