Package: PIUMA 1.3.0

Mattia Chiesa

PIUMA: Phenotypes Identification Using Mapper from topological data Analysis

The PIUMA package offers a tidy pipeline of Topological Data Analysis frameworks to identify and characterize communities in high and heterogeneous dimensional data.

Authors:Mattia Chiesa [aut, cre], Arianna Dagliati [aut], Alessia Gerbasi [aut], Giuseppe Albi [aut], Laura Ballarini [aut], Luca Piacentini [aut]

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PIUMA.pdf |PIUMA.html✨
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NEWS

# Install 'PIUMA' in R:

install.packages('PIUMA', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/bioinfomonzino/piuma/issues

Datasets:

df_test_proj - A dataset to test the 'dfToProjection' and 'dfToDistance' funtions of 'PIUMA' package.
tda_test_data - A TDAobj to test the 'PIUMA' package.
vascEC_meta - Example datasets for PIUMA package
vascEC_norm - We tested PIUMA on a subset of the single-cell RNA Sequencing dataset (GSE:GSE193346 generated and published by Feng et al. (2022) on Nature Communication to demonstrate that distinct transcriptional profiles are present in specific cell types of each heart chambers, which were attributed to have roles in cardiac development. In this tutorial, our aim will be to exploit PIUMA for identifying sub-population of vascular endothelial cells, which can be associated with specific heart developmental stages. The original dataset consisted of three layers of heterogeneity: cell type, stage and zone (i.e., heart chamber). Our testing dataset was obtained by subsetting vascular endothelial cells (cell type) by Seurat object, extracting raw counts and metadata. Thus, we filtered low expressed genes and normalized data by DaMiRseq

On BioConductor:PIUMA-1.3.0(bioc 3.21)PIUMA-1.2.0(bioc 3.20)

clustering graphandnetwork dimensionreduction network classification

5.15 score 4 stars 2 scripts 110 downloads 31 exports 106 dependencies

Last updated 2 months agofrom:223a516951. Checks:OK: 1 WARNING: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Dec 19 2024
R-4.5-win	WARNING	Dec 19 2024
R-4.5-linux	WARNING	Dec 19 2024
R-4.4-win	WARNING	Dec 19 2024
R-4.4-mac	WARNING	Dec 19 2024
R-4.3-win	WARNING	Dec 19 2024
R-4.3-mac	WARNING	Dec 19 2024

Exports:checkNetEntropy checkScaleFreeModel dfToDistance dfToProjection getComp getDfMapper getDistMat getJacc getNodeDataMat getOrigData getOutcome getOutcomeFact getScaledData jaccard jaccardMatrix makeTDAobj makeTDAobjFromSE mapperCore plot_projection_plot plot_ScaleFreeLaw scaleData_01 setComp setDfMapper setDistMat setJacc setNodeDataMat setOrigData setOutcome setOutcomeFact setScaledData tdaDfEnrichment

Dependencies:abind askpass backports base64enc Biobase BiocGenerics bslib cachem checkmate cli cluster colorspace cpp11 crayon curl data.table dbscan DelayedArray digest evaluate fansi farver fastmap fontawesome foreign Formula fs generics GenomeInfoDb GenomeInfoDbData GenomicRanges ggplot2 glue gridExtra gtable here highr Hmisc htmlTable htmltools htmlwidgets httr igraph IRanges isoband jquerylib jsonlite kernlab knitr labeling lattice lifecycle magrittr MASS Matrix MatrixGenerics matrixStats memoise mgcv mime munsell nlme nnet openssl patchwork permute pillar pkgconfig png R6 rappdirs RColorBrewer Rcpp RcppEigen RcppTOML reticulate rlang rmarkdown rpart rprojroot RSpectra rstudioapi S4Arrays S4Vectors sass scales SparseArray stringi stringr SummarizedExperiment sys tibble tinytex tsne UCSC.utils umap utf8 vctrs vegan viridis viridisLite withr xfun XVector yaml zlibbioc

The PIUMA package - Phenotypes Identification Using Mapper from topological data Analysis

Mattia Chiesa, Laura Ballarini, Alessia Gerbasi, Giuseppe Albi, Arianna Dagliati, Luca Piacentini

Rendered fromPIUMA_vignette.Rmdusingknitr::rmarkdownon Dec 19 2024.

