jaleesr / bitfam Goto Github PK

Hello! Thank you for your tool!
I faced with the prob

lem while running BITFAM on 1 sample (~1500cells)
Error in if (p$diagnostics$pareto_k > 1) {:
missing value where TRUE/FALSE is required
Calls: BITFAM
Execution stopped

It's right after finishing the first chain. What should I do?
Note: I run it locally on macOS
Thank in advance!

Hello - another question for you. I have not been able to use the BITFAM_weights function. I can use the same BITFAM list normally for BITFAM_activities but it throws an error when using BITFAM_weights. Please advise, thanks!

Z <- BITFAM_activities(BITFAM_res)
bw = BITFAM_weights(BITFAM_res)
Error in extract(BITFAM_list$Model, result, "W") :
object 'result' not found

Hi, I was wondering if anyone has seen this issue before. After loading in my normalized seurat object, loading the relevant packages, I get the following error when running the BITFAM() command.

BITFAM_res <- BITFAM(data = normalized_data_matrix, species = "human", scATAC_obj = NA, ncores = parallel::detectCores())
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Warning: The following arguments are not used: drop
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'i' in selecting a method for function '[': comparison (!=) is possible only for atomic and list types

Additionally, the preprocessing command produces a similar issue:

data_matrix_normalized <- BITFAM_preprocess(raw_data = d_matrix)
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'x' in selecting a method for function 'colSums': comparison (>) is possible only for atomic and list types

My R version is 4.3.0.

Thanks a lot for making th the BITFAM method available. It seems the DESCRIPTION file still contains the boilerplate code from the usethis package that can be run to specify a license:

Perhaps you can pick a license to be explicit about use & reuse of the codebase?
Many thanks in advance!

Thanks to all the group for the interesting package

I am trying to run BITFAM with my single-cell RNA-Seq dataset following the instruction provided in the GitHub pages
It looks running correctly but even if I specify in the arguments to use all the cores of my machine, it looks like running with a single core:

BITFAM_res <- BITFAM(data = data_matrix_normalized, species = "human", scATAC_obj = NA, ncores = parallel::detectCores())

I tested it on my local machine and on a remote VM but the result is always the same. I also try to use BiocParallel but the BPPARAM parameter wasn't used. Is there anything I am missing or that I can do to parallelize the computation?

EDIT:
I tested BiocParallel to verify that I use more than a CPU per time and it worked, and this is the warning I receive when I run BITFAM:

rstan (Version 2.21.8, GitRev: 2e1f913d3ca3)
For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores()).
To avoid recompilation of unchanged Stan programs, we recommend calling
rstan_options(auto_write = TRUE)

Thanks for the help
Jacopo

Hello,

I am trying to upload a CHIP-seq matrix into BITFAM. Is this possible?
See: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128537

The documentation doesn't show how to build BITFAM from scratch, would appreciate any help.

Thank you

I'm running into the following error. Any idea how to solve this?
(Apple silicon, OS X 14, R 4.3.1)
Chain 1: 5700 -30414956.781 0.005 0.005
Chain 1: 5800 -30272309.137 0.005 0.005
Chain 1: 5900 -30145405.238 0.005 0.005 MEAN ELBO CONVERGED
Chain 1:
Chain 1: Drawing a sample of size 300 from the approximate posterior...
Chain 1: COMPLETED.
Error in if (p$diagnostics$pareto_k > 1) { :
missing value where TRUE/FALSE needed

Hey,

Loving the package so far! I was hoping to get an idea of the activity of the target genes for the detected TFs, and couldn't find any documentation on it, I then found a (seemingly) undocumented function called BITFAM_weights, which seems to be intended for what I'm looking for, however it pulls an error. I imagine if there's no documentation then it's possible this function is still in development, or maybe there's a better way for me to extract this information. Code and error below, thanks in advance for any assistance!

