- dynamo.ext.scribe(adata, genes=None, TFs=None, Targets=None, gene_filter_rate=0.1, cell_filter_UMI=10000, motif_ref='https://www.dropbox.com/s/s8em539ojl55kgf/motifAnnotations_hgnc.csv?dl=1', nt_layers=['X_new', 'X_total'], normalize=False, do_CLR=True, drop_zero_cells=True, TF_link_ENCODE_ref='https://www.dropbox.com/s/bjuope41pte7mf4/df_gene_TF_link_ENCODE.csv?dl=1')[source]
Apply Scribe to calculate causal network from spliced/unspliced, metabolic labeling based and other “real” time series datasets. Note that this function can be applied to both of the metabolic labeling based single-cell assays with newly synthesized and total RNA as well as the regular single cell assays with both the unspliced and spliced transcripts. Furthermore, you can also replace the either the new or unspliced RNA with dynamo estimated cell-wise velocity, transcription, splicing and degradation rates for each gene (similarly, replacing the expression values of transcription factors with RNA binding, ribosome, epigenetics or epitranscriptomic factors, etc.) to infer the total regulatory effects, transcription, splicing and post-transcriptional regulation of different factors.
AnnData.) – adata object that includes both newly synthesized and total gene expression of cells. Alternatively, the object should include both unspliced and spliced gene expression of cells.
list]) – The list of gene names that will be used for casual network inference. By default, it is None and thus will use all genes.
list]) – The list of transcription factors that will be used for casual network inference. When it is None gene list included in the file linked by motif_ref will be used.
list]) – The list of target genes that will be used for casual network inference. When it is None gene list not included in the file linked by motif_ref will be used.
float) – minimum percentage of expressed cells for gene filtering.
int) – minimum number of UMIs for cell filtering.
str) – It provides the list of TFs gene names and is used to parse the data to get the list of TFs and Targets for the causal network inference from those TFs to Targets. But currently the motif based filtering is not implemented. By default it is a dropbox link that store the data from us. Other motif reference can bed downloaded from RcisTarget: https://resources.aertslab.org/cistarget/. For human motif matrix, it can be downloaded from June’s shared folder: https://shendure-web.gs.washington.edu/content/members/cao1025/public/nobackup/sci_fate/data/hg19-tss- centered-10kb-7species.mc9nr.feather
list) – The two keys for layers that will be used for the network inference. Note that the layers can be changed flexibly. See the description of this function above. The first key corresponds to the transcriptome of the next time point, for example unspliced RNAs (or estimated velocitym, see Fig 6 of the Scribe preprint: https://www.biorxiv.org/content/10.1101/426981v1) from RNA velocity, new RNA from scSLAM-seq data, etc. The second key corresponds to the transcriptome of the initial time point, for example spliced RNAs from RNA velocity, old RNA from scSLAM-seq data.
bool) – Whether to drop cells that with zero expression for either the potential regulator or potential target. This can signify the relationship between potential regulators and targets, speed up the calculation, but at the risk of ignoring strong inhibition effects from certain regulators to targets.
bool) – Whether to perform context likelihood relatedness analysis on the reconstructed causal network
str) – The path to the TF chip-seq data. By default it is a dropbox link from us that stores the data. Other data can be downloaded from: https://amp.pharm.mssm.edu/Harmonizome/dataset/ENCODE+Transcription+Factor+Targets.
- Return type:
An updated adata object with a new key causal_net in .uns attribute, which stores the inferred causal network.