Cell Bender Functionality & Plotting
-Compiled: October 25, 2022
+Compiled: November 10, 2022
Source:vignettes/Cell_Bender_Functions.Rmd
Cell_Bender_Functions.RmdCompiled: October 25, 2022
}Cell Bender Functionality
+CellBender Functionality
-
Cell Bender is software for elimination of technical artifacts in scRNA-seq/snRNA-seq that uses deep generative model for unsupervised removal of ambient RNA and chimeric PCR artifacts. You can find out more info about Cell Bender from the bioRxiv Preprint, GitHub Repo, and Documentation.
-Following completion of Cell Bender scCustomize contains a couple of functions that may be helpful when creating and visualizing data in Seurat.
+CellBender is software for elimination of technical artifacts in scRNA-seq/snRNA-seq that uses deep generative model for unsupervised removal of ambient RNA and chimeric PCR artifacts. You can find out more info about CellBender from the bioRxiv Preprint, GitHub Repo, and Documentation.
+Following completion of CellBender scCustomize contains a couple of functions that may be helpful when creating and visualizing data in Seurat.
library(tidyverse)
library(patchwork)
@@ -160,11 +160,21 @@ Cell Bender Functionality
Importing Cell Bender H5 Outputs
-The output from Cell Bender is an H5 file that is styled and can be read like 10X Genomics H5 file. However, Seurat::Read10X_h5() assumes that the file contains no name prefix.
-However,Read10X_h5_Multi_Directory() can be used when reading in Cell Bender files. and contains additional parameters related to Cell Bender imports.
+The output from CellBender is an H5 file that is styled and can be read like 10X Genomics H5 file. In CellBender pre-v3 this file could be read by Seurat::Read10X_h5() (although that function assumes that the file contains no name prefix). However, v3+ contains extra information that causes Read10X_h5 to fail.
+scCustomize contains function Read_CellBender_h5_Mat() can be used when reading in CellBender h5 files regardless of the version of CellBender used.
+
+cell_bender_mat <- Read_CellBender_h5_Mat(file_name = "PATH/SampleA_out_filtered.h5")
+
Reading multiple CellBender files with single function +
+scCustomize also contains two wrapper functions to easily read multiple CellBender files stored either in single directory or in multiple sub-directories Read_CellBender_h5_Multi_File and Read_CellBender_h5_Multi_Directory.
Cell Bender Output Structure
+Read in H5 outputs from multiple sub-directories with Read_CellBender_h5_Multi_Directory().
+
+Cell Bender Output Structure
+
The following is typical output directory structure for Cell Bender files with sub-directory labeled with sample name and each file also prefixed with the sample name.
Parent_Directory
├── sample_01
@@ -180,31 +190,45 @@ Cell Bender Output Structure
-Read in H5 outputs
-
+
+Read Files
+
All we have to do is adjust the parameters to account for cell bender file names and directory structure.
-
-
secondary_path = "" as the files are directly within the immediate sub-directory
+secondary_path In this case we can leave as NULL because samples are located in immediate subdirectory (secondary path must be the same for all samples).
-
-
default_10X_path = FALSE because these are not 10X outputs.
--
-
h5_filename = "_out.h5" specifies the shared aspect of file name that is not part of sample name. (Can also specify “_out_filtered.h5” depending on which file is desired).
+custom_name = "_out.h5" This specifies what the common file suffix of all files are.
-
-cell_bender_merged <- Read10X_h5_Multi_Directory(base_path = "assets/cell_bender/", secondary_path = "",
- default_10X_path = FALSE, h5_filename = "_out.h5", cell_bender = TRUE, merge = TRUE, sample_names = c("WT1",
- "WT2"), parallel = TRUE, num_cores = 2)
+Optional Parameters
+* parallel and num_cores to use multiple core processing. * sample_list By default Read_CellBender_h5_Multi_Directory will read in all sub-directories present in parent directory. However a subset can be specified by passing a vector of sample directory names. * sample_names As with other functions by default Read_CellBender_h5_Multi_Directory will use the sub-directory names within parent directory to name the output list entries. Alternate names for the list entries can be provided here if desired. These names will also be used to add cell prefixes if merge = TRUE (see below).
+* merge logical (default FALSE). Whether to combine all samples into single sparse matrix and using sample_names to provide sample prefixes.