BITFAM_res <- BITFAM(data = GetAssayData(seu.int, slot = "data"), species = "mouse", scATAC_obj = NA, ncores = 4)
Z <- BITFAM_activities(BITFAM_res)
weights <- BITFAM_weights(BITFAM_list = BITFAM_res)
Error in extract(BITFAM_list$Model, result, "W") : 
  object 'result' not found

Hello,
Thank you for this package. Its really helpful. I am exactly following the tutorial. However, I am getting the following error. Could you please help me with the following issue-

fit_rf <- randomForest(D3~., data = Z1_D3)
Error in eval(predvars, data, env) : object 'NKX2-2' not found
D3_tf_top10 <- importance(fit_rf)[order(importance(fit_rf)[, 1], decreasing = T), ][1:10]
Error in importance(fit_rf) : object 'fit_rf' not found

Hi
I couldn't get this to work. Error is
Error in Seurat::FindVariableFeatures(tiss@data) :
object 'tiss' not found

BITFAM code contain the line
variable_genes <- Seurat::FindVariableFeatures(tiss@data)

shouldn't that just be ?
variable_genes <- Seurat::FindVariableFeatures(data)

Hi

Can you please help with the following error. thanks.

BITFAM_res <- BITFAM(data = data_matrix_normalized, species = "mouse", scATAC_obj = NA, 
                      iter = 8, ncores = 1)

Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
rstan (Version 2.21.8, GitRev: 2e1f913d3ca3)
For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores()).
To avoid recompilation of unchanged Stan programs, we recommend calling
rstan_options(auto_write = TRUE)
Do not specify '-march=native' in 'LOCAL_CPPFLAGS' or a Makevars file
make cmd is
make -f "C:/PROGRA1/R/R-421.3/etc/x64/Makeconf" -f "C:/PROGRA1/R/R-421.3/share/make/winshlib.mk" CXX='$(CXX14) $(CXX14STD)' CXXFLAGS='$(CXX14FLAGS)' CXXPICFLAGS='$(CXX14PICFLAGS)' SHLIB_LDFLAGS='$(SHLIB_CXX14LDFLAGS)' SHLIB_LD='$(SHLIB_CXX14LD)' SHLIB="file4dfc22eb2c08.dll" WIN=64 TCLBIN= OBJECTS="file4dfc22eb2c08.o"

make would use
g++ -std=gnu++14 -I"C:/PROGRA1/R/R-421.3/include" -DNDEBUG -I"C:/Users/Hemant Gujar/AppData/Local/R/win-library/4.2/Rcpp/include/" -I"C:/Users/Hemant Gujar/AppData/Local/R/win-library/4.2/RcppEigen/include/" -I"C:/Users/Hemant Gujar/AppData/Local/R/win-library/4.2/RcppEigen/include/unsupported" -I"C:/Users/Hemant Gujar/AppData/Local/R/win-library/4.2/BH/include" -I"C:/Users/Hemant Gujar/AppData/Local/R/win-library/4.2/StanHeaders/include/src/" -I"C:/Users/Hemant Gujar/AppData/Local/R/win-library/4.2/StanHeaders/include/" -I"C:/Users/Hemant Gujar/AppData/Local/R/win-library/4.2/RcppParallel/include/" -I"C:/Users/Hemant Gujar/AppData/Local/R/win-library/4.2/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include "C:/Users/Hemant Gujar/AppData/Local/R/win-library/4.2/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp" -std=c++1y -I"c:/rtools42/x86_64-w64-mingw32.static.posix/include" -O2 -Wall -mfpmath=sse -msse2 -mstackrealign -c file4dfc22eb2c08.cpp -o file4dfc22eb2c08.o
if test "zfile4dfc22eb2c08.o" != "z"; then
if test -e "file4dfc22eb2c08-win.def"; then
echo g++ -shared -s -static-libgcc -o file4dfc22eb2c08.dll file4dfc22eb2c08-win.def file4dfc22eb2c08.o -L"c:/rtools42/x86_64-w64-mingw32.static.posix/lib/x64" -L"c:/rtools42/x86_64-w64-mingw32.static.posix/lib" -L"C:/PROGRA1/R/R-421.3/bin/x64" -lR ;
g++ -shared -s -static-libgcc -o file4dfc22eb2c08.dll file4dfc22eb2c08-win.def file4dfc22eb2c08.o -L"c:/rtools42/x86_64-w64-mingw32.static.posix/lib/x64" -L"c:/rtools42/x86_64-w64-mingw32.static.posix/lib" -L"C:/PROGRA1/R/R-421.3/bin/x64" -lR ;
else
echo EXPORTS > tmp.def;
nm file4dfc22eb2c08.o | sed -n 's/^.* [BCDRT] / /p' | sed -e '/[.]refptr[.]/d' -e '/[.]weak[.]/d' | sed 's/[^ ][^ ]*/"&"/g' >> tmp.def;
echo g++ -shared -s -static-libgcc -o file4dfc22eb2c08.dll tmp.def file4dfc22eb2c08.o -L"c:/rtools42/x86_64-w64-mingw32.static.posix/lib/x64" -L"c:/rtools42/x86_64-w64-mingw32.static.posix/lib" -L"C:/PROGRA1/R/R-421.3/bin/x64" -lR ;
g++ -shared -s -static-libgcc -o file4dfc22eb2c08.dll tmp.def file4dfc22eb2c08.o -L"c:/rtools42/x86_64-w64-mingw32.static.posix/lib/x64" -L"c:/rtools42/x86_64-w64-mingw32.static.posix/lib" -L"C:/PROGRA1/R/R-421.3/bin/x64" -lR ;
rm -f tmp.def;
fi
fi
Error in compileCode(f, code, language = language, verbose = verbose) :
from C:/Users/Hemant Gujar/AppData/Local/R/win-library/4.2/StanHeaders/include/src/stan/model/model_header.hpp:4, from file4dfc22eb2c08.cpp:14:C:/Users/Hemant Gujar/AppData/Local/R/win-library/4.2/StanHeaders/include/stan/math/rev/core/set_zero_all_adjoints.hpp:14:13: warning: 'void stan::math::set_zero_all_adjoints()' defined but not used [-Wunused-function] static void set_zero_all_adjoints() { ^make: *** [C:/PROGRA1/R/R-421.3/etc/x64/Makeconf:260: file4dfc22eb2c08.o] Error 1
Error in sink(type = "output") : invalid connection