Last update: 2024-05-17
Started: 2024-03-22

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Compute the Network Entropy	checkNetEntropy
Assessment of Scale-Free model fitting	checkScaleFreeModel
A dataset to test the 'dfToProjection' and 'dfToDistance' funtions of 'PIUMA' package.	df_test_proj
Compute the Distance Matrix from TDAobj	dfToDistance
Data projection using a Dimensionality Reduction Method	dfToProjection
Getter method for the 'comp' slot of a TDAobj object.	getComp getComp,PIUMA-getComp getComp,TDAobj-method
Getter method for the 'dfMapper' slot of a TDAobj object.	getDfMapper getDfMapper,PIUMA-getDfMapper getDfMapper,TDAobj-method
Getter method for the 'dist_mat' slot of a TDAobj object.	getDistMat getDistMat,PIUMA-getDistMat getDistMat,TDAobj-method
Getter method for the 'jacc' slot of a TDAobj object.	getJacc getJacc,PIUMA-getJacc getJacc,TDAobj-method
Getter method for the 'node_data_mat' slot of a TDAobj object.	getNodeDataMat getNodeDataMat,PIUMA-getNodeDataMat getNodeDataMat,TDAobj-method
Getter method for the 'orig_data' slot of a TDAobj object.	getOrigData getOrigData,PIUMA-getOrigData getOrigData,TDAobj-method
Getter method for the 'outcome' slot of a TDAobj object.	getOutcome getOutcome,PIUMA-getOutcome getOutcome,TDAobj-method
Getter method for the 'outcomeFact' slot of a TDAobj object.	getOutcomeFact getOutcomeFact,PIUMA-getOutcomeFact getOutcomeFact,TDAobj-method
Getter method for the 'scaled_data' slot of a TDAobj object.	getScaledData getScaledData,PIUMA-getScaledData getScaledData,TDAobj-method
Compute the Matrix of Jaccard Indexes	jaccardMatrix
Import data and generate the TDAobj object	makeTDAobj
Import SummarizedExperiment data and generate the TDAobj object	makeTDAobjFromSE
Implement the TDA Mapper algorithm on TDAobj	mapperCore
PIUMA: Phenotypes Identification Using Mapper from topological data Analysis	PIUMA
Setter method for the 'comp' slot of a TDAobj object.	setComp setComp,PIUMA-setComp setComp,TDAobj-method
Setter method for the 'dfMapper' slot of a TDAobj object.	setDfMapper setDfMapper,PIUMA-setDfMapper setDfMapper,TDAobj-method
Setter method for the 'dist_mat' slot of a TDAobj object.	setDistMat setDistMat,PIUMA-setDistMat setDistMat,TDAobj-method
Setter method for the 'jacc' slot of a TDAobj object.	setJacc setJacc,PIUMA-setJacc setJacc,TDAobj-method
Setter method for the 'node_data_mat' slot of a TDAobj object.	setNodeDataMat setNodeDataMat,PIUMA-setNodeDataMat setNodeDataMat,TDAobj-method
Setter method for the 'orig_data' slot of a TDAobj object.	setOrigData setOrigData,PIUMA-setOrigData setOrigData,TDAobj-method
Setter method for the 'outcome' slot of a TDAobj object.	setOutcome setOutcome,PIUMA-setOutcome setOutcome,TDAobj-method
Setter method for the 'outcomeFact' slot of a TDAobj object.	setOutcomeFact setOutcomeFact,PIUMA-setOutcomeFact setOutcomeFact,TDAobj-method
Setter method for the 'scaled_data' slot of a TDAobj object.	setScaledData setScaledData,PIUMA-setScaledData setScaledData,TDAobj-method
A TDAobj to test the 'PIUMA' package.	tda_test_data
Add information to TDAobj	tdaDfEnrichment
The object 'TDAobj'	TDAobj-class
Example datasets for PIUMA package	vascEC_meta
We tested PIUMA on a subset of the single-cell RNA Sequencing dataset (GSE:GSE193346 generated and published by Feng et al. (2022) on Nature Communication to demonstrate that distinct transcriptional profiles are present in specific cell types of each heart chambers, which were attributed to have roles in cardiac development. In this tutorial, our aim will be to exploit PIUMA for identifying sub-population of vascular endothelial cells, which can be associated with specific heart developmental stages. The original dataset consisted of three layers of heterogeneity: cell type, stage and zone (i.e., heart chamber). Our testing dataset was obtained by subsetting vascular endothelial cells (cell type) by Seurat object, extracting raw counts and metadata. Thus, we filtered low expressed genes and normalized data by DaMiRseq	vascEC_norm