Package 'CACIMAR'

Title: cross-species analysis of cell identities, markers and regulations
Description: A toolkit to perform cross-species analysis based on scRNA-seq data. CACIMAR contains 5 main features. (1) identify Markers in each cluster. (2) Cell type annotaion (3) identify conserved markers. (4) identify conserved cell types. (5) identify conserved modules of regulatory networks.
Authors: Junyao Jiang [aut, cre]
Maintainer: Junyao Jiang <[email protected]>
License: MIT + file LICENSE
Version: 1.0.0
Built: 2025-02-13 06:02:12 UTC
Source: https://github.com/jiang-junyao/cacimar

Help Index

filter cellchat CCC networks

Description

Use conserved CCC to filter cellchat CCC networks

Usage

add_cellchat_prob(conserved_ccc_df, cellchat_df)

Arguments

cellchat_df

Build homologous gene database

Description

Build homologous gene database according to Vertebrate Homology data in MGI database. This function currently supports ten species: cattle, chicken, chimpanzee, dog, frog, human, macaque, mouse, rat, and zebrafish. After building the database, this function also integrates biomaRt to add ENSEMBEL ID for each gene in the database.

Usage

buildHomDatabase(MGI, Species_name1, Species_name2)

Arguments

MGI

MGI database, download from http://www.informatics.jax.org/
Species_name1

The name of the first species. input 'mm' for mouse, 'hs' for human, 'zf' for zebrafish, 'ch' for chicken, 'cf' for dog, 'pt' for chimpanzee, 'xt' for frog, 'rn' for rat, 'bt' for cattle, and 'rh' for macaque.
Species_name2

The name of the second species. input 'mm' for mouse, 'hs' for human, 'zf' for zebrafish, 'ch' for chicken, 'cf' for dog, 'pt' for chimpanzee, 'xt' for frog, 'rn' for rat, 'bt' for cattle, and 'rh' for macaque.

Value

homologous gene database of two species

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)

Caculate conserved score of cell-cell interaction

Description

caculate the conserved score of cell-cell interaction with summed weights of ligand-receptor interactions for two species

Usage

Caculate_cell_pair_cci_score(
  conserved_result_df,
  species1_cci,
  species2_cci,
  conserved_cell_types_df,
  species_name1,
  species_name2
)

Arguments

conserved_result_df

dataframe, result from function Identify_Conserved_CCI1
species1_cci

dataframe, result from SingleCellSignalR of species1
species2_cci

dataframe, result from SingleCellSignalR of species2
conserved_cell_types_df

dataframe, contain the conserved cell type for each species, like conserved_cell_types_mm_zf <- data.frame("mm" = c("mmRods", "mmRod BC", "mmCones", "mmPericytes", "mmV/E cells", "mmRGC", "mmGABAergic AC", "mmRPE", "mmResting MG", "mmActivated MG", "mmMicroglia"), "zf" = c("zfRods", "zfCone BC", "zfCones", "zfPericytes", "zfV/E cells", "zfRGC", "zfGABAergic AC", "zfRPE", "zfResting MG", "zfActivated MG", "zfMicroglia"))
species_name1

two character to represent species1, like "mm". You should set this value from dataframe species_names_ref.rda
species_name2

two character to represent species2, like "zf". You should set this value from dataframe species_names_ref.rda

Value

dataframe, conserved Weights table

Examples

conserved_cell_types_mm_zf <- data.frame("mm" = c("mmRods", "mmRod BC", "mmCones", "mmPericytes", "mmV/E cells", "mmRGC", "mmGABAergic AC", "mmRPE", "mmResting MG", "mmActivated MG", "mmMicroglia"), "zf" = c("zfRods", "zfCone BC", "zfCones", "zfPericytes", "zfV/E cells", "zfRGC", "zfGABAergic AC", "zfRPE", "zfResting MG", "zfActivated MG", "zfMicroglia"))
cci_conserved_Weights_table_mm_zf <- Caculate_cell_pair_cci_score(conserved_result_df=conserved_result_mm_zf,
species1_cci = SingleCellSignalR_mouse_result,
species2_cci = SingleCellSignalR_zebrafish_result,
conserved_cell_types_df = conserved_cell_types_mm_zf,
species_name1 = "mm",
species_name2 = "zf")

Caculate conserved score of cell-cell interaction for three species

Description

caculate the conserved score of cell-cell interaction with summed weights of ligand-receptor interactions for three species

Usage

Caculate_cell_pair_cci_score2(
  conserved_result_df,
  species1_cci,
  species2_cci,
  species3_cci,
  conserved_cell_types_df,
  species_name1,
  species_name2,
  species_name3
)

Arguments

conserved_result_df

dataframe, result from function Identify_Conserved_CCI2
species1_cci

dataframe, result from SingleCellSignalR of species1
species2_cci

dataframe, result from SingleCellSignalR of species2
species3_cci

dataframe, result from SingleCellSignalR of species3
conserved_cell_types_df

dataframe, contain the conserved cell type for each species, like conserved_cell_types_mm_zf_ch <- data.frame('mm' = c("mmResting MG","mmGABAergic AC", "mmRGC", "mmCones"), "zf" = c("zfResting MG", "zfGABAergic AC", "zfRGC", "zfCones"), 'ch' = c("chResting MG","chGABAergic AC", "chRGC", "chCones"))
species_name1

two character to represent species1, like "mm". You should set this value from dataframe species_names_ref.rda
species_name2

two character to represent species2, like "zf". You should set this value from dataframe species_names_ref.rda
species_name3

two character to represent species3, like "ch". You should set this value from dataframe species_names_ref.rda

Value

dataframe, conserved Weights table

Sum weight of cell-cell interactions

Description

Here calculate overall weight of cell-cell interactions within each pair of cell types

Usage

calculate_Weights(
  species1_cci,
  species2_cci,
  specie_name1 = "Mm",
  specie_name2 = "Zf"
)

Arguments

species1_cci

data frame, cell-cell interactions result with perform_CCI_analysis of species 1
species2_cci

data frame, cell-cell interactions result with perform_CCI_analysis of species 2
specie_name1

character, name for species 1, like "Mm"
specie_name2

character, name for species 2, like "Zf"

