From 0e5639ca541df2e7d9c63e32d2ccc080189832ff Mon Sep 17 00:00:00 2001 From: Andrew Roth Date: Mon, 31 Aug 2020 14:56:41 -0700 Subject: [PATCH] Updating README and adding examples --- README.md | 113 +- examples/synthetic.tsv | 401 +++ examples/tracerx.tsv | 7357 ++++++++++++++++++++++++++++++++++++++++ 3 files changed, 7863 insertions(+), 8 deletions(-) create mode 100644 examples/synthetic.tsv create mode 100644 examples/tracerx.tsv diff --git a/README.md b/README.md index d5c4d37..6429776 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Overview -PyClone-VI is fast method for inferring clonal population structure. +PyClone-VI is a fast method for inferring clonal population structure. ## Installation @@ -21,10 +21,9 @@ conda create -n pyclone-vi --file requirements.txt --yes ``` source activate pyclone-vi ``` -> Note: You will have to do this whenver you open a new terminal and want to run PyClone-VI. +> Note: You will have to do this whenever you open a new terminal and want to run PyClone-VI. 4. Install PyClone-VI - ``` pip install git+ssh://git@github.com/aroth85/pyclone-vi.git ``` @@ -36,13 +35,33 @@ pyclone-vi --help ## Usage +## Quick start + +1. Activate the `conda` environment +``` +source activate pyclone-vi +``` + +2. Fit the model to the data here we use the TRACERx file provided in the `examples` folder. +This assumes you are running in the base directory of the git repo. +Here we run allowing for up to 40 clusters (clones), using the Beta-Binomial distribution and performing 10 random restarts. +This should take a under five minutes. +``` +pyclone-vi fit -i examples/tracerx.tsv -o tracerx.h5 -c 40 -d beta-binomial -r 100 +``` + +3. Next we output the final results from the best random restart. +``` +pyclone-vi write-results-file -i tracerx.h5 -o tracerx.tsv +``` + ### Input format To run a PyClone-VI analysis you will need to prepare an input file. The file should be in tab delimited format and have the following columns. -> Note: There is an example file in examples/data/mixing.tsv +> Note: There is an example file in examples/data/tracerx.tsv -1. mutation_id - Unique identifier for the mutation. +1. mutation_id - Unique identifier for the mutation. This is free form but should match across all samples. > Note: PyClone-VI will remove any mutations without entries for all detected samples. If you have mutations with no data in some samples set their ref/alt counts to 0 for the corresponding sample. @@ -63,16 +82,93 @@ For autosome this will be two and male sex chromosomes one. You can include the following optional columns. 1. tumour_content - The tumour content (cellularity) of the sample. -Default value is 1.0 if column is not present. +Default value is 1.0 if column is not present. > Note: In principle this could be different for each mutations/sample. However it most cases it should be the same for all mutations in a sample. 2. error_rate - Sequencing error rate. -Default value is 0.001 if column is not present. +Default value is 0.001 if column is not present. +> Note: Most users will not need to change this value. + +### Output format + +The results file output by `write-results-file` is in tab delimited format. +There six columns: + +1. mutation_id - Mutation identifier as used in the input file. + +2. sample_id - Unique identifier for the sample as used in the input file. + +3. cluster_id - Most probable cluster or clone the mutation was assigned to. + +4. cellular_prevalence - Proportion of malignant cells with the mutation in the sample. +This is also called cancer cell fraction (CCF) in the literature. + +5. cellular_prevalence_std - Standard error of the cellular_prevalence estimate. + +6. cluster_assignment_prob - Posterior probability the mutation is assigned to the cluster. +This can be used as a confidence score to remove mutations with low probability of belonging to a cluster. + ### Running PyClone-VI -TODO +PyClone-VI has two sub-commands `fit` and `write-results-file`. +Typical usage is to run `fit` to perform inference and then `write-results-file` to select the best fit and post-process the results. + +#### `fit` command + +The `fit` command is used for performing the inference step. +It supports performing multiple restarts, the best of which will be selected by the `write-results-file` command. + +There are a few mandatory arguments: + +* `-i, --in-file` - Path where the input file is located. +This file should be in the format specified above. + +* `-o, --out-file` - Path where the output file will be written. +The output file is in HDF5 file format. +Most users will execute the `write-results-file` to extract the final results from this file. + +There are several optional arguments: + +* `-c, --num-clusters` - The number of clusters to use while fitting. +This should be set to a value larger than the expected number of clusters. +The software will then automatically determine how many to use. +Usually a value of 10-40 will work. +In general this value should increase if as more samples are used. + +* `-d, --density` - The probability density used to model the read count data. +Choices are `beta-binomial` and `binomial`. +`binomial` is a common choice for sequencing data. +`beta-binomial` is useful when the data is over-dispersed which has been observed frequently in sequencing data. + +* `-g, --num-grid-points` - Number of grid points used for approximating the posterior distribution. +Higher values should be used for deeply sequenced data. +The default value of 100 will likely work for most users. + +* `-r, --num-restarts` - Number of random restarts of variational inference. +More restarts will have a higher probability of finding the optimal variational approximation. +This also increases running time. +Usually a value of 10-100 will work. + +Additional arguments can be viewed by running `pyclone-vi fit --help` + +#### `write-results-file` command + +The `write-results-file` will select the best solution found by the `fit` command and post-process the results. +The output format is tab delimited file which an be imported and manipulated using tools such as R and Python. + +The are two mandatory arguments: + +* `-i, --in-file` - Path to the output file generated by the `fit` command. + +* `-o, --out-file` - Path where the final results will be written in tab delimited format. + +There is one optional argument: + +* `-c, --compress` - If set the output will be compressed using gzip. +This is useful where a large number mutations are input to reduce the size of the results file. + # License @@ -82,3 +178,4 @@ PyClone-VI is licensed under the GPL v3, see the LICENSE.txt file for details. ## 0.1.0 +First release. diff --git a/examples/synthetic.tsv b/examples/synthetic.tsv new file mode 100644 index 0000000..5fb65d7 --- /dev/null +++ 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