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Error occurred when I run the demo
The command and error is below:
./fitclone.py
<string>:582: SyntaxWarning: "is not" with a literal. Did you mean "!="?
Traceback (most recent call last):
File "/data/xieduo/fitclone/fitclone/./fitclone.py", line 8, in <module>
CondExp().run_with_config_file('../data/SA501/sa501_config.yaml')
File "<string>", line 34, in run_with_config_file
TypeError: load() missing 1 required positional argument: 'Loader'
Could you please help me to spot where went wrong?
sa501_config.yaml config parameters
Hi Sohrab,
In sa501_config.yaml file, you have true_x0 parameter, can you explain it? I don't see any comment from readme file.
true_x0: [0.42112125868690475, 0.10256939082931406, 0.029992607701921473, 0.19326294873370334]
Thanks,
Hoa
The question of futher using
Hi Thank you for your help! I have slove the previoius problem and i now have a new question of futher using this model. The Fitclone used single-cell sequencing data in your paper. Can the results of whole exon sequencing be analyzed using Fitclone?
Running fitclone with Clone IDs / K that are not consecutive numbers
Hi @sohrabsa
I am using fitclone for a project where my clones have particular IDs I'd like to track. Unfortunately these IDs are not respected by fitclone. From reading the code and testing, it seems like the python part of fitclone rename the K's internally as 0..n-1 in the order they are provided.
Most R scripts make a similar assumption, although the CloneIDs are named from 1 to n (instead of 0 to n-1). The only exception is for generating the "full_original_data_trace_.pdf" figure, where the K are taken directly from the input file. "violin_hist.pdf", "theta_hist.pdf" and "theta_hist_single.pdf" show numbers from 1 to n, disregarding the IDs used in the input file. "predict_trace.pdf" and "infer_trace.pdf" fail as they read the IDs from the input file and fails to match the re-assigned IDs from the python output (note also that the plots are wrong if the K in the input file are not ordered properly, i.e. if the input file contains the entries for say clone 3 before clone 0).
I am not sure I can contribute much to the python code, but I am happy to modify the R scripts to support non-consecutive numbers by simply reading the K identifiers in the input file and matching them in order to the output of fitclone). However, there are some options/parameters (like M_data for instance) that are not documented but can be relevant to the plots. Let me know if you'd be interested in this or if you think it would be best to modify the python code first (if relevant).
Regards
Javier
Switch to PyMem_Malloc for more efficient memory allocation
Arrays defined via cdef double* _buffer = <double*>PyMem_Malloc(l * sizeof(double)) and respecting C- for Fortran contiguous design will potentially be faster.
Error when running the demo dataset SA501
Hi Sohrab,
When I test your fitclone package with your provided demo dataset, there are some small issues, so I noted it here first.
Error:
[1] "Error - Error in if (!is.null(mcmc_options$observation_model) | mcmc_options$multinomial_error) {: argument is of length zero\n"
Log:
[1] "Compuing RMSE"
[1] "Compuing MAE"
[1] "JUS PLotting"
[1] "../results/_ERFNY_202007-28-193055.435388"
No id variables; using all as measure variables
[1] "../results/_ERFNY_202007-28-193055.435388"
No id variables; using all as measure variables
[1] -6.641435 NA
Thanks,
Hoa
Single time point?
Dear Developers,
It seems a cool method and I would like to give it a try!
However I do not have a time-course data,
I wonder does it work on estimating subclone fitness given only one time point?
Thank you very much and have a good weekend!
Cheers,
Siao-Han
Installation issue (gcc error)
Hello! I'm running into some issues with installation, and I'm hoping you can help. I've narrowed it down to a problem in the cyconvert.sh script, where I get the same error on every line:
(fitclone) [vleblanc@marrahost02 fitclone]$ python3 setup_cython/setup.py build_ext --inplace
Compiling wf_sample_parallel.pyx because it changed.