+
+cell_bender_merged <- Read_CellBender_h5_Multi_Directory(base_path = "assets/Cell_Bender_Example/",
+ custom_name = "_out.h5", sample_names = c("WT1", "WT2"), merge = TRUE)
+
-These matrices can then be used to create Seurat objects as desired (can also be used to create other analysis objects)
+Read in H5 outputs from single directory with Read_CellBender_h5_Multi_File().
-
-cell_bender_seurat <- CreateSeuratObject(counts = cell_bender_merged, names.field = 1, names.delim = "_")
+
If all output files are in single directory you can use Read_CellBender_h5_Multi_File to read in all of the files with single function.
+Parent_Directory
+├── CellBender_Outputs
+│ └── sample_01_out.h5
+│ └── sample_02_out.h5
+
+cell_bender_merged <- Read_CellBender_h5_Multi_File(data_dir = "assets/Cell_Bender_Example/", custom_name = "_out.h5",
+ sample_names = c("WT1", "WT2"))
Read_CellBender_h5_Multi_Directory().
Cell Bender Output Structure +
The following is typical output directory structure for Cell Bender files with sub-directory labeled with sample name and each file also prefixed with the sample name.
Parent_Directory
├── sample_01
@@ -180,31 +190,45 @@ Cell Bender Output Structure
-Read in H5 outputs
-
+
+Read Files
+
All we have to do is adjust the parameters to account for cell bender file names and directory structure.
-
-
secondary_path = "" as the files are directly within the immediate sub-directory
+secondary_path In this case we can leave as NULL because samples are located in immediate subdirectory (secondary path must be the same for all samples).
-
-
default_10X_path = FALSE because these are not 10X outputs.
--
-
h5_filename = "_out.h5" specifies the shared aspect of file name that is not part of sample name. (Can also specify “_out_filtered.h5” depending on which file is desired).
+custom_name = "_out.h5" This specifies what the common file suffix of all files are.
-
-cell_bender_merged <- Read10X_h5_Multi_Directory(base_path = "assets/cell_bender/", secondary_path = "",
- default_10X_path = FALSE, h5_filename = "_out.h5", cell_bender = TRUE, merge = TRUE, sample_names = c("WT1",
- "WT2"), parallel = TRUE, num_cores = 2)
+Optional Parameters
+* parallel and num_cores to use multiple core processing. * sample_list By default Read_CellBender_h5_Multi_Directory will read in all sub-directories present in parent directory. However a subset can be specified by passing a vector of sample directory names. * sample_names As with other functions by default Read_CellBender_h5_Multi_Directory will use the sub-directory names within parent directory to name the output list entries. Alternate names for the list entries can be provided here if desired. These names will also be used to add cell prefixes if merge = TRUE (see below).
+* merge logical (default FALSE). Whether to combine all samples into single sparse matrix and using sample_names to provide sample prefixes.
+
+cell_bender_merged <- Read_CellBender_h5_Multi_Directory(base_path = "assets/Cell_Bender_Example/",
+ custom_name = "_out.h5", sample_names = c("WT1", "WT2"), merge = TRUE)
+
These matrices can then be used to create Seurat objects as desired (can also be used to create other analysis objects)
+Read in H5 outputs from single directory with Read_CellBender_h5_Multi_File().
-
-cell_bender_seurat <- CreateSeuratObject(counts = cell_bender_merged, names.field = 1, names.delim = "_")
+
Read_CellBender_h5_Multi_File().
-cell_bender_seurat <- CreateSeuratObject(counts = cell_bender_merged, names.field = 1, names.delim = "_")If all output files are in single directory you can use Read_CellBender_h5_Multi_File to read in all of the files with single function.
Parent_Directory
+├── CellBender_Outputs
+│ └── sample_01_out.h5
+│ └── sample_02_out.h5
+cell_bender_merged <- Read_CellBender_h5_Multi_File(data_dir = "assets/Cell_Bender_Example/", custom_name = "_out.h5",
+ sample_names = c("WT1", "WT2"))Create Seurat Object with Corrected Counts +
+Creating a Seurat object from merged CellBender matrices then is identical to creating any other Seurat object.
+
+cell_bender_seurat <- CreateSeuratObject(counts = cell_bender_merged, names.field = 1, names.delim = "_")Creating Dual Assay Objects
Sometimes it can be helpful to create object that contains both the cell ranger values and cell bender values (we’ll come to why below). scCustomize contains a helper function Create_CellBender_Merged_Seurat() to handle object creation in one quick step.
