Package 'SCOPRO'

filter_in_vitro

Description

For a given gene in in marker_all, if the fraction of cells in one or more clusters with an expression above threshold is greater than fraction, then the gene is kept

Usage

filter_in_vitro(
  norm_vitro,
  cluster_vitro,
  marker_all,
  fraction = 0.1,
  threshold = 0
)

Arguments

norm_vitro	Norm count matrix (n_genes X n_cells) for in vitro dataset
cluster_vitro	cluster for in vitro dataset
marker_all	First element of the list given as output by the function select_top_markers
fraction	Numeric value.
threshold	Numeric value

Value

Character vector with the names of kept genes

Author(s)

Gabriele Lubatti [email protected]

---

plot_score

Description

plot_score

Usage

plot_score(
  SCOPRO_output,
  marker_stages,
  marker_stages_filter,
  selected_stages,
  name_vivo,
  y_name,
  fill_name,
  title_name
)

Arguments

SCOPRO_output	output given by function SCOPRO
marker_stages	Second element of the list given as output by the function select_top_markers
marker_stages_filter	output from the function filter_in_vitro
selected_stages	In vivo stages for which the markers where computed with the function select_top_markers
name_vivo	name of the in vivo stage on which SCOPRO is run
y_name	Character value
fill_name	Character value.
title_name	Character value.

Value

ggplot2::ggplot2 object.

Author(s)

Gabriele Lubatti [email protected]

---

plot_score_genes

Description

plot_score_genes

Usage

plot_score_genes(
  markers_to_plot,
  label_1,
  label_2,
  norm_vitro,
  norm_vivo,
  cluster_vitro,
  cluster_vivo,
  final_name,
  max_size = 9,
  text_size = 9.5,
  title_name
)

Arguments

markers_to_plot	Character vector with the names of the genes to plot.
label_1	Character value. Label for the in vitro dataset
label_2	Character value. Label for the in vivo dataset
norm_vitro	Norm count matrix (n_genes X n_cells) for in vitro dataset
norm_vivo	Norm count matrix (n_genes X n_cells) for in vivo dataset
cluster_vitro	cluster for in vitro dataset
cluster_vivo	cluster for in vivo dataset
final_name	Character vector with the names of the genes to show in the plot.
max_size	Numeric value, specifying the size of the dot.
text_size	Numeric value, specifying the size of the text in the plot.
title_name	Character value.

Value

ggplot2::ggplot2 object showing balloon plot.

Author(s)

Gabriele Lubatti [email protected]

---

SCOPRO

Description

The mean expression profile of marker_stages_filter genes is computed for each cluster in the in vivo and in vitro dataset. For a given cluster, a connectivity matrix is computed with number of rows and number of columns equal to the length of marker_stages_filter. Each entry (i,j) in the matrix can be 1 if the fold_change between gene i and gene j is above fold_change. Otherwise is 0. Finally the connectivity matrix of a given name_vivo stage and all the clusters in the in vitro dataset are compared. A gene i is considered to be conserved between name_vivo and an in vitro cluster if the jaccard index of the links of gene i is above threshold.

Usage

SCOPRO(
  norm_vitro,
  norm_vivo,
  cluster_vitro,
  cluster_vivo,
  name_vivo,
  marker_stages_filter,
  threshold = 0.1,
  number_link = 1,
  fold_change = 3,
  threshold_fold_change = 0.1,
  marker_stages,
  selected_stages
)

Arguments

norm_vitro	Norm count matrix (n_genes X n_cells) for in vitro dataset
norm_vivo	Norm count matrix (n_genes X n_cells) for in vivo dataset
cluster_vitro	cluster for in vitro dataset
cluster_vivo	cluster for in vivo dataset
name_vivo	name of the in vivo stage on which SCOPRO is run
marker_stages_filter	output from the function filter_in_vitro
threshold	Numeric value. For a given gene, the jaccard index between the links from the in vivo and in vitro datasets is computed. If the jaccard index is above threshold, then the gene is considered to be conserved between the two datasets.
number_link	Numeric value. For a given gene in the in vivo dataset with links above number_link, the jaccard index between the links from in vitro and in vivo dataset is computed.
fold_change	Numeric value. For a given gene, the fold change between all the other genes is computed. If fold change is above fold_change, then there is a link with weight 1 between the two genes.
threshold_fold_change	Numeric value. Above threshold the fold change between genes is computed. Below threshold the difference between genes is computed.
marker_stages	Second element of the list given as output by the function select_top_markers
selected_stages	In vivo stages for which the markers where computed with the function select_top_markers