[1/1] Cythonizing wf_sample_parallel.pyx
/projects/vleblanc_prj/tools/anaconda3/envs/fitclone/lib/python3.7/site-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /projects/vleblanc_prj/tools/fitclone/fitclone/wf_sample_parallel.pyx
tree = Parsing.p_module(s, pxd, full_module_name)
running build_ext
building 'wf_sample_parallel' extension
creating build
creating build/temp.linux-x86_64-3.7
gcc -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/projects/vleblanc_prj/tools/anaconda3/envs/fitclone/lib/python3.7/site-packages/numpy/core/include -I/projects/vleblanc_prj/tools/anaconda3/envs/fitclone/include/python3.7m -c wf_sample_parallel.c -o build/temp.linux-x86_64-3.7/wf_sample_parallel.o -fopenmp
In file included from /projects/vleblanc_prj/tools/anaconda3/envs/fitclone/lib/python3.7/site-packages/numpy/core/include/numpy/ndarraytypes.h:1822,
from /projects/vleblanc_prj/tools/anaconda3/envs/fitclone/lib/python3.7/site-packages/numpy/core/include/numpy/ndarrayobject.h:12,
from /projects/vleblanc_prj/tools/anaconda3/envs/fitclone/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h:4,
from wf_sample_parallel.c:629:
/projects/vleblanc_prj/tools/anaconda3/envs/fitclone/lib/python3.7/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning "Using deprecated NumPy API, disable it with " "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
17 | #warning "Using deprecated NumPy API, disable it with " \
| ^~~~~~~
gcc -pthread -shared -B /projects/vleblanc_prj/tools/anaconda3/envs/fitclone/compiler_compat -L/projects/vleblanc_prj/tools/anaconda3/envs/fitclone/lib -Wl,-rpath=/projects/vleblanc_prj/tools/anaconda3/envs/fitclone/lib -Wl,--no-as-needed -Wl,--sysroot=/ build/temp.linux-x86_64-3.7/wf_sample_parallel.o -o /projects/vleblanc_prj/tools/fitclone/fitclone/wf_sample_parallel.cpython-37m-x86_64-linux-gnu.so -fopenmp
/projects/vleblanc_prj/tools/anaconda3/envs/fitclone/compiler_compat/ld: warning: /projects/vleblanc_prj/tools/anaconda3/envs/fitclone/bin/../lib/gcc/x86_64-conda-linux-gnu/11.2.0/crtbeginS.o: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010002
/projects/vleblanc_prj/tools/anaconda3/envs/fitclone/compiler_compat/ld: warning: /projects/vleblanc_prj/tools/anaconda3/envs/fitclone/bin/../lib/gcc/x86_64-conda-linux-gnu/11.2.0/crtbeginS.o: unsupported GNU_PROPERTY_TYPE (5) type: 0xc0010001
/projects/vleblanc_prj/tools/anaconda3/envs/fitclone/compiler_compat/ld: build/temp.linux-x86_64-3.7/wf_sample_parallel.o: unable to initialize decompress status for section .debug_info
/projects/vleblanc_prj/tools/anaconda3/envs/fitclone/compiler_compat/ld: build/temp.linux-x86_64-3.7/wf_sample_parallel.o: unable to initialize decompress status for section .debug_info
/projects/vleblanc_prj/tools/anaconda3/envs/fitclone/compiler_compat/ld: build/temp.linux-x86_64-3.7/wf_sample_parallel.o: unable to initialize decompress status for section .debug_info
/projects/vleblanc_prj/tools/anaconda3/envs/fitclone/compiler_compat/ld: build/temp.linux-x86_64-3.7/wf_sample_parallel.o: unable to initialize decompress status for section .debug_info
