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changeset 3:b5c7ba11401d draft

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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/anndata/ commit dc9d19d1f902f3ed54009cd0e68c8518c284b856"
author iuc
date Mon, 06 Jan 2020 13:45:13 -0500
parents e175d4067b00
children 080eab96d846
files import.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, 596 insertions(+), 92 deletions(-) [+]
line wrap: on
line diff
--- a/import.xml	Thu Dec 12 09:23:27 2019 -0500
+++ b/import.xml	Mon Jan 06 13:45:13 2020 -0500
@@ -1,4 +1,4 @@
-<tool id="anndata_import" name="Import AnnData" version="@VERSION@+@GALAXY_VERSION@">
+<tool id="anndata_import" name="Import Anndata and loom" version="@VERSION@+@GALAXY_VERSION@">
     <description>from different format</description>
     <macros>
         <import>macros.xml</import>
@@ -17,124 +17,155 @@
     </expand>
     <expand macro="version_command"/>
     <command detect_errors="exit_code"><![CDATA[
-#if $in.adata_format == 'mtx'
-mkdir mtx
-    #if $in.tenx.use == 'legacy_10x'
-&& cp '$in.matrix' 'mtx/matrix.mtx'
-&& cp '$in.tenx.genes' 'mtx/genes.tsv'
-&& cp '$in.tenx.barcodes' 'mtx/barcodes.tsv'
-    #else if $in.tenx.use == 'v3_10x'
-&& cp '$in.matrix' 'mtx/matrix.mtx'
-&& gzip 'mtx/matrix.mtx'
-&& cp '$in.tenx.features' 'mtx/features.tsv'
-&& gzip 'mtx/features.tsv'
-&& cp '$in.tenx.barcodes' 'mtx/barcodes.tsv'
-&& gzip 'mtx/barcodes.tsv'
+#if $hd5_format.filetype == 'anndata'
+    #if $hd5_format.in.adata_format == 'mtx'
+        mkdir mtx
+        #if $hd5_format.in.tenx.use == 'legacy_10x'
+            && cp '$hd5_format.in.matrix' 'mtx/matrix.mtx'
+            && cp '$hd5_format.in.tenx.genes' 'mtx/genes.tsv'
+            && cp '$hd5_format.in.tenx.barcodes' 'mtx/barcodes.tsv'
+        #else if $hd5_format.in.tenx.use == 'v3_10x'
+            && cp '$hd5_format.in.matrix' 'mtx/matrix.mtx'
+            && gzip 'mtx/matrix.mtx'
+            && cp '$hd5_format.in.tenx.features' 'mtx/features.tsv'
+            && gzip 'mtx/features.tsv'
+            && cp '$hd5_format.in.tenx.barcodes' 'mtx/barcodes.tsv'
+            && gzip 'mtx/barcodes.tsv'
+        #end if
+        &&
+    #else if $hd5_format.in.adata_format == 'umi_tools'
+        gzip '$hd5_format.in.input'
+        &&
     #end if
-&&
-#else if $in.adata_format == 'umi_tools'
-gzip '$in.input'
-&&
-#end if
+
+    @CMD@
+
+    #if $hd5_format.in.adata_format == 'mtx'
+        && rm -rf mtx
+    #end if
 
-@CMD@
-
-#if $in.adata_format == 'mtx'
-&& rm -rf mtx
+#else:
+        python '$__tool_directory__/tsv_to_loompy.py'
+        -c '${hd5_format.coldata}'
+        -r '${hd5_format.rowdata}'
+        -f '${hd5_format.mainmatrix}'
+        #if $hd5_format.other_files:
+            '${hd5_format.other_files}'
+        #end if
 #end if
       ]]></command>
     <configfiles>
         <configfile name="script_file"><![CDATA[
 @CMD_imports@
-#if $in.adata_format == 'loom'
+#if $hd5_format.filetype == 'anndata'
+#if $hd5_format.in.adata_format == 'loom'
 adata = ad.read_loom(
-    '$in.input',
-    sparse=$in.sparse,
-    cleanup=$in.cleanup,
-    X_name='$in.x_name',
-    obs_names='$in.obs_names',
-    var_names='$in.var_names')
+    '$hd5_format.in.input',
+    sparse=$hd5_format.in.sparse,
+    cleanup=$hd5_format.in.cleanup,
+    X_name='$hd5_format.in.x_name',
+    obs_names='$hd5_format.in.obs_names',
+    var_names='$hd5_format.in.var_names')
 
