Change UI wording to make more tech agnostic

src/components/data-processing/CellSizeDistribution/CalculationConfig.jsx

@@ -26,7 +26,7 @@ const CellSizeDistributionConfig = (props) => {
 
       <Form.Item disabled label='Minimum #UMIs per cell'>
         <Space>
-          <Tooltip title='The cut-off is automatically calculated as the inflection point of the knee plot. The inflection point estimates the boundary between empty drops and droplets that contain cells. The number of UMIs per cell varies depending on cell type. The typical minimum threshold range approx. 500-2000.'>
+          <Tooltip title='The cut-off is automatically calculated as the inflection point of the knee plot. The inflection point estimates the boundary between background (ambient RNA) and real cells. The number of UMIs per cell varies depending on cell type. The typical minimum threshold range approx. 500-2000.'>
             <InfoCircleOutlined />
           </Tooltip>
           <InputNumber

src/components/data-processing/Classifier/CalculationConfig.jsx

@@ -30,8 +30,7 @@ const ClassifierConfig = (props) => {
                 {' '}
                 function
               </a>
-              . This
-              distinguishes between droplets containing cells and ambient RNA. The FDR range is
+              . This distinguishes between real cells and background (ambient RNA). The FDR range is
               [0-1]. The default FDR value is 0.01, where only barcodes with FDR &lt; 0.01
               are retained.
             </span>

src/pages/experiments/[experimentId]/data-processing/index.jsx

@@ -175,7 +175,7 @@ const DataProcessingPage = ({ experimentId, experimentData }) => {
     {
       key: 'classifier',
       name: getUserFriendlyQCStepName('classifier'),
-      description: 'The Classifier filter is based on the ‘emptyDrops’ method which distinguishes between droplets containing cells and ambient RNA. Droplets are filtered based on the False Discovery Rate (FDR) value - the red line on the density plot. In the knee plot, the ‘mixed’ population shown in grey contains some cells that are filtered out and some that remain and can be filtered further in the next filter.',
+      description: 'The Classifier filter is based on the ‘emptyDrops’ method which distinguishes between real cells and background (ambient RNA). Barcodes are filtered based on the False Discovery Rate (FDR) value - the red line on the density plot. In the knee plot, the ‘mixed’ population shown in grey contains some cells that are filtered out and some that remain and can be filtered further in the next filter.',
       multiSample: true,
       render: (key) => (
         <SingleComponentMultipleDataContainer
@@ -200,7 +200,7 @@ const DataProcessingPage = ({ experimentId, experimentData }) => {
     {
       key: 'cellSizeDistribution',
       name: getUserFriendlyQCStepName('cellSizeDistribution'),
-      description: 'The number of unique molecular identifiers (#UMIs) per cell distinguishes real cells (high #UMIs per cell) from empty droplets (low #UMIs per cell). This filter is used to detect empty droplets and fine-tunes the Classifier filter. In some datasets this filter might be used instead of the Classifier filter.',
+      description: 'The number of unique molecular identifiers (#UMIs) per cell distinguishes real cells (high #UMIs per cell) from background (low #UMIs per cell). This filter is used to detect background and fine-tunes the Classifier filter. In some datasets this filter might be used instead of the Classifier filter.',
       multiSample: true,
       render: (key) => (
         <SingleComponentMultipleDataContainer
@@ -224,7 +224,7 @@ const DataProcessingPage = ({ experimentId, experimentData }) => {
     {
       key: 'mitochondrialContent',
       name: getUserFriendlyQCStepName('mitochondrialContent'),
-      description: 'A high percentage of mitochondrial reads is an indicator of cell death. UMIs mapped to mitochondrial genes are calculated as a percentage of total UMIs. The percentage of mitochondrial reads depends on the cell type. The typical cut-off range is 10-50%, with the default cut-off set to 3 median absolute deviations above the median.',
+      description: 'A high percentage of mitochondrial reads is an indicator of cell death. UMIs mapped to mitochondrial genes are calculated as a percentage of total UMIs. The percentage of mitochondrial reads depends on the cell type. The typical cut-off range is 5-30%, with the default cut-off set to 3 median absolute deviations above the median.',
       multiSample: true,
       render: (key) => (
         <SingleComponentMultipleDataContainer
@@ -248,7 +248,7 @@ const DataProcessingPage = ({ experimentId, experimentData }) => {
     {
       key: 'numGenesVsNumUmis',
       name: getUserFriendlyQCStepName('numGenesVsNumUmis'),
-      description: 'The number of expressed genes per cell and number of UMIs per cell is expected to have a linear relationship. This filter is used to exclude outliers (e.g. many UMIs originating from only a few genes).',
+      description: 'The number of expressed genes per cell and number of UMIs per cell is expected to have a linear relationship, until the maximum number of genes is reached and the curve tends to plateau. This filter is used to exclude outliers (e.g. many UMIs originating from only a few genes).',
       multiSample: true,
       render: (key) => (
         <SingleComponentMultipleDataContainer
@@ -275,7 +275,7 @@ const DataProcessingPage = ({ experimentId, experimentData }) => {
       name: getUserFriendlyQCStepName('doubletScores'),
       description: (
         <span>
-          Droplets may contain more than one cell.
+          A single barcode might correspond to more than one cell.
           In such cases, it is not possible to distinguish which reads came from which cell.
           Such “cells” cause problems in the downstream analysis as they appear as an intermediate type.
           “Cells” with a high probability of being a doublet should be excluded.

src/redux/reducers/componentConfig/initialState.js

@@ -648,8 +648,8 @@ const classifierKneePlot = {
   },
   axes: {
     ...axesBaseState,
-    xAxisText: 'Droplet Rank',
-    yAxisText: 'Droplet #UMIs',
+    xAxisText: 'Barcode Rank',
+    yAxisText: 'Barcode #UMIs',
   },
   axesRanges: {
     ...axesRangesBaseState,