Package: HIPPO 1.25.0

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_1.25.0.tar.gz
HIPPO_1.25.0.zip(r-4.7)HIPPO_1.25.0.zip(r-4.6)HIPPO_1.25.0.zip(r-4.5)
HIPPO_1.25.0.tgz(r-4.6-any)HIPPO_1.25.0.tgz(r-4.5-any)
HIPPO_1.25.0.tar.gz(r-4.7-any)HIPPO_1.25.0.tar.gz(r-4.6-any)
HIPPO_1.25.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
HIPPO/json (API)
NEWS

# Install 'HIPPO' in R:

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

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-1.25.0(bioc 3.24)HIPPO-1.24.0(bioc 3.23)

sequencing singlecell geneexpression differentialexpression clustering

6.18 score 19 stars 8 scripts 335 downloads 8 mentions 18 exports 64 dependencies

Last updated from:d96a769e67. Checks:1 WARNING, 7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	165
linux-devel-x86_64	NOTE	318
source / vignettes	OK	230
linux-release-x86_64	NOTE	335
macos-release-arm64	NOTE	190
macos-oldrel-arm64	NOTE	167
windows-devel	NOTE	216
windows-release	NOTE	223
windows-oldrel	NOTE	207
wasm-release	OK	126

Exports:%>%get_data_from_sce get_hippo get_hippo_diffexp hippo hippo_diagnostic_plot hippo_diffexp hippo_dimension_reduction hippo_feature_heatmap hippo_pca_plot hippo_tsne_plot hippo_umap_plot nb_prob_zero pois_prob_zero preprocess_heterogeneous preprocess_homogeneous zero_proportion_plot zinb_prob_zero

Dependencies:abind askpass Biobase BiocGenerics cli cpp11 DelayedArray dplyr farver generics GenomicRanges ggplot2 ggrepel glue gridExtra gtable here IRanges irlba isoband jsonlite labeling lattice lifecycle magrittr Matrix MatrixGenerics matrixStats openssl pillar pkgconfig plyr png R6 rappdirs RColorBrewer Rcpp RcppEigen RcppTOML reshape2 reticulate rlang rprojroot RSpectra Rtsne S4Arrays S4Vectors S7 scales Seqinfo SingleCellExperiment SparseArray stringi stringr SummarizedExperiment sys tibble tidyselect umap utf8 vctrs viridisLite withr XVector

Feature Selection and Hierarchical Clustering of cells in Zhengmix4eq

Rendered fromexample.Rmdusingknitr::rmarkdownon May 30 2026.

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
re-export magrittr pipe operator	%>%
A reference data frame that matches ENSG IDs to HGNC symbols	ensg_hgnc
Access data from SCE object	get_data_from_sce
Access hippo object from SingleCellExperiment object.	get_hippo
Return hippo_diffexp object	get_hippo_diffexp
HIPPO's hierarchical clustering	hippo
Conduct feature selection by computing test statistics for each gene	hippo_diagnostic_plot
HIPPO's differential expression	hippo_diffexp
compute t-SNE or umap of each round of HIPPO	hippo_dimension_reduction
HIPPO's feature heatmap	hippo_feature_heatmap
visualize each round of hippo through t-SNE	hippo_pca_plot
visualize each round of hippo through t-SNE	hippo_tsne_plot
visualize each round of hippo through UMAP	hippo_umap_plot
Expected zero proportion under Negative Binomial	nb_prob_zero
Expected zero proportion under Poisson	pois_prob_zero
Preprocess UMI data without cell label so that each row contains information about each gene	preprocess_heterogeneous
Preprocess UMI data with inferred or known labels	preprocess_homogeneous
A sample single cell sequencing data subsetted from Zheng2017	toydata
visualize each round of hippo through zero proportion plot	zero_proportion_plot
Expected zero proportion under Negative Binomial	zinb_prob_zero