Creating Dual Assay Objects
Read in both sets of data
-
-cell_bender_merged <- Read10X_h5_Multi_Directory(base_path = "assets/cell_bender/", secondary_path = "",
- default_10X_path = FALSE, h5_filename = "_out.h5", cell_bender = TRUE, merge = TRUE, sample_names = c("WT1",
- "WT2"), parallel = TRUE, num_cores = 2)
+
+cell_bender_merged <- Read_CellBender_h5_Multi_Directory(base_path = "assets/Cell_Bender_Cell_Ranger_Data/Cell_Bender_Example/",
+ custom_name = "_out.h5", sample_names = c("WT1", "WT2"), merge = TRUE)
-cell_ranger_merged <- Read10X_h5_Multi_Directory(base_path = "assets/cell_ranger/", default_10X_path = TRUE,
- h5_filename = "filtered_feature_bc_matrix.h5", cell_bender = F, merge = TRUE, sample_names = c("WT1",
+cell_ranger_merged <- Read10X_h5_Multi_Directory(base_path = "assets/Cell_Bender_Cell_Ranger_Data/Cell_Ranger_Example/",
+ default_10X_path = FALSE, h5_filename = "filtered_feature_bc_matrix.h5", merge = TRUE, sample_names = c("WT1",
"WT2"), parallel = TRUE, num_cores = 2)
Create Dual Assay Seurat Object
To run the function the user simply needs to provide the names of the two matrices and a name for assay containing the Cell Ranger counts (by default this is named “RAW”).
-
+
dual_seurat <- Create_CellBender_Merged_Seurat(raw_cell_bender_matrix = cell_bender_merged, raw_counts_matrix = cell_ranger_merged,
raw_assay_name = "RAW")
@@ -233,7 +256,7 @@ Create Dual Assay Seurat ObjectOptional Parameters
Users can specify any additional parameters normally passed to Seurat::CreateSeuratObject() when using this function.
-
+
dual_seurat <- Create_CellBender_Merged_Seurat(raw_cell_bender_matrix = cell_bender_merged, raw_counts_matrix = cell_ranger_merged,
raw_assay_name = "RAW", min_cells = 5, min_features = 200)
@@ -245,12 +268,11 @@ Pre/Post Cell Bender Analysis
Add Pre/Post to Meta Data
-scCustomize includes function Add_Cell_Bender_Diff() to help with this process. This function will take the nCount and nFeature statistics from both assays in the object and calculate the difference and return 2 new columns (“nCount_Diff” and “nFeature_Diff”) to the object meta.data.
-
-astrocytes_cortex <- Add_Cell_Bender_Diff(seurat_object = astrocytes_cortex, raw_assay_name = "RAW",
- cell_bender_assay_name = "RNA")
+scCustomize includes function Add_CellBender_Diff() to help with this process. This function will take the nCount and nFeature statistics from both assays in the object and calculate the difference and return 2 new columns (“nCount_Diff” and “nFeature_Diff”) to the object meta.data.
+
+dual_seurat <- Add_CellBender_Diff(seurat_object = dual_seurat, raw_assay_name = "RAW", cell_bender_assay_name = "RNA")
-head(astrocytes_cortex@meta.data, 5)
+head(dual_seurat@meta.data, 5)
+
+Calculate per sample averages
+
+We can then use Median_Stats() to calculate per sample averages across all cells by supplying the new variables to the median_var parameter.
+
+median_stats <- Median_Stats(seurat_object = dual_seurat, group_by_var = "orig.ident", median_var = c("nCount_Diff",
+ "nFeature_Diff"))
+
+
+
+orig.ident
+
+
+Median_nCount_RNA
+
+
+Median_nFeature_RNA
+
+
+Median_nCount_Diff
+
+
+Median_nFeature_Diff
+
+
+
+
+
+WT1
-2504
+7724.5
-1235
-
-
-4
-
-
-4
+2936
-142
+216
-251
+73
-WT-2a_AAACGCTCATGTTACG-1
-
-
-WT-2a
-
-
-2685
-
-
-1518
+WT2
-2999
+7404.5
-1694
+2803
-0.0744879
+212
-0.8193669
+61
-
-0.8938547
+
+
+
+Totals (All Cells)
-2720
+7565.0
-1509
-
-
-4
-
-
-4
+2869
-176
+214
-314
+67
-Calculate per sample averages
+Examine most changed features
-
We can then use Median_Stats() to calculate per sample averages across all cells by supplying the new variables to the median_var parameter.
