Probe Annotations
Probe Annotations

The table displays the probe annotations that are in use for the current analysis.

Download CSV

To customize or edit these annotations, click the Download CSV button and save the CSV file locally. This file can be edited and then imported back into the advanced analysis. See the comments at the top of the CSV file for more details regarding this process.

Import CSV

Use the Import CSV button to import probe annotations for your analysis. This can be either a probe annotation file that you've obtained from NanoString or a file that was previously downloaded and customized.

Analysis Type
 
Quick Analysis

A quick analysis will perform experimental setup QC and differential expression testing using a single sample annotation field to group samples. Note that the baseline reference for the selected sample annotation is specified on the sample annotation page. Warning: If normalized data was chosen, do NOT run Quick Analysis. Only Custom Analysis should be used for Normalized Data.

 
Select a sample annotation to be used in quick analysis
 
Custom Analysis

This option allows you to choose which modules to run and customize all of the settings. Warning: If starting from Normalized Data as input, "Normalization" module must be deselected.


 
 
To upload or create annotations, click here.
General Options
Experiment Type:
Experiment Type

Select the type of experiment that is being analyzed. After inspecting the current data, nSolver has attempted to choose the correct value for this setting. You can change this if necessary.

Standard

A standard experiment with data from a single RLF.

MultiRLF

MultiRLF Merge (standard experiments merged)

Choose modules to run:
Overview

Selection of this option generates descriptive plots, including unsupervised clustering heatmaps, principal component analysis (PCA) plots and summaries of covariate (annotation) distribution across samples. This option is recommended for all analyses. These descriptive plots can be broken down across subsets of probes by selecting an appropriate probe annotation.

Normalization

Selection of these options specifies that the advanced analysis module should attempt to normalize the data. Normalization options vary by analyte type. Normalization is recommended.

Differential Expression

For each gene, The Optimal method infers differential expression with respect to specified covariate(s) using a negative binomial mixture model for low expression probes or a simplified negative binomial model for high expression probes. The Fast method uses the simplified negative binomial model for all probes. In situations of the mixture model algorithm not converging, the simplified model will be used instead. If the simplified model does not converge, the loglinear regression model will be used. High or low expression is determined by how high the probe mean is across all samples relative to the negative controls. At least one covariate must be selected as the predictor. Analysis will take into account the selected confounders but results will only be displayed for covariates designated as predictors.

Pathway Scoring

This module condenses each gene set's expression data into a pathway score, creating a more manageable dataset of samples X pathway scores. The module allows for exploration of scores relative to selected covariates of interest (e.g. change in MAPK pathway scores relative to treatment type). Currently scores are based on the 1st principle component of the pathway probes in the dataset. At least three samples need to be present for this module to work.

Cell type Profiling

Estimates cell abundance based on expression of cell-specific marker genes. Each cell type's abundance is estimated with the mean of its marker genes' expression. A variety of summary statistics and plots are created, including QC plots verifying the utility of these marker genes in any particular dataset.

Probe Descriptive

The module provides descriptive plots for up to 15 user-selected probes relative to user-defined covariates of interest. Depending on whether a single probe or multiple probes are selected, bivariate or multivariate analyses plots are generated. Furthermore, the module provides trend plots when trending covariates are provided.

Related Analytes

This module enables descriptive analyses of groups of probes corresponding to the same gene or genomic locus. These probes may target the same analyte types (e.g. ERK protein and phospho-ERK protein) or different analyte types (CD14 mRNA and CD14 protein). Use this module to investigate questions like how a gene and its protein are co-regulated and how they jointly respond to biological variables.

Cell.Type Annotations Required

This module requires values in the Cell.Type column of the probe annotations.

Related.Probes Annotations Required

This module requires a multi-analyte dataset and values in the Related.Probes column of the probe annotations.

Choose a annotation for defining probe sets

Choose the annotation column for all probe set-based analyses, including descriptive plots, DE and GSA. By default, probe sets will be annotated by using the information defined in the Probe.Annotation column.

