Package: HIPPO 1.19.0

Tae Kim

HIPPO: Heterogeneity-Induced Pre-Processing tOol

For scRNA-seq data, it selects features and clusters the cells simultaneously for single-cell UMI data. It has a novel feature selection method using the zero inflation instead of gene variance, and computationally faster than other existing methods since it only relies on PCA+Kmeans rather than graph-clustering or consensus clustering.

Authors:Tae Kim [aut, cre], Mengjie Chen [aut]

HIPPO_1.19.0.tar.gz
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HIPPO.pdf |HIPPO.html
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NEWS

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

Peer review:

Bug tracker:https://github.com/tk382/hippo/issues

Datasets:
  • ensg_hgnc - A reference data frame that matches ENSG IDs to HGNC symbols
  • toydata - A sample single cell sequencing data subsetted from Zheng2017

On BioConductor:HIPPO-1.19.0(bioc 3.21)HIPPO-1.18.0(bioc 3.20)

sequencingsinglecellgeneexpressiondifferentialexpressionclustering

6.16 score 18 stars 4 scripts 130 downloads 8 mentions 18 exports 75 dependencies

Last updated 2 months agofrom:3720af6268. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 18 2024
R-4.5-winNOTEDec 18 2024
R-4.5-linuxNOTEDec 18 2024
R-4.4-winNOTEDec 18 2024
R-4.4-macNOTEDec 18 2024
R-4.3-winNOTEDec 18 2024
R-4.3-macNOTEDec 18 2024

Exports:%>%get_data_from_sceget_hippoget_hippo_diffexphippohippo_diagnostic_plothippo_diffexphippo_dimension_reductionhippo_feature_heatmaphippo_pca_plothippo_tsne_plothippo_umap_plotnb_prob_zeropois_prob_zeropreprocess_heterogeneouspreprocess_homogeneouszero_proportion_plotzinb_prob_zero

Dependencies:abindaskpassBiobaseBiocGenericsclicolorspacecrayoncurlDelayedArraydplyrfansifarvergenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2ggrepelgluegridExtragtableherehttrIRangesirlbaisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimemunsellnlmeopensslpillarpkgconfigplyrpngR6rappdirsRColorBrewerRcppRcppEigenRcppTOMLreshape2reticulaterlangrprojrootRSpectraRtsneS4ArraysS4VectorsscalesSingleCellExperimentSparseArraystringistringrSummarizedExperimentsystibbletidyselectUCSC.utilsumaputf8vctrsviridisLitewithrXVectorzlibbioc

Feature Selection and Hierarchical Clustering of cells in Zhengmix4eq

Rendered fromexample.Rmdusingknitr::rmarkdownon Dec 18 2024.

Last update: 2020-03-26
Started: 2019-12-30