Value

data frame of weight result

Examples

all_weight_df_long <- calculate_Weights(species1_cci = SingleCellSignalR_mouse_result,
species2_cci = SingleCellSignalR_zebrafish_result)

head(all_weight_df_long)
            source       target    weight species scale_weight             Source            Source2
1     Activated MG Activated MG 152.07967      Mm  0.008256281     MmActivated MG     MmActivated MG
2       Astrocytes Activated MG 230.97254      Mm  0.012539310       MmAstrocytes       MmAstrocytes
3            Cones Activated MG  51.89929      Mm  0.002817570            MmCones            MmCones
4     GABAergic AC Activated MG  99.92955      Mm  0.005425093     MmGABAergic AC     MmGABAergic AC
5   Glycinergic AC Activated MG  78.95619      Mm  0.004286467   MmGlycinergic AC   MmGlycinergic AC
6 Horizontal cells Activated MG 106.23687      Mm  0.005767513 MmHorizontal cells MmHorizontal cells

Make a ChordDiagram

Description

Construct a ChordDiagram to show the conservation score for intercellular interactions

Usage

ChordDiagram(
  net,
  Score_factor_size = 20,
  brewer_pal_used = "Set3",
  grid.col = NULL,
  grid.border = "#F8F8F8",
  is_link_colors_threshold = TRUE,
  link_colors_threshold = 0.5,
  link_colors_down_threshold_col = "grey",
  filename = "chordDiagram.pdf",
  order_grid = NULL,
  directional = 1,
  direction.type = c("arrows"),
  diffHeight = -0.03,
  annotationTrack = "grid",
  reduce = -1,
  link.arr.type = "big.arrow",
  link.border = "white",
  link.lwd = 1,
  link.sort = TRUE,
  link.decreasing = TRUE,
  link.largest.ontop = TRUE,
  link.overlap = F,
  transparency = 0,
  cex = 1.5,
  ylim_edit = 0,
  facing = "clockwise",
  niceFacing = TRUE,
  col = "black",
  adj = c(0, 0.5),
  start.degree = -5,
  magnify_set = TRUE,
  picture_width = 14,
  picture_height = 10
)

Arguments

net

data frame, a data frame contain the source, target, and weight
Score_factor_size

numeric, scale the weight score for showing
brewer_pal_used

character, the brewer_pal in RColorBrewer, like "Set1", "Set3", "Paired"
grid.col

vector, color vectors with names. By default, it is NULL and it will asign colors automatically.
grid.border

character, corlors for borders of grids. If it is NULL, the border color is same as grid color
is_link_colors_threshold

logical, whether change the colors of links which below a threshold to a specific color
link_colors_threshold

numeric, the threshold used to change colors for links
link_colors_down_threshold_col

character, specific corlor for links which below a threshold
filename

character, save the plot with this filename
order_grid

vector, a ordered vector for grid. By default, it is NULL
directional

Directions for the links. 1 means the direction is from the first column in df to the second column, -1 is the reverse, 0 is no direction, and 2 for two directional. The value can be a vector which has same length as number of rows in df
direction.type

type for representing directions. Can be one or two values in "diffHeight" and "arrows"
diffHeight

numeric, The difference of height between two 'roots' if directional is set to TRUE
annotationTrack

annotation for the track. It can set to c("name","grid", "axis"), or one of them.
reduce

numeric, if the ratio of the width of certain grid compared to the whole circle is less than this value, the grid is removed on the plot. Set it to value less than zero if you want to keep all tiny grid.
link.arr.type

type for the arrow, "big.arrow" is set by default
link.border

character, corlors for links
link.lwd

numeric, width for link borders
link.sort

logical, whether sort links on every sector based on the width of the links on it
link.decreasing

logical, for link.sort
link.largest.ontop

logical, controls the order of adding links
link.overlap

logical, whether the links that come or end in a same sector overlap
transparency

numeric, transparency for link colors
cex

numeric, font size for the text in annotationTrack
ylim_edit

numeric, control the text height from the grid
facing

control the direction of the text in annotationTrack, it can be one of c("inside", "outside", "reverse.clockwise", "clockwise", "downward", "bending", "bending.inside", "bending.outside")
niceFacing

logical, adjusted the text to fit human eyes
col

character, colors for the text in annotationTrack
adj

offset for text, like c(0, 0.5). By default the text position adjustment is either horizontal or vertical in the canvas coordinate system.
start.degree

numeric, rotation the ChordDiagram with this angle
picture_width

numeric, width of the ChordDiagram for saving
picture_height

numeric, height of the ChordDiagram for saving

Value

saving ChordDiagram in current directory

Examples

load(system.file("extdata", "CCC_conserved_sumary.rda", package = "CACIMAR"))
cci_data = CCC_conserved_sumary[, c("Source", "Target", "score_weight")]
ChordDiagram(net = cci_data, filename = "chordDiagram.pdf", link_colors_threshold = 0.75)

Caculate conserved interaction score and Make data for ChordDiagram

Description

Caculate conserved interaction score and Make data for ChordDiagram

Usage

conserved_interaction_score(
  conserved_result_species,
  SingleCellSignalR_sp1_result,
  SingleCellSignalR_sp2_result,
  SingleCellSignalR_sp3_result,
  conserved_cell_types_df,
  species_name1 = "mm",
  species_name2 = "zf",
  species_name3 = "ch"
)

Arguments

conserved_result_species

dataframe, result from function Identify_Conserved_CCI2
SingleCellSignalR_sp1_result

dataframe, result from SingleCellSignalR of species1
SingleCellSignalR_sp2_result

dataframe, result from SingleCellSignalR of species2
SingleCellSignalR_sp3_result

dataframe, result from SingleCellSignalR of species3
conserved_cell_types_df

dataframe, contain the conserved cell type for each species, like conserved_cell_types_mm_zf_ch <- data.frame('mm' = c("mmResting MG","mmGABAergic AC", "mmRGC", "mmCones"), "zf" = c("zfResting MG", "zfGABAergic AC", "zfRGC", "zfCones"), 'ch' = c("chResting MG","chGABAergic AC", "chRGC", "chCones"))
species_name1

two character to represent species1, like "mm". You should set this value from dataframe species_names_ref.rda
species_name2

two character to represent species2, like "zf". You should set this value from dataframe species_names_ref.rda
species_name3

two character to represent species3, like "ch". You should set this value from dataframe species_names_ref.rda

Value

list of conserved Weights table and data for chordDiagram

Examples

#not run
#conserved_result_mm_zf_ch is the result from function Identify_Conserved_CCI2
conserved_cci_result <- conserved_interaction_score(conserved_result_species = conserved_result_mm_zf_ch,
SingleCellSignalR_sp1_result = SingleCellSignalR_mouse_result,
SingleCellSignalR_sp2_result = SingleCellSignalR_zebrafish_result,
SingleCellSignalR_sp3_result = SingleCellSignalR_chick_result,
conserved_cell_types_df = conserved_cell_types_mm_zf_ch,
species_name1 = "mm",
species_name2 = "zf",
species_name3 = "ch")