-#else if $in.adata_format == 'tabular'
-    #set delimiter=$in.input.metadata.delimiter
+#else if $hd5_format.in.adata_format == 'tabular'
+    #set delimiter=$hd5_format.in.input.metadata.delimiter
     #if $delimiter != ','
         #set delimiter='\\t'
     #end if
 adata = ad.read_csv(
-    '$in.input',
+    '$hd5_format.in.input',
     delimiter='$delimiter',
-    first_column_names=$in.first_column_names)
+    first_column_names=$hd5_format.in.first_column_names)
 
-#else if $in.adata_format == 'mtx'
-    #if $in.tenx.use == 'no'
-adata = ad.read_mtx(filename='$in.matrix')
+#else if $hd5_format.in.adata_format == 'mtx'
+    #if $hd5_format.in.tenx.use == 'no'
+adata = ad.read_mtx(filename='$hd5_format.in.matrix')
     #else
 import scanpy as sc
 adata = sc.read_10x_mtx(
     'mtx',
-    var_names='$in.tenx.var_names',
-    make_unique=$in.tenx.make_unique,
+    var_names='$hd5_format.in.tenx.var_names',
+    make_unique=$hd5_format.in.tenx.make_unique,
     cache=False,
-    gex_only=$in.tenx.gex_only)
+    gex_only=$hd5_format.in.tenx.gex_only)
     #end if
 
-#else if $in.adata_format == 'umi_tools'
-adata = ad.read_umi_tools('${in.input}.gz')
+#else if $hd5_format.in.adata_format == 'umi_tools'
+adata = ad.read_umi_tools('${hd5_format.in.input}.gz')
 
