HIPPO
Single cell UMI analysis tool that focuses on zero-inflation to detect biological heterogeneity
Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
Prerequisites HIPPO works on the SingleCellExperiment object. You can install the library like the following.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("SingleCellExperiment")Installing
You can download the most recent version of HIPPO package as below. Please allow up to 5 minutes to completely compile the vignette.
devtools::install_github("tk382/HIPPO", build_vignettes = TRUE)Read the data
Many single-cell data sets used in the manuscript are available in DuoClustering2018 package (link).
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(version='devel')
BiocManager::install("DuoClustering2018")For example, you can load Zhengmix4eq data set like below.
sce = DuoClustering2018::sce_full_Zhengmix4eq(metadata = FALSE)For this vignette, we use a smaller toydata, a subset of Zhengmix4eq. The toydata is included in the HIPPO package.
data(toydata)
sce = toydataAlternatively, you can start from a matrix object and create SingleCellExperiment object.
X = readRDS("../zhengmix4eq_counts.rds")
sce = SingleCellExperiment(assays = list(counts = X))Diagnostic Plot
This plot shows the zero inflation compared to the expected Poisson line. If most genes don’t align with the black line, it shows that there is cell heterogeneity driving the zero inflation.
hippo_diagnostic_plot(sce,
show_outliers = TRUE,
zvalue_thresh = 3)
Feature Selection and Hierarchical Clustering
HIPPO assumes that the count matrix is placed in sce@assays@data$counts. Some objects that we found online have the count matrix in sce@assays$data$counts. In this case, HIPPO will throw an error because it cannot found a count matrix. In this case, you have to create another SingleCellExperiment object to assign the count matrix in the correct slot.
Next, you can run hippo function to do the pre-processing that simutlaneously conducts feature selection and hierarchcial clustering. There are three arguments that help you decide the stopping criterion of clustering procedure.
K is the maximum number of clusters that you want. HIPPO will return the clustering results for all k = 2, 3, …, K, so you can overestimate the number of potential clusters. The default is 10, but users are highly recommended to adjust this.
z_threshold is the feature selection criterion. For each round of hierarchical clustering, hippo will find outlier genes where the z-value of significance is greater than the threshold. For example, if you would like to select genes with p-values less than 0.05, z_threshold would be 1.96. The default threshold is 2, but users can use their discretion to change this value.
outlier_proportion is the number of outlier genes to allow. The default is 0.01 (1%) which means the clustering procedure will automatically stop if there are less than 1% of genes remain as important features. With the example data set, the default choice has empirically worked well.
set.seed(20200610)
sce = hippo(sce,
feature_method = "zero_inflation",
clustering_method = "kmeans",
K = 3,
outlier_proportion = 0.00001)Dimension Reduction for Each Round of HIPPO
We offer two dimension reduction methods: umap and tsne. And we offer two separate visualization functions.
sce = hippo_dimension_reduction(sce, method="umap")
hippo_umap_plot(sce)
sce = hippo_dimension_reduction(sce, method="tsne")
hippo_tsne_plot(sce)
Visualize the selected features at each round
This function shows how the zero-inflation decreases as HIPPO proceeds in the clustering. This function has arguments called switch_to_hgnc and ref. These aim to provide the users an option to change the gene names from ENSG IDs to HGNC symbols for ease of understanding. Many SingleCellExperiment objects have such data embedded in rowData(sce). Users can create a data frame with ensg and hgnc columns for the genes, and HIPPO will automatically switch the row names of the count matrix from ENSG IDs to HGNC symbols. The default is set to FALSE, assuming that the row names are already HGNC symbols.
data(ensg_hgnc)
zero_proportion_plot(sce,
switch_to_hgnc = TRUE,
ref = ensg_hgnc)
hippo_feature_heatmap(sce, k = 2,
switch_to_hgnc = TRUE,
ref = ensg_hgnc,
top.n = 20)
hippo_feature_heatmap(sce, k = 3,
switch_to_hgnc = TRUE,
ref = ensg_hgnc,
top.n = 20)
Differential Expression Example
We also offer a differential expression analysis tool.
This function also has an option to switch the gene names to HGNC symbols. top.n argument lets users choose how many top genes to show in the box plot. The default is 5.
The labels of boxplots are aligned with the t-SNE or UMAP plots above. When K is equal to 2, the color codes match with the cell groups as separated in the dimension reduction plot.
sce = hippo_diffexp(sce,
top.n = 15,
switch_to_hgnc = TRUE,
ref = ensg_hgnc)
Each round of differential expression test results are also saved in the list of data frames.
head(get_hippo_diffexp(sce, 1))
#> genes null_dev alt_dev pval
#> 1 ENSG00000101439 1005.8606 418.53653 9.570942e-130
#> 2 ENSG00000087086 677.0435 249.54309 5.687571e-95
#> 3 ENSG00000251562 1097.4217 709.70432 2.599070e-86
#> 4 ENSG00000011600 440.4111 87.47779 9.735996e-79
#> 5 ENSG00000227507 518.0729 308.59588 1.786349e-47
#> 6 ENSG00000137154 551.1884 344.74232 8.189475e-47
head(get_hippo_diffexp(sce, 2))
#> genes null_dev alt_dev pval
#> 1 ENSG00000019582 1701.8180 166.8158 0.000000e+00
#> 2 ENSG00000204287 1088.8127 112.4015 2.408126e-214
#> 3 ENSG00000223865 904.2276 142.0947 9.232007e-168
#> 4 ENSG00000251562 1097.4217 364.1340 1.727066e-161
#> 5 ENSG00000196126 765.7430 146.7529 1.239977e-136
#> 6 ENSG00000087086 677.0435 158.7387 9.893622e-115