Create sankey plot

Description

Build a sankey plot to show the cell-cell interaction profile of two species

Usage

create_sankey(
  links,
  specie_name1 = "Mm",
  specie_name2 = "Zf",
  output_file = "sankey.html",
  colors_file = NULL,
  brewer.pal_set = "Set3",
  node_width = 18,
  node_padding = 15,
  node_stroke_width = 0,
  node_corner_radius = 0,
  drag_x = TRUE,
  drag_y = TRUE,
  units = "TWh",
  node_pos_x = "group",
  align = "none",
  scale_node_breadths_by_string = F,
  show_node_values = FALSE,
  height = 1400,
  width = 1800,
  linkColor = "#A0A0A0",
  link_type = "bezier",
  curvature = 0.5,
  link_opacity = 5,
  link_gradient = FALSE,
  node_shadow = FALSE,
  node_label_margin = 5,
  zoom = TRUE,
  font_family = NULL,
  iterations = 0,
  x_scaling_factor = 1.4,
  font_size = 16,
  ...
)

Arguments

links

dataframe, a links file containing source, target, and weight
specie_name1

character, name for specie 1
specie_name2

character, name for specie 2
output_file

character, save sankey plot with this file name
colors_file

character, file path for the color file, it is NULL by default and it will assign colors automatically
brewer.pal_set

if colors_file is null, the brewer.pal can be choose, like "Set1", "Set3", "Paired", ......
node_width

numeric, width of the node
node_padding

numeric, distance between the node
node_stroke_width

numeric, width of the stroke around nodes
node_corner_radius

numeric, the radius of the node
drag_x

logical, TRUE indicates that the plot can be horizontally dragged
drag_y

logical, TRUE indicates that the plot can be vertically dragged
units

character, name for units
node_pos_x

character, variable used for grouping nodes on the x-axis
align

character, alignment of the nodes. One of 'right', 'left', 'justify', 'center', 'none'. If 'none', then the labels of the nodes are always to the right of the node
scale_node_breadths_by_string

logical, Put nodes at positions relatively to string lengths - only work well currently with align='none'
show_node_values

logical, Show values above nodes. Might require and increased node margin.
height

numeric, height of the sankey plot for saving
width

numeric, width of the sankey plot for saving
linkColor

numeric, color of links
link_type

character, one of 'bezier', 'l-bezier', 'trapezoid', 'path1' and 'path2'
curvature

numeric, curvature parameter for bezier links - between 0 and 1
link_opacity

numeric, opacity of links
link_gradient

logical, add a gradient to the links
node_shadow

logical, add a shadow to the nodes
node_label_margin

numeric, distance between the node and font
zoom

logical, value to enable (TRUE) or disable (FALSE) zooming
iterations

numeric, number of iterations in the diagramm layout for computation of the depth (y-position) of each node. Note: this runs in the browser on the client so don't push it too high
x_scaling_factor

numeric, scale the computed x position of the nodes by this value
font_size

numeric, size of the font of the plot
...

param of sankeyD3::sankeyNetwork()

Value

a sankey plot

Examples

create_sankey(links = all_weight_df_long[, c("Source2", "target", "scale_weight")],
output_file = "sankey_scale_weight.html",
specie_name1 = "Mm",
specie_name2 = "Zf",
colors_file = NULL)

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 Identify_Markers

Examples

data("pbmc_small")
all.markers <- Identify_Markers(pbmc_small)
all.markers2 <- Format_Markers_Frac(all.markers)

Format Conserved markers to plot heatmap

Description

Format Conserved markers to plot heatmap

Usage

FormatConservedMarkers(ConservedMarker)

Arguments

ConservedMarker

Result from 'Identify_ConservedMarkers'

Value

list contains two data.frame

Examples

load(system.file("extdata", "zf_mm_markers.rda", package = "CACIMAR"))
ConservedMarker <- Identify_ConservedMarkers(OrthG_Mm_Zf,Mm_marker,Zf_marker,
Species_name1 = 'mm',Species_name2 = 'zf')
MarkersPlot <- FormatConservedMarkers(ConservedMarker)

Geometric mean of ligand and receptor

Description

This function caculates the geometric mean of ligand and receptor. Average expression of ligand comes from source celltype, and verage expression of receptor comes from target celltype

Usage

get_average_expression(
  specie_conserved_CCC,
  seurat_object,
  avg_group,
  species_set,
  assay_set
)

Arguments

specie_conserved_CCC

dataframe, also result of the ligand receptor analysis(for example, ConservedCCI$Conserved_ligand_receptor$sp1_orthg_ccc_df). It must contain 4 column for ligand, receptor, source celltype, target celltype
seurat_object

a seurat object contains the expression data for celltypes of source and target
avg_group

a character, which variable used to average the expression, usally "celltype"
species_set

species_set should only contain two characters, like "Mm" to label the species
assay_set

Assay to get the expression, like "RNA", or "SCT"

Examples

LRpair_show <- get_average_expression(CCC_conserved_sumary = ConservedCCI[[2]], specie_conserved_CCC = ConservedCCI$Conserved_ligand_receptor$sp1_orthg_ccc_df, seurat_object = Mm_seurat_object, avg_group = "celltype", species_set = "Mm")

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 = T,
  cluster_rows = F,
  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)

Title

Description

Title

Usage

Identify_Conserved_CCI(OrthG, sp1_ccc, sp2_ccc, Species_name1, Species_name2)

Arguments

OrthG

ortholog genes database
sp1_ccc

cell-cell interactions in species1. First column should be ligand, second column should be target, third column should be corresponding cell type of ligand, fourth column should be corresponding cel type of target, fifth column should be weight of ligand-receptor interaction
sp2_ccc

cell-cell interactions in species2. First column should be ligand, second column should be target, third column should be corresponding cell type of ligand, fourth column should be corresponding cel type of target, fifth column should be weight of ligand-receptor interaction
Species_name1

character, indicating the species names of Species1_GRN
Species_name2

character, indicating the species names of Species2_GRN

Identify conserved cell-cell interaction from two species

Description

get conserved cell-cell interaction between two species

Usage

Identify_Conserved_CCI1(
  species1_cci,
  species2_cci,
  species_names_ref = NULL,
  conserved_cell_types_df,
  species_name1 = "mm",
  species_name2 = "zf",
  HOM_matrix = NULL
)

