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Changeset 3:6db1b06e6bbb (2020-01-06)

Previous changeset 2:a56baceb1900 (2019-12-12) Next changeset 4:4000634ece52 (2020-01-18)

Commit message:
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/anndata/ commit dc9d19d1f902f3ed54009cd0e68c8518c284b856"

modified:
macros.xml

added:
loompy_to_tsv.py
modify_loom.py
test-data/addlayer1.tsv
test-data/addloomout1.loom
test-data/addloomout2.loom
test-data/addloomout3.loom
test-data/addtest.loom
test-data/cols.tsv
test-data/converted.loom.test
test-data/finallayer.tsv
test-data/firstlayer.tsv
test-data/loomtest.loom
test-data/rows.tsv
test-data/secondlayer.tsv
tsv_to_loompy.py

diff -r a56baceb1900 -r 6db1b06e6bbb loompy_to_tsv.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/loompy_to_tsv.py Mon Jan 06 13:44:18 2020 -0500

[

@@ -0,0 +1,82 @@
+#!/usr/bin/env python
+
+"""Converts a loompy file to tsv file(s). Each layer becomes a new file."""
+
+import argparse
+
+import loompy
+
+parser = argparse.ArgumentParser(description="Loompy file converter flags")
+parser.add_argument('--version', action='version', version='%(prog)s 0.1.0',
+                    help="Displays tool version")
+parser.add_argument("-f", "--file", help="loom file to import")
+args = parser.parse_args()
+
+file = args.file
+
+matrices = []
+allcols = []
+colstrings = []
+allrows = []
+
+# Build background info for all attributes and layers
+loompyfile = loompy.connect(file)
+row_attributes = loompyfile.ra.keys()  # List of row attributes
+for row in row_attributes:  # Each list represents rownames for row_attributes
+    c_row = loompyfile.ra[row]
+    c_row = [str(r) for r in c_row]
+    allrows.append(c_row)
+col_attributes = loompyfile.ca.keys()  # List of column attributes
+for col in col_attributes:  # each list represents colnames for col_attributes
+    c_col = loompyfile.ca[col]
+    c_col = [str(c) for c in c_col]
+    allcols.append(c_col)
+layers = loompyfile.layers.keys()  # List of layers
+for layer in layers:  # List with each element being a loompy layer
+    c_layer = loompyfile[layer]
+    c_layer = c_layer[:, :]
+    c_layer = c_layer.astype(str)
+    matrices.append(c_layer)
+
+# Create column attribute output
+with open("attributes/col_attr.tsv", "w") as colout:
+    col_attributes = "\t".join(col_attributes) + "\n"
+    colout.write(col_attributes)
+    for length in range(0, len(c_col)):
+        attributestring = ""
+        for col in allcols:
+            attributestring = attributestring + col[length] + "\t"
+        while attributestring[-1] == "\t":
+            attributestring = attributestring[:-1]
+        colout.write(attributestring)
+        colout.write("\n")
+# Create row attribute output
+with open("attributes/row_attr.tsv", "w") as rowout:
+    row_attributes = "\t".join(row_attributes) + "\n"
+    rowout.write(row_attributes)
+    for length in range(0, len(c_row)):
+        attributestring = ""
+        for row in allrows:
+            attributestring = attributestring + row[length] + "\t"
+        while attributestring[-1] == "\t":
+            attributestring = attributestring[:-1]
+        rowout.write(attributestring)
+        rowout.write("\n")
+
+# Build output files for each layer
+for x in range(0, len(layers)):
+    # Output file name generation
+    if layers[x] in layers[0: x]:  # Different output names if layers have same names somehow
+        repeats = layers[0, x].count(layer[x])
+        outputname = "output/" + layers[x] + repeats + ".tsv"
+    elif layers[x] == "":  # Empty layer name
+        outputname = "output/mainmatrix.tsv"
+    else:
+        outputname = "output/" + str(layers[x]) + ".tsv"  # Usual case
+# Matrix output
+    with open(outputname, "w") as outputmatrix:
+        for line in matrices[x]:
+            line = "\t".join(line)
+            line += "\n"
+            line = line
+            outputmatrix.write(line)

diff -r a56baceb1900 -r 6db1b06e6bbb macros.xml
--- a/macros.xml Thu Dec 12 09:22:35 2019 -0500
+++ b/macros.xml Mon Jan 06 13:44:18 2020 -0500

