diff --git a/nPYc/StudyDesigns/SOP/Generic.json b/nPYc/StudyDesigns/SOP/Generic.json
index 545e2a7a..1d341494 100644
--- a/nPYc/StudyDesigns/SOP/Generic.json
+++ b/nPYc/StudyDesigns/SOP/Generic.json
@@ -17,10 +17,18 @@
 		"LinearityReference": "red", "Linearity Reference": "red",
 		"MethodReference": "blue", "Method Reference": "blue",
 		"ProceduralBlank": "aquamarine", "Blank": "aquamarine",
-		"Other": "grey", "UnknownRole": "grey", "Unknown": "grey", "nan": "grey", "NaN":  "grey", "NA":  "grey",
-		"UnknownType": "grey", "UnknownRole": "grey", "Unknown": "grey", "nan": "grey", "NaN":  "grey", "NA":  "grey"
+		"Other": "grey", "UnknownRole": "grey", "Unknown": "grey", "UnknownType": "grey", "nan": "grey", "NaN":  "grey", "NA":  "grey"
 	},
 	"sampleTypeMarkers":{
+		"StudySample": "8", "Assay": "8", "Study Sample": "8", "Sample": "x",
+		"StudyPool": "1", "Study Reference": "1", "Study Pool": "1",
+		"ExternalReference": "2", "Long-Term Reference": "2", "Long Term Reference": "2",
+		"LinearityReference": "4", "Linearity Reference": "4",
+		"MethodReference": "3", "Method Reference": "3",
+		"ProceduralBlank": "x", "Blank": "x",
+		"UnknownType": "d", "UnknownRole": "d", "Unknown": "d", "nan": "d", "NaN":  "d", "NA":  "d"
+	},
+	"matplotlibMarkers":{
 		"StudySample": "o", "Assay": "o", "Study Sample": "o", "Sample": "X",
 		"StudyPool": "v", "Study Reference": "v", "Study Pool": "v",
 		"ExternalReference": "^", "Long-Term Reference": "^", "Long Term Reference": "^",
diff --git a/nPYc/enumerations/_enumerations.py b/nPYc/enumerations/_enumerations.py
index 0702b4a4..d65e9cbb 100644
--- a/nPYc/enumerations/_enumerations.py
+++ b/nPYc/enumerations/_enumerations.py
@@ -46,6 +46,7 @@ class SampleType(enum.Enum):
 	* *External Reference* a sample of a comparable :term:`matrix` to the *Study Samples*, but not a sample (or mixture) derived from samples acquired as part of the study. Acquired, for example, for assessing analytical quality between studies.
 	* *Method Reference* consists of a synthetic mixture of known chemical standards
 	* *Procedural Blank* a blank sample not expected to contain any signals from the sample matrix
+	* *Linearity Reference - for dilution series? What do I know (jms3)
 	* *Unknown Type* a sample of unknown or undefined type
 	"""
 	def __repr__(self):
@@ -63,6 +64,7 @@ def __str__(self):
 	MethodReference = 'Method Reference'
 	ProceduralBlank = 'Procedural Blank'
 	UnknownType = 'Unknown Type'
+	LinearityReference = 'Linearity Reference'
 
 class AssayRole(enum.Enum):
 	"""
diff --git a/nPYc/plotting/_plotBatchAndROCorrection.py b/nPYc/plotting/_plotBatchAndROCorrection.py
index fe84a88c..99281a0c 100644
--- a/nPYc/plotting/_plotBatchAndROCorrection.py
+++ b/nPYc/plotting/_plotBatchAndROCorrection.py
@@ -141,13 +141,19 @@ def plotBatchAndROCorrection(msData, msDatacorrected, featureList, addViolin=Tru
 						msDatacorrected.intensityData[sample, feature]),
 						xytext=(mdates.date2num(msData.sampleMetadata.loc[sample, 'Acquired Time']),
 						msData.intensityData[sample, feature]),
-						arrowprops=dict(edgecolor=sTypeColourDict[SampleType.StudyPool], facecolor=sTypeColourDict[SampleType.StudyPool], alpha=0.9, arrowstyle = '-|>', shrinkA=0, shrinkB=0),
+						arrowprops=dict(edgecolor=sTypeColourDict[SampleType.StudyPool],
+										facecolor=sTypeColourDict[SampleType.StudyPool],
+										alpha=0.9, arrowstyle = '-|>', shrinkA=0, shrinkB=0),
 						clip_on=True)
 