 #end if
-
 adata.write('anndata.h5ad')
+#end if
 ]]></configfile>
     </configfiles>
     <inputs>
-        <conditional name="in">
-            <param name="adata_format" type="select" label="Format for the annotated data matrix">
-                <option value="loom">Loom</option>
-                <option value="tabular">Tabular, CSV, TSV</option>
-                <option value="mtx">Matrix Market (mtx), from Cell ranger or not</option>
-                <option value="umi_tools">UMI tools</option>
+        <conditional name="hd5_format">
+            <param name="filetype" type="select" label="hd5 format to be created">
+                <option value="anndata" selected="true">Anndata file</option>
+                <option value="loom">Loom file</option>
             </param>
-            <when value="loom">
-                <param name="input" type="data" format="loom" label="Annotated data matrix"/>
-                <param name="sparse" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Is the data matrix to read sparse?"/>
-                <param name="cleanup" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Cleanup?"/>
-                <param name="x_name" type="text" value="spliced" label="X_name"/>
-                <param name="obs_names" type="text" value="CellID" label="obs_names"/>
-                <param name="var_names" type="text" value="Gene" label="var_names"/>
-            </when>
-            <when value="tabular">
-                <param name="input" type="data" format="tabular,csv,tsv" label="Annotated data matrix"/>
-                <param name="first_column_names" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Does the first column store the row names?"/>
-            </when>
-            <when value="mtx">
-                <param name="matrix" type="data" format="mtx" label="Matrix"/>
-                <conditional name="tenx">
-                    <param name="use" type="select" label="Use 10x Genomics formatted mtx">
-                        <option value="no">No</option>
-                        <option value="legacy_10x">Output from Cell Ranger v2 or earlier versions</option>
-                        <option value="v3_10x">Output from Cell Ranger v3 or later versions</option>
+            <when value="anndata">
+                <conditional name="in">
+                    <param name="adata_format" type="select" label="Format for the annotated data matrix">
+                        <option value="loom">Loom</option>
+                        <option value="tabular">Tabular, CSV, TSV</option>
+                        <option value="mtx">Matrix Market (mtx), from Cell ranger or not</option>
+                        <option value="umi_tools">UMI tools</option>
                     </param>
-                    <when value="no"/>
-                    <when value="legacy_10x">
-                        <param name="genes" type="data" format="tabular" label="Genes"/>
-                        <expand macro="params_10x"/>
+                    <when value="loom">
+                        <param name="input" type="data" format="loom" label="Annotated data matrix"/>
+                        <param name="sparse" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Is the data matrix to read sparse?"/>
+                        <param name="cleanup" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Cleanup?"/>
+                        <param name="x_name" type="text" value="spliced" label="X_name"/>
+                        <param name="obs_names" type="text" value="CellID" label="obs_names"/>
+                        <param name="var_names" type="text" value="Gene" label="var_names"/>
+                    </when>
+                    <when value="tabular">
+                        <param name="input" type="data" format="tabular,csv,tsv" label="Annotated data matrix"/>
+                        <param name="first_column_names" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Does the first column store the row names?"/>
                     </when>
-                    <when value="v3_10x">
-                        <param name="features" type="data" format="tabular" label="Features"/>
-                        <expand macro="params_10x"/>
+                    <when value="mtx">
+                        <param name="matrix" type="data" format="mtx" label="Matrix"/>
+                        <conditional name="tenx">
+                            <param name="use" type="select" label="Use 10x Genomics formatted mtx">
+                                <option value="no">No</option>
+                                <option value="legacy_10x">Output from Cell Ranger v2 or earlier versions</option>
+                                <option value="v3_10x">Output from Cell Ranger v3 or later versions</option>
+                            </param>
+                            <when value="no"/>
+                            <when value="legacy_10x">
+                                <param name="genes" type="data" format="tabular" label="Genes"/>
+                                <expand macro="params_10x"/>
+                            </when>
+                            <when value="v3_10x">
+                                <param name="features" type="data" format="tabular" label="Features"/>
+                                <expand macro="params_10x"/>
+                            </when>
+                        </conditional>
+                    </when>
+                    <when value="umi_tools">
+                        <param name="input" type="data" format="tabular" label="condensed count matrix from UMI tools"/>
                     </when>
                 </conditional>
             </when>
-            <when value="umi_tools">
-                <param name="input" type="data" format="tabular" label="condensed count matrix from UMI tools"/>
+            <when value="loom">
+                <param name="mainmatrix" type="data" format="tabular" label="File for main layer of loom file." help="All subsequent tsv must be the same dimensions as this file. When converted back to tsv using hd5 export, this will be labeled as 'mainmatrix.tsv'"/>
+                <param name="other_files" type="data" format="tabular" multiple="true" optional="true" label="Add layers" help="Adds layers of same dimension to the loom file. When converted to tsv using hd5 export, these layers will retain their names."/>
+                <param name="coldata" type="data" format="tabular" label="Tsv of column data." help="First row is column attributes, subsequent are values."/>
+                <param name="rowdata" type="data" format="tabular" label="Tsv of row data." help="First row is row attributes, subsequent are values."/>
             </when>
         </conditional>
     </inputs>
     <outputs>
-        <data name="anndata" format="h5ad" from_work_dir="anndata.h5ad" label="${tool.name} on ${on_string}"/>
+        <data name="anndata" format="h5ad" from_work_dir="anndata.h5ad" label="Anndata import on ${on_string}">
+            <filter>hd5_format['filetype'] == 'anndata'</filter>
+        </data>
+        <data name="loomdata" format="loom" from_work_dir="converted.loom" label="Loom import on ${on_string}">
+            <filter>hd5_format['filetype'] == 'loom'</filter>
+        </data>
     </outputs>
     <tests>
-        <test>
+        <test expect_num_outputs="1">
             <conditional name="in">
                 <param name="adata_format" value="loom"/>
                 <param name="input" value="krumsiek11.loom" />
@@ -154,7 +185,7 @@
             </assert_stdout>
             <output name="anndata" value="import.loom.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/>
         </test>
-        <test>
+        <test expect_num_outputs="1">
             <conditional name="in">
                 <param name="adata_format" value="tabular"/>
                 <param name="input" value="adata.csv"/>
@@ -167,7 +198,7 @@
             </assert_stdout>
             <output name="anndata" value="import.csv.h5ad" ftype="h5ad" compare="sim_size"/>
         </test>
-        <test>
+        <test expect_num_outputs="1">
             <conditional name="in">
                 <param name="adata_format" value="tabular"/>
                 <param name="input" value="adata.tsv"/>
@@ -180,7 +211,7 @@
             </assert_stdout>
             <output name="anndata" value="import.tsv.h5ad" ftype="h5ad" compare="sim_size"/>
         </test>
-        <!--<test>
+        <!--<test expect_num_outputs="1">
             <conditional name="in">
                 <param name="adata_format" value="mtx"/>
                 <param name="matrix" value="matrix_10x_v1.2.0.mtx"/>
@@ -220,23 +251,32 @@
             </conditional>
             <output name="anndata" value="import.mtx.v3_10x.h5ad" ftype="h5ad" compare="sim_size"/>
         </test>!-->
-        <test>
+        <test expect_num_outputs="1">
+            <param name="filetype" value="anndata"/>
             <conditional name="in">
                 <param name="adata_format" value="umi_tools"/>
                 <param name="input" value="umi_tools.tsv"/>
             </conditional>
             <output name="anndata" value="import.umi_tools.h5ad" ftype="h5ad" compare="sim_size"/>
         </test>
+        <test expect_num_outputs="1">
+            <param name="filetype" value="loom"/>
+            <param name="mainmatrix" value="firstlayer.tsv"/>
+            <param name="other_files" value="secondlayer.tsv"/>
+            <param name="coldata" value="cols.tsv"/>
+            <param name="rowdata" value="rows.tsv"/>
+            <output name="loomdata" value="converted.loom.test" ftype="loom" compare="sim_size"/>
+        </test>
     </tests>
     <help><![CDATA[
 **What it does**
 