Arguments

species1_cci

result of SingCellSignalR of species1
species2_cci

result of SingCellSignalR of species2
species_names_ref

dataframe, if you construct new homolog database and use new species homolog information in this analysis, you should provide the names corresponding relationship. it should be a dataframe contain at lease two column full_name and sy_name.
conserved_cell_types_df

dataframe, like conserved_cell_types_mm_zf <- data.frame("mm" = c("mmRods", "mmRod BC", "mmCones", "mmPericytes", "mmV/E cells", "mmRGC", "mmGABAergic AC", "mmRPE", "mmResting MG", "mmActivated MG", "mmMicroglia"), "zf" = c("zfRods", "zfCone BC", "zfCones", "zfPericytes", "zfV/E cells", "zfRGC", "zfGABAergic AC", "zfRPE", "zfResting MG", "zfActivated MG", "zfMicroglia"))
species_name1

two character to represent species1, like "mm". You should set this value from dataframe species_names_ref.rda
species_name2

two character to represent species2, like "zf". You should set this value from dataframe species_names_ref.rda
HOM_matrix

dataframe, default is NULL, if you used new created homolog database, you should put it here

Value

list for conserved result for species1 and species2

Examples

conserved_cell_types_mm_zf <- data.frame("mm" = c("mmRods", "mmRod BC", "mmCones", "mmPericytes", "mmV/E cells", "mmRGC", "mmGABAergic AC", "mmRPE", "mmResting MG", "mmActivated MG", "mmMicroglia"), "zf" = c("zfRods", "zfCone BC", "zfCones", "zfPericytes", "zfV/E cells", "zfRGC", "zfGABAergic AC", "zfRPE", "zfResting MG", "zfActivated MG", "zfMicroglia"))
conserved_result_mm_zf <- Identify_Conserved_CCI1(species1_cci=SingleCellSignalR_mouse_result,
                                                  species2_cci=SingleCellSignalR_zebrafish_result,
                                                  conserved_cell_types_df=conserved_cell_types_mm_zf,
                                                  species_name1 = "mm",
                                                  species_name2 = "zf")

Identify conserved cell-cell interaction from three species

Description

find the conserved cell-cell interaction from three species and caculate the conserved score of cell-cell interaction

Usage

Identify_Conserved_CCI2(
  species1_cci,
  species2_cci,
  species3_cci,
  species_names_ref = NULL,
  conserved_cell_types_df,
  species_name1 = "mm",
  species_name2 = "zf",
  species_name3 = "ch",
  HOM_matrix = NULL
)

Arguments

species1_cci

result of SingCellSignalR of species1
species2_cci

result of SingCellSignalR of species2
species3_cci

result of SingCellSignalR of species3
species_names_ref

dataframe, if you construct new homolog database and use new species homolog information in this analysis, you should provide the names corresponding relationship. it should be a dataframe contain at lease two column full_name and sy_name.
conserved_cell_types_df

dataframe, like conserved_cell_types_mm_zf_ch <- data.frame('mm' = c("mmResting MG","mmGABAergic AC", "mmRGC", "mmCones"), "zf" = c("zfResting MG", "zfGABAergic AC", "zfRGC", "zfCones"), 'ch' = c("chResting MG","chGABAergic AC", "chRGC", "chCones"))
species_name1

two character to represent species1, like "mm". You should set this value from dataframe species_names_ref.rda
species_name2

two character to represent species2, like "zf". You should set this value from dataframe species_names_ref.rda
species_name3

two character to represent species3, like "ch". You should set this value from dataframe species_names_ref.rda
HOM_matrix

dataframe, default is NULL, if you used new created homolog database, you should put it here

Value

list for conserved result for each species

Identify conserved gene

Description

Identify conserved gene

Usage

identify_conserved_gene(
  OrthG,
  spc1_marker,
  spc2_marker,
  Species_name1,
  Species_name2
)

Arguments

OrthG

ortholog genes database
spc1_marker

vector, indicating the gene of species 1
spc2_marker

vector, indicating the gene of species 2
Species_name1

character, indicating the species names of Species1_Marker_table.
Species_name2

character, indicating the species names of Species2_Marker_table

Identify conserved ligand receptor interaction

Description

Identify conserved cell-cell interactions in conserved cell types

Usage

Identify_Conserved_LR(
  OrthG,
  Species1_CCI,
  Species2_CCI,
  ConservedCellType = NULL,
  Species_name1,
  Species_name2
)

Arguments

OrthG

ortholog genes database
Species1_CCI

cell-cell interactions in species1. First column should be ligand, second column should be target, third column should be corresponding cell type of ligand, fourth column should be corresponding cel type of target
Species2_CCI

cell-cell interactions in species2. First column should be ligand, second column should be target, third column should be corresponding cell type of ligand, fourth column should be corresponding cel type of target
Species_name1

character, indicating the species names of Species1_GRN
Species_name2

character, indicating the species names of Species2_GRN

Examples

load(system.file("extdata", "cci_test.rda", package = "CACIMAR"))
Identify_ConservedCCI(OrthG_Hs_Mm,hs_cci_test,mm_cci_test,celltype,'hs','mm')

Identify conserved markers in conserved celltype

Description

Identify orthologs marker genes for two species based on orthologs database

Usage

identify_conserved_marker(
  OrthG,
  Species1_Marker_table,
  Species2_Marker_table,
  Species_name1,
  Species_name2,
  conserved_celltype_pair
)

Arguments

OrthG

ortholog genes database
Species1_Marker_table

data.frame of species 1, should contain 'gene' and 'Allcluster' columns.
Species2_Marker_table

data.frame of species 2, should contain 'gene' and 'Allcluster' columns.
Species_name1

character, indicating the species names of Species1_Marker_table.
Species_name2

character, indicating the species names of Species2_Marker_table
conserved_celltype_pair

character, indicating the conserved celltypes

Details

Identify orthologs marker genes for two species

Identify conserved pair

Description

Identify conserved pair

Usage

identify_conserved_pair(conserved_score, quantile_threshold = 0.75)

Arguments

conserved_score

conserved celltype/GRN score
quantile_threshold

numeric, indicating

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

Author(s)

Jie Wang

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,
  filter_marker = TRUE
)

Arguments

OrthG

ortholog genes database
Species1_Marker_table

data.frame of species 1, should contain 'gene' and 'Allcluster' columns.
Species2_Marker_table

data.frame of species 2, should contain 'gene' and 'Allcluster' columns.
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
filter_marker

logical, indicating whether filter markers

Value

Data frame of conserved markers

Examples

load(system.file("extdata", "zf_mm_markers.rda", package = "CACIMAR"))
ConservedMarker <- Identify_ConservedMarkers(OrthG_Mm_Zf,Mm_marker,Zf_marker,
Species_name1 = 'mm',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 conserved cell type specific regulatory networks