Additional Image Types

Select optional additional image output format - .png files will be created automatically

Omit Low Count Data

Removes probes from the analysis based on a specified threshold count value and observation frequency across all samples. Probes that fall below the threshold at a frequency greater than the specified observation frequency will be removed from the analysis. To change our defaults, first de-select the "auto" checkbox.

Threshold count value: (min = 0, max = 100)
Observation frequency: (min = 0, max = 1)

   Adjust Parameters
Fusion Analysis Parameters
Fusion parameters:

Specify Fusion Parameters: adjust the p-value threshold for Junction probe detection and End probe imbalance expression.

SNV Analysis Parameters
Customize SNV analysis parameters:

Reassign SNV References by covariate type (default "Is Reference" refers to the assignment made during nSolver experiment creation) or by file name (manual selection). Adjust parameters defining the detection thresholds (Detected and Not Detected), based on the relative ratio of variant probes to reference probes (log2FC) and p-value. EM (Expectation maximization) performs an iterative refinement of background estimate after an initial call. Debias performs centering of reference probe data to prevent systematic bias due to normalization. By default, these options are selected; they work optimally in most circumstances. If you are troubleshooting unexpected results, uncheck EM and Debias one at a time and review the results or adjust the thresholds, as appropriate.

Warning: The Normalization option must be turned off if normalized data is selected for analysis.
Normalization Parameters
 
 
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Warning: The Normalization option must be turned off if normalized data is selected for analysis.
Pathway Scoring
Pathway Scoring

Each pathway score is derived using the 1st principal component of a gene set's data, but there is no guarantee a gene set's first PC captures interesting biology.

Plot Pathway scores vs :Each gene's expression data is regressed on the variables selected here, and pathway scores are fit to the residuals of these regressions.

Adjust Pathway Score for: If you think an uninteresting variable or a technical variable could be highly correlated with gene expression and therefore threatens to hijack the first PC, select that variable as confounder to scrub its influence from the data before calculating pathway scores.

 
 
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Cell type Profiling
Available Annotations

Select sample annotations by which samples shall be grouped for analysis and display.

 
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Column Specifying the Cell Types' Characteristic Probes
Column Specifying the Cell Types' Characteristic Probes

Specifies the gene annotation column that identifies cell type specific genes.

Creating Signatures:
Creating Signatures

By default, cell type specific probes that do not mirror the expression of their counterparts will be removed before estimation of cell type abundance. Choosing the "Use All Probes" options skips this QC measure and uses all of a cell type's probes to measure its abundance.

P-value Threshold for Reporting Cell Type Abundance:
P-value Threshold for Reporting Cell Type Abundance

Defines the significance threshold for reporting a cell type abundance estimate. Cell types whose evidence for cell type-specific expression does not meet this level of evidence will be discarded. By default, this value is set to 1, returning results for all cell types regardless of how well their probes exhibit cell type-specific expression in your data. This choice relies on the accuracy of the literature around these probes (Bindea 2013). Choose a value of 0.05 or lower to only see results for cell types whose quantification is further supported by your data.

Show Results for:
Show Results for

Use these options to choose how expression activity abundance estimates are displayed. Raw cell type abundances show the estimated abundance for each individual cell type, e.g. T-cells, NK cells, etc... It is often the case that all the raw cell types scores are highly correlated due to variable amounts of total immune infiltrate in your samples. When this occurs, it can be useful to look at relative cell type measurement to reveal subtler patterns in the TIL landscape.

Relative cell types show contrasts between raw cell types, e.g. CD8 T cells vs. Tregs or NK cells vs. total TILs.

You must select at least one of raw and relative cell type abundances.

 
Cell Type Contrasts:
 
Differential Expression
Differential Expression Annotations

For each probe, infers differential expression with respect to each specified covariate using a multivariate linear regression model with terms for all selected predictors and confounders. Results will only be displayed for covariates designated as predictors, but will have taken into account the effect of selected confounders. At least one covariate must be selected as a predictor for differential expression analysis to occur.

