| Type: | Package |
| Title: | Cluster Independent Algorithm for Rare Cell Types Identification |
| Version: | 0.1.0 |
| Author: | Gabriele Lubatti |
| Maintainer: | Gabriele Lubatti <gabriele.lubatti@helmholtz-muenchen.de> |
| Description: | Identification of markers of rare cell types by looking at genes whose expression is confined in small regions of the expression space https://github.com/ScialdoneLab. |
| License: | Artistic-2.0 |
| Depends: | R (≥ 4.0) |
| Imports: | Biobase, ggplot2, ggraph, magrittr |
| Suggests: | circlize, clustree, ComplexHeatmap, plotly, Seurat (≥ 4.0), testthat, knitr, rmarkdown |
| biocViews: | software |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.1 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2022-02-22 11:07:19 UTC; gabriele.lubatti |
| Repository: | CRAN |
| Date/Publication: | 2022-02-22 20:00:02 UTC |
CIARA
Description
It selects highly localized genes as specified in CIARA_gene, starting from genes in background
Usage
CIARA(
norm_matrix,
knn_matrix,
background,
cores_number = 1,
p_value = 0.001,
odds_ratio = 2,
local_region = 1,
approximation = FALSE
)
Arguments
norm_matrix |
Norm count matrix (n_genes X n_cells). |
knn_matrix |
K-nearest neighbors matrix (n_cells X n_cells). |
background |
Vector of genes for which the function CIARA_gene is run. |
cores_number |
Integer.Number of cores to use. |
p_value |
p value returned by the function fisher.test with parameter alternative = "g" |
odds_ratio |
odds_ratio returned by the function fisher.test with parameter alternative = "g" |
local_region |
Integer. Minimum number of local regions (cell with its knn neighbours) where the binarized gene expression is enriched in 1. |
approximation |
Logical.For a given gene, the fisher test is run in the local regions of only the cells where the binarized gene expression is 1. |
Value
Dataframe with n_rows equal to the length of background . Each row is the output from CIARA_gene.
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
CIARA_gene
Description
The gene expression is binarized (1/0) if the value in a given cell is above/below the median. Each of cell with its first K nearest neighbors defined a local region. If there are at least local_region enriched in 1 according to fisher.test, then the gene is defined as highly localized and a final p value is assigned to it. The final p value is the minimum of the p values from all the enriched local regions. If there are no enriched local regions, then the p value by default is set to 1
Usage
CIARA_gene(
norm_matrix,
knn_matrix,
gene_expression,
p_value = 0.001,
odds_ratio = 2,
local_region = 1,
approximation = FALSE
)
Arguments
norm_matrix |
Norm count matrix (n_genes X n_cells). |
knn_matrix |
K-nearest neighbors matrix (n_cells X n_cells). |
gene_expression |
numeric vector with the gene expression (length equal to n_cells). The gene expression is binarized (equal to 0/1 in the cells where the value is below/above the median) |
p_value |
p value returned by the function fisher.test with parameter alternative = "g" |
odds_ratio |
odds_ratio returned by the function fisher.test with parameter alternative = "g" |
local_region |
Integer. Minimum number of local regions (cell with its knn neighbours) where the binarized gene expression is enriched in 1. |
approximation |
Logical.For a given gene, the fisher test is run in the local regions of only the cells where the binarized gene expression is 1. |
Value
List with one element corresponding to the p value of the gene.
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
See Also
https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/fisher.test
cluster_analysis_integrate_rare
Description
cluster_analysis_integrate_rare
Usage
cluster_analysis_integrate_rare(
raw_counts,
project_name,
resolution,
neighbors,
max_dimension,
feature_genes = NULL
)
Arguments
raw_counts |
Raw count matrix (n_genes X n_cells). |
project_name |
Character name of the Seurat project. |
resolution |
Numeric value specifying the parameter resolution used in the Seurat function FindClusters. |
neighbors |
Numeric value specifying the parameter k.param in the Seurat function FindNeighbors |
max_dimension |
Numeric value specifying the maximum number of the PCA dimensions used in the parameter dims for the Seurat function FindNeighbors |
feature_genes |
vector of features specifying the argument features in the Seurat function RunPCA. |
Value
Seurat object including raw and normalized counts matrices, UMAP coordinates and cluster result.
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
See Also
https://www.rdocumentation.org/packages/Seurat/versions/4.0.1/topics/FindClusters https://www.rdocumentation.org/packages/Seurat/versions/4.0.1/topics/FindNeighbors https://www.rdocumentation.org/packages/Seurat/versions/4.0.1/topics/RunPCA
cluster_analysis_sub
Description
cluster_analysis_sub
Usage
cluster_analysis_sub(
raw_counts,
resolution,
neighbors,
max_dimension,
name_cluster
)
Arguments
raw_counts |
Raw count matrix (n_genes X n_cells). |
resolution |
Numeric value specifying the parameter resolution used in the Seurat function FindClusters. |
neighbors |
Numeric value specifying the parameter k.param in the Seurat function FindNeighbors |
max_dimension |
Numeric value specifying the maximum number of the PCA dimensions used in the parameter dims for the Seurat function FindNeighbors |
name_cluster |
Character.Name of the original cluster for which the sub clustering is done. |
Value
Seurat object including raw and normalized counts matrices and cluster result.
