Mercurial > repos > iuc > anndata_export
changeset 3:8623710d083c draft
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/anndata/ commit dc9d19d1f902f3ed54009cd0e68c8518c284b856"
| author | iuc |
|---|---|
| date | Mon, 06 Jan 2020 13:44:46 -0500 |
| parents | e0395cca2c57 |
| children | 016308443041 |
| files | export.xml loompy_to_tsv.py macros.xml 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 |
| diffstat | 17 files changed, 533 insertions(+), 30 deletions(-) [+] |
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--- a/export.xml Thu Dec 12 09:23:01 2019 -0500 +++ b/export.xml Mon Jan 06 13:44:46 2020 -0500 @@ -1,54 +1,79 @@ -<tool id="anndata_export" name="Export AnnData" version="@VERSION@+@GALAXY_VERSION@"> - <description>to different formats</description> +<tool id="anndata_export" name="Export AnnData and loom files" version="@VERSION@+@GALAXY_VERSION@"> + <description>Interconvert AnnData and Loom formats</description> <macros> <import>macros.xml</import> </macros> <expand macro="requirements"/> <expand macro="version_command"/> <command detect_errors="exit_code"><![CDATA[ -@CMD@ +#if $hd5_format.filetype == 'anndata': + @CMD@ +#else: + @LOOMCMD@ +#end if ]]></command> <configfiles> <configfile name="script_file"><![CDATA[ @CMD_imports@ - -adata = ad.read('$input') -#if $output_format == 'loom' +#if $hd5_format.filetype == 'anndata': +adata = ad.read('$hd5_format.input') + #if $hd5_format.output_format == 'loom': adata.write_loom('anndata.loom') -#else if $output_format == 'tabular' + #else if $hd5_format.output_format == 'tabular': adata.write_csvs('.', sep="\t", skip_data = False) + #end if #end if -]]></configfile> - </configfiles> + ]]></configfile> +</configfiles> <inputs> - <param name="input" type="data" format="h5ad" label="Annotated data matrix"/> - <param name="output_format" type="select" label="Format to write the annotated data matrix"> - <option value="loom">loom</option> - <option value="tabular">Tabular</option> - </param> + <conditional name="hd5_format"> + <param name="filetype" type="select" label="hd5 format"> + <option value="anndata" selected="true">Anndata file</option> + <option value="loom">Loom file</option> + </param> + <when value="anndata"> + <param name="input" type="data" format="h5ad" label="Annotated data matrix"/> + <param name="output_format" type="select" label="Format to write the annotated data matrix"> + <option value="loom">loom</option> + <option value="tabular">Tabular</option> + </param> + </when> + <when value="loom"> + <param name="input" type="data" format="loom" label="Loom file to be exported to a series of tabular files."/> + </when> + </conditional> </inputs> <outputs> <data name="loom_output" format="loom" from_work_dir="anndata.loom" label="${tool.name} on ${on_string}: Annotated data matrix"> - <filter>output_format == 'loom'</filter> + <filter>hd5_format['filetype'] == 'anndata' and hd5_format['output_format'] == 'loom'</filter> </data> <data name="tabular_x" format="tabular" from_work_dir="X.csv" label="${tool.name} on ${on_string}: Matrix table (X)"> - <filter>output_format == 'tabular'</filter> + <filter>hd5_format['filetype'] == 'anndata' and hd5_format['output_format'] == 'tabular'</filter> </data> <data name="tabular_obs" format="tabular" from_work_dir="obs.csv" label="${tool.name} on ${on_string}: Key-indexed one-dimensional observations annotation (obs)"> - <filter>output_format == 'tabular'</filter> + <filter>hd5_format['filetype'] == 'anndata' and hd5_format['output_format'] == 'tabular'</filter> </data> <data name="tabular_obsm" format="tabular" from_work_dir="obsm.csv" label="${tool.name} on ${on_string}: Key-indexed multi-dimensional observations annotation (obsm)"> - <filter>output_format == 'tabular'</filter> + <filter>hd5_format['filetype'] == 'anndata' and hd5_format['output_format'] == 'tabular'</filter> </data> <data name="tabular_var" format="tabular" from_work_dir="var.csv" label="${tool.name} on ${on_string}: Key-indexed one-dimensional variables annotation (var)"> - <filter>output_format == 'tabular'</filter> + <filter>hd5_format['filetype'] == 'anndata' and hd5_format['output_format'] == 'tabular'</filter> </data> <data name="tabular_varm" format="tabular" from_work_dir="varm.csv" label="${tool.name} on ${on_string}: