# HG changeset patch
# User iuc
# Date 1578336258 18000
# Node ID 6db1b06e6bbb0c319daaa38ae9d91f06c99f8986
# Parent a56baceb1900eaf25b6db6c3cbfee4e25e9f7e61
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/anndata/ commit dc9d19d1f902f3ed54009cd0e68c8518c284b856"
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 @@
-
+
+
+
@@ -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 `__) 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 `__) 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
`__
+
+
+**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).
+
]]>
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
@@ -0,0 +1,9 @@
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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
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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
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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
@@ -0,0 +1,9 @@
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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
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+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
@@ -0,0 +1,9 @@
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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