Merge pull request #38 from genenetwork/feature/wgcna_analysis

script for wgcna analysis

11 files changed, 665 insertions, 1 deletions

diff --git a/gn3/api/wgcna.py b/gn3/api/wgcna.py
new file mode 100644
index 0000000..fa044cf
--- /dev/null
+++ b/gn3/api/wgcna.py
@@ -0,0 +1,25 @@
+"""endpoint to run wgcna analysis"""
+from flask import Blueprint
+from flask import request
+from flask import current_app
+from flask import jsonify
+
+from gn3.computations.wgcna import call_wgcna_script
+
+wgcna = Blueprint("wgcna", __name__)
+
+
+@wgcna.route("/run_wgcna", methods=["POST"])
+def run_wgcna():
+    """run wgcna:output should be a json with a the data"""
+
+    wgcna_data = request.json
+
+    wgcna_script = current_app.config["WGCNA_RSCRIPT"]
+
+    results = call_wgcna_script(wgcna_script, wgcna_data)
+
+    if results.get("data") is None:
+        return jsonify(results), 401
+
+    return jsonify(results), 200
diff --git a/gn3/app.py b/gn3/app.py
index 5e852e1..a25332c 100644
--- a/gn3/app.py
+++ b/gn3/app.py
@@ -13,6 +13,7 @@ from gn3.api.general import general
 from gn3.api.heatmaps import heatmaps
 from gn3.api.correlation import correlation
 from gn3.api.data_entry import data_entry
+from gn3.api.wgcna import wgcna
 
 def create_app(config: Union[Dict, str, None] = None) -> Flask:
     """Create a new flask object"""
@@ -42,4 +43,5 @@ def create_app(config: Union[Dict, str, None] = None) -> Flask:
     app.register_blueprint(heatmaps, url_prefix="/api/heatmaps")
     app.register_blueprint(correlation, url_prefix="/api/correlation")
     app.register_blueprint(data_entry, url_prefix="/api/dataentry")
+    app.register_blueprint(wgcna, url_prefix="/api/wgcna")
     return app
diff --git a/gn3/computations/wgcna.py b/gn3/computations/wgcna.py
new file mode 100644
index 0000000..fd508fa
--- /dev/null
+++ b/gn3/computations/wgcna.py
@@ -0,0 +1,51 @@
+"""module contains code to preprocess and call wgcna script"""
+
+import os
+import json
+import uuid
+from gn3.settings import TMPDIR
+
+from gn3.commands import run_cmd
+
+
+def dump_wgcna_data(request_data: dict):
+    """function to dump request data to json file"""
+    filename = f"{str(uuid.uuid4())}.json"
+
+    temp_file_path = os.path.join(TMPDIR, filename)
+
+    with open(temp_file_path, "w") as output_file:
+        json.dump(request_data, output_file)
+
+    return temp_file_path
+
+
+def compose_wgcna_cmd(rscript_path: str, temp_file_path: str):
+    """function to componse wgcna cmd"""
+    # (todo):issue relative paths to abs paths
+    cmd = f"Rscript ./scripts/{rscript_path}  {temp_file_path}"
+    return cmd
+
+
+def call_wgcna_script(rscript_path: str, request_data: dict):
+    """function to call wgcna script"""
+    generated_file = dump_wgcna_data(request_data)
+    cmd = compose_wgcna_cmd(rscript_path, generated_file)
+
+    try:
+
+        run_cmd_results = run_cmd(cmd)
+
+        with open(generated_file, "r") as outputfile:
+
+            if run_cmd_results["code"] != 0:
+                return run_cmd_results
+            return {
+                "data": json.load(outputfile),
+                **run_cmd_results
+            }
+    except FileNotFoundError:
+        # relook  at handling errors gn3
+        return {
+            "output": "output file not found"
+        }
diff --git a/gn3/settings.py b/gn3/settings.py
index 9d7bba3..150d96d 100644
--- a/gn3/settings.py
+++ b/gn3/settings.py
@@ -25,6 +25,8 @@ GN2_BASE_URL = "http://www.genenetwork.org/"
 # biweight script
 BIWEIGHT_RSCRIPT = "~/genenetwork3/scripts/calculate_biweight.R"
 