 		# LTR
 		if sum(ERmask) > 0:
 
-			ax.plot_date([pandas.to_datetime(d) for d in msData.sampleMetadata.loc[ERmask, 'Acquired Time']], msData.intensityData[ERmask, feature], c=sTypeColourDict[SampleType.ExternalReference], fmt='o', ms=4, alpha=0.9, label='Long-Term Reference')
+			ax.plot_date([pandas.to_datetime(d) for d in msData.sampleMetadata.loc[ERmask, 'Acquired Time']],
+						 msData.intensityData[ERmask, feature],
+						 c=sTypeColourDict[SampleType.ExternalReference],
+						 fmt='o', ms=4,
+						 alpha=0.9, label='Long-Term Reference')
 
 			ER = numpy.where(ERmask==True)
 			temp = ER[0]
@@ -156,14 +162,22 @@ def plotBatchAndROCorrection(msData, msDatacorrected, featureList, addViolin=Tru
 						msDatacorrected.intensityData[sample, feature]),
 						xytext=(mdates.date2num(msData.sampleMetadata.loc[sample, 'Acquired Time']),
 						msData.intensityData[sample, feature]),
-						arrowprops=dict(edgecolor=sTypeColourDict[SampleType.ExternalReference], facecolor=sTypeColourDict[SampleType.ExternalReference], alpha=0.9, arrowstyle = '-|>', shrinkA=0, shrinkB=0),
+						arrowprops=dict(edgecolor=sTypeColourDict[SampleType.ExternalReference],
+										facecolor=sTypeColourDict[SampleType.ExternalReference],
+										alpha=0.9, arrowstyle = '-|>', shrinkA=0, shrinkB=0),
 						clip_on=True)
 
 
 		# SRD
 		if sum(LRmask) > 0:
 
-			ax.plot_date([pandas.to_datetime(d) for d in msData.sampleMetadata.loc[LRmask, 'Acquired Time']], msData.intensityData[LRmask, feature], c=sTypeColourDict[SampleType.MethodReference], fmt='s', ms=4, alpha=0.9, label='Serial Dilution')
+			ax.plot_date([pandas.to_datetime(d) for d in msData.sampleMetadata.loc[LRmask, 'Acquired Time']],
+						 msData.intensityData[LRmask, feature],
+						 c=sTypeColourDict[SampleType.LinearityReference],
+						 fmt='s',
+						 ms=4,
+						 alpha=0.9,
+						 label='Serial Dilution')
 
 
 		# Plot fit coloured by batch
diff --git a/nPYc/plotting/_plotTIC.py b/nPYc/plotting/_plotTIC.py
index c33b047f..4dbece16 100644
--- a/nPYc/plotting/_plotTIC.py
+++ b/nPYc/plotting/_plotTIC.py
@@ -254,7 +254,7 @@ def plotTIC(dataset, addViolin=True, addBatchShading=False,
 	if colourType in {'continuous', 'continuousCentered'}:
 		cbar = plt.colorbar(sc)
 		cbar.set_label(colourBy)
-		leg = ax.legend(loc='upper left', bbox_to_anchor=(1, 1))
+		#leg = ax.legend(loc='upper left', bbox_to_anchor=(1, 1))
 	elif addViolin is False:
 		ax.legend(loc='upper left', bbox_to_anchor=(1, 1))
 