Hi!
I tried to use BITFAM on scRNa seq data following your instructions. However I received an error after:
BITFAM_res <- BITFAM(data = mtx_preprocess, species = "human", scATAC_obj = NA, ncores = parallel::detectCores())

Error in Sys.setenv(R_MAKEVARS_USER = NULL) : wrong length for argument
In addition: Warning message:
In .warn_march_makevars() :
Detected -march=native in the Makevars file at 'C:/Users/semina/Documents/.R/Makevars'. Compiling with the -march=native flag on windows with Rtools can cause crashes because of the compiler implementation. rstan will ignore the Makevars file until -march=native is removed. You can disable this by setting rstan_options(disable_march_warning = TRUE)

Hi, great package!
I run this:

data_matrix_normalized <- BITFAM_preprocess(raw_data = seurat@assays$RNA@counts)
BITFAM_res <- BITFAM(data = data_matrix_normalized, species = "mouse", scATAC_obj = NA, 
                     ncores = 30)

Error in (function ()  : 
  org.Mm.egPFAM is defunct. Please use select() if you need access to PFAM or PROSITE accessions.

I search they said: "Using this form PACKAGE::select() (within rstan) should solve this problem for biology-focused users" , But how to do that? thanks!

Hello - I have been using BITFAM and it has been great, thanks. I am working on human data and trying to use the interseted TF option though this does not seem to be working currently - below is the line of code. Please let me know thanks.

BITFAM_res <- BITFAM(data = data_matrix_normalized, interseted_TF = c("IRF3", "IRF7"), species = "human", scATAC_obj = NA, ncores = parallel::detectCores())

I was wondering if it makes sense to merge multiple samples such as control and stimulation and then run BITFAM on the merged object. Thank you!

Hello,
I was running BITFAM on a subset of 200 cells:
BITFAM_res <- BITFAM(data = data_matrix_normalized, species = "human", scATAC_obj = NA, ncores = parallel::detectCores() )
And it would not finish even in 3 days. I have 128GB RAM and 32 CPUs.
Thanks !

Hi, just testing out the tool, thanks so much for this!

I think the problem is when checking whether interseted_TF is NA or not.