+# wgcna script
+WGCNA_RSCRIPT = "wgcna_analysis.R"
 # qtlreaper command
 REAPER_COMMAND = "{}/bin/qtlreaper".format(os.environ.get("GUIX_ENVIRONMENT"))
 
diff --git a/guix.scm b/guix.scm
index fb97142..02c67b2 100644
--- a/guix.scm
+++ b/guix.scm
@@ -84,7 +84,6 @@
                       #:recursive? #t
                       #:select? git-file?))
   (propagated-inputs `(("coreutils" ,coreutils)
-                       ("csvdiff" ,go-github-com-aswinkarthik-csvdiff)
                        ("gemma-wrapper" ,gemma-wrapper)
                        ("gunicorn" ,gunicorn)
                        ("python" ,python-wrapper)
@@ -104,6 +103,8 @@
                        ("r-optparse" ,r-optparse)
                        ("r-qtl" ,r-qtl)
                        ("r-stringi" ,r-stringi)
+                       ("r-wgcna" ,r-wgcna)
+                       ("r-rjson" ,r-rjson)
                        ("python-plotly" ,python-plotly)
                        ("python-pandas" ,python-pandas)
                        ("rust-qtlreaper" ,rust-qtlreaper)
diff --git a/scripts/output.json b/scripts/output.json
new file mode 100644
index 0000000..9caf837
--- /dev/null
+++ b/scripts/output.json
@@ -0,0 +1,161 @@
+{
+   "ModEigens":{
+      "MEturquoise":[
+         0.0646677768085351,
+         0.137200224277058,
+         0.63451113720732,
+         -0.544002665501479,
+         -0.489487590361863,
+         0.197111117570427
+      ]
+   },
+   "soft_threshold":{
+      "Power":[
+         1,
+         2,
+         3,
+         4,
+         5,
+         6,
+         7,
+         8,
+         9,
+         10,
+         12,
+         14,
+         16,
+         18,
+         20
+      ],
+      "SFT.R.sq":[
+         0.0181093215847335,
+         0.00011984142485113,
+         0.000171967046945159,
+         0.0105462010616537,
+         0.0341444584348834,
+         0.0687163726151286,
+         0.0306423506274298,
+         0.0492567394226327,
+         0.0789539269997996,
+         0.0944880158122527,
+         0.122195040270446,
+         0.0340768567186139,
+         0.0625860126119281,
+         0.100082257137014,
+         0.128277841930818
+      ],
+      "slope":[
+         3.81011386139005,
+         -0.170826531149538,
+         0.159161787390082,
+         -1.01047981107833,
+         -1.55943531024794,
+         -1.93420125756514,
+         -1.16799247295814,
+         -1.33414063070555,
+         -1.54984944650438,
+         -1.54715757057087,
+         -1.49931213589121,
+         -1.80460140377151,
+         -2.19055343583319,
+         -2.52135805259606,
+         -2.58599487577447
+      ],
+      "truncated.R.sq":[
+         0.768989700952805,
+         0.522025793450566,
+         0.329341226670409,
+         0.110265719555879,
+         0.0195649645183151,
+         -0.0503253079741786,
+         0.0507498358330625,
+         -0.0129255450167765,