@@ -263,7 +263,9 @@ def plotTIC(dataset, addViolin=True, addBatchShading=False,
 	# Save or output
 	if savePath:
 		try:
-			plt.savefig(savePath, bbox_extra_artists=(leg, ), bbox_inches='tight', format=figureFormat, dpi=dpi)
+			#plt.savefig(savePath, bbox_extra_artists=(leg, ), bbox_inches='tight', format=figureFormat, dpi=dpi)
+			plt.savefig(savePath, bbox_inches='tight', format=figureFormat, dpi=dpi)
+
 		except UnboundLocalError:
 			plt.savefig(savePath, bbox_inches='tight', format=figureFormat, dpi=dpi)
 		plt.close()
@@ -273,7 +275,9 @@ def plotTIC(dataset, addViolin=True, addBatchShading=False,
 
 
 def plotTICinteractive(dataset, x='Run Order', y='TIC', labelBy='Run Order',
-					   colourBy='Correction Batch', withExclusions=True,
+					   colourBy='Correction Batch',
+						colourDict=None, markerDict=None, abbrDict=None,
+					   withExclusions=True,
 					   destinationPath=None, autoOpen=True, opacity=.6):
 	"""
 	Interactively visualise TIC or intensity for a given feature with plotly, provides tooltips to allow identification of samples.
@@ -283,6 +287,9 @@ def plotTICinteractive(dataset, x='Run Order', y='TIC', labelBy='Run Order',
 	:param str y: Y-axis of plot, either ``TIC`` for sum of all features, or a specific feature name
 	:param str labelBy: dataset.sampleMetadata column entry to display in tooltips
 	:param str colourBy: dataset.sampleMetadata column entry to colour data points by
+	:param dict colourDict:
+	:param dict markerDict:
+	:param dict abbrDict:
 	:param bool withExclusions: If ``True``, only report on features and samples not masked by the sample and feature masks
 	:param str destinationPath: file path to save html version of plot
 	:param bool autoOpen: If ``True``, opens html version of plot
@@ -379,16 +386,27 @@ def plotTICinteractive(dataset, x='Run Order', y='TIC', labelBy='Run Order',
 		data.append(CLASSplot)
 
 	# Plot categorical values by unique groups
+	# add colour/marker dictionaries here?
 	else:
 		uniq = numpy.unique(classes[plotnans == False])
+		if colourDict is None:
+			colourDict = {}
+			for u in uniq:
+				colourDict[u] = 'magenta'
+			markerDict = {}
+			for u in uniq:
+				markerDict[u] = 'diamond-dot'
+
 		for i in uniq:
 			CLASSplot = go.Scatter(
 				x=msData.sampleMetadata.loc[classes == i, x],
 				y=values[classes == i],
 				mode='markers',
 				marker=dict(
-					colorscale='Portland',
-					symbol='circle',
+					#colorscale='Portland',
+					#symbol='circle',
+					color=colourDict[i],
+					symbol=markerDict[i]
 					),
 				text=hovertext[classes == i],
 				name=str(i),
@@ -425,7 +443,8 @@ def plotTICinteractive(dataset, x='Run Order', y='TIC', labelBy='Run Order',
 			mode='markers',
 			marker=dict(
 				color='rgb(198, 83, 83)',
-				symbol='x'
+				symbol='star',
+
 			),
 			text=hovertext[LTRmask],
 			name='Long-Term Reference',
diff --git a/nPYc/plotting/_plotting.py b/nPYc/plotting/_plotting.py
index 0afa7823..f6793659 100644
--- a/nPYc/plotting/_plotting.py
+++ b/nPYc/plotting/_plotting.py
@@ -194,7 +194,7 @@ def histogram(values, inclusionVector=None, quantiles=None, title='', xlabel='',
 
 
 def plotLRTIC(msData, sampleMask=None, colourByDetectorVoltage=False, title='', label=False, savePath=None,
-              figureFormat='png', dpi=72, figureSize=(11, 7), opacity=.4):
+              figureFormat='png', dpi=72, figureSize=(11, 7), opacity=.6):
     """
 	Visualise TIC for linearity reference (LR) samples (either all or a subset) coloured by either dilution value or detector voltage.