View source: R/core_functions.R
SCOPRO | R Documentation |
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.
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 )
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 |
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 |
Gabriele Lubatti gabriele.lubatti@helmholtz-muenchen.de
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")
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