[

@@ -15,13 +15,19 @@
         </citations>
     </xml>
     <xml name="version_command">
-        <version_command><![CDATA[python -c "import anndata as ad;print('anndata version: %s' % ad.__version__)"]]></version_command>
+        <version_command><![CDATA[python -c "import anndata as ad;print('anndata version: %s' % ad.__version__); import loompy;print('\nloompy version: %s' % loompy.__version__)"]]></version_command>
     </xml>
     <token name="@CMD@"><![CDATA[
cat '$script_file' &&
python '$script_file'
     ]]>
     </token>
+    <token name="@LOOMCMD@"><![CDATA[
+mkdir ./output &&
+mkdir ./attributes &&
+python '$__tool_directory__/loompy_to_tsv.py' -f '${hd5_format.input}'
+    ]]>
+    </token>
     <token name="@CMD_imports@"><![CDATA[
import anndata as ad
     ]]>
@@ -33,15 +39,27 @@

AnnData stores a data matrix `X` together with annotations of observations `obs`, variables `var` and unstructured annotations `uns`.

-.. image:: https://falexwolf.de/img/scanpy/anndata.svg
+.. image:: https://falexwolf.de/img/scanpy/anndata.svg

-AnnData stores observations (samples) of variables (features) in the rows of a matrix. This is the convention of the modern classics
-of statistics (`Hastie et al., 2009 <https://web.stanford.edu/~hastie/ElemStatLearn/>`__)  and machine learning (Murphy, 2012), the convention of dataframes both in R and Python and the established statistics
+AnnData stores observations (samples) of variables (features) in the rows of a matrix. This is the convention of the modern classics
+of statistics (`Hastie et al., 2009 <https://web.stanford.edu/~hastie/ElemStatLearn/>`__)  and machine learning (Murphy, 2012), the convention of dataframes both in R and Python and the established statistics
and machine learning packages in Python (statsmodels, scikit-learn).

More details on the `AnnData documentation
<https://anndata.readthedocs.io/en/latest/anndata.AnnData.html>`__
+
+
+**Loom data**
+
+Loom files are an efficient file format for very large omics datasets, consisting of a main matrix, optional additional layers, a variable number of row and column annotations, and sparse graph objects.
+
+.. image:: https://linnarssonlab.org/loompy/_images/Loom_components.png
+
+
+Loom files to store single-cell gene expression data: the main matrix contains the actual expression values (one column per cell, one row per gene); row and column annotations contain metadata for genes
+and cells, such as Name, Chromosome, Position (for genes), and Strain, Sex, Age (for cells).
+
     ]]>
     </token>
     <xml name="params_chunk_X">

diff -r a56baceb1900 -r 6db1b06e6bbb modify_loom.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/modify_loom.py Mon Jan 06 13:44:18 2020 -0500