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
See Also
https://www.rdocumentation.org/packages/Seurat/versions/4.0.1/topics/RunPCA https://www.rdocumentation.org/packages/Seurat/versions/4.0.1/topics/FindVariableFeatures
find_resolution
Description
find_resolution
Usage
find_resolution(seurat_object, resolution_vector)
Arguments
seurat_object |
Seurat object as returned by cluster_analysis_integrate_rare |
resolution_vector |
vector with all values of resolution for which the Seurat function FindClusters is run |
Value
Clustree object showing the connection between clusters obtained at different level of resolution as specified in resolution_vector.
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
See Also
https://CRAN.R-project.org/package=clustree
get_background_full
Description
get_background_full
Usage
get_background_full(
norm_matrix,
threshold = 1,
n_cells_low = 3,
n_cells_high = 20
)
Arguments
norm_matrix |
Norm count matrix (n_genes X n_cells). |
threshold |
threshold in expression for a given gene |
n_cells_low |
minimum number of cells where a gene is expressed at a level above threshold |
n_cells_high |
maximum number of cells where a gene is expressed at a level above threshold |
Value
Character vector with all genes expressed at a level higher than threshold in a number of cells between n_cells and n_cells_high.
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
markers_cluster_seurat
Description
The Seurat function FindMarkers is used to identify general marker for each cluster (specific cluster vs all other cluster). This list of markers is then filtered keeping only the genes that appear as markers in a unique cluster.
Usage
markers_cluster_seurat(seurat_object, cluster, cell_names, number_top)
Arguments
seurat_object |
Seurat object as returned by cluster_analysis_sub or by cluster_analysis_integrate_rare. |
cluster |
Vector of length equal to the number of cells, with cluster assignment. |
cell_names |
Vector of length equal to the number of cells, with cell names. |
number_top |
Integer. Number of top marker genes to keep for each cluster. |
Value
List of three elements. The first is a vector with number_top marker genes for each cluster. The second is a vector with number_top marker genes and corresponding cluster. The third element is a vector with all marker genes for each cluster.
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
See Also
https://www.rdocumentation.org/packages/Seurat/versions/4.0.1/topics/FindMarkers
merge_cluster
Description
merge_cluster
Usage
merge_cluster(old_cluster, new_cluster, max_number = NULL)
Arguments
old_cluster |
original cluster assignment that need to be updated |
new_cluster |
new cluster assignment that need to be integrated with old_cluster. |
max_number |
Threshold in size for clusters in new_cluster. Only cluster with number of cells smaller than max_number will be integrated in old cluster. If max_number is NULL, then all the clusters in new_cluster are integrated in old cluster. |
Value
Numeric vector of length equal to old_cluster showing the merged cluster assignment between old cluster and new_cluster.
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
plot_balloon_marker
Description
plot_balloon_marker
Usage
plot_balloon_marker(
norm_counts,
cluster,
marker_complete,
max_number,
max_size = 5,
text_size = 7
)
Arguments
norm_counts |
Norm count matrix (genes X cells). |
cluster |
Vector of length equal to the number of cells, with cluster assignment. |
marker_complete |
Third element of the output list as returned by the function markers_cluster_seurat |
max_number |
Integer. Maximum number of markers for each cluster for which we want to plot the expression. |
max_size |
Integer. Size of the dots to be plotted. |
text_size |
Size of the text in the heatmap plot. |
Value
ggplot2 object showing balloon plot.
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
plot_gene
Description
Cells are coloured according to the expression of gene_id and plotted according to coordinate_umap.
Usage
plot_gene(norm_counts, coordinate_umap, gene_id, title_name)
Arguments
norm_counts |
Norm count matrix (genes X cells). |
coordinate_umap |
Data frame with dimensionality reduction coordinates. Number of rows must be equal to the number of cells |
gene_id |
Character name of the gene. |
title_name |
Character name. |
Value
ggplot2 object.
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
See Also
https://CRAN.R-project.org/package=ggplot2
plot_genes_sum
Description
The sum of each gene in genes_relevant across all cells is first normalized to 1. Then for each cell, the sum from the (normalized) genes expression is computed and shown in the output plot.
Usage
plot_genes_sum(coordinate_umap, norm_counts, genes_relevant, name_title)
Arguments
coordinate_umap |
Data frame with dimensionality reduction coordinates. Number of rows must be equal to the number of cells |
norm_counts |
Norm count matrix (genes X cells). |
genes_relevant |
Vector with gene names for which we want to visualize the sum in each cell. |
name_title |
Character value. |
Value
ggplot2 object.