-
-median_stats <- Median_Stats(seurat_object = astrocytes_cortex, group_by_var = "orig.ident", median_var = c("nCount_Diff",
- "nFeature_Diff"))
+It can also be helpful to understand what features may have changed the most. scCustomize provides the function CellBender_Feature_Diff to determine changes in features. This will return a data.frame with rowSums for each feature, the difference in each feature, and the percent change of each feature.
+
+feature_diff <- CellBender_Feature_Diff(seurat_object = dual_seurat, raw_assay = "RAW", cell_bender_assay = "RNA")
-orig.ident
-
-
-Median_nCount_RNA
-Median_nFeature_RNA
-
-
-Median_percent_mito
+Raw_Counts
-Median_percent_ribo
+CellBender_Counts
-Median_percent_mito_ribo
-
-
-Median_nCount_Diff
+Count_Diff
-Median_nFeature_Diff
+Pct_Diff
-WT-2a
-
-
-2949.5
-
-
-1527.5
-
-
-0.0224121
+Gm42418
-0.0673027
+256834
-0.0948317
+3198
-280
+253636
-156
+98.75484
-WT-6a
+Uqcr11
-2765.0
+1315
-1451.5
+20
-0.0352609
+1295
-0.0656276
+98.47909
+
+
+
+
+Gm48099
-0.1065341
+277
-279
+5
-154
+272
+
+
+98.19495
-Totals (All Cells)
+Tmsb4x
+
+
+6010
-2875.0
+116
-1498.5
+5894
-0.0293169
+98.06988
+
+
+
+
+Rps21
-0.0665447
+2597
-0.1006627
+61
-279
+2536
-156
+97.65114
+
+Plot feature differences
+
+In addition to returning the data.frame it can be useful to visually examine the changes/trends after running CellBender. The function CellBender_Diff_Plot takes the data.frame from CellBender_Feature_Diff as input and plots the results.
+
+Optional Parameters
+
+-
+
pct_diff_threshold plot genes that exhibit a change equal to or greater than this threshold.
+
+-
+
num_features instead of plotting genes above a threshold simply plot the top X changed genes.
+
+-
+
num_labels change how many genes are labeled.
+
+-
+
label logical, whether or not to label features.
+
+-
+
custom_labels specify vector of specific features to label.
+
+
+p1 <- CellBender_Diff_Plot(feature_diff_df = feature_diff)
+p2 <- CellBender_Diff_Plot(feature_diff_df = feature_diff, pct_diff_threshold = 50)
+p3 <- CellBender_Diff_Plot(feature_diff_df = feature_diff, num_features = 500, pct_diff_threshold = NULL)
+p4 <- CellBender_Diff_Plot(feature_diff_df = feature_diff, num_labels = 10)
+p5 <- CellBender_Diff_Plot(feature_diff_df = feature_diff, label = F)
+p6 <- CellBender_Diff_Plot(feature_diff_df = feature_diff, custom_labels = "Gm48099")
+
+wrap_plots(p1, p2, p3, p4, p5, p6, ncol = 2)
+
+
-cell_bender_merged <- Read10X_h5_Multi_Directory(base_path = "assets/cell_bender/", secondary_path = "",
- default_10X_path = FALSE, h5_filename = "_out.h5", cell_bender = TRUE, merge = TRUE, sample_names = c("WT1",
- "WT2"), parallel = TRUE, num_cores = 2)
+
+cell_bender_merged <- Read_CellBender_h5_Multi_Directory(base_path = "assets/Cell_Bender_Cell_Ranger_Data/Cell_Bender_Example/",
+ custom_name = "_out.h5", sample_names = c("WT1", "WT2"), merge = TRUE)
-cell_ranger_merged <- Read10X_h5_Multi_Directory(base_path = "assets/cell_ranger/", default_10X_path = TRUE,
- h5_filename = "filtered_feature_bc_matrix.h5", cell_bender = F, merge = TRUE, sample_names = c("WT1",
+cell_ranger_merged <- Read10X_h5_Multi_Directory(base_path = "assets/Cell_Bender_Cell_Ranger_Data/Cell_Ranger_Example/",
+ default_10X_path = FALSE, h5_filename = "filtered_feature_bc_matrix.h5", merge = TRUE, sample_names = c("WT1",
"WT2"), parallel = TRUE, num_cores = 2)
Create Dual Assay Seurat Object
To run the function the user simply needs to provide the names of the two matrices and a name for assay containing the Cell Ranger counts (by default this is named “RAW”).
