<p>Correlation of bacteriome and mycobiome was determined for (A) uninfected and (B) HIV-infected study participants using R statistical computing software (Spearman's correlation and two-tailed probability of <i>t</i> for each correlation) for the two groups. Interactions among different fungi in the mycobiome of (C) uninfected and (D) HIV-infected study participants was also determined using similar approach. Red: Positive correlation; Blue: negative correlation; diameter of circles represent the absolute value of correlation for each pair of the microbe-microbe matrix.</p
Motivation: Network interpretation using correlations has several known difficulties. Firstly, the d...
<p>Spearman’s rank correlation matrix of OTUs with > = 0.1% abundance in at least 5 saliva samples. ...
Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial inte...
<p>For correlation analysis, microbiome abundance data was divided into independent data matrices (d...
<p><b>Intra-kingdom correlations within the bacteriome and mycobiome for (A,D) HIV-infected non-smok...
<p>After unbiased analysis of potential associations between host gene mRNA levels and bacterial DNA...
<p>Correlation matrices comparing viral taxa to bacteria at the Family level. The color and size of ...
<p>Microbial populations listed represent at least 1% of the bacterial, archaeal, ciliate, or fungal...
Pearson’s linear correlation coefficient was calculated between SCC and relative abundances of micro...
<p>The legend is common for figures A, B and D: The correlation network represents features that are...
<p>Correlation coefficients (r<sub>s</sub>) are shown for each comparison with the p-value and numbe...
<p>A. Strong (>0.7) correlations between Microbiota at time point 1. B. Intersection of strong corre...
<p>A. Spearman correlation method. B. SparCC method. The nodes represent microbial species from the ...
<p><b>Principal coordinates analysis (PCoA) of (A-F) bacteriome and (G-L) mycobiome data at differen...
Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial inte...
Motivation: Network interpretation using correlations has several known difficulties. Firstly, the d...
<p>Spearman’s rank correlation matrix of OTUs with > = 0.1% abundance in at least 5 saliva samples. ...
Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial inte...
<p>For correlation analysis, microbiome abundance data was divided into independent data matrices (d...
<p><b>Intra-kingdom correlations within the bacteriome and mycobiome for (A,D) HIV-infected non-smok...
<p>After unbiased analysis of potential associations between host gene mRNA levels and bacterial DNA...
<p>Correlation matrices comparing viral taxa to bacteria at the Family level. The color and size of ...
<p>Microbial populations listed represent at least 1% of the bacterial, archaeal, ciliate, or fungal...
Pearson’s linear correlation coefficient was calculated between SCC and relative abundances of micro...
<p>The legend is common for figures A, B and D: The correlation network represents features that are...
<p>Correlation coefficients (r<sub>s</sub>) are shown for each comparison with the p-value and numbe...
<p>A. Strong (>0.7) correlations between Microbiota at time point 1. B. Intersection of strong corre...
<p>A. Spearman correlation method. B. SparCC method. The nodes represent microbial species from the ...
<p><b>Principal coordinates analysis (PCoA) of (A-F) bacteriome and (G-L) mycobiome data at differen...
Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial inte...
Motivation: Network interpretation using correlations has several known difficulties. Firstly, the d...
<p>Spearman’s rank correlation matrix of OTUs with > = 0.1% abundance in at least 5 saliva samples. ...
Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial inte...