To install all python and R dependencies, create a conda enviroment using the provided conda environment file:
conda env create -f e2g_jamboree_env.yml
To activate the enviroment after installation, simply use:
conda activate e2g_jamboree
Multiome experiments often contain cells from multiple cell types or cell states. To apply E2G methods to individual cell types, ATAC-seq and RNA-seq data has to be extracted for cell barcodes assigned to different cell types or states.
To extract separate fragment files for all annotated cell types in a multiome fragment file, use the
extract_cell_types_frag_file.py python script. This takes a fragment file and a cell annotation
table as input. It also allows to specify a lane identifier to add to the cell barcodes in the
fragment file, which is important if an experiments has files from multiple lanes.
To extract fragments for all cell types, use a command similar to this:
python3 extract_cell_types_frag_file.py -f fragments.tsv.gz -c metadata_IGVF10.csv -l 1 -o output/fragments_lane1
By default, this script assumes that the cell type for each barcode is listed in the 'sample' column
in the cell annotation table. To specify another column, use the -s or --sample_col argument.
This will create a new directory called output/fragments_lane1 containing fragment files for all
cell types that were annotated for the input fragment file. Use this script on fragment files for
all lanes of an experiment to get demultiplexed fragment files for all lanes.
To combined fragment files from multiple lanes for one cell type, we need to combine, sort, compress and tabix index the files. This can be done using these commands:
cat output/fragments_lane1/fragments_cell_type.tsv output/fragments_lane2/fragments_cell_type.tsv > output/fragments_combined/fragments_cell_type.tsv
sort -k1,1 -k2,2n output/fragments_combined/fragments_cell_type.tsv > output/fragments_combined/fragments_cell_type.sorted.tsv
rm output/fragments_combined/fragments_cell_type.tsv # delete file not longer needed
bgzip output/fragments_combined/fragments_cell_type.sorted.tsv
tabix -p bed output/fragments_combined/fragments_cell_type.sorted.tsv.gz
To extract RNA counts for all annotated cell types from a .mtx file, use the
extract_cell_types_mtx.R R script. This takes a RNA .mtx file and a cell annotation table as
input. It also allows to specify a lane identifier to add to the cell barcodes in the matrix file,
which is important if an experiments has files from multiple lanes.
To extract RNA counts for all cell types, use a command similar to this:
Rscript extract_cell_types_mtx.R -m cells_x_genes.total.mtx -c metadata_IGVF10.csv -l 1 -o output/rna_lane1
By default, this script assumes that the cell type for each barcode is listed in the 'sample' column
in the cell annotation table. To specify another column, use the -s or --sample_col argument.
The script will create a new directory called output/rna_lane1 containing matrix, barcode and gene
name files for all cell types that were annotated for the input matrix. Use this script on matrix
files for all lanes of an experiment to get demultiplexed RNA matrix files for all lanes.
To combine RNA matrix files from multiple lanes for one cell type, simply use the
combine_rna_mtx.R R script. This takes a comma separated list of input RNA matrix files as input
and will combine them into the specified output file. This will also create combined barcode and
gene name files in the same directory.
Rscript combine_rna_mtx.R -i output/rna_lane1/cells_x_genes.total.cell_type.mtx,output/rna_lane2/cells_x_genes.total.cell_type.mtx -o output/rna_combined/cells_x_genes.total.cell_type.mtx
Once E2G methods have been applied, they need to be reformatted to the IGVF E2G format. Following scripts can be used to reformat predictions produced during the jamboree:
Use the reformat_multiome_e2g_predictions.R to reformat these predictions to the IGVF format. This
script also needs a gene id table, which can be downloaded from
here. When running this script, cell type, model name
and versions are specified as input arguments. In case of thresholded predictions, the used
thresholding strategy can be provided as a string to be added to the metadata header.
Example command:
# full predictions
Rscript reformat_multiome_e2g_predictions.R -i K562_10XMultiome_Xu2022_archr.tsv -o K562_10XMultiome_Xu2022_archr.e2g.tsv.gz -g gencode_v43_gene_tss.tsv.gz -c K562 -m ArchR -v 0.0
# thresholded predictions
Rscript reformat_multiome_e2g_predictions.R -i K562_10XMultiome_Xu2022_archr_thresholded_score_0.45.tsv -o K562_10XMultiome_Xu2022_archr_thresholded_score_0.45.e2g.tsv.gz -g gencode_v43_gene_tss.tsv.gz -c K562 -m ArchR -v 0.0 -t 'score > 0.45'
scE2G predictions can be reformatted into the IGVF E2G format using the
reformat_scE2G_predictions.R script. When running this script, cell type, model name and versions
are specified as input arguments. In case of thresholded predictions, the used thresholding strategy
can be provided as a string to be added to the metadata header.
Example command:
# full predictions
Rscript reformat_scE2G_predictions.R -i encode_e2g_predictions.tsv.gz -o K562_10XMultiome_Xu2022_scE2G_multiome.e2g.tsv.gz -c K562 -m scE2G -v 0.0
# thresholded predictions
Rscript reformat_scE2G_predictions.R -i encode_e2g_predictions.tsv.gz -o K562_10XMultiome_Xu2022_scE2G_multiome.e2g.tsv.gz -c K562 -m scE2G -v 0.0 -t 'score > 0.164'