+         -0.035717519210676,
+         -0.0807094793662766,
+         -0.0967803564932559,
+         0.00172686282662393,
+         -0.0340811003657508,
+         -0.0390284600100592,
+         -0.0489269837827069
+      ],
+      "mean.k.":[
+         4.20178789454309,
+         3.44556816856968,
+         2.98532344074325,
+         2.65297323828966,
+         2.39517682414009,
+         2.18846935370095,
+         2.01963258852289,
+         1.87999059876872,
+         1.76335304166057,
+         1.66510080962817,
+         1.51038968360321,
+         1.39583176924843,
+         1.30882729664563,
+         1.24120316437299,
+         1.18753154238216
+      ],
+      "median.k.":[
+         4.65733789933094,
+         4.13585224131512,
+         3.75980713552836,
+         3.43775457512904,
+         3.15305040369031,
+         2.89933881967523,
+         2.67225531456956,
+         2.46825611568646,
+         2.28437024155601,
+         2.118086531192,
+         1.83011067501282,
+         1.59073325345641,
+         1.38991168639473,
+         1.2201000051609,
+         1.07553194658444
+      ],
+      "max.k.":[
+         4.81522245318686,
+         4.21987143583501,
+         3.83876962723542,
+         3.55526976885104,
+         3.32904938849614,
+         3.14312441404036,
+         2.98828051379132,
+         2.85837136671219,
+         2.74879840851137,
+         2.65594228759455,
+         2.50929962297015,
+         2.40113981571731,
+         2.31993775805391,
+         2.25792900175825,
+         2.2098218130451
+      ]
+   },
+   "blockGenes":[
+      1,
+      2,
+      3,
+      4,
+      5,
+      6,
+      7
+   ],
+   "net_colors":{
+      "X1":"turquoise",
+      "X2":"turquoise",
+      "X3":"turquoise",
+      "X4":"turquoise",
+      "X5":"turquoise",
+      "X6":"turquoise",
+      "X7":"turquoise"
+   },
+   "net_unmerged":{
+      "X1":"turquoise",
+      "X2":"turquoise",
+      "X3":"turquoise",
+      "X4":"turquoise",
+      "X5":"turquoise",
+      "X6":"turquoise",
+      "X7":"turquoise"
+   },
+   "imageLoc":"/WGCNAoutput_1uujpTIpC.png"
+}
\ No newline at end of file
diff --git a/scripts/wgcna_analysis.R b/scripts/wgcna_analysis.R
new file mode 100644
index 0000000..17b3537
--- /dev/null
+++ b/scripts/wgcna_analysis.R
@@ -0,0 +1,115 @@
+
+
+library(WGCNA);
+library(stringi);
+library(rjson)
+
+options(stringsAsFactors = FALSE);
+
+imgDir = Sys.getenv("GENERATED_IMAGE_DIR")
+# load expression data **assumes from json files row(traits)(columns info+samples)
+# pass the file_path as arg
+# pass the file path to read json data
+
+args = commandArgs(trailingOnly=TRUE)
+
+if (length(args)==0) {
+  stop("Argument for the file location is required", call.=FALSE)
+} else {
+  # default output file
+  json_file_path  = args[1]
+}
+
+inputData <- fromJSON(file = json_file_path)
+
+