Key-indexed multi-dimensional variables annotation (varm)"> - <filter>output_format == 'tabular'</filter> + <filter>hd5_format['filetype'] == 'anndata' and hd5_format['output_format'] == 'tabular'</filter> </data> + <collection name="layer_tsvs" type="list" label="Layer matrices" > + <filter>hd5_format['filetype'] == 'loom'</filter> + <discover_datasets pattern="__designation__" format="tabular" directory="output" visible="false" /> + </collection> + <collection name="attribute_tsvs" type="list" label="Attribute matrices" > + <filter>hd5_format['filetype'] == 'loom'</filter> + <discover_datasets pattern="__designation__" format="tabular" directory="attributes" visible="false" /> + </collection> </outputs> <tests> - <test> + <test expect_num_outputs="1"> + <param name="filetype" value="anndata"/> <param name="input" value="krumsiek11.h5ad"/> <param name="output_format" value="loom"/> <assert_stdout> @@ -56,7 +81,8 @@ </assert_stdout> <output name="loom_output" value="export.krumsiek11.loom" ftype="loom" compare="sim_size"/> </test> - <test> + <test expect_num_outputs="5"> + <param name="filetype" value="anndata"/> <param name="input" value="krumsiek11.h5ad"/> <param name="output_format" value="tabular"/> <assert_stdout> @@ -68,13 +94,26 @@ <output name="tabular_var" value="export.krumsiek11.var.tabular" ftype="tabular"/> <output name="tabular_varm" value="export.krumsiek11.varm.tabular" ftype="tabular"/> </test> + <test expect_num_outputs="2"> + <param name="filetype" value="loom"/> + <param name="input" value="loomtest.loom"/> + <output_collection name="layer_tsvs" type="list"> + <element name="mainmatrix.tsv" value="firstlayer.tsv" ftype="tabular"/> + <element name="extralayer.tsv" value="secondlayer.tsv" ftype="tabular"/> + <element name="thirdlayer.tsv" value="finallayer.tsv" ftype="tabular"/> + </output_collection> + <output_collection name="attribute_tsvs" type="list"> + <element name="row_attr.tsv" value="rows.tsv" ftype="tabular"/> + <element name="col_attr.tsv" value="cols.tsv" ftype="tabular"/> + </output_collection> + </test> </tests> <help><![CDATA[ -**What it does** +This tool exports an AnnData dataset to a Loom file +(`write_loom method <https://anndata.readthedocs.io/en/latest/anndata.AnnData.write_loom.html>`__) +or a Tabular file (`write_csvs method <https://anndata.readthedocs.io/en/latest/anndata.AnnData.write_csvs.html>`__) -This tool exports a AnnData dataset to a Loom file -(`write_loom method <https://anndata.readthedocs.io/en/latest/anndata.AnnData.write_loom.html>`__) -or a Tabular file (`write_csvs method <https://anndata.readthedocs.io/en/latest/anndata.AnnData.write_csvs.html>`__) +It can also create a series of tabular files from an input loom dataset. @HELP@ ]]></help>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/loompy_to_tsv.py Mon Jan 06 13:44:46 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)
--- a/macros.xml Thu Dec 12 09:23:01 2019 -0500 +++ b/macros.xml Mon Jan 06 13:44:46 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">
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/modify_loom.py Mon Jan 06 13:44:46 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]
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/addlayer1.tsv Mon Jan 06 13:44:46 2020 -0500 @@ -0,0 +1,9 @@ +-4.38397705861083 14.292813312163 -3.50113245239144 0.822254333007829 1.00150289648448 2.43591204698924 -0.356677013622828 -6.11947364969354 2.56281003005924 6.63909009032078 -0.78946499862756 4.69845868698249 0.555316773542253 1.14493347970535 -2.13170480396421 0.671242754395511 -1.24598424970653 2.35762885939 0.808249063291586 2.13338650412044 -0.442400711567299 0.185262494040311 2.11898976841304 1.33325569029047 -1.59297725345935 -0.966752781646448 3.58792591654639 1.66406580309397 0.63327385793388 -0.308925411126234 1.62030751470511 -0.551680746030133 0.481109445913889 -1.29522624182828 -1.2921145302749 1.35092954319461 0.357971803776218 0.602387478388324 -0.86450428384588 -0.253516849675122 -0.191991523300002 -1.23045693386899 1.10601149579208 0.474927307151347 0.0295127518668576 0.347320109574423 -0.124263697792595 -1.1355438083115 -0.408955132576027 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tsv_to_loompy.py Mon Jan 06 13:44:46 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