grvanderploeg - Network

I'm excited to share that my R package, parafac4microbiome, has been updated to version 1.1.2 and is now available on CRAN:...

I'm excited to share that my R package, parafac4microbiome, has been updated to version 1.1.2 and is now available on CRAN: https://cran.rstudio.com//web/packages/parafac4microbiome/index.html. This new release includes the full dataset from the vanderPloeg2024 paper (https://www.nature.com/articles/s41522-024-00565-x), along with numerous bug fixes to improve model selection, an enhanced version of CORCONDIA, updated vignettes, and many other enhancements! #RStats

I presented my poster today at the Amsterdam Microbiome Expertise Center (AMEC) day at Artis zoo. I'm excited to share it here too. Feel...

I presented my poster today at the Amsterdam Microbiome Expertise Center (AMEC) day at Artis zoo. I'm excited to share it here too. Feel free to message me with any questions! #AMEC2025 #Microbiome #DataAnalysis

(8/8) I hope that this was helpful. If you got this far, thank you very much for your interest. Feel free to DM me if you have any...

(8/8) I hope that this was helpful. If you got this far, thank you very much for your interest. Feel free to DM me if you have any questions. Also check out my paper (https://buff.ly/4hDLpFw) and the parafac4microbiome R package (https://buff.ly/4hFVtOD) for more details.

(7/8) PARAFAC reveals distinct microbial growth patterns: Proteobacteria show exponential growth in case subjects, while Actinobacteria...

(7/8) PARAFAC reveals distinct microbial growth patterns: Proteobacteria show exponential growth in case subjects, while Actinobacteria exhibit a sigmoid growth curve in controls. This method interprets time-resolved microbial differences between groups more effectively than PCA.

(6/8) In contrast, Parallel factor analysis decomposes a three-way data array into components with three loading vectors each: one for the...

(6/8) In contrast, Parallel factor analysis decomposes a three-way data array into components with three loading vectors each: one for the subjects, one for the microbial relative abundances, and one for time.

(5/8) The biplot of the PCA shows that the first principal component distinguishes case and control samples by Actinobacteria abundance,...

(5/8) The biplot of the PCA shows that the first principal component distinguishes case and control samples by Actinobacteria abundance, while the second separates different time points. However, the time profiles of each subject group remain unclear.

(4/8) Principal Component Analysis would decompose a two-way data table like this into components with scores for the measurements and...

(4/8) Principal Component Analysis would decompose a two-way data table like this into components with scores for the measurements and loadings for the microbial relative abundances. This does not take the longitudinal study design into account.

(3/8) The mock data contains three phyla corresponding to 25 case and 25 control subjects across 10 time points. Initially, all subjects are...

(3/8) The mock data contains three phyla corresponding to 25 case and 25 control subjects across 10 time points. Initially, all subjects are similar. By time point 10, case subjects show reduced Actinobacteria and increased Proteobacteria, while controls show the opposite.

(2/8) I will highlight how this helps using a a mock intervention dataset, where we have obtained count data of three phyla corresponding to...

(2/8) I will highlight how this helps using a a mock intervention dataset, where we have obtained count data of three phyla corresponding to 25 case and 25 control subjects across 10 time points.

(1/8) Longitudinal microbiome data is shown as a two-way table with rows for the measurements (subject-time point combinations) and columns...

(1/8) Longitudinal microbiome data is shown as a two-way table with rows for the measurements (subject-time point combinations) and columns for microbial abundances, which can be transformed into a three-way array by adding time points as a third dimension.

Here's a thread 🧵 on using the parafac4microbiome package to track microbial changes with tensors over time. Check out my paper...

Here's a thread 🧵 on using the parafac4microbiome package to track microbial changes with tensors over time. Check out my paper (https://buff.ly/4hDLpFw) and the parafac4microbiome R package (https://buff.ly/4hFVtOD) for more details. #RStats

My second paper on applying Parallel Factor Analysis (PARAFAC) to microbiome datasets is submitted and available on @biorxivpreprint :...

My second paper on applying Parallel Factor Analysis (PARAFAC) to microbiome datasets is submitted and available on @biorxivpreprint : https://buff.ly/3PNae61. It features my R package, parafac4microbiome, downloadable from CRAN: https://buff.ly/4apC9CT.

#rStats

Thrilled to share my 1st paper in @nature.portfolio npj Biofilms & Microbiomes! We show how Parallel Factor Analysis reveals microbial...

Thrilled to share my 1st paper in @nature.portfolio npj Biofilms & Microbiomes! We show how Parallel Factor Analysis reveals microbial subcommunities & pathways in gingivitis. Thanks to my coauthors!

https://buff.ly/421t1C5

#Microbiome #AcademicTwitter #phdlife

I thoroughly enjoyed making an R package that create Parallel Factor Analysis (PARAFAC) models for longitudinal microbiome data analysis....

I thoroughly enjoyed making an R package that create Parallel Factor Analysis (PARAFAC) models for longitudinal microbiome data analysis. Learn more about it on https://buff.ly/4a6z3mX or my preprint at https://buff.ly/3W2nQhn #Microbiome #Bioinformatics #DataScience #RStats

Happy to share that my personal website grvanderploeg.com is live. You can view my CV, publications and presentations on there if you're...

Happy to share that my personal website grvanderploeg.com is live. You can view my CV, publications and presentations on there if you're interested.

Happy to be on this platform!

Happy to be on this platform!