-This tool creates an AnnData dataset from several input types:
+This tool creates an AnnData or loom dataset from several input types:
 
-- Loom (`read_loom method <https://anndata.readthedocs.io/en/latest/anndata.read_loom.html>`__) 
-- Tabular (`read_csv method <https://anndata.readthedocs.io/en/latest/anndata.read_csv.html>`__) 
-- Matrix Market (mtx), from Cell ranger or not (`read_mtx method <https://anndata.readthedocs.io/en/latest/anndata.read_mtx.html>`__) 
-- UMI tools (`read_umi_tools method <https://anndata.readthedocs.io/en/latest/anndata.read_umi_tools.html>`__) 
+- Loom (`read_loom method <https://anndata.readthedocs.io/en/latest/anndata.read_loom.html>`__)
+- Tabular (`read_csv method <https://anndata.readthedocs.io/en/latest/anndata.read_csv.html>`__)
+- Matrix Market (mtx), from Cell ranger or not (`read_mtx method <https://anndata.readthedocs.io/en/latest/anndata.read_mtx.html>`__)
+- UMI tools (`read_umi_tools method <https://anndata.readthedocs.io/en/latest/anndata.read_umi_tools.html>`__)
 
 @HELP@
     ]]></help>
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/loompy_to_tsv.py	Mon Jan 06 13:45:13 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 09:23:27 2019 -0500
+++ b/macros.xml	Mon Jan 06 13:45:13 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 13:45:13 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 13:45:13 2020 -0500
@@ -0,0 +1,9 @@
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Binary file test-data/addtest.loom has changed
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cols.tsv	Mon Jan 06 13:45:13 2020 -0500
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Binary file test-data/converted.loom.test has changed
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/firstlayer.tsv	Mon Jan 06 13:45:13 2020 -0500
@@ -0,0 +1,9 @@
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Binary file test-data/loomtest.loom has changed
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/rows.tsv	Mon Jan 06 13:45:13 2020 -0500
@@ -0,0 +1,10 @@
+Gene	Protein	Testing	Testing2
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+3	3	6	18
+4	4	7	19
+5	5	8	20
+6	6	9	21
+7	7	10	22
+8	8	11	23
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/secondlayer.tsv	Mon Jan 06 13:45:13 2020 -0500
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/tsv_to_loompy.py	Mon Jan 06 13:45:13 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