Merge pull request #576 from macs3-project/feat/macs3/hmmratac

MACS3 b3 update `hmmratac` and `Cython` issue

.github/workflows/build-and-test-MACS3-macos12.yml

@@ -0,0 +1,57 @@
+# This workflow will install Python dependencies, run tests and lint with a single version of Python
+# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: MACS3 CI MacOS 12
+
+on: [push, pull_request]
+
+jobs:
+  build:
+    runs-on: macos-12
+    name: Build on MacOS 12 with Python 3.11
+    steps:
+      - name: Checkout MACS
+        uses: actions/checkout@v3
+        with:
+          submodules: 'true'
+      - name: Cache pip
+        uses: actions/cache@v3
+        with:
+          # This path is specific to Ubuntu
+          path: ~/.cache/pip
+          # Look to see if there is a cache hit for the corresponding requirements file
+          key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}
+          restore-keys: |
+            ${{ runner.os }}-pip-
+            ${{ runner.os }}-        
+      - name: Setup venv
+        run: |
+          # create venv
+          python3 -m venv macs3venv
+      - name: Install dependencies
+        run: |
+          # make sure pip is the lastest
+          source macs3venv/bin/activate
+          python3 -m pip install --upgrade pip
+          pip3 install pytest
+          if [ -f requirements.txt ]; then pip3 install --upgrade -r requirements.txt; fi
+      - name: Install MACS
+        run: |
+          # we use pip install instead of old fashion setup.py install
+          source macs3venv/bin/activate
+          pip install .
+      - name: Test with pytest
+        run: |
+          source macs3venv/bin/activate
+          pytest --runxfail && cd test && ./cmdlinetest-nohmmratac macs3
+      - name: Build sdist
+        run: |
+          source macs3venv/bin/activate
+          python setup.py sdist
+      - name: Archive sdist
+        uses: actions/upload-artifact@v3
+        with:
+          name: sdist file
+          path: |
+            dist/*.tar.gz
+

.github/workflows/build-and-test-MACS3-non-x64.yml

@@ -1,7 +1,7 @@
 # This workflow will install Python dependencies, run tests and lint with a single version of Python
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: CI non x64
+name: MACS3 CI non x64
 
 on: [push, pull_request]
 
@@ -14,11 +14,11 @@ jobs:
         include:
           - arch: aarch64
             distro: bullseye        
-          - arch: armv7 # scipy installation issue before, let's retest...
+          - arch: armv7
             distro: bullseye
           - arch: ppc64le
             distro: bullseye
-          - arch: s390x # scipy installation issue before, let's retest...
+          - arch: s390x
             distro: bullseye
     steps:
       - name: Checkout MACS
@@ -57,6 +57,7 @@ jobs:
             apt-get update -qq -y
             apt-get install apt-utils
             apt-get install -yq libblas3 liblapack3 libblas-dev liblapack-dev
+            # we rely on Debian/Linux python packages for numpy/scipy/sklearn 
             apt-get install -yq python3 python3-pip python3-virtualenv python3-wheel python3-numpy python3-scipy python3-sklearn cython3 
             apt-get install -yq procps zlib1g zlib1g-dev gfortran
           run: |

.github/workflows/build-and-test-MACS3-x64.yml

@@ -1,7 +1,7 @@
 # This workflow will install Python dependencies, run tests and lint with a single version of Python
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: CI x64
+name: MACS3 CI x64
 
 on: [push, pull_request]
 
@@ -38,8 +38,6 @@ jobs:
           # make sure pip is the lastest
           python -m pip install --upgrade pip
           pip install pytest
-          # install cython first...
-          pip install cython
           if [ -f requirements.txt ]; then pip install --upgrade -r requirements.txt; fi
       - name: Install MACS
         run: |

.github/workflows/publish-to-anaconda.yml

@@ -1,7 +1,7 @@
 # This workflows will upload a Python Package using Twine when a release is created
 # For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
 
-name: Publish to Anaconda/macs3
+name: MACS3 Publish to Anaconda/macs3
 
 on:
   release:
@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy: 
       matrix: 
-        python-version: [3.9, 3.10, 3.11]
+        python-version: ['3.11']
         arch: ['x64']
     name: Build conda package in x64 under Python ${{ matrix.python-version }}
     steps:

.github/workflows/publish-to-pypi.yml

@@ -1,7 +1,7 @@
 # This workflows will upload a Python Package using Twine when a release is created
 # For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
 
-name: Upload to PYPI
+name: MACS3 Upload to PYPI
 
 on:
   release:
@@ -19,12 +19,10 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v3
       with:
-        python-version: '3.9'
+        python-version: '3.11'
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        # install cython first...
-        pip install cython
         if [ -f requirements.txt ]; then pip install --upgrade -r requirements.txt; fi
         # setuptools, wheel and twine are needed to upload to pypi
         pip install setuptools wheel twine

ChangeLog

@@ -1,39 +1,20 @@
-2023-06-23  Tao Liu  <vladimir.liu@gmail.com>
-	MACS 3.0.0b2
+2023-07-28  Tao Liu  <vladimir.liu@gmail.com>
+	MACS 3.0.0b3
+
 	* New features in MACS3:
 
 	1) Speed/memory optimization.  Use the cykhash to replace python
 	dictionary. Use buffer (10MB) to read and parse input file (not
 	available for BAM file parser). And many optimization tweaks. We
 	added memory monitoring to the runtime messages.
 
