| Title: | Cross-Species Analysis of Cell Identities, Markers and Regulations |
| Version: | 1.0.0 |
| Maintainer: | Junyao Jiang <jiangjunyao789@163.com> |
| Author: | Junyao Jiang <jiangjunyao789@163.com> |
| Description: | A toolkit to perform cross-species analysis based on scRNA-seq data. This package contains 5 main features. (1) identify Markers in each cluster. (2) Cell type annotation (3) identify conserved markers. (4) identify conserved cell types. (5) identify conserved modules of regulatory networks. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| Imports: | pheatmap, pbapply, psych, ROCR, reshape2, dplyr, grDevices, stats, methods, viridisLite |
| Depends: | R (≥ 4.0), Seurat |
| Suggests: | knitr, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| URL: | https://github.com/jiang-junyao/CACIMAR |
| BugReports: | https://github.com/jiang-junyao/CACIMAR/issues |
| NeedsCompilation: | no |
| Packaged: | 2022-05-17 12:49:03 UTC; jiangjunyao |
| Repository: | CRAN |
| Date/Publication: | 2022-05-18 08:20:02 UTC |
CACIMAR colors palette
Description
CACIMAR colors palette
Usage
CACIMAR_cols(color_number)
Arguments
color_number |
numeric, indicating used colors number |
Value
vector of colors
Examples
CACIMAR_cols(10)
CACIMAR_cols(20)
Format marker genes for plotting
Description
Order the gene expression in each cluster to make the heatmap look better
Usage
Format_Markers_Frac(Marker_genes)
Arguments
Marker_genes |
data.frame, generated by |
Value
Markers corresponding to certain cluster
Examples
data("pbmc_small")
all.markers <- Identify_Markers(pbmc_small)
all.markers2 <- Format_Markers_Frac(all.markers)
plot the heatmap of marker genes across different species
Description
plot the heatmap of marker genes across different species
Usage
Heatmap_Cor(
RNA1,
RowType1 = "",
ColType1 = "",
cluster_cols = TRUE,
cluster_rows = FALSE,
Color1 = NULL,
...
)
Arguments
RNA1 |
correlation of expression in each cell type |
RowType1 |
character, indicating the cell types that you want to show on the row in heatmap. RowType1=” means show all cell types |
ColType1 |
character, indicating the cell types that you want to show on the column in heatmap. RowType1=” means show all cell types |
cluster_cols |
boolean values determining if columns should be clustered or hclust object |
cluster_rows |
boolean values determining if rows should be clustered or hclust object |
Color1 |
vector of colors used in heatmap |
... |
parameter in pheatmap |
Value
pheatmap object
Examples
load(system.file("extdata", "network_example.rda", package = "CACIMAR"))
n1 <- Identify_ConservedNetworks(OrthG_Mm_Zf,mmNetwork,zfNetwork,'mm','zf')
Heatmap_Cor(n1[[2]],cluster_cols=TRUE, cluster_rows=FALSE)
Identify cell type of each cluster
Description
This function has three steps to identify cell type of each cluster. (1) Calculate the power of each known marker based on AUC (area under the receiver operating characteristic curve of gene expression) which indicates the capability of marker i from cell type m to distinguish cluster j and the other clusters. (2) Calculate the united power (UP) for cell type m across each cluster j. (3) For each cluster j we determine the cell type according to UP. Generally, the cluster beongs to the cell type which have the highest united power or higher than the threshold of the united power (for example > 0.9 power).
Usage
Identify_CellType(seurat_object, Marker_gene_table)
Arguments
seurat_object |
seurat object |
Marker_gene_table |
data.frame, indicating marker gene and its corresponding cell type. Marker_gene_table should contain two columns: 'CellType' represent correseponding cell types of each marker and 'Marker' represent Markers |
Value
Cell type with the highest power in each cluster
Examples
KnownMarker=data.frame(c('AIF1','BID','CCL5','CD79A','CD79B','MS4A6A'),c('a','a','a','b','b','b'))
data("pbmc_small")
colnames(KnownMarker)=c('Marker','CellType')
CT <- Identify_CellType(pbmc_small,KnownMarker)
Identify conserved cell types based on power of genes and orthologs database
Description
Identify conserved cell types based on power of genes and orthologs database
Usage
Identify_ConservedCellTypes(
OrthG,
Species1_Marker_table,
Species2_Marker_table,
Species_name1,
Species_name2
)
Arguments
OrthG |
ortholog genes database |
Species1_Marker_table |
data.frame of species 1, should contain three column: 'gene', 'cluster' and 'power' |
Species2_Marker_table |
data.frame of species 2, should contain three column: 'gene', 'cluster' and 'power' |
Species_name1 |
character, indicating the species names of Species1_Marker_table |
Species_name2 |
character, indicating the species names of Species2_Marker_table |
Value
list contains two elements: first one is details of conserved cell types, second one is matrix of cell types conserved score
Examples
load(system.file("extdata", "CellTypeAllMarkers.rda", package = "CACIMAR"))
expression <- Identify_ConservedCellTypes(OrthG_Mm_Zf,mm_Marker[1:30,],zf_Marker[1:30,],'mm','zf')
Identify orthologs marker genes for two species
Description
Identify orthologs marker genes for two species based on orthologs database
Usage
Identify_ConservedMarkers(
OrthG,
Species1_Marker_table,
Species2_Marker_table,
Species_name1,
Species_name2,
match_cell_name = NULL
)
Arguments
OrthG |
ortholog genes database |
Species1_Marker_table |
data.frame of species 1, first column should be gene name, second column should be Clusters corresponding to marker gene |
Species2_Marker_table |
data.frame of species 2, first column should be gene name, second column should be Clusters corresponding to marker gene of marker genes. |