+trait_sample_data <- do.call(rbind, inputData$trait_sample_data)
+
+dataExpr <- data.frame(apply(trait_sample_data, 2, function(x) as.numeric(as.character(x))))
+# transform expressionData
+
+dataExpr <- data.frame(t(dataExpr))
+gsg = goodSamplesGenes(dataExpr, verbose = 3)
+
+if (!gsg$allOK)
+{
+if (sum(!gsg$goodGenes)>0)
+printFlush(paste("Removing genes:", paste(names(dataExpr)[!gsg$goodGenes], collapse = ", ")));
+if (sum(!gsg$goodSamples)>0)
+printFlush(paste("Removing samples:", paste(rownames(dataExpr)[!gsg$goodSamples], collapse = ", ")));
+# Remove the offending genes and samples from the data:
+dataExpr <- dataExpr[gsg$goodSamples, gsg$goodGenes]
+}
+
+## network constructions and modules
+
+names(dataExpr) = inputData$trait_names
+
+# Allow multi-threading within WGCNA
+enableWGCNAThreads()
+
+# choose softthreshhold (Calculate soft threshold)
+# xtodo allow users to pass args
+
+powers <- c(c(1:10), seq(from = 12, to=20, by=2)) 
+sft <- pickSoftThreshold(dataExpr, powerVector = powers, verbose = 5)
+
+# check the power estimate
+
+if (is.na(sft$powerEstimate)){
+  powerEst = 1
+}else{
+  powerEst = sft$powerEstimate
+}
+
+# pass user options
+network <- blockwiseModules(dataExpr,
+                  #similarity  matrix options
+                  corType = inputData$corType,
+                  #adjacency  matrix options
+
+                  power = powerEst,
+                  networkType = "unsigned",
+                  #TOM options
+                  TOMtype =  inputData$TOMtype,
+
+                  #module indentification
+                  verbose = 3,
+
+                  minmodulesSize = inputData$minModuleSize,
+                  deepSplit = 3,
+                  PamRespectsDendro = FALSE
+                )
+
+
+
+genImageRandStr <- function(prefix){
+
+	randStr <- paste(prefix,stri_rand_strings(1, 9, pattern = "[A-Za-z0-9]"),sep="_")
+
+	return(paste(randStr,".png",sep=""))
+}
+
+mergedColors <- labels2colors(network$colors)
+
+imageLoc <- file.path(imgDir,genImageRandStr("WGCNAoutput"))
+png(imageLoc,width=1000,height=600,type='cairo-png')
+
+plotDendroAndColors(network$dendrograms[[1]],mergedColors[network$blockGenes[[1]]],
+"Module colors",
+dendroLabels = FALSE, hang = 0.03,
+addGuide = TRUE, guideHang = 0.05)
+
+
+json_data <- list(input = inputData,
+  output = list(ModEigens=network$MEs,
+   soft_threshold=sft$fitIndices,
+   blockGenes =network$blockGenes[[1]],
+   net_colors =network$colors,
+   power_used_for_analy=powerEst,
+   net_unmerged=network$unmergedColors,
+   imageLoc=imageLoc))
+
+json_data <- toJSON(json_data)
+
+write(json_data,file= json_file_path)
\ No newline at end of file
diff --git a/scripts/wgcna_test_data.csv b/scripts/wgcna_test_data.csv
new file mode 100644
index 0000000..8db9598
--- /dev/null
+++ b/scripts/wgcna_test_data.csv
@@ -0,0 +1,9 @@
+129S1/SvImJ,A/J,AKR/J,B6D2F1,BALB/cByJ,BALB/cJ