Description

Identify conserved cell type specific regulatory networks

Usage

identify_ct_ConservedNetworks(
  OrthG,
  Species1_GRN,
  Species2_GRN,
  Species_name1,
  Species_name2,
  network_regulation_num = NULL
)

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

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)

Identify conserved node and edge in the networks Based on the Conservation analysis result from Identify_ConservedNetworks, this function further identify the conserved node and edge in the network(graph)

Description

Identify conserved node and edge in the networks Based on the Conservation analysis result from Identify_ConservedNetworks, this function further identify the conserved node and edge in the network(graph)

Usage

identify_network_relationships(
  Species1_GRN,
  Species2_GRN,
  ConservedNetworkTable,
  Species1_group = "",
  Species2_group = ""
)

Arguments

Species1_GRN

gene regulatory network of species 1
Species2_GRN

gene regulatory network of species 2
ConservedNetworkTable

result from Identify_ConservedNetworks
Species1_group

character,indicating interested GRN group in species 1
Species2_group

character,indicating interested GRN group in species 2

Make data fit for ChordDiagram

Description

Make data fit for ChordDiagram

Usage

Make_ChordDiagram_data(
  cci_conserved_Weights_table,
  species_name1,
  species_name2
)

Arguments

cci_conserved_Weights_table

dataframe, result from function Caculate_cell_pair_cci_score
species_name1

two character to represent species1, like "mm"
species_name2

two character to represent species1, like "zf"

Value

dataframe, data for ChordDiagram

Examples

cci_data_mm_zf <- Make_ChordDiagram_data(cci_conserved_Weights_table = cci_conserved_Weights_table_mm_zf, species_name1 = "mm", species_name2 = "zf")

Make data fit for ChordDiagram for conserved cell types from three species

Description

Make data fit for ChordDiagram for conserved cell types from three species

Usage

Make_ChordDiagram_data2(
  cci_conserved_Weights_table,
  species_name1,
  species_name2,
  species_name3
)

Arguments

cci_conserved_Weights_table

dataframe, result from function Caculate_cell_pair_cci_score2
species_name1

two character to represent species1, like "mm"
species_name2

two character to represent species2, like "zf"
species_name3

two character to represent species3, like "ch"

Value

dataframe, data for ChordDiagram

Examples

# cci_conserved_Weights_table_sp1_sp2_sp3 is the result of function Caculate_cell_pair_cci_score2
 cci_data_sp1_sp2_sp3 <- Make_ChordDiagram_data2(cci_conserved_Weights_table = cci_conserved_Weights_table_sp1_sp2_sp3,
 species_name1 = "mm",
 species_name2 = "zf",
 species_name3 = "ch")

Make ligand-receptor data for pheatmap

Description

prepared average expression data for pheatmap

Usage

make_pheatmap_LR_data(
  cci_conserved_results,
  seurat_object_sp1,
  seurat_object_sp2,
  seurat_object_sp3,
  species_name1 = "mm",
  species_name2 = "zf",
  species_name3 = "ch",
  avg_group = "cellname",
  cci_conserved_Weights,
  subset_quantile = 0.75,
  assay_set = "RNA"
)

Arguments

cci_conserved_results

dataframe, the result from function Identify_Conserved_CCI2
seurat_object_sp1

seurat object of species1
seurat_object_sp2

seurat object of species2
seurat_object_sp3

seurat object of species3
species_name1

character, two character the representation the species1, like "mm"
species_name2

character, two character the representation the species2, like "zf"
species_name3

character, two character the representation the species2, like "ch"
avg_group

character, which group used to calculate the average expression, like "celltype" in metadata
cci_conserved_Weights

dataframe, the result from function Caculate_cell_pair_cci_score
subset_quantile

numeric, the value to filter the conserved score of the cell-cell interaction, default is 0.75 of the consered score of cell-cell interaction
assay_set

which assay used for average expression, like "RNA", or "SCT"

Value

list, contains the dataframe for heatmap, and the average expression for each species

Examples

merge_avg_width_df <- make_pheatmap_LR_data(cci_conserved_results = conserved_result_mm_zf_ch,
seurat_object_sp1 = Mm_seurat,
seurat_object_sp2 = Zf_seurat,
seurat_object_sp3 = ch_seurat,
avg_group = "cellname",
species_name1 = "mm",
species_name2 = "zf",
species_name3 = "ch",
cci_conserved_Weights = conserved_cci_result$cci_conserved_Weights)

Make ligand-receptor data for pheatmap

Description

prepared average expression data for pheatmap

Usage

make_pheatmap_LR_data1(
  cci_conserved_results,
  seurat_object_sp1,
  seurat_object_sp2,
  species_name1 = "mm",
  species_name2 = "zf",
  species_name3 = "ch",
  avg_group = "cellname",
  cci_conserved_Weights,
  subset_quantile = 0.75,
  assay_set = "RNA"
)

Arguments

cci_conserved_results

dataframe, the result from function Identify_Conserved_CCI2
seurat_object_sp1

seurat object of species1
seurat_object_sp2

seurat object of species2
species_name1

character, two character the representation the species1, like "mm"
species_name2

character, two character the representation the species2, like "zf"
avg_group

character, which group used to calculate the average expression, like "celltype" in metadata
cci_conserved_Weights

dataframe, the result from function Caculate_cell_pair_cci_score
subset_quantile

numeric, the value to filter the conserved score of the cell-cell interaction, default is 0.75 of the consered score of cell-cell interaction
assay_set

which assay used for average expression, like "RNA", or "SCT"

Value

list, contains the dataframe for heatmap, and the average expression for each species

Examples

merge_avg_width_df <- make_pheatmap_LR_data1(cci_conserved_results = conserved_result_mm_zf,
seurat_object_sp1 = Mm_seurat,
seurat_object_sp2 = Zf_seurat,
avg_group = "cellname",
species_name1 = "mm",
species_name2 = "zf",
cci_conserved_Weights = conserved_cci_result$cci_conserved_Weights)

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 16754 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.

Cell-cell interaction analysis with SingleCellSignalR algorithm

Description

Perform cell-cell interaction with SingleCellSignalR algorithm contained in liana package.