Species_name1 |
character, indicating the species names of Species1_Marker_table. |
Species_name2 |
character, indicating the species names of Species2_Marker_table |
match_cell_name |
characters contained in both cell names to match similar cell types |
Value
Data frame of conserved markers
Examples
load(system.file("extdata", "CellMarkers.rda", package = "CACIMAR"))
o1 <- Identify_ConservedMarkers(OrthG_Mm_Zf,Mm_marker_cell_type,
Zf_marker_cell_type,Species_name1 = 'mm',Species_name2 = 'zf')
o2 <- Identify_ConservedMarkers(OrthG_Zf_Ch,Ch_marker_cell_type,
Zf_marker_cell_type,Species_name1 = 'ch',Species_name2 = 'zf')
Identify conserved regulatory networks
Description
Use Score of Conserved network to identify conserved regulatory network modules based on homologous genes databased and topology of networks
Usage
Identify_ConservedNetworks(
OrthG,
Species1_GRN,
Species2_GRN,
Species_name1,
Species_name2
)
Arguments
OrthG |
ortholog genes database |
Species1_GRN |
gene regulatory network of species 1 |
Species2_GRN |
gene regulatory network of species 2 |
Species_name1 |
character, indicating the species names of Species1_GRN |
Species_name2 |
character, indicating the species names of Species2_GRN |
Value
list contains two df. First df contains details of conserved regulatory network, second df contains NCS between module pairs
Examples
load(system.file("extdata", "gene_network.rda", package = "CACIMAR"))
n1 <- Identify_ConservedNetworks(OrthG_Mm_Zf,mm_gene_network,zf_gene_network,'mm','zf')
Identify markers of each cluster
Description
This function first identify marker genes in each cluster with Roc threshold > RocThr. Then, based on marker genes identified above, this function calculates the difference and power of marker genes in each cluster, and marker genes with Difference threshold > DiffThr will be retained. Next, gene with the largest power in which cluster will be the marker gene in this cluster. Eventually, make fisher test for power of each cluster, cluster with p.value < 0.05 will be retained as the final cluster for marker gene
Usage
Identify_Markers(
Seurat_object,
PowerCutoff = 0.4,
DifferenceCutoff = 0,
PvalueCutoff = 0.05
)
Arguments
Seurat_object |
Seurat object, should contain cluster information |
PowerCutoff |
numeric, indicating the cutoff of gene power to refine marker genes |
DifferenceCutoff |
numeric, indicating the cutoff of difference in marker genes between clusters to refine marker genes |
PvalueCutoff |
numeric, indicating the p.value cutoff of chi-square test to refine marker genes |
Value
Data frame of conserved markers
Examples
data("pbmc_small")
all.markers <- Identify_Markers(pbmc_small)
Orthologs genes database for homo sapiens and zebrafish
Description
Orthologs genes database for homo sapiens and zebrafish
Usage
OrthG_Hs_Ch
Format
An object of class data.frame with 12219 rows and 5 columns.
Orthologs genes database for homo sapiens and mus musculus
Description
Orthologs genes database for homo sapiens and mus musculus
Usage
OrthG_Hs_Mm
Format
An object of class data.frame with 16754 rows and 5 columns.
Orthologs genes database for homo sapiens and zebrafish
Description
Orthologs genes database for homo sapiens and zebrafish
Usage
OrthG_Hs_Zf
Format
An object of class data.frame with 12017 rows and 5 columns.
Orthologs genes database for mus musculus and chicken
Description
Orthologs genes database for mus musculus and chicken
Usage
OrthG_Mm_Ch
Format
An object of class data.frame with 62661 rows and 5 columns.
Orthologs genes database for mus musculus and zebrafish
Description
Orthologs genes database for mus musculus and zebrafish
Usage
OrthG_Mm_Zf
Format
An object of class data.frame with 65631 rows and 5 columns.
Orthologs genes database for mus zebrafish and chicken
Description
Orthologs genes database for mus zebrafish and chicken
Usage
OrthG_Zf_Ch
Format
An object of class data.frame with 38394 rows and 5 columns.
Plot Markers in each cell type
Description
This function integrate R package pheatmap to plot markers in each cell type
Usage
Plot_MarkersHeatmap(
ConservedMarker,
start_col = 2,
module_colors = NA,
heatmap_colors = NA,
cluster_rows = FALSE,
cluster_cols = FALSE,
show_rownames = FALSE,
show_colnames = FALSE,
cellwidth = NA,
cellheight = NA,
legend = FALSE,
annotation_legend = FALSE,
annotation_names_row = FALSE,
...
)
Arguments
ConservedMarker |
Markers table |
start_col |
numeric, indicating the start column of marker power in each cell type |
module_colors |
vector, indicating colors of modules (annotation_colors) |
heatmap_colors |
vector, indicating colors used in heatmap |
cluster_rows |
boolean values determining if rows should be clustered or hclust object |
cluster_cols |
boolean values determining if columns should be clustered or hclust object |
show_rownames |
boolean specifying if column names are be shown |
show_colnames |
boolean specifying if column names are be shown |
cellwidth |
individual cell width in points. If left as NA, then the values depend on the size of plotting window |
cellheight |
individual cell height in points. If left as NA, then the values depend on the size of plotting window |
legend |
logical to determine if legend should be drawn or not |
annotation_legend |
boolean value showing if the legend for annotation tracks should be drawn |
annotation_names_row |
boolean value showing if the names for row annotation tracks should be drawn |
... |
parameter in pheatmap |
Value
pheatmap object
Examples
data("pbmc_small")
all.markers <- Identify_Markers(pbmc_small)
all.markers <- Format_Markers_Frac(all.markers)
Plot_MarkersHeatmap(all.markers[,c(2,6,7,8)])