+7.142,7.31,7.49,6.899,7.172,7.396

+7.071,7.05,7.313,6.999,7.293,7.117

+7.221,7.246,7.754,6.866,6.752,7.269

+9.221,9.246,9.954,6.866,6.952,9.269

+7.221,7.246,7.754,6.866,6.752,7.269

+7.221,7.246,7.754,6.866,6.752,7.269

+11.221,11.246,11.1154,6.866,6.1152,11.269

+

diff --git a/scripts/wgcna_test_data.json b/scripts/wgcna_test_data.json
new file mode 100644
index 0000000..1d469f4
--- /dev/null
+++ b/scripts/wgcna_test_data.json
@@ -0,0 +1,65 @@
+{
+  "trait_names":["1455537_at","1425637_at","1449593_at","1421945_a_at","1450423_s_at","1423841_at","1451144_at"],
+   "trait_sample_data":[
+  {
+    "129S1/SvImJ": 7.142,
+    "A/J": 7.31,
+    "AKR/J": 7.49,
+    "B6D2F1": 6.899,
+    "BALB/cByJ": 7.172,
+    "BALB/cJ": 7.396
+  },
+  {
+    "129S1/SvImJ": 7.071,
+    "A/J": 7.05,
+    "AKR/J": 7.313,
+    "B6D2F1": 6.999,
+    "BALB/cByJ": 7.293,
+    "BALB/cJ": 7.117
+  },
+  {
+    "129S1/SvImJ": 7.221,
+    "A/J": 7.246,
+    "AKR/J": 7.754,
+    "B6D2F1": 6.866,
+    "BALB/cByJ": 6.752,
+    "BALB/cJ": 7.269
+  },
+  {
+    "129S1/SvImJ": 9.221,
+    "A/J": 9.246,
+    "AKR/J": 9.954,
+    "B6D2F1": 6.866,
+    "BALB/cByJ": 6.952,
+    "BALB/cJ": 9.269
+  },
+  {
+    "129S1/SvImJ": 7.221,
+    "A/J": 7.246,
+    "AKR/J": 7.754,
+    "B6D2F1": 6.866,
+    "BALB/cByJ": 6.752,
+    "BALB/cJ": 7.269
+  },
+  {
+    "129S1/SvImJ": 7.221,
+    "A/J": 7.246,
+    "AKR/J": 7.754,
+    "B6D2F1": 6.866,
+    "BALB/cByJ": 6.752,
+    "BALB/cJ": 7.269
+  },
+  {
+    "129S1/SvImJ": 11.221,
+    "A/J": 11.246,
+    "AKR/J": 11.1154,
+    "B6D2F1": 6.866,
+    "BALB/cByJ": 6.1152,
+    "BALB/cJ": 11.269
+  }
+   ],
+   "TOMtype": "unsigned",
+   "minModuleSize": 30,
+   "corType": "pearson"
+
+}
diff --git a/tests/integration/test_wgcna.py b/tests/integration/test_wgcna.py
new file mode 100644
index 0000000..078449d
--- /dev/null
+++ b/tests/integration/test_wgcna.py
@@ -0,0 +1,73 @@
+"""integration tests for wgcna"""
+
+from unittest import TestCase
+from unittest import mock
+
+from gn3.app import create_app
+
+
+class WgcnaIntegrationTest(TestCase):
+    """class contains wgcna integration tests"""
+
+    def setUp(self):
+        self.app = create_app().test_client()
+
+    @mock.patch("gn3.api.wgcna.call_wgcna_script")
+    def test_wgcna_endpoint(self, mock_wgcna_script):
+        """test /api/wgcna/run_wgcna endpoint"""
+
+        wgcna_output_data = {
+            "code": 0,
+            "output": "run script successfully",
+            "data": {
+                "ModEigens": {
+                    "MEturquoise": [
+                        0.0646677768085351,
+                        0.137200224277058,
+                        0.63451113720732,
+                        -0.544002665501479,
+                        -0.489487590361863,
+                        0.197111117570427
+                    ]
+                },
+                "net_colors": {
+                    "X1": "turquoise",
+                    "X2": "turquoise",
+                    "X3": "turquoise",
+                    "X4": "turquoise"
+                },
+                "imageLoc": "/WGCNAoutput_1uujpTIpC.png"
+            }
+        }
+
+        request_data = {
+            "trait_names": [
+                "1455537_at",
+                "1425637_at"
+            ],
+            "trait_sample_data": [
+                {
+                    "129S1/SvImJ": 6.142,
+                    "A/J": 5.31,
+                    "AKR/J": 3.49,
+                    "B6D2F1": 2.899,
+                    "BALB/cByJ": 1.172,
+                    "BALB/cJ": 7.396
+                },
+                {
+                    "129S1/SvImJ": 1.42,
+                    "A/J": 2.31,
+                    "AKR/J": 5.49,
+                    "B6D2F1": 3.899,
+                    "BALB/cByJ": 1.172,
+                    "BALB/cJ": 7.396
+                }
+            ]