Usage

perform_CCI_analysis(
  seurat_obj,
  expre_cell = 10,
  select_DB = "Consensus",
  target_organism,
  method = "sca",
  LRscore_threshold = 0.5,
  scale_score = TRUE
)

Arguments

seurat_obj

seurat object, a seurat object with cell types in active idents
expre_cell

numeric, filter genes expressed in more than expre_cell cells
select_DB

character, databased used for analysis, more option can run liana::show_resources()
target_organism

ncbi_taxid' or 'name' of the target organism. See ‘show_homologene' for available organisms via OmnipathR’s 'HomoloGene'
method

character, method use for cell-cell interaction analysis. By default, we set it to SingleCellSignalR
LRscore_threshold

numeric, the threshold the filter the cell-cell interactions, the larger the lesser interactions left
scale_score

logical, whether scale the score of the interaction,default is TRUE

Value

data frame of cell cell interaction analysis result

Examples

# Mouse analysis
SingleCellSignalR_mouse_result <- perform_CCI_analysis(seurat_obj=Mm_seurat, target_organism=10090)

SingleCellSignalR_zebrafish_result <- perform_CCI_analysis(seurat_obj=Zf_seurat, target_organism=7955)

Create a pheatmap for average expression of ligand-receptor pairs

Description

make a pheatmap for average expression of ligand-receptor pairs using geometric mean of corresponding cell type

Usage

pheatmap_LR_multi(
  width_df,
  filename = "pheatmap_LR.pdf",
  color_pheatmap_set = NULL,
  species_colors = NULL,
  species_name1 = "mm",
  species_name2 = "zf",
  species_name3 = "ch",
  annotation_names_col = TRUE,
  border_color = "white",
  scale = "none",
  cluster_rows = FALSE,
  cluster_cols = FALSE,
  legend = TRUE,
  legend_breaks = NA,
  legend_labels = NA,
  show_rownames = T,
  show_colnames = T,
  fontsize = 10,
  cellwidth = 16,
  cellheight = 12,
  width = 14,
  height = 14,
  ...
)

Arguments

width_df

width dataframe, result of function make_pheatmap_LR_data
filename

name to save the pheatmap, default is "pheatmap_LR.pdf"
color_pheatmap_set

colors for the pheatmap, default is viridis::viridis(8)
species_colors

vector, colors vectors for the species
species_name1

two character, like "mm", for species1
species_name2

two character, like "zf", for species2
species_name3

two character, like "ch", for species3
annotation_names_col

logical, whether show the column title name
border_color

character, for the color of the border, like "white"
scale

how to scale the data, one of "none", "row", "column"
cluster_rows

logical, whether cluster the row
cluster_cols

logical, whether cluster the column
legend

logical, whether show the legend
legend_breaks

break legend with this vector value
legend_labels

labels for the break legend
show_rownames

logical, whether show the rownames
show_colnames

logical, whether show the colnames
fontsize

numeric, size of the font
cellwidth

numeric, width of the cell
cellheight

numeric, heigth of the cell
width

numeric, width of the heatmap
height

numeric, height of the heatmap
...

other parameters in pheatmap

Value

pheatmap

Examples

merge_avg_width_df <- make_pheatmap_LR_data(cci_conserved_results = conserved_result_mm_zf_ch,
seurat_object_sp1 = Mm_seurat,
seurat_object_sp2 = Zf_seurat,
seurat_object_sp3 = ch_seurat,
avg_group = "cellname",
species_name1 = "mm",
species_name2 = "zf",
species_name3 = "ch",
cci_conserved_Weights = conserved_cci_result$cci_conserved_Weights)
pheatmap_LR_multi(width_df = merge_avg_width_df$merge_width_df, filename = "lr_avg_pheatmap.pdf")

Create a pheatmap for average expression of ligand-receptor pairs

Description

make a pheatmap for average expression of ligand-receptor pairs using geometric mean of corresponding cell type

Usage

pheatmap_LR1(
  width_df,
  filename = "pheatmap_LR.pdf",
  color_pheatmap_set = NULL,
  species_colors = NULL,
  species_name1 = "mm",
  species_name2 = "zf",
  annotation_names_col = TRUE,
  border_color = "white",
  scale = "none",
  cluster_rows = FALSE,
  cluster_cols = FALSE,
  legend = TRUE,
  legend_breaks = NA,
  legend_labels = NA,
  show_rownames = T,
  show_colnames = T,
  fontsize = 10,
  cellwidth = 16,
  cellheight = 12,
  width = 14,
  height = 14,
  ...
)

Arguments

width_df

width dataframe, result of function make_pheatmap_LR_data1
filename

name to save the pheatmap, default is "pheatmap_LR.pdf"
color_pheatmap_set

colors for the pheatmap, default is viridis::viridis(8)
species_colors

vector, colors vectors for the species
species_name1

two character, like "mm", for species1
species_name2

two character, like "zf", for species2
annotation_names_col

logical, whether show the column title name
border_color

character, for the color of the border, like "white"
scale

how to scale the data, one of "none", "row", "column"
cluster_rows

logical, whether cluster the row
cluster_cols

logical, whether cluster the column
legend

logical, whether show the legend
legend_breaks

break legend with this vector value
legend_labels

labels for the break legend
show_rownames

logical, whether show the rownames
show_colnames

logical, whether show the colnames
fontsize

numeric, size of the font
cellwidth

numeric, width of the cell
cellheight

numeric, heigth of the cell
width

numeric, width of the heatmap
height

numeric, height of the heatmap
...

other parameters in pheatmap

Value

pheatmap

Examples

merge_avg_width_df <- make_pheatmap_LR_data1(cci_conserved_results = conserved_result_mm_zf,
seurat_object_sp1 = Mm_seurat,
seurat_object_sp2 = Zf_seurat,
avg_group = "cellname",
species_name1 = "mm",
species_name2 = "zf",
cci_conserved_Weights = conserved_cci_result$cci_conserved_Weights)
pheatmap_LR_multi(width_df = merge_avg_width_df$merge_width_df, filename = "lr_avg_pheatmap.pdf")

Plot communication between cell types

Description

This function takes a dataframe containing information about communication between cell types, and plots a graph to visualize the communication pattern. The graph represents sender and receiver cell types as nodes, and the strength of communication as the width of edges connecting them.

Usage

Plot_Celltype.Communication(
  graph_df,
  sendercolumn,
  receivercolumn,
  widthcolumn,
  colors = NULL,
  useLabels = TRUE,
  nodeSize = 5,
  vertex.label.color,
  edge.label.color,
  vertex.label.cex,
  ...
)

Arguments

graph_df

The input dataframe containing communication data, see the example in tutorial.
sendercolumn

single column name, The name of the column containing sender cell types.
receivercolumn

single column name, The name of the column containing receiver cell types.
widthcolumn

single column name, The name of the column containing the width of edges (communication strength).
colors

Optional vector of colors for nodes and edges.
useLabels

Logical value indicating whether to display labels for nodes.
nodeSize

Size of the nodes.
vertex.label.color

Color of node labels.
vertex.label.cex

Size of node labels.
...