build/temp.linux-x86_64-3.7/wf_sample_parallel.o: file not recognized: file format not recognized
collect2: error: ld returned 1 exit status
error: command 'gcc' failed with exit status 1
I've tried to hunt down these errors on various forums and it seems that it may be an issue of using a different version of the compiler that was used to compile the files originally. I don't think the cython language level or the deprecated NumPy API are the issue, but perhaps I'm wrong (and please let me know if I should also address these). Could I please get your help in solving this? I'd love to try out this tool :)
In case it's helpful, here's everything installed in my fitclone conda environment:
(fitclone) [vleblanc@marrahost02 fitclone]$ conda list
# packages in environment at /projects/vleblanc_prj/tools/anaconda3/envs/fitclone:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 1_gnu conda-forge
atk 2.36.0 ha770c72_4 conda-forge
atk-1.0 2.36.0 h516909a_2 conda-forge
binutils_impl_linux-64 2.36.1 h193b22a_2 conda-forge
blas 1.0 openblas
bzip2 1.0.8 h7f98852_4 conda-forge
ca-certificates 2021.10.26 h06a4308_2 intel
cairo 1.16.0 h18b612c_1001 conda-forge
cycler 0.11.0 pyhd8ed1ab_0 conda-forge
cython 0.29.21 py37he6710b0_0 anaconda
dbus 1.13.6 he372182_0 conda-forge
expat 2.4.7 h27087fc_0 conda-forge
fontconfig 2.13.96 h8e229c2_2 conda-forge
freetype 2.10.4 h0708190_1 conda-forge
fribidi 1.0.10 h36c2ea0_0 conda-forge
gcc 11.2.0 h702ea55_8 conda-forge
gcc_impl_linux-64 11.2.0 h82a94d6_14 conda-forge
gdk-pixbuf 2.32.2 1 bioconda
gettext 0.19.8.1 hf34092f_1004 conda-forge
glib 2.66.1 h680cd38_0 conda-forge
gobject-introspection 1.66.1 py37h619baee_1 conda-forge
graphite2 1.3.13 h58526e2_1001 conda-forge
gstreamer 1.14.5 h36ae1b5_2 conda-forge
gtk2 2.24.32 h90f3771_0 conda-forge
harfbuzz 2.4.0 h37c48d4_1 conda-forge
hdf5 1.10.7 hb1b8bf9_0 anaconda
icu 58.2 hf484d3e_1000 conda-forge
jbig 2.1 h7f98852_2003 conda-forge
joblib 1.0.1 py37h3f38642_0 intel
jpeg 9e h7f98852_0 conda-forge
kernel-headers_linux-64 2.6.32 he073ed8_15 conda-forge
kiwisolver 1.4.0 py37h7cecad7_0 conda-forge
ld_impl_linux-64 2.36.1 hea4e1c9_2 conda-forge
lerc 3.0 h9c3ff4c_0 conda-forge
libblas 3.8.0 17_openblas conda-forge
libcblas 3.8.0 17_openblas conda-forge
libdeflate 1.10 h7f98852_0 conda-forge
libffi 3.2.1 he1b5a44_1007 conda-forge
libgcc 7.2.0 h69d50b8_2 conda-forge
libgcc-devel_linux-64 11.2.0 h0952999_14 conda-forge
libgcc-ng 11.2.0 h1d223b6_14 conda-forge
libgfortran-ng 7.5.0 h14aa051_20 conda-forge
libgfortran4 7.5.0 h14aa051_20 conda-forge
libgomp 11.2.0 h1d223b6_14 conda-forge
libiconv 1.16 h516909a_0 conda-forge
liblapack 3.8.0 17_openblas conda-forge
liblapacke 3.8.0 17_openblas conda-forge
libopenblas 0.3.10 pthreads_hb3c22a3_5 conda-forge
libpng 1.6.37 h21135ba_2 conda-forge
libsanitizer 11.2.0 he4da1e4_14 conda-forge
libstdcxx-ng 11.2.0 he4da1e4_14 conda-forge
libtiff 4.3.0 h542a066_3 conda-forge