+        }
+        mock_wgcna_script.return_value = wgcna_output_data
+
+        response = self.app.post("/api/wgcna/run_wgcna",
+                                 json=request_data, follow_redirects=True)
+
+        self.assertEqual(response.status_code, 200)
+        self.assertEqual(response.get_json(), wgcna_output_data)
diff --git a/tests/unit/computations/test_wgcna.py b/tests/unit/computations/test_wgcna.py
new file mode 100644
index 0000000..ec81d94
--- /dev/null
+++ b/tests/unit/computations/test_wgcna.py
@@ -0,0 +1,160 @@
+"""module contains python code for wgcna"""
+from unittest import TestCase
+from unittest import mock
+
+from gn3.computations.wgcna import dump_wgcna_data
+from gn3.computations.wgcna import compose_wgcna_cmd
+from gn3.computations.wgcna import call_wgcna_script
+
+
+class TestWgcna(TestCase):
+    """test class for wgcna"""
+
+    @mock.patch("gn3.computations.wgcna.run_cmd")
+    @mock.patch("gn3.computations.wgcna.compose_wgcna_cmd")
+    @mock.patch("gn3.computations.wgcna.dump_wgcna_data")
+    def test_call_wgcna_script(self,
+                               mock_dumping_data,
+                               mock_compose_wgcna,
+                               mock_run_cmd):
+        """test for calling wgcna script"""
+
+        # pylint: disable = line-too-long
+        mock_dumping_data.return_value = "/tmp/QmQPeNsJPyVWPFDVHb77w8G42Fvo15z4bG2X8D2GhfbSXc-test.json"
+
+        mock_compose_wgcna.return_value = "Rscript/GUIX_PATH/scripts/r_file.R /tmp/QmQPeNsJPyVWPFDVHb77w8G42Fvo15z4bG2X8D2GhfbSXc-test.json"
+
+        request_data = {
+            "trait_names": ["1455537_at", "1425637_at", "1449593_at", "1421945_a_at", "1450423_s_at", "1423841_at", "1451144_at"],
+            "trait_sample_data": [
+                {
+                    "129S1/SvImJ": 7.142,
+                    "A/J": 7.31,
+                    "AKR/J": 7.49,
+                    "B6D2F1": 6.899,
+                    "BALB/cByJ": 7.172,
+                    "BALB/cJ": 7.396
+                },
+                {
+                    "129S1/SvImJ": 7.071,
+                    "A/J": 7.05,
+                    "AKR/J": 7.313,
+                    "B6D2F1": 6.999,
+                    "BALB/cByJ": 7.293,
+                    "BALB/cJ": 7.117
+                }]}
+
+        mock_run_cmd_results = {
+
+            "code": 0,
+            "output": "Flagging genes and samples with too many missing values...\n  ..step 1\nAllowing parallel execution with up to 3 working processes.\npickSoftThreshold: will use block size 7.\n pickSoftThreshold: calculating connectivity for given powers...\n   ..working on genes 1 through 7 of 7\n   Flagging genes and samples with too many missing values...\n    ..step 1\n ..Working on block 1 .\n    TOM calculation: adjacency..\n    ..will not use multithreading.\nclustering..\n ....detecting modules..\n ....calculating module eigengenes..\n ....checking kME in modules..\n ..merging modules that are too close..\n     mergeCloseModules: Merging modules whose distance is less than 0.15\n     mergeCloseModules: less than two proper modules.\n      ..color levels are turquoise\n      ..there is nothing to merge.\n       Calculating new MEs...\n"
+        }
+
+        json_output = "{\"inputdata\":{\"trait_sample_data \":{},\"minModuleSize\":30,\"TOMtype\":\"unsigned\"},\"outputdata\":{\"eigengenes\":[],\"colors\":[]}}"
+
+        expected_output = {
+
+            "data": {
+                "inputdata": {
+                    "trait_sample_data ": {},
+                    "minModuleSize": 30,
+                    "TOMtype": "unsigned"
+                },
+
+                "outputdata": {
+                    "eigengenes": [],
+                    "colors": []
+                }
+            },
+
+            **mock_run_cmd_results
+
+        }
+
+        with mock.patch("builtins.open", mock.mock_open(read_data=json_output)):