-	2) Code cleanup. Reorganize source codes. 
-
-	3) Unit testing. 
-
-	4) R wrappers for MACS -- MACSr
-
-	5) Switch to Github Action for CI, support multi-arch testing 
-	including x64, armv7, aarch64, s390x and ppc64le. 
+	2) Call variants in peak regions directly from BAM files. The
+	function was originally developed under code name SAPPER. Now
+	SAPPER has been merged into MACS. Also, `simde` has been added as
+	a submodule in order to support fermi-lite library under non-x64
+	architectures.
 
-	6) MACS tag-shifting model has been refined. Now it will use a 
-	naive peak calling approach to find ALL possible paired peaks at +
-	and - strand, then use all of them to calculate the 
-	cross-correlation. (a related bug has been fix #442) 
-
-	7) Call variants in peak regions directly from BAM files. The 
-	function was originally developed under code name SAPPER. Now 
-	SAPPER has been merged into MACS. Also, `simde` has been added as 
-	a submodule in order to support fermi-lite library under non-x64 
-	architectures. 
-
-	8) BAI index and random access to BAM file now is supported. #449 
-	And user can use original BAM file (instead of the subset of BAM 
-	file as in SAPPER) in the `callvar` command. 
-
-	9) Support of Python > 3.10 #497 #498 
-
-	10) HMMRATAC module is added. HMMRATAC is a dedicated software to
+	3) HMMRATAC module is added. HMMRATAC is a dedicated software to
 	analyze ATAC-seq data. The basic idea behind HMMRATAC is to digest
 	ATAC-seq data according to the fragment length of read pairs into
 	four signal tracks: short fragments, mononucleosomal fragments,
@@ -44,19 +25,45 @@
 	in JAVA, by Evan Tarbell. We implemented it in Python/Cython and
 	optimize the whole process using existing MACS functions and
 	hmmlearn. Now it can run much faster than the original JAVA
-	version. Note: evaluation of the peak calling results is underway.
+	version. Note: evaluation of the peak calling results is underway.	
+
+	4) Code cleanup. Reorganize source codes. 
+
+	5) Unit testing. 
+
+	6) R wrappers for MACS -- MACSr
+
+	7) Switch to Github Action for CI, support multi-arch testing
+	including x64, armv7, aarch64, s390x and ppc64le. We also test on
+	Mac OS 12.
+
+	8) MACS tag-shifting model has been refined. Now it will use a 
+	naive peak calling approach to find ALL possible paired peaks at +
+	and - strand, then use all of them to calculate the 
+	cross-correlation. (a related bug has been fix #442) 
+
+	9) BAI index and random access to BAM file now is supported. #449
+	And user can use original BAM file (instead of the subset of BAM
+	file as in SAPPER) in the `callvar` command.
+
+	10) Support of Python > 3.10 #497 #498 
 
 	11) The effective genome size parameters have been updated
 	according to deeptools. #508
 
-	12) Memory usage now will be displayed in the runtime message.
-
-	13) Multiple updates regarding dependencies, anaconda built, CI/CD
+	12) Multiple updates regarding dependencies, anaconda built, CI/CD
 	process.
-
+
+	13) Cython support to ~0.29. Cython 3 is not supported yet.
+
 	* Other:
 	1) Missing header line while no peaks can be called #501 #502
 
+	2) Note: different numpy, scipy, sklearn may give slightly
+	different results for hmmratac results. The current standard
+	results for automated testing in `/test` directory are from Numpy
+	1.25.1, Scipy 1.11.1, and sklearn 1.3.0.
+
 2020-04-11  Tao Liu  <vladimir.liu@gmail.com>
 	MACS version 2.2.7.1
 

MACS3/Commands/hmmratac_cmd.py

@@ -1,4 +1,4 @@
-# Time-stamp: <2023-06-08 11:03:46 Tao Liu>
+# Time-stamp: <2023-07-28 12:10:12 Tao Liu>
 
 """Description: Main HMMR command
 
@@ -143,7 +143,7 @@ def run( args ):
 