libuuid 2.32.1 h7f98852_1000 conda-forge
libwebp-base 1.2.2 h7f98852_1 conda-forge
libxcb 1.13 h7f98852_1004 conda-forge
libxml2 2.9.9 h13577e0_2 conda-forge
libzlib 1.2.11 h36c2ea0_1013 conda-forge
llvm-openmp 8.0.1 hc9558a2_0 conda-forge
lz4-c 1.9.3 h9c3ff4c_1 conda-forge
matplotlib 2.2.3 py37h8a2030e_1 conda-forge
matplotlib-base 2.2.3 py37h60b886d_1 conda-forge
ncurses 6.3 h9c3ff4c_0 conda-forge
numpy 1.16.2 py37h8b7e671_1 conda-forge
numpy-base 1.17.0 py37h2f8d375_0 r
openmp 8.0.1 0 conda-forge
openssl 1.0.2s 0 intel
pandas 0.24.2 py37hb3f55d8_1 conda-forge
pango 1.42.4 h7062337_4 conda-forge
pcre 8.45 h9c3ff4c_0 conda-forge
pip 22.0.4 pyhd8ed1ab_0 conda-forge
pixman 0.38.0 h516909a_1003 conda-forge
pthread-stubs 0.4 h36c2ea0_1001 conda-forge
pyparsing 3.0.7 pyhd8ed1ab_0 conda-forge
pyqt 5.6.0 py37h13b7fb3_1008 conda-forge
python 3.7.1 hd21baee_1001 conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python_abi 3.7 2_cp37m conda-forge
pytz 2022.1 pyhd8ed1ab_0 conda-forge
pyyaml 5.3.1 py37h5e8e339_2 conda-forge
qt 5.6.2 hce4f676_1013 conda-forge
readline 7.0 hf8c457e_1001 conda-forge
scikit-learn 0.22.2.post1 py37hcdab131_0 conda-forge
scipy 1.1.0 py37he2b7bc3_2
setuptools 59.8.0 py37h89c1867_0 conda-forge
sip 4.18.1 py37hf484d3e_1000 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
sqlite 3.28.0 h8b20d00_0 conda-forge
sysroot_linux-64 2.12 he073ed8_15 conda-forge
tk 8.6.12 h27826a3_0 conda-forge
tornado 6.1 py37h5e8e339_2 conda-forge
typing-extensions 4.1.1 hd8ed1ab_0 conda-forge
typing_extensions 4.1.1 pyha770c72_0 conda-forge
wheel 0.37.1 pyhd8ed1ab_0 conda-forge
xarray 0.11.0 py37_1000 conda-forge
xorg-kbproto 1.0.7 h7f98852_1002 conda-forge
xorg-libice 1.0.10 h7f98852_0 conda-forge
xorg-libsm 1.2.3 hd9c2040_1000 conda-forge
xorg-libx11 1.7.2 h7f98852_0 conda-forge
xorg-libxau 1.0.9 h7f98852_0 conda-forge
xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge
xorg-libxext 1.3.4 h7f98852_1 conda-forge
xorg-libxrender 0.9.10 h7f98852_1003 conda-forge
xorg-libxt 1.2.1 h7f98852_2 conda-forge
xorg-renderproto 0.11.1 h7f98852_1002 conda-forge
xorg-xextproto 7.3.0 h7f98852_1002 conda-forge
xorg-xproto 7.0.31 h7f98852_1007 conda-forge
xz 5.2.5 h516909a_1 conda-forge
yaml 0.2.5 h7f98852_2 conda-forge
zlib 1.2.11 h36c2ea0_1013 conda-forge
zstd 1.5.2 ha95c52a_0 conda-forge
About estimate clonal fractions
Hi, in the supplementary of _Clonal fitness inferred from time-series modelling of single-cell cancer genomes ,you mentioned that using βγ = ν to get γ(page 8th), what I want to ask you is: (1)is β a 0-1 matrix? If not, how to identify its value?(2)to figure out γ, β must be a square matrix, but why β is a square matrix(or why the number of mutation-cluster are equal to clones')?
THANK YOU
Consult
Hi
I have some 10x single cell time series design sample data，I wonder that if I can use this tool based InferCNV package results ? Thank you !
Issue with clang during install
Good Afternoon,
I am trying to install fitClone but am running into an error.