-+dual_seurat <- Create_CellBender_Merged_Seurat(raw_cell_bender_matrix = cell_bender_merged, raw_counts_matrix = cell_ranger_merged, raw_assay_name = "RAW")
Create Dual Assay Seurat ObjectOptional Parameters
Users can specify any additional parameters normally passed to Seurat::CreateSeuratObject() when using this function.
+dual_seurat <- Create_CellBender_Merged_Seurat(raw_cell_bender_matrix = cell_bender_merged, raw_counts_matrix = cell_ranger_merged, raw_assay_name = "RAW", min_cells = 5, min_features = 200)
Pre/Post Cell Bender Analysis
Add Pre/Post to Meta Data
-scCustomize includes function Add_Cell_Bender_Diff() to help with this process. This function will take the nCount and nFeature statistics from both assays in the object and calculate the difference and return 2 new columns (“nCount_Diff” and “nFeature_Diff”) to the object meta.data.
-
-astrocytes_cortex <- Add_Cell_Bender_Diff(seurat_object = astrocytes_cortex, raw_assay_name = "RAW",
- cell_bender_assay_name = "RNA")
+scCustomize includes function Add_CellBender_Diff() to help with this process. This function will take the nCount and nFeature statistics from both assays in the object and calculate the difference and return 2 new columns (“nCount_Diff” and “nFeature_Diff”) to the object meta.data.
+
+dual_seurat <- Add_CellBender_Diff(seurat_object = dual_seurat, raw_assay_name = "RAW", cell_bender_assay_name = "RNA")
-head(astrocytes_cortex@meta.data, 5)
+head(dual_seurat@meta.data, 5)
Add_Cell_Bender_Diff() to help with this process. This function will take the nCount and nFeature statistics from both assays in the object and calculate the difference and return 2 new columns (“nCount_Diff” and “nFeature_Diff”) to the object meta.data.
-astrocytes_cortex <- Add_Cell_Bender_Diff(seurat_object = astrocytes_cortex, raw_assay_name = "RAW",
- cell_bender_assay_name = "RNA")
+scCustomize includes function Add_CellBender_Diff() to help with this process. This function will take the nCount and nFeature statistics from both assays in the object and calculate the difference and return 2 new columns (“nCount_Diff” and “nFeature_Diff”) to the object meta.data.
+
+dual_seurat <- Add_CellBender_Diff(seurat_object = dual_seurat, raw_assay_name = "RAW", cell_bender_assay_name = "RNA")
-head(astrocytes_cortex@meta.data, 5)
+head(dual_seurat@meta.data, 5)Calculate per sample averages +
+We can then use Median_Stats() to calculate per sample averages across all cells by supplying the new variables to the median_var parameter.
+median_stats <- Median_Stats(seurat_object = dual_seurat, group_by_var = "orig.ident", median_var = c("nCount_Diff",
+ "nFeature_Diff"))| +orig.ident + | ++Median_nCount_RNA + | ++Median_nFeature_RNA + | ++Median_nCount_Diff + | ++Median_nFeature_Diff + | +||||
|---|---|---|---|---|---|---|---|---|
| +WT1 | -2504 +7724.5 | -1235 - | --4 - | --4 +2936 | -142 +216 | -251 +73 | ||
| -WT-2a_AAACGCTCATGTTACG-1 - | --WT-2a - | --2685 - | --1518 +WT2 | -2999 +7404.5 | -1694 +2803 | -0.0744879 +212 | -0.8193669 +61 | --0.8938547 + |
| +Totals (All Cells) | -2720 +7565.0 | -1509 - | --4 - | --4 +2869 | -176 +214 | -314 +67 |
Calculate per sample averages
+Examine most changed features
-
We can then use Median_Stats() to calculate per sample averages across all cells by supplying the new variables to the median_var parameter.