Value

The plotted graph as an invisible object

Examples

plot_graph_network(long_df, sendercolumn = "celltype1", receivercolumn = "celltype2", widthcolumn = "score", useLabels = T, colors = colors, nodeSize = 10, vertex.label.color = "black", vertex.label.cex = 1.5)

Heapmap of cell-cell interaction

Description

Heapmap of cell-cell interaction

Usage

plot_interaction_heatmap(
  SingleCellSignalR_sp1_result,
  SingleCellSignalR_sp2_result,
  SingleCellSignalR_sp3_result,
  value_name = "scale_weight",
  used_col = c("source", "target", "LRscore_scale"),
  species_name1 = "mm",
  species_name2 = "zf",
  species_name3 = "ch",
  sp1_conserved_celltype,
  sp2_conserved_celltype,
  sp3_conserved_celltype,
  celltype_colors = NULL,
  brewer.pal_set = "Set3",
  species_colors = NULL,
  ph_colors = viridis::viridis(8),
  border_color = "white",
  scale = "none",
  cluster_rows = FALSE,
  cluster_cols = FALSE,
  legend = TRUE,
  show_rownames = F,
  show_colnames = F,
  fontsize = 8,
  cellwidth = 20,
  cellheight = 24,
  angle_col = "45",
  annotation_names_col = TRUE,
  annotation_names_row = TRUE,
  filename = "CCI_pheatmap.pdf",
  width = 18,
  height = 10,
  ...
)

Arguments

SingleCellSignalR_sp1_result

dataframe, result of the SingCellSignalR, must contain "source", "target", "LRscore_scale"
SingleCellSignalR_sp2_result

dataframe, result of the SingCellSignalR, must contain "source", "target", "LRscore_scale"
SingleCellSignalR_sp3_result

dataframe, result of the SingCellSignalR, must contain "source", "target", "LRscore_scale"
value_name

character, names of the sum weight, default is "scale_weight"
used_col

default is c("source", "target", "LRscore_scale"), they should be in the column of result of SingCellSignalR
species_name1

character, species_name must be only two characters, like "mm"
species_name2

character, species_name must be only two characters, like "zf"
species_name3

character, species_name must be only two characters, like "ch"
sp1_conserved_celltype

vector, a vector contained conserved cell type names in species1
sp2_conserved_celltype

vector, a vector contained conserved cell type names in species2
sp3_conserved_celltype

vector, a vector contained conserved cell type names in species3
celltype_colors

vector, colors vector with names. names of celltype_colors must be paste0(species_name, celltype_name), like "mmCones"
brewer.pal_set

character, pal in RColorBrewer
species_colors

vector, colors vector without names. colors for species
ph_colors

vector, colors for pheatmap
border_color

character, colors for border
scale

how to scale the data, one of "none", "row", "column"
cluster_rows

logical, whether cluster the row
cluster_cols

logical, whether cluster the column
legend

logical, whether show the legend
show_rownames

logical, whether show the rownames
show_colnames

logical, whether show the colnames
fontsize

numeric, size of the font
cellwidth

numeric, width of the cell
cellheight

numeric, heigth of the cell
angle_col

numeric, angle of the colnames
annotation_names_col

logical, whether show the name of column
annotation_names_row

logical, whether show the name of row
filename

the names used to save the heatmap
width

numeric, width of the heatmap
height

numeric, height of the heatmap
...

other parameters in pheatmap

Value

a list contain the data and the pheatmap object

Functions

  • plot_interaction_heatmap(): heapmap to show interaction between conserved cell types

Examples

mm_conserved_celltype <- c("Activated MG", "Cones", "GABAergic AC","Microglia", "Pericytes", "Resting MG", "RGC", "Rod BC", "Rods", "RPE", "V/E cells")
zf_conserved_celltype <- c("Activated MG",  "Cones", "GABAergic AC", "Microglia", "Pericytes", "Resting MG", "RGC","Cone BC", "Rods", "RPE", "V/E cells")
ch_conserved_celltype <- c("Activated MG", "Cones", "GABAergic AC", "Resting MG", "RGC", "Cone BC", "Rods")
plot_interaction_heatmap(SingleCellSignalR_sp1_result = SingleCellSignalR_mouse_result,
                         SingleCellSignalR_sp2_result = SingleCellSignalR_zebrafish_result,
                         SingleCellSignalR_sp3_result = SingleCellSignalR_chick_result,
                         species_name1 = "mm",
                         species_name2 = "zf",
                         species_name3 = "ch",
                         sp1_conserved_celltype = mm_conserved_celltype,
                         sp2_conserved_celltype = zf_conserved_celltype,
                         sp3_conserved_celltype = ch_conserved_celltype)

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 = F,
  cluster_cols = F,
  show_rownames = F,
  show_colnames = F,
  cellwidth = 6,
  cellheight = 2,
  legend = F,
  annotation_legend = F,
  annotation_names_row = F,
  ...
)

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

logicalal 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)])

Plot phylogenetic tree

Description

Build a phylogenetic tree to identify the conservation of the cell types across species

Usage

Plot_phylogenetic_tree(
  SCT_matrix,
  tree_method = "hierarchical clustering",
  hcluster.method = "average",
  species.vector,
  layout.tree = "rectangular",
  tree_size = 1.4,
  xlim_tree = 0.65,
  fontface_for_tiplab = TRUE,
  set_fontface_for_tiplab = c("bold.italic", "italic", "plain"),
  conserved_hm_celltype = NULL,
  bar_width = 0.025,
  Show_Pairwise_distances = FALSE,
  boot_times = 100,
  offset = 0.04,
  offset2 = 0.022,
  offset_tiplab = 0.07,
  fontface_tiplab_with_colors = "bold",
  tiplab.size = 7,
  tiplab_cols = NULL,
  tippoint.shape = 21,
  tippoint.shape.size = 0,
  geom_nodepoint = 5,
  show_branch.length = FALSE,
  round_x = 2,
  fill_brandlength = "lightgreen",
  size_brandlength = 6,
  annotation_colors_df = NULL,
  brewer_pal_used = "Set3",
  col.value = c(rgb(102/255, 46/255, 115/255), rgb(31/255, 153/255, 139/255)),
  colors_labels = NULL,
  show_colnames = FALSE,
  colnames_angle = 0,
  colnames_position = "top",
  font.size_colnames = 5,
  colnames_offset_y = 0.8,
  custom_column_labels = c("Species", "Celltypes"),
  bootstrap_value_size = 0,
  bootstrap_value_col = "black",
  legend.text_size = 18,
  width = 13,
  height = 22,
  plot.margin = margin(10, 10, 10, -10),
  legend.position = "top"
)

