| Type: | Package |
| Title: | Poisson Hurdle Clustering for Sparse Microbiome Data |
| Version: | 0.1.0 |
| Author: | Zhili Qiao |
| Maintainer: | Zhili Qiao <zlqiao@iastate.edu> |
| Description: | Clustering analysis for sparse microbiome data, based on a Poisson hurdle model. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.2 |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Depends: | R (≥ 2.10) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2022-02-07 19:38:00 UTC; zlqia |
| Repository: | CRAN |
| Date/Publication: | 2022-02-08 16:20:11 UTC |
Calculate optimal number of clusters.
Description
This function estimates the optimal number of clusters for a given dataset.
Usage
Hybrid(data, absolute = FALSE, Kstart = NULL, Treatment)
Arguments
data |
Data matrix with dimension N*P indicating N features and P samples. |
absolute |
Logical. Whether we should use absolute (TRUE) or relative (FALSE) abundance of features to determine clusters. |
Kstart |
Positive integer. The number of clusters for starting the hybrid merging algorithm. Should be relatively large to ensure that Kstart > optimal number of clusters. Uses max(50, sqrt(N)) by default. |
Treatment |
Vector of length p, indicating replicates of different treatment groups. For example, Treatment = c(1,1,2,2,3,3) indicates 3 treatment groups, each with 2 replicates. |
Value
A positive integer indicating the optimal number of clusters
Examples
######## Run the following codes in order:
##
## This is a sample data set which has 100 features, and 4 treatment groups with 4 replicates each.
data('sample_data')
head(sample_data)
set.seed(1)
##
## Finding the optimal number of clusters
K <- Hybrid(sample_data, Kstart = 4, Treatment = rep(c(1,2,3,4), each = 4))
##
## Clustering result from EM algorithm
result <- PHcluster(sample_data, rep(c(1,2,3,4), each = 4), K, method = 'EM', nstart = 1)
print(result$cluster)
##
## Plot the feature abundance level for each cluster
plot_abundance(result, sample_data, Treatment = rep(c(1,2,3,4), each = 4))
Poisson hurdle clustering
Description
This function gives the clustering result based on a Poisson hurdle model.
Usage
PHcluster(
data,
Treatment,
nK,
method = c("EM", "SA"),
absolute = FALSE,
cool = 0.9,
nstart = 1
)
Arguments
data |
Data matrix with dimension N*P indicating N features and P samples. The cluster analysis is done feature-wised. |
Treatment |
Vector of length P. Indicating replicates of different treatment groups. For example, Treatment = c(1,1,2,2,3,3) indicates 3 treatment groups, each with 2 replicates. |
nK |
Positive integer. Number of clusters. |
method |
Method for the algorithm. Can choose between "EM" as Expectation Maximization or "SA" as Simulated Annealing. |
absolute |
Logical. Whether we should use absolute (TRUE) or relative (False) abundance of features to determine clusters. |
cool |
Real number between (0, 1). Cooling rate for the "SA" algorithm. Uses 0.9 by default. |
nstart |
Positive integer. Number of starts for the entire algorithm. Note that as nstart increases the computational time also grows linearly. Uses 1 by default. |
Value
- cluster
Vector of length N consisting of integers from 1 to nK. Indicating final clustering result. For evaluating the clustering result please check NMI for Normalized Mutual Information.
- prob
N*nK matrix. The (i, j)th element representing the probability that observation i belongs to cluster j.
- log_l
Scaler. The Poisson hurdle log-likelihood of the final clustering result.
- alpha
Vector of length N. The geometric mean abundance level for each feature, across all treatment groups.
- Normalizer
vector of length P. The normalizing constant of sequencing depth for each sample.
Examples
######## Run the following codes in order:
##
## This is a sample data set which has 100 features, and 4 treatment groups with 4 replicates each.
data('sample_data')
head(sample_data)
set.seed(1)
##
## Finding the optimal number of clusters
K <- Hybrid(sample_data, Kstart = 4, Treatment = rep(c(1,2,3,4), each = 4))
##
## Clustering result from EM algorithm
result <- PHcluster(sample_data, rep(c(1,2,3,4), each = 4), K, method = 'EM', nstart = 1)
print(result$cluster)
##
## Plot the feature abundance level for each cluster
plot_abundance(result, sample_data, Treatment = rep(c(1,2,3,4), each = 4))
Plot of feature abundance level
Description
This function plots the feature abundance level for each cluster, after extracting the effect of sample-wise normalization factors and feature-wise geometric mean.
Usage
plot_abundance(result, data, Treatment)
Arguments
result |
Clustering result from function PHclust(). |
data |
Data matrix with dimension N*P indicating N features and P samples. |
Treatment |
Vector of length P. Indicating replicates of different treatment groups. For example, Treatment = c(1,1,2,2,3,3) indicates 3 treatment groups, each with 2 replicates. |
Value
A plot for feature abundance level will be shown. No value is returned.
Examples
######## Run the following codes in order:
##
## This is a sample data set which has 100 features, and 4 treatment groups with 4 replicates each.
data('sample_data')
head(sample_data)
set.seed(1)
##
## Finding the optimal number of clusters
K <- Hybrid(sample_data, Kstart = 4, Treatment = rep(c(1,2,3,4), each = 4))
##
## Clustering result from EM algorithm
result <- PHcluster(sample_data, rep(c(1,2,3,4), each = 4), K, method = 'EM', nstart = 1)
print(result$cluster)
##
## Plot the feature abundance level for each cluster
plot_abundance(result, sample_data, Treatment = rep(c(1,2,3,4), each = 4))
Sample of sparse microbiome count data
Description
A sample data matrix with 100 features in 2 true clusters, 4 treatment groups with 4 replicates in each group.
Usage
sample_data
Format
The dataset contains 16 columns, indexed as A1 ~ A4, B1 ~ B4, C1 ~ C4, D1 ~ D4 to represent 4 treatment groups.
Examples
head(sample_data)