+
+            mock_run_cmd.return_value = mock_run_cmd_results
+
+            results = call_wgcna_script(
+                "Rscript/GUIX_PATH/scripts/r_file.R", request_data)
+
+            mock_dumping_data.assert_called_once_with(request_data)
+
+            mock_compose_wgcna.assert_called_once_with(
+                "Rscript/GUIX_PATH/scripts/r_file.R",
+                "/tmp/QmQPeNsJPyVWPFDVHb77w8G42Fvo15z4bG2X8D2GhfbSXc-test.json")
+
+            mock_run_cmd.assert_called_once_with(
+                "Rscript/GUIX_PATH/scripts/r_file.R /tmp/QmQPeNsJPyVWPFDVHb77w8G42Fvo15z4bG2X8D2GhfbSXc-test.json")
+
+            self.assertEqual(results, expected_output)
+
+    @mock.patch("gn3.computations.wgcna.run_cmd")
+    @mock.patch("gn3.computations.wgcna.compose_wgcna_cmd")
+    @mock.patch("gn3.computations.wgcna.dump_wgcna_data")
+    def test_call_wgcna_script_fails(self, mock_dumping_data, mock_compose_wgcna, mock_run_cmd):
+        """test for calling wgcna script\
+        fails and generates the expected error"""
+        # pylint: disable = line-too-long,
+        mock_dumping_data.return_value = "/tmp/QmQPeNsJPyVWPFDVHb77w8G42Fvo15z4bG2X8D2GhfbSXc-test.json"
+
+        mock_compose_wgcna.return_value = "Rscript/GUIX_PATH/scripts/r_file.R /tmp/QmQPeNsJPyVWPFDVHb77w8G42Fvo15z4bG2X8D2GhfbSXc-test.json"
+
+        expected_error = {
+            "code": 2,
+            "output": "could not read the json file"
+        }
+
+        with mock.patch("builtins.open", mock.mock_open(read_data="")):
+
+            mock_run_cmd.return_value = expected_error
+            self.assertEqual(call_wgcna_script(
+                "input_file.R", ""), expected_error)
+
+    def test_compose_wgcna_cmd(self):
+        """test for composing wgcna cmd"""
+        wgcna_cmd = compose_wgcna_cmd(
+            "wgcna.r", "/tmp/wgcna.json")
+        self.assertEqual(
+            wgcna_cmd, "Rscript ./scripts/wgcna.r  /tmp/wgcna.json")
+
+    @mock.patch("gn3.computations.wgcna.TMPDIR", "/tmp")
+    @mock.patch("gn3.computations.wgcna.uuid.uuid4")
+    def test_create_json_file(self, file_name_generator):
+        """test for writing the data to a csv file"""
+        # # All the traits we have data for (should not contain duplicates)
+        # All the strains we have data for (contains duplicates)
+
+        trait_sample_data = {"1425642_at": {"129S1/SvImJ": 7.142,
+                                            "A/J": 7.31, "AKR/J": 7.49,
+                                            "B6D2F1": 6.899, "BALB/cByJ": 7.172,
+                                            "BALB/cJ": 7.396},
+                             "1457784_at": {"129S1/SvImJ": 7.071, "A/J": 7.05,
+                                            "AKR/J": 7.313,
+                                            "B6D2F1": 6.999, "BALB/cByJ": 7.293,
+                                            "BALB/cJ": 7.117},
+                             "1444351_at": {"129S1/SvImJ": 7.221, "A/J": 7.246,
+                                            "AKR/J": 7.754,
+                                            "B6D2F1": 6.866, "BALB/cByJ": 6.752,
+                                            "BALB/cJ": 7.269}
+
+                             }
+
+        expected_input = {
+            "trait_sample_data": trait_sample_data,
+            "TOMtype": "unsigned",
+            "minModuleSize": 30
+        }
+
+        with mock.patch("builtins.open", mock.mock_open()) as file_handler:
+
+            file_name_generator.return_value = "facb73ff-7eef-4053-b6ea-e91d3a22a00c"
+
+            results = dump_wgcna_data(
+                expected_input)
+
+            file_handler.assert_called_once_with(
+                "/tmp/facb73ff-7eef-4053-b6ea-e91d3a22a00c.json", 'w')
+
+            self.assertEqual(
+                results, "/tmp/facb73ff-7eef-4053-b6ea-e91d3a22a00c.json")