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authorBonfaceKilz2021-09-28 11:21:07 +0300
committerGitHub2021-09-28 11:21:07 +0300
commit674dc56b9df38e7cf1bbc65a2fc6bf3cc16f7231 (patch)
treef35b34525843542aca50c4f22c6b62ca59d1a057
parent0cbb6ecde0315b7d6f021cb17406f5e5197e8a05 (diff)
parent16235188d4ee2ad21a667832baf6cbbea6d8856a (diff)
downloadgenenetwork3-674dc56b9df38e7cf1bbc65a2fc6bf3cc16f7231.tar.gz
Merge pull request #38 from genenetwork/feature/wgcna_analysis
script for wgcna analysis
-rw-r--r--gn3/api/wgcna.py25
-rw-r--r--gn3/app.py2
-rw-r--r--gn3/computations/wgcna.py51
-rw-r--r--gn3/settings.py2
-rw-r--r--guix.scm3
-rw-r--r--scripts/output.json161
-rw-r--r--scripts/wgcna_analysis.R115
-rw-r--r--scripts/wgcna_test_data.csv9
-rw-r--r--scripts/wgcna_test_data.json65
-rw-r--r--tests/integration/test_wgcna.py73
-rw-r--r--tests/unit/computations/test_wgcna.py160
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")