After I run the command ./make.sh I get the following error towards the end of the install: clang: error: unsupported option '-fopenmp'.
I am using MAC OS Sonoma 14.4.1, and have tried changing the make.sh file to have ./cyconvert_bigsur.sh as the final line, but this did not seem to change anything during the install. Any advice you can provide would be greatly appreciated.
If it is helpful, here are all the packages installed in my fitness environment:
(fitness) MLC02D47RGMD6M:fitclone 80019141$ conda list
# packages in environment at /Users/80019141/anaconda3/envs/fitness:
#
# Name Version Build Channel
blas 1.0 openblas
ca-certificates 2023.08.22 hecd8cb5_0 anaconda
certifi 2020.6.20 pyhd3eb1b0_3 anaconda
cycler 0.11.0 pyhd3eb1b0_0
cython 0.29 py37h0a44026_0
flit-core 3.6.0 pyhd3eb1b0_0
freetype 2.12.1 hd8bbffd_0
hdf5 1.10.6 hdbbcd12_0 anaconda
intel-openmp 2021.4.0 hecd8cb5_3538
kiwisolver 1.4.4 py37hcec6c5f_0
libcxx 14.0.6 h9765a3e_0
libffi 3.4.4 hecd8cb5_1
libgfortran 3.0.1 h93005f0_2
libopenblas 0.3.20 h9a5756b_0
libpng 1.6.39 h6c40b1e_0
libxcrypt 4.4.36 h10d778d_1 conda-forge
matplotlib 2.2.3 py37h54f8f79_0
mkl 2021.4.0 hecd8cb5_637
mkl-service 2.4.0 py37h9ed2024_0
ncurses 6.4 hcec6c5f_0
numpy 1.16.2 py37h926163e_0
numpy-base 1.16.2 py37ha711998_0
openssl 1.1.1w hca72f7f_0 anaconda
pandas 0.24.2 py37h0a44026_0
pip 22.3.1 py37hecd8cb5_0
pyparsing 3.0.9 py37hecd8cb5_0
python 3.7.16 h218abb5_0
python-dateutil 2.8.2 pyhd3eb1b0_0
pytz 2022.7 py37hecd8cb5_0
pyyaml 5.3.1 py37haf1e3a3_1
readline 8.2 hca72f7f_0
scipy 1.1.0 py37h1a1e112_2
setuptools 65.6.3 py37hecd8cb5_0
six 1.16.0 pyhd3eb1b0_1
sqlite 3.45.3 h6c40b1e_0
tk 8.6.12 h5d9f67b_0
tornado 6.2 py37hca72f7f_0
typing_extensions 4.4.0 py37hecd8cb5_0
wheel 0.38.4 py37hecd8cb5_0
xarray 0.11.0 py37_0
xz 5.4.6 h6c40b1e_1
yaml 0.2.5 haf1e3a3_0
zlib 1.2.13 h4b97444_1
Switch to mamba for faster installation
Install mamba:
conda install mamba -n base -c conda-forge
Correct data produced by SA501?
I've gotten everything installed correctly, but I'm having trouble determining if the test dataset is being run correctly. Based on the change in allele/clone frequencies in the test data, clone k=3 is undergoing positive selection and should have a selection coefficient (s) that is positive. But when I look at the infer_theta.tsv, which contains the "posterior of the selection coefficients" almost all the values are negative. If these are probability posteriors, shouldn't they be positive, as how can probability be negative? Also, all of the values in llhood_theta.tsv are also negative. When I look at the theta_hists.pdf generated by the R scripts, the estimate s values are also all negative (I also assume k number in these plots is 0=1, 1=2, 2=3, etc.). What is the "ESS = 1.08, 1.01, 1.82, 0.66" in the violin_hist.pdf plot? Is there a way to calculate the probability of positive selection (PPS) using the values generated after running the script, as is displayed in Figure 1D of the publication?
I might be misunderstanding the significance of these values, so I apologize for my ignorance, especially when it comes to these particular statistical genetics terms, many thanks!
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