-median_stats <- Median_Stats(seurat_object = astrocytes_cortex, group_by_var = "orig.ident", median_var = c("nCount_Diff",
- "nFeature_Diff"))It can also be helpful to understand what features may have changed the most. scCustomize provides the function CellBender_Feature_Diff to determine changes in features. This will return a data.frame with rowSums for each feature, the difference in each feature, and the percent change of each feature.
+feature_diff <- CellBender_Feature_Diff(seurat_object = dual_seurat, raw_assay = "RAW", cell_bender_assay = "RNA")| -orig.ident - | --Median_nCount_RNA | -Median_nFeature_RNA - | --Median_percent_mito +Raw_Counts | -Median_percent_ribo +CellBender_Counts | -Median_percent_mito_ribo - | --Median_nCount_Diff +Count_Diff | -Median_nFeature_Diff +Pct_Diff |
|---|---|---|---|---|---|---|---|
| -WT-2a - | --2949.5 - | --1527.5 - | --0.0224121 +Gm42418 | -0.0673027 +256834 | -0.0948317 +3198 | -280 +253636 | -156 +98.75484 |
| -WT-6a +Uqcr11 | -2765.0 +1315 | -1451.5 +20 | -0.0352609 +1295 | -0.0656276 +98.47909 + | +|||
| +Gm48099 | -0.1065341 +277 | -279 +5 | -154 +272 + | ++98.19495 | |||
| -Totals (All Cells) +Tmsb4x + | ++6010 | -2875.0 +116 | -1498.5 +5894 | -0.0293169 +98.06988 + | +|||
| +Rps21 | -0.0665447 +2597 | -0.1006627 +61 | -279 +2536 | -156 +97.65114 |
Plot feature differences +
+In addition to returning the data.frame it can be useful to visually examine the changes/trends after running CellBender. The function CellBender_Diff_Plot takes the data.frame from CellBender_Feature_Diff as input and plots the results.
Optional Parameters
+-
+
-
+
pct_diff_thresholdplot genes that exhibit a change equal to or greater than this threshold.
+
+ -
+
num_featuresinstead of plotting genes above a threshold simply plot the top X changed genes.
+
+ -
+
num_labelschange how many genes are labeled.
+
+ -
+
labellogical, whether or not to label features.
+
+ -
+
custom_labelsspecify vector of specific features to label.
+
+p1 <- CellBender_Diff_Plot(feature_diff_df = feature_diff)
+p2 <- CellBender_Diff_Plot(feature_diff_df = feature_diff, pct_diff_threshold = 50)
+p3 <- CellBender_Diff_Plot(feature_diff_df = feature_diff, num_features = 500, pct_diff_threshold = NULL)
+p4 <- CellBender_Diff_Plot(feature_diff_df = feature_diff, num_labels = 10)
+p5 <- CellBender_Diff_Plot(feature_diff_df = feature_diff, label = F)
+p6 <- CellBender_Diff_Plot(feature_diff_df = feature_diff, custom_labels = "Gm48099")
+
+wrap_plots(p1, p2, p3, p4, p5, p6, ncol = 2)
Dual Assay Plotting
@@ -666,12 +671,12 @@ Example Plotting
Ambient RNA gene
-First let’s plot gene that represents ambient RNA as it’s restricted in expression to neurons (synaptic gene). If Cell Bender has worked well we expect that expression of this gene will be very different between the two assays. 
+First let’s plot gene that represents ambient RNA as it’s restricted in expression to neurons (synaptic gene). If Cell Bender has worked well we expect that expression of this gene will be very different between the two assays. 
Ambient RNA gene
-First let’s plot gene that represents ambient RNA as it’s restricted in expression to neurons (synaptic gene). If Cell Bender has worked well we expect that expression of this gene will be very different between the two assays.
First let’s plot gene that represents ambient RNA as it’s restricted in expression to neurons (synaptic gene). If Cell Bender has worked well we expect that expression of this gene will be very different between the two assays.
Non-Ambient RNA gene
-Now let’s plot normally astrocyte restricted gene. If Cell Bender has worked well we expect that expression of this gene shouldn’t be very different between the two assays. 
Now let’s plot normally astrocyte restricted gene. If Cell Bender has worked well we expect that expression of this gene shouldn’t be very different between the two assays. 