Value

A list with five elements:

common_link	Vector with the names of the genes conserved between name_vivo and all the clusters in the vitro dataset
no_common_link	Vector with the names of the genes not conserved between name_vivo and the clusters in the vitro dataset
link_kept	List with the names of the genes conserved between name_vivo and each single cluster in the vitro dataset
link_no_kept	List with the names of the genes not conserved between name_vivo and each single cluster in the vitro dataset
final_score	Numeric value, given by the fraction of conserved markers of name_vivo and each single cluster in the in vitro dataset

Author(s)

Gabriele Lubatti [email protected]

Examples

load(system.file("extdata", "norm_es_vitro_small.Rda", package = "SCOPRO"))
n_es= norm_es_vitro_small
load(system.file("extdata", "norm_vivo_small.Rda", package = "SCOPRO"))
n_v = norm_vivo_small
load(system.file("extdata", "cluster_es_vitro_small.Rda", package = "SCOPRO"))
c_es=cluster_es_vitro_small
load(system.file("extdata", "cluster_vivo_small.Rda", package = "SCOPRO"))
c_v=cluster_vivo_small
load(system.file("extdata", "marker_stages_filter.Rda", package = "SCOPRO"))
m_s_f = marker_stages_filter
load(system.file("extdata", "marker_stages.Rda", package = "SCOPRO"))
m_s = marker_stages
stages = c("Late_2_cell","epiblast_4.5","epiblast_5.5","epiblast_6.5")
output_SCOPRO = SCOPRO(n_es,n_v,c_es,c_v,"Late_2_cell",m_s_f,0.1,1,3,0.1,m_s,stages)
plot_score(output_SCOPRO,m_s,m_s_f,stages,"Late_2_cell","Score","Cluster","2-cells")

---

select_common_genes

Description

select_common_genes

Usage

select_common_genes(
  SCOPRO_output,
  marker_stages,
  selected_stages,
  name_vivo,
  cluster_vitro,
  name_vitro
)

Arguments

SCOPRO_output	output given by function SCOPRO
marker_stages	Second element of the list given as output by the function select_top_markers
selected_stages	In vivo stages for which the markers where computed with the function select_top_markers
name_vivo	name of the in vivo stage on which SCOPRO is run
cluster_vitro	cluster for in vitro dataset
name_vitro	name of the in vitro stage for which we want to know the conserved markers with the name_vivo stage

Value

Character vector with the names of the conserved markers of name_vivo stage in the name_vitro stage

Author(s)

Gabriele Lubatti [email protected]

---

select_no_common_genes

Description

select_no_common_genes

Usage

select_no_common_genes(
  SCOPRO_output,
  marker_stages,
  selected_stages,
  name_vivo,
  cluster_vitro,
  name_vitro
)

Arguments

SCOPRO_output	output given by function SCOPRO
marker_stages	Second element of the list given as output by the function select_top_markers
selected_stages	In vivo stages for which the markers where computed with the function select_top_markers
name_vivo	name of the in vivo stage on which SCOPRO is run
cluster_vitro	cluster for in vitro dataset
name_vitro	name of the in vitro stage for which we want to know the non-conserved markers with the name_vivo stage

Value

Character vector with the names of the non-conserved markers of name_vivo stage in the name_vitro stage

Author(s)

Gabriele Lubatti [email protected]

---

select_top_markers

Description

For each stage in selected_stages, starting from the markers given by markers_cluster_seurat function of the package CIARA, only the markers with a median above threshold in the stage and below threshold in all the other stages are kept.

Usage

select_top_markers(
  selected_stages,
  cluster_vivo,
  norm_vivo,
  markers_small,
  max_number = 100,
  threshold = 0.1
)

Arguments

selected_stages	Character vector with the name of the selected in vivo stages
cluster_vivo	cluster for in vivo dataset
norm_vivo	Norm count matrix (n_genes X n_cells) for in vivo dataset
markers_small	Output given by the function markers_cluster_seurat of the package CIARA
max_number	Numeric value. Maximum number of top markers to consider for each stage in selected_stages
threshold	Numeric value.

Value

A list with two elements:

marker_all	Vector with the union of all the top_number markers for each stage in selected_stages
marker_stages	List with length equal to number of stages in selected_stages . Each element contains the top_number markers for a given stage in selected_stages

Author(s)

Gabriele Lubatti [email protected]

See Also

https://CRAN.R-project.org/package=CIARA

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