Identifying key genes and cell subclusters for time-series single cell sequencing data
For users with differnt needs, two modes are provided. In the ‘full’ mode, only a file containing gene expression matrix (file_data), the status of the matrix (indication), the needs of cell annotation (annotation),the name of root cluster and fdr level are required.
scTITANS_fullIn the ‘full’ mode, details about the parameters are shown as follows.
data_file file for gene expression matrix (row genes, column cells)cell_metadata file for cell metadata (row cells);if not provided, provide “none”gene_metadata file for gene metadata (row genes,header must contain gene_short_name); if not provided, provide “none”indication status of the matrix (normalized, filtered, none); if raw data is used, provide “none”; if the gene matrix has been filter, provide “filtered”; if the gene matrix has been filtered and noralized, provide “normalized”.annotation if cell annotation is required, provide T; else, provide Fsp must be Human or Mouse. If anntation is “F”, provide “”tissue organ, such as Liver; if not sure, provide “none”. If annotation is “F”, provide “”root_type the name of root cluster; if not sure, provide “none”fdr_level fdr level
For users with a raw matrix obtained from human liver with CellRanger, the following command will be ok (no cell annotation).
In this example, besieds the file for gene expression matrix, users chose no cell annotation and fdr level of 0.01 in identifying key genes and cell subclusters. Moreover, no root cell type is provided.
result = scTITANS_full(data_file,"none","none","none","F","","","none",0.01)
For users with a raw matrix obtained from human liver with CellRanger, the following command will be ok (cell annotation required).
In this example, besieds the file for gene expression matrix, users chose cell annotation and a fdr leve of 0.01 in identifying key genes and cell subclusters.
In this case, users must also provide the information for sp and organ.
result = scTITANS_full(data_file,"none","none","none","T","Human","Liver","none",0.01)
The above two command will both return two txt files named “SigGenes.fdr0.01.txt” and “SigClusters.fdr0.01.txt”, respectively. If something wrong happens in identifying significant clusters, an error information will be displayed.
scTITANS_partialFor users who have finished trajectory inference analysis, the ‘partial’ mode will much more suitable.
data_file file for gene expression matrix (row genes, column cells)cell_metadata file for cell metadata (row cells)gene_meta file for gene metadata (row genes,header must contain gene_short_name)root_type the name of root cluster; if not sure, provide “none”fdr_level fdr level
In this example, besides the file for gene expression matrix, users must also provide files for cell_metadata and gene_metadata. In the file for cell_metada, information for cell clusters and/or cell types are required.
This mode can work in cases where root cell type is provided or not.
For a user with results from trajectory inference analysis and without information about root cell type, the following command will be ok.
result = scTITANS_partial(data_file,cell_meta,gene_meta,"none",0.01)
The above command will both return two txt files named “SigGenes.fdr0.01.txt” and “SigClusters.fdr0.01.txt”, respectively. If something wrong happens in identifying significant clusters, an error information will be displayed.
Shao et al., Identify differential genes and cell subclusters from time-series scRNA-seq data using scTITANS, Computational and Structural Biotechnology Journal, Volume 19, 2021, Pages 4132-4141, https://doi.org/10.1016/j.csbj.2021.07.016