[

@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+"""This program adds layers, row attributes or column attributes for loom files"""
+
+import argparse
+
+import loompy
+import numpy as np
+
+parser = argparse.ArgumentParser(description="Loompy file converter flags")
+parser.add_argument('--VERSION', action='version', version='%(prog)s 0.1.0',
+                    help="Displays tool version")
+parser.add_argument('--file', '-f',
+                    help="Loom file to which data will be added")
+parser.add_argument('--rowfile', '-r', help="File of row attributes & values")
+parser.add_argument('--colfile', '-c',
+                    help="File of column attributes and values")
+parser.add_argument('--layers', '-l', nargs='*',
+                    help="Input tsv files. First file becomes main layer.")
+parser.add_argument('--add', '-a', choices=["rows", "cols", "layers"],
+                    help="Selects rows, columns or layers to be added to file")
+args = parser.parse_args()
+
+lfile = args.file
+if args.rowfile:
+    rowfile = args.rowfile
+if args.colfile:
+    colfile = args.colfile
+if args.layers:
+    alllayers = args.layers
+addselect = args.add
+# Check proper flags for chosen attributes are being added
+if addselect == "cols" and not args.colfile:
+    raise Exception("To add column attributes, column flag and file must be provided")
+if addselect == "rows" and not args.rowfile:
+    raise Exception("To add row attributes, row flag and file must be provided")
+if addselect == "layers" and not args.layers:
+    raise Exception("To add layers, a layer flag and file(s) must be provided")
+
+layernames = []
+rowdict = {}
+coldict = {}
+
+with loompy.connect(lfile) as loomfile:
+    # Loom file dimensions
+    nrow = loomfile.shape[0]
+    ncol = loomfile.shape[1]
+    if addselect == "layers":
+        layernames = []
+        # Generate layer names based on file names
+        for x in range(0, len(alllayers)):
+            layer = alllayers[x]
+            layer = layer.split("/")[-1].split(".")[-2]  # Takes away path, takes off extension
+            layernames.append(layer)
+        # Add in the layers themselves
+        for layer in range(0, len(alllayers)):
+            matrix = ""
+            with open(alllayers[layer], "r") as infile:
+                rows = 0
+                count = 0
+                for line in infile:
+                    if count == 0:
+                        cols = len(line.split("\t"))
+                        if cols != ncol:
+                            raise Exception("Dimensions of new matrix incorrect for this loom file. New matrices must be %d by %d" % (nrow, ncol))
+                    matrix = matrix + line + "\t"
+                    rows += 1
+                if rows != nrow:
+                    raise Exception("Dimensions of new matrix incorrect for this loom file. New matrices must be %d by %d")
+            matrix = matrix.split("\t")
+            matrix = [float(n) for n in matrix[:-1]]
+            matrix = np.asarray(matrix).reshape(nrow, ncol)
+            loomfile[layernames[layer]] = matrix
+    elif addselect == "rows":
+        with open(rowfile, "r") as rows:
+            count = 0
+            for line in rows:
+                line = line.strip().split("\t")
+                if count == 0:  # First time through
+                    row_attributes = line
+                    for x in row_attributes:
+                        rowdict[x] = []
+                    count += 1
+                else:
+                    for x in range(0, len(line)):
+                        rowdict[row_attributes[x]].append(line[x])
+        for x in row_attributes:
+            if len(rowdict[x]) != nrow:
+                raise Exception("Incorrect length of row. Row length must be: %d" % nrow)
+            loomfile.ra[x] = rowdict[x]
+    elif addselect == "cols":
+        with open(colfile, "r") as cols:
+            count = 0
+            for line in cols:
+                line = line.replace('\"', "")
+                line = line.replace(' ', "")
+                line = line.strip().split("\t")
+                if count == 0:  # First time through
+                    col_attributes = line
+                    for x in col_attributes:
+                        coldict[x] = []
+                    count += 1
+                else:
+                    for x in range(0, len(line)):
+                        coldict[col_attributes[x]].append(line[x])
+        for y in col_attributes:
+            if len(coldict[y]) != ncol:
+                raise Exception("Incorrect length of column. Column length must be: %d" % ncol)
+            loomfile.ca[y] = coldict[y]

diff -r a56baceb1900 -r 6db1b06e6bbb test-data/addlayer1.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/addlayer1.tsv Mon Jan 06 13:44:18 2020 -0500

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diff -r a56baceb1900 -r 6db1b06e6bbb test-data/addloomout1.loom

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diff -r a56baceb1900 -r 6db1b06e6bbb test-data/addloomout2.loom

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diff -r a56baceb1900 -r 6db1b06e6bbb test-data/addloomout3.loom

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diff -r a56baceb1900 -r 6db1b06e6bbb test-data/addtest.loom

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diff -r a56baceb1900 -r 6db1b06e6bbb test-data/cols.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cols.tsv Mon Jan 06 13:44:18 2020 -0500

@@ -0,0 +1,101 @@
+Testing testing testing2
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diff -r a56baceb1900 -r 6db1b06e6bbb test-data/converted.loom.test

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diff -r a56baceb1900 -r 6db1b06e6bbb test-data/finallayer.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/finallayer.tsv Mon Jan 06 13:44:18 2020 -0500

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diff -r a56baceb1900 -r 6db1b06e6bbb test-data/firstlayer.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/firstlayer.tsv Mon Jan 06 13:44:18 2020 -0500

diff -r a56baceb1900 -r 6db1b06e6bbb test-data/loomtest.loom

Binary file test-data/loomtest.loom has changed

diff -r a56baceb1900 -r 6db1b06e6bbb test-data/rows.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/rows.tsv Mon Jan 06 13:44:18 2020 -0500

@@ -0,0 +1,10 @@
+Gene Protein Testing Testing2
+0 0 3 15
+1 1 4 16
+2 2 5 17
+3 3 6 18
+4 4 7 19
+5 5 8 20
+6 6 9 21
+7 7 10 22
+8 8 11 23

diff -r a56baceb1900 -r 6db1b06e6bbb test-data/secondlayer.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/secondlayer.tsv Mon Jan 06 13:44:18 2020 -0500