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
See Also
https://CRAN.R-project.org/package=ggplot2
plot_heatmap_marker
Description
plot_heatmap_marker
Usage
plot_heatmap_marker(
marker_top,
marker_all_cluster,
cluster,
condition,
norm_counts,
text_size
)
Arguments
marker_top |
First element returned by markers_cluster_seurat |
marker_all_cluster |
Second element returned by markers_cluster_seurat |
cluster |
Vector of length equal to the number of cells, with cluster assignment. |
condition |
Vector or length equal to the number of cells, specifying the condition of the cells (i.e. batch, dataset of origin..) |
norm_counts |
Norm count matrix (genes X cells). |
text_size |
Size of the text in the heatmap plot. |
Value
Heatmap class object.
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
See Also
https://www.rdocumentation.org/packages/ComplexHeatmap/versions/1.10.2/topics/Heatmap
plot_interactive
Description
It shows in an interactive plot which are the highly localized genes in each cell. It is based on plotly library
Usage
plot_interactive(
coordinate_umap,
color,
text,
min_x = NULL,
max_x = NULL,
min_y = NULL,
max_y = NULL
)
Arguments
coordinate_umap |
Data frame with dimensionality reduction coordinates. Number of rows must be equal to the number of cells |
color |
vector of length equal to n_rows in coordinate_umap.Each cell will be coloured following a gradient according to the corresponding value of this vector. |
text |
Character vector specifying the highly localized genes in each cell. It is the output from selection_localized_genes. |
min_x |
Set the min limit on the x axis. |
max_x |
Set the max limit on the x axis. |
min_y |
Set the min limit on the y axis. |
max_y |
Set the min limit on the y axis. |
Value
plotly object given by plot_ly function (from library plotly).
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
See Also
plot_umap
Description
plot_umap
Usage
plot_umap(coordinate_umap, cluster)
Arguments
coordinate_umap |
Data frame with dimensionality reduction coordinates. Number of rows must be equal to the number of cells |
cluster |
Vector of length equal to the number of cells, with cluster assignment. |
Value
ggplot2 object.
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
See Also
https://CRAN.R-project.org/package=ggplot2
Objects exported from other packages
Description
These objects are imported from other packages. Follow the links below to see their documentation.
- ggraph
selection_localized_genes
Description
selection_localized_genes
Usage
selection_localized_genes(
norm_counts,
localized_genes,
min_number_cells = 4,
max_number_genes = 10
)
Arguments
norm_counts |
Norm count matrix (genes X cells). |
localized_genes |
vector of highly localized genes as provided by the last element of the list given as output from CIARA_mixing_final. |
min_number_cells |
Minimum number of cells where a genes must be expressed (> 0). |
max_number_genes |
Maximum number of genes to show for each cell in the interactive plot from plot_interactive. |
Value
Character vector where each entry contains the name of the top max_number_genes for the corresponding cell.
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
test_hvg
Description
For each cluster in cluster, HVGs are defined with Seurat function FindVariableFeatures. A Fisher test is performed to see if there is a statistically significant enrichment between the top number_hvg and the localized_genes
Usage
test_hvg(
raw_counts,
cluster,
localized_genes,
background,
number_hvg,
min_p_value
)
Arguments
raw_counts |
Raw count matrix (n_genes X n_cells). |
cluster |
Vector of length equal to the number of cells, with cluster assignment. |
localized_genes |
Character vector with localized genes detected by CIARA. |
background |
Character vector with all the genes names to use as background for the Fisher test. |
number_hvg |
Integer value. Number of top HVGs provided by the Seurat function FindVariableFeatures. |
min_p_value |
Threshold on p values provided by Fisher test. |
Value
A list with two elements.
first element |
The first one is a list with length equal to the number of clusters. Each entry is list of three elements. The first two elements contain the p value and the odds ration given by the Fisher test The third is a vector with genes names that are present both in localized_genes and in top number_hvg HVGs . |
second element |
a character vector with the name of the cluster that have a p value smaller than min_p_value. |
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
See Also
https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/fisher.test
white_black_markers
Description
A white-marker is a gene whose median expression across cells belong to single_cluster is greater than threshold and in all the other clusters is equal to zero.
Usage
white_black_markers(
cluster,
single_cluster,
norm_counts,
marker_list,
threshold = 0
)
Arguments
cluster |
Vector of length equal to the number of cells, with cluster assignment. |
single_cluster |
Character. Label of one specify cluster |
norm_counts |
Norm count matrix (genes X cells). |
marker_list |
Third element of the output list as returned by the function markers_cluster_seurat |
threshold |
Numeric. The median of the genes across cells belong to single_cluster has to be greater than threshold in order to be consider as a white-black marker for single_cluster |
Value
Logical vector of length equal to marker_list, with TRUE/FALSE if the gene is/is not a white-black marker for single_cluster.
Author(s)
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de