Package: ProFAST 1.9

Wei Liu

ProFAST: Probabilistic Factor Analysis for Spatially-Aware Dimension Reduction

Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations. More details can be referred to Wei Liu, et al. (2023) <doi:10.1101/2023.07.11.548486>.

Authors:Wei Liu [aut, cre], Xiao Zhang [aut], Jin Liu [aut]

ProFAST_1.9.tar.gz
ProFAST_1.9.zip(r-4.7-x86_64)ProFAST_1.9.zip(r-4.7-arm64)ProFAST_1.9.zip(r-4.6-x86_64)ProFAST_1.9.zip(r-4.6-arm64)
ProFAST_1.9.tar.gz(r-4.7-arm64)ProFAST_1.9.tar.gz(r-4.7-x86_64)ProFAST_1.9.tar.gz(r-4.6-arm64)ProFAST_1.9.tar.gz(r-4.6-x86_64)
ProFAST_1.9.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
ProFAST/json (API)

# Install 'ProFAST' in R:
install.packages('ProFAST', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/feiyoung/profast/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • CosMx_subset - A Seurat object including spatial transcriptomics dataset from CosMx platform
  • pbmc3k_subset - A Seurat object including scRNA-seq PBMC dataset
  • top5_signatures - A data.frame object including top five signature genes in scRNA-seq PBMC dataset

On CRAN:

Conda:

openblascpp

5.03 score 2 packages 24 scripts 615 downloads 22 exports 149 dependencies

Last updated from:8d9bf6e477. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK290
linux-devel-x86_64OK376
source / vignettesOK451
linux-release-arm64OK354
linux-release-x86_64OK358
windows-devel-arm64OK418
windows-devel-x86_64OK416
windows-release-arm64OK447
windows-release-x86_64OK344
wasm-releaseOK217

Exports:AddAdjAddParSettingFASTcoembed_plotcoembedding_umapdiagnostic.cor.eigsFASTFAST_runFAST_singleFAST_structurefind.signature.genesget_r2_mcfaddenget.top.signature.datIntegrateSRTDataiscmeb_runmodel_set_FASTNCFMNCFM_fastpdistanceRunHarmonyLouvainRuniSCMEBSelectHKgenestransferGeneNames

Dependencies:abindaskpassbase64encBHBiocGenericsbitopsbslibcachemcaToolscliclustercodetoolscommonmarkCompQuadFormcowplotcpp11crosstalkcurldata.tabledeldirdigestdotCall64dplyrdqrngDR.SCevaluatefarverfastDummiesfastmapfitdistrplusFNNfontawesomefsfurrrfuturefuture.applygenericsggplot2ggrepelggridgesglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevallifecyclelistenvlmtestmagrittrMASSMatrixmatrixStatsmclustmemoisemimeminiUInlmeopensslotelparallellypatchworkpbapplypillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrR6RANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppThreadRcppTOMLreshape2reticulaterlangrmarkdownROCRrprojrootRSpectraRtsneS4VectorsS7sassscalesscattermoresctransformSeuratSeuratObjectshinysitmosourcetoolsspspamspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringistringrsurvivalsystensortibbletidyrtidyselecttinytexutf8uwotvctrsviridisLitewithrxfunxtableyamlzoo

CoFAST: NSCLC CosMx data coembedding
Load and view data | Preprocessing | Coembedding using FAST | Downstream analysis
Rendered fromCosMx.Rmdusingknitr::rmarkdown

Last update: 2024-03-19
Started: 2024-03-19

CoFAST: PBMC scRNA-seq data coembedding
Load and view data | Preprocessing | Coembedding using non-centered factor model | Downstream analysis
Rendered frompbmc3k.Rmdusingknitr::rmarkdown

Last update: 2024-03-19
Started: 2024-03-19

FAST: single DLPFC section
View the DLPFC data | Preprocessing | Fit FAST for this data | Evaluate the adjusted McFadden's pseudo R-square | Clustering analysis based on FAST embedding | Visualization | Differential epxression analysis
Rendered fromFASTdlpfc.Rmdusingknitr::rmarkdown

Last update: 2024-03-19
Started: 2024-03-19

FAST: two DLPFC sections
View the DLPFC data | Create a PRECASTObject object | Prepare the PRECASTObject with preprocessing step. | Fit FAST for this data | Add the model setting | Fit FAST | Evaluate the adjusted McFadden's pseudo R-square | Embedding alignment and spatial clustering using iSC-MEB | Remove unwanted variations in the log-normalized expression matrices | Visualization | Combined differential epxression analysis
Rendered fromFASTdlpfc2.Rmdusingknitr::rmarkdown

Last update: 2024-03-19
Started: 2024-01-12

FAST: simulation
View the simulated data | Create a PRECASTObject object | Prepare the PRECASTObject with preprocessing step. | Fit FAST using simulated data | Add the model setting | Fit FAST | Evaluate the adjusted McFadden's pseudo R-square | Embedding alignment and clustering using Harmony and Louvain | Remove unwanted variations in the log-normalized expression matrices | Visualization | Combined differential epxression analysis
Rendered fromFASTsimu.Rmdusingknitr::rmarkdown

Last update: 2024-01-12
Started: 2024-01-12

Readme and manuals

Help Manual

Help pageTopics
Calculate the adjacency matrix given a spatial coordinate matrixAddAdj
Add FAST model settings for a PRECASTObj objectAddParSettingFAST
Coembedding dimensional reduction plotcoembed_plot
Calculate UMAP projections for coembedding of cells and featurescoembedding_umap
A Seurat object including spatial transcriptomics dataset from CosMx platformCosMx_subset
Determine the dimension of low dimensional embeddingdiagnostic.cor.eigs diagnostic.cor.eigs.default diagnostic.cor.eigs.Seurat
Run FAST model for a PRECASTObj objectFAST
(Varitional) ICM-EM algorithm for implementing FAST modelFAST_run
Fit FAST model for single-section SRT dataFAST_single
(Varitional) ICM-EM algorithm for implementing FAST model with structurized parametersFAST_structure
Find the signature genes for each group of cell/spotsfind.signature.genes
Calcuate the the adjusted McFadden's pseudo R-squareget_r2_mcfadden
Obtain the top signature genes and related informationget.top.signature.dat
Integrate multiple SRT data into a Seurat objectIntegrateSRTData
Fit an iSC-MEB model using specified multi-section embeddingsiscmeb_run
Set parameters for FAST modelmodel_set_FAST
Cell-feature coembedding for scRNA-seq dataNCFM
Cell-feature coembedding for SRT dataNCFM_fast
A Seurat object including scRNA-seq PBMC datasetpbmc3k_subset
Calculate the cell-feature distance matrixpdistance
Embedding alignment and clustering based on the embeddings from FASTRunHarmonyLouvain
Fit an iSC-MEB model using the embeddings from FASTRuniSCMEB
Select housekeeping genesSelectHKgenes
A data.frame object including top five signature genes in scRNA-seq PBMC datasettop5_signatures
Transfer gene names from one fortmat to the other formattransferGeneNames