Impute high resolution cell type-specific expression from bulk genomic profiles
docker run <bind_mounts> cibersortx/hires [options] --mixture <file> --sigmatrix <file>
CIBERSORTxHiRes
imputes sample-level gene expression variation of distinct cell types from a collection of bulk tissue transcriptomes. Unlike CIBERSORTxGEP
, the output is an expression matrix for each cell type rather than a single transcriptome profile. CIBERSORTxHiRes
is useful for exploring cell type expression variation without prior knowledge of biological or functional groupings (e.g. relating cell type specific gene expression to survival).
docker
or singularity
is required to run this tool. You can run
docker pull cibersortx/hires
to obtain a copy of this tool. You also need a token that you will provide every time you run the CIBERSORTx
executables. You can obtain the token from the CIBERSORTx website.
CIBERSORTx
will assume that data are in log space, and will anti-log all expression values by $2^x$.CIBERSORTx
to generate one for you if you have single-cell RNA-seq reference available.CIBERSORTx
cell fractions [default: run CIBERSORT]CIBERSORTxGEP
The main result of CIBERSORTxHiRes
is a set of expression matrix .txt files and heatmaps for each individual cell type, showing the cell-type specific expression of individual genes at the sample level.
The name of each file contains the name of the cell type, followed by the window size used for the job (e.g. CIBERSORTxHiRes_job1_Mastcells_Window40.txt).
The "1" values in the expression matrix txt files are genes with insufficient evidence of expression (these genes are either not expressed or have inadequate statistical power to be imputed). The NA values are genes that have inadequate statistical power to be imputed.
If you have provided a gene subset list via --subsetgenes, these files will have all the genes in the list that are found in the original mixture file given as input. If some genes are still missing, this could be due to different annotations or gene symbols between the gene subset list and the mixture file.
CIBERSORTxHiRes
runs both CIBERSORTxFractions
and CIBERSORTxGEP
, and a set of output files is generated from each analysis.
This examples imputes cell type specific gene expression profiles from bulk follicular lymphoma samples profiled on microarray, using the signature matrix LM22 collapsed to 4 major cell types. In addition the results are compared to ground truth reference profiles obtained from FACS-sorted cell subsets.
docker run -v absolute/path/to/input/dir:/src/data -v absolute/path/to/output/dir:/src/outdir cibersortx/hires \--username email_address_registered_on_CIBERSORTx_website
\--token token_obtained_from_CIBERSORTx_website
\--mixture Fig4a-arrays-SimulatedMixtures.MAS5.txt
\--sigmatrix Fig4a-LM4.txt
\--classes Fig4a-LM4-mergedclasses.txt
\--window 20
--QN TRUE
This examples imputes cell type specific gene expression profiles from bulk NSCLC samples profiled by RNA-Seq and compares the results to ground truth reference profiles obtained from FACS-sorted cell subsets.
docker run -v absolute/path/to/input/dir:/src/data -v absolute/path/to/output/dir:/src/outdir cibersortx/hires \--username email_address_registered_on_CIBERSORTx_website
\--token token_obtained_from_CIBERSORTx_website
\--mixture SuppFig11-DLBCL_CHOP_Lenz-arrays-bulktumors.MAS5.txt
\--sigmatrix LM22.txt
--classes SuppFig11-LM22_10_merged_classes.txt
\--subsetgenes SuppFig11-DLBCL-GCBABC-genes
--QN TRUE