     # now we will prepare the weights for each fragment length for
     # each of the four distributions based on the EM results
-    weight_mapping = generate_weight_mapping( fl_list, em_means, em_stddevs )
+    weight_mapping = generate_weight_mapping( fl_list, em_means, em_stddevs, min_frag_p = options.min_frag_p )
 
     options.info( f"#  Generate short, mono-, di-, and tri-nucleosomal signals")
     digested_atac_signals = generate_digested_signals( petrack, weight_mapping )
@@ -189,7 +189,7 @@ def run( args ):
 
         # Let MACS3 do the cutoff analysis to help decide the lower and upper cutoffs
         with open(cutoffanalysis_file, "w") as ofhd_cutoff:
-            ofhd_cutoff.write( fc_bdg.cutoff_analysis( min_length=minlen, max_gap=options.hmm_training_flanking ) )
+            ofhd_cutoff.write( fc_bdg.cutoff_analysis( min_length=minlen, max_gap=options.hmm_training_flanking, max_score = 1000 ) )
         #raise Exception("Cutoff analysis only.")
         sys.exit(1)
 
@@ -201,6 +201,19 @@ def run( args ):
     if options.hmm_file:
         # skip this step if hmm_file is given
         options.info( f"#3 Skip this step of looking for training set since a Hidden Markov Model file has been provided!")
+    elif options.hmm_training_regions: 
+        # if a training region file is provided - need to read in the bedfile and skip the peak calling step
+        options.info(f"#3 Read training regions from BED file: {options.hmm_training_regions}")
+        # from refinepeak_cmd.py:
+        peakio = open(options.hmm_training_regions,"rb")
+        peaks = PeakIO()
+        for l in peakio:
+            fs = l.rstrip().split()
+            peaks.add( chromosome=fs[0], start=int(fs[1]), end=int(fs[2])) #change based on what expected input file should contain
+        peakio.close()
+        training_regions = Regions()
+        training_regions.init_from_PeakIO( peaks )
+        options.info("#  Training regions have been read from bedfile")
     else:
         # Find regions with fold change within determined range to use as training sites.
         # Find regions with zscore values above certain cutoff to exclude from viterbi.
@@ -223,7 +236,7 @@ def run( args ):
 
         # Let MACS3 do the cutoff analysis to help decide the lower and upper cutoffs
         with open(cutoffanalysis_file, "w") as ofhd_cutoff:
-            ofhd_cutoff.write( fc_bdg.cutoff_analysis( min_length=minlen, max_gap=options.hmm_training_flanking ) )
+            ofhd_cutoff.write( fc_bdg.cutoff_analysis( min_length=minlen, max_gap=options.hmm_training_flanking, max_score = 1000 ) )
 
         # we will check if anything left after filtering
         if peaks.total > options.hmm_maxTrain:
@@ -299,6 +312,11 @@ def run( args ):
         # write hmm into model file
         options.info( f"#  Write HMM parameters into JSON: {hmm_modelfile}")
         hmm_model_save( hmm_modelfile, hmm_model, options.hmm_binsize, i_open_region, i_nucleosomal_region, i_background_region )
+
+        # if --modelonly option provided, exit script after hmm model is saved 
+        if options.hmm_modelonly:
+            options.info( f"#  Complete - HMM model was saved, program exited (--modelonly option was provided) ")
+            sys.exit()
 
     # Now tell users the parameters of the HMM
     assignments = [ "", "", "" ]

MACS3/Signal/BedGraph.pyx

@@ -1,6 +1,6 @@
 # cython: language_level=3
 # cython: profile=True
-# Time-stamp: <2023-06-08 10:52:59 Tao Liu>
+# Time-stamp: <2023-07-28 12:06:48 Tao Liu>
 
 """Module for BedGraph data class.
 
@@ -1131,7 +1131,7 @@ cdef class bedGraphTrackI:
         #ret.merge_regions()
         return ret
 
-    cpdef str cutoff_analysis ( self, int32_t max_gap, int32_t min_length, int32_t steps = 100 ):
+    cpdef str cutoff_analysis ( self, int32_t max_gap, int32_t min_length, int32_t steps = 100, float32_t max_score = 1000 ):
         """
         Cutoff analysis function for bedGraphTrackI object.
     
@@ -1171,8 +1171,8 @@ cdef class bedGraphTrackI:
 
         #midvalue = self.minvalue/2 + self.maxvalue/2
         #s = float(self.minvalue - midvalue)/steps
-        minv = self.minvalue
-        maxv = self.maxvalue
+        minv = max( 0, self.minvalue )
+        maxv = min( self.maxvalue, max_score )
 
         s = float(maxv - minv)/steps
 
@@ -1265,4 +1265,4 @@ cdef np.ndarray calculate_elbows( np.ndarray values, float32_t threshold=0.01):
     # Find all points where the change in slope is significantly larger than the average
     elbows = np.where(delta_slopes > avg_delta_slope + threshold)[0]
 
-    return elbows
+    return elbows