 
 
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Warning/Optimal method:

Running the Optimal model is computationally intensive and run time is proportionate to data size and number low expression probes. It may take several 10s of minutes depending on the data size and count distribution.

Fast/Recommended

All genes are fit with the negative binomial model.

P-value adjustment

Outputs the Bonferroni adjusted p-value or the Benjamini-Hochberg or Benjamini-Yekutieli False Discovery Rate (FDR). These adjusted p-values better summarize the strength of evidence for a gene's differential expression than regular univariate p-values.

Probe.Annotation Required

GSA requires that a valid probe set annotation column be selected on the General Options tab.

Gene Set Analysis (GSA)

Summarizes the statistical significance of all genes in a pathway using their mean squared t-statistic.

GSA will utilize the value of the Probe Sets Column selected on the General tab.

KEGG.Pathways Annotations Required

This module requires values in the KEGG.Pathways column of the probe annotations.

Custom Pathway Select
KEGG ID KEGG Pathway
Probe Descriptive
Probe List

Enter up to 15 probes for further analysis. If five or more probes are entered, PCA plots will also be generated. Probes used as housekeepers or removed from the analysis via a low count threshold will not be included in the output.

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Grouping Annotations

Expression data will be grouped by the levels of the selected annotations. At least one annotation must be selected.

 
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Generate Trend Plots

Creates trend plot for a data series defined by the Series ID across a set of levels identified by Interval ID. The interval ID can be an ordered categorical or continuous variable. Additionally, trends across distinct sample annotation groups can be examined by specifying an optional stratifying annotation.

Interval ID:
Series ID:
Stratifying Annotation (optional):
Generate Interaction Network

This option generates a network that best describes the conditional inter-relation between your selected probes. You can opt to adjust for a covariate that is expected to influence these probes.

Adjust For (optional):
Related Analytes
Related Analytes

For each set of related probes, this function creates trend plot for a data series defined by the Series ID across a set of levels identified by Interval ID. The interval ID can be an ordered categorical or continuous variable. Additionally, trends across distinct sample annotation groups can be examined by specifying an optional Stratifying Variable.

 
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Interval ID:
Series ID:
Stratifying Annotation (optional):
Summary

Please note

In order for many of the analyses performed by this module to provide robust results, multiple samples are needed. A minimum of 3 samples per condition is recommended, i.e. at least 3 samples per analysis involving continuous variables and at least 3 samples per level for categorical variables.

  Categories
 
Select SNV Reference Sample And Threshold Settings For Variant Call
 
 
 
 
 
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Specify Fusion Parameters
 
 
 
 
Non-default Cell Type Gene Annotation Column

Warning: A non-default cell type gene annotation column has been selected, which may interfere with calculation of relative cell type abundance.

Calculation of relative cell abundance relies on a .csv file specifying a "cell type contrasts matrix". The default cell type contrasts matrix assumes that the default cell type gene annotation column has been used. Use of a custom cell type gene annotation column may limit the utility of the default cell type contrasts matrix: new cell type names will not yield relative cell type abundance results, and removed cell type names will eliminate their associated cell type contrasts.

The default cell type contrasts matrix uses the following raw cell type measurements:

  • B-cells
  • CD4 activated
  • CD8 T-cells
  • Cytytoxic cells
  • DC
  • Macrophages
  • Mast cells
  • Neutrophils
  • NK cells
  • Normal mucosa
  • T-cells
  • T helper cells
  • Th1 cells
  • Th2 cells
  • Treg

To calculate the following contrasts:

  • B-cells,CD8 vs Treg
  • CD8 vs Tcells
  • T helpers vs T cells
  • Mast cells norm
  • CD8 vs CD4
  • CD8 vs cytotoxic
  • DC,NK,Th1 vs Th2
 
Unsupported Analyte Type
 

Warning:

The nSolver Advanced Analysis doesn't support CNV or miRNA data.