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diff -r a56baceb1900 -r 6db1b06e6bbb tsv_to_loompy.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tsv_to_loompy.py Mon Jan 06 13:44:18 2020 -0500

[

@@ -0,0 +1,109 @@
+#!/usr/bin/env python
+"""This module converts a tsv file into a binary loom file"""
+
+import argparse
+import os
+
+import loompy
+import numpy as np
+
+parser = argparse.ArgumentParser(description="Loompy file converter flags")
+parser.add_argument('--VERSION', action='version', version='%(prog)s 0.1.0',
+                    help="Displays tool version")
+parser.add_argument('--rowfile', '-r', help="File of row attributes & values")
+parser.add_argument('--colfile', '-c',
+                    help="File of column attributes and values")
+parser.add_argument('--output', '-o', help="Output file name")
+parser.add_argument('--files', '-f', nargs='*',
+                    help="Input tsv files. First file becomes main layer.")
+args = parser.parse_args()
+
+colsfile = args.colfile
+rowsfile = args.rowfile
+if args.output:
+    filename = args.output
+else:
+    filename = "converted.loom"
+alldata = args.files
+alayers = []
+layernames = []
+rowdict = {}
+coldict = {}
+
+#  Creates dictionary based on row file
+#  For each attribute:
+#  Attribute: [attribute values]
+with open(rowsfile, "r") as rows:
+    count = 0
+    for line in rows:
+        line = line.strip().split("\t")
+        if count == 0:  # First time through
+            row_attributes = line
+            for x in row_attributes:
+                rowdict[x] = []
+            count += 1
+        else:
+            for x in range(0, len(line)):
+                rowdict[row_attributes[x]].append(line[x])
+#  Same as above, but for columns
+with open(colsfile, "r") as cols:
+    count = 0
+    for line in cols:
+        line = line.replace('\"', "")
+        line = line.replace(' ', "")
+        line = line.strip().split("\t")
+        if count == 0:  # First time through
+            col_attributes = line
+            for x in col_attributes:
+                coldict[x] = []
+            count += 1
+        else:
+            for x in range(0, len(line)):
+                coldict[col_attributes[x]].append(line[x])
+#  Finding dimensions for the loom layers
+rowshape = len(rowdict[list(rowdict.keys())[0]])
+colshape = len(coldict[list(coldict.keys())[0]])
+
+#  Creates a list with each element being entire matrix of
+#  each layer file as floats
+for file in range(0, len(alldata)):
+    layer = alldata[file][:-4]
+    layer = layer.split("/")[-1]
+    if layer == "":
+        raise Exception("Please only use named files")
+    layernames.append(layer)
+    cfile = alldata[file]
+    with open(cfile, "r") as tsv:
+        cmatrix = []
+        for line in tsv:
+            line = line.strip().split("\t")
+            line = [float(i) for i in line]
+            cmatrix += line
+        alayers.append(cmatrix)
+
+#  Loompy cannot overwright existing files. If somehow it finds
+#  a second file with the same name, it must be deleted
+if os.path.isfile(filename):
+    os.remove(filename)
+#  To create the file properly, the first row and column attributes must be
+#  added separately in the form of individual dictionaries
+row_attrs = {row_attributes[0]: np.asarray(rowdict[row_attributes[0]])}
+col_attrs = {col_attributes[0]: np.asarray(coldict[col_attributes[0]])}
+matrix = np.asarray(alayers[0])
+matrix = matrix.astype(float)
+matrix = matrix.reshape(rowshape, colshape)
+#  Creation of initial loom file
+if "loom" not in filename[-5:]:
+    filename = filename + ".loom"
+loompy.create(filename, matrix, row_attrs, col_attrs)
+#  Adding all row and column attributes, then all layers
+with loompy.connect(filename) as loomfile:
+    for x in row_attributes:
+        loomfile.ra[x] = rowdict[x]
+    for y in col_attributes:
+        loomfile.ca[y] = coldict[y]
+    for z in range(1, len(alayers)):
+        matrix = np.asarray(alayers[z])
+        matrix = matrix.astype(float)
+        matrix = matrix.reshape(rowshape, colshape)
+        loomfile[layernames[z]] = matrix