Arguments

SCT_matrix

matrix, contain the conservation scores of cell types across species
tree_method

character, method used for constructing phylogenetic tree, it can be "hierarchical clustering", or "classic Neighbor-Joining"
hcluster.method

character, method used for clustering phylogenetic tree, it can be one of "average", "single", "complete", "mcquitty", "median", "centroid"
species.vector

vector, length is equal to nrow(SCT_matrix), used for annotation bar
layout.tree

character, method used for the layout of the tree. it can be one of 'rectangular', 'dendrogram', 'fan', 'circular'
tree_size

numeric, size of the tree's branch
xlim_tree

numeric, x limit for the picture, used for the width of the picture
fontface_for_tiplab

logical, whether used fontface for tip label. If it is TRUE, then the tiplab_cols for tip label won't work
set_fontface_for_tiplab

if fontface_for_tiplab is TRUE, the parameter will work. By default, it is set with c("bold.italic", "italic", "plain")
conserved_hm_celltype

vector, contain the conserved cell types in heatmap, or celltypes defined by yourself
bar_width

integer, width of annotation bar
Show_Pairwise_distances

logical, whether save the plot of pairwise distances between input data and constructed tree or not
boot_times

integer, the times for bootstrap test, usually 100, or you can set larger 1000
offset

numeric, the distance of celltype annotation bar from tip
offset2

numeric, the distance of species annotation bar from tip
offset_tiplab

numeric, the distance of labels from tip
fontface_tiplab_with_colors

charcter, colors for tip label. if fontface_for_tiplab is FALSE, then can set one fontface for all tip labels, like "bold", "italic", "plain"
tiplab.size

numeric, the size of tip labels
tiplab_cols

vector, colors for tip group
tippoint.shape

numeric, the shape used for tip point, such as 21, 17, 22, ......
tippoint.shape.size

numeric, the size of the tip point shape
geom_nodepoint

integer, the size of the node point
show_branch.length

logical, whether show the branch length, default is TRUE
round_x

numeric, the decimal places contained for round() function for branch length number
fill_brandlength

character, colors for branch length
size_brandlength

numeric, the size for the branch length label
annotation_colors_df

dataframe, must contain two column, named "colors" and "celltype", default is null
brewer_pal_used

character, the brewer_pal in RColorBrewer, like "Set1", "Set3", "Paired". It set colors for tip label. It works when fontface_for_tiplab is FALSE
col.value

chacterize vector, colors used for species bar annotation, must be the same length of the number of species
colors_labels

chacterize vector, labels for the legend, the length of colors_breaks
show_colnames

logical, whether show the colnames of annotation bar
colnames_angle

integer, the angle of the colnames of annotation bar
colnames_position

character, the position of the colnames of annotation bar, can be top or bottom
font.size_colnames

integer, the size of the colnames of annotation bar
colnames_offset_y

numeric, the distance of the colnames from the annotation bar
custom_column_labels

character, edit the colnames of annotation bar by yourself
bootstrap_value_size

numeric, the size of the bootstrap values showed in the tree. If you don't want to showed the bootstrap values in the tree, you can set the parameter to 0
bootstrap_value_col

character, the color of the bootstrap values showed in the tree
legend.text_size

integer, the size of the legend
width

numeric, the width of the plot
height

numeric, the height of the plot
plot.margin

the plot margin, it should be something like margin(10, 10, 10, -10), the four number represent top, right, bottom, left, respectively
legend.position

character, one of "right", "left", "top", "bottom", the position of the legend
fontface_tiplab

charcter, the fontface used for tip label

Value

save plot in present file directory

Examples

library(CACIMAR, lib.loc = "/usr/local/lib/R/site-library")
load(system.file("extdata", "zf_mm_markers.rda", package = "CACIMAR"))
expression <- CACIMAR::Identify_ConservedCellTypes(OrthG_Mm_Zf,Zf_marker,Mm_marker,'zf','mm')
SCT_matrix <- expression[[2]]
# get the species names
species.vector <- substr(rownames(SCT_matrix), 1, 2)
# get the conserved celltypes in heatmap
SNT_h <- SCT_matrix[grep('mm',rownames(SCT_matrix)),as.numeric(grep('zf',colnames(SCT_matrix)))]
conserved_hm_celltypes <- get_conserved_hm_celltypes(SNT_h)
Plot_phylogenetic_tree(SCT_matrix = SCT_matrix,
                       species.vector = species.vector,
                       conserved_hm_celltype = conserved_hm_celltypes)

Plot Phylogenetic Tree of Different Species Cell Types

Description

Plot a tree of different species cell types using the conserved score of cell types

Usage

Plot_Species_CellType_Tree(
  dist_matrix,
  hcluster.method = c("average", "single", "complete"),
  species.vector,
  layout.tree = c("rectangular", "dendrogram", "fan", "circular"),
  offset = 0.01,
  tiplab.size = 5,
  tippoint.shape = c(22, 21),
  tippoint.shape.size = 4,
  geom_nodepoint = 0,
  col.value = c(rgb(102/255, 46/255, 115/255), rgb(31/255, 153/255, 139/255))
)

Arguments

dist_matrix

Distance matrix
hcluster.method

chariter, Hierarchical clustering method, possible values are "average" (default), "single", "complete"
species.vector

vector, Species vector
layout.tree

Tree layout ("rectangular", "dendrogram", "fan", "circular"), default is "rectangular"
offset

Tiplab offset, default is 0.01
tiplab.size

Tiplab size, default is 5
tippoint.shape

Tippoint shape, default is 22
tippoint.shape.size

Tippoint size, default is 4
geom_nodepoint

Nodepoint size, default is 0
col.value

Color vector for species, default is c(rgb(102/255,46/255,115/255), rgb(31/255,153/255,139/255))

Value

a list, which contain the Tree data and the Tree plot

Examples

load(system.file("extdata", "zf_mm_markers.rda", package = "CACIMAR"))
expression <- Identify_ConservedCellTypes(OrthG_Mm_Zf,Zf_marker,Mm_marker,'zf','mm')
dist_matrix <- expression[[2]]
species.vector <- substr(rownames(dist_matrix), 1, 2)
Species_CellType_Tree <- Plot_Species_CellType_Tree(dist_matrix = dist_matrix, species.vector = species.vector, hcluster.method = "average", geom_nodepoint = 0, layout.tree = "rectangular", offset = 0.02, tiplab.size = 5, tippoint.shape = 22, tippoint.shape.size = 4)
Species_CellType_Tree$Plot