Data once uploaded and filtered through the preceding tabs can be normalized and corrected for batch-effect. This guide particularly focuses on normalization of data for downstream analysis which can be achieved through various methods and options integrated together in a single interface. Generally, users can use one of the provided normalization methods integrated from other packages, or use transformation options to manually normalize/scale data. Additionally, these transformation options can be applied to normalized data as well depending upon the type of the transformation.
|Seurat - LogNormalize||Butler et al., Nature Biotechnology 2018 & Stuart, Butler, et al., Cell 2019|
|Seurat - CLR||Butler et al., Nature Biotechnology 2018 & Stuart, Butler, et al., Cell 2019|
|Seurat - RC||Butler et al., Nature Biotechnology 2018 & Stuart, Butler, et al., Cell 2019|
|Seurat - SCTransform||Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression, Butler et al., Nature Biotechnology 2018 & Stuart, Butler, et al., Cell 2019|
|Scater - LogNormCounts||McCarthy DJ, Campbell KR, Lun ATL, Willis QF (2017). “Scater: pre-processing, quality control, normalisation and visualisation of single-cell RNA-seq data in R.” Bioinformatics, 33, 1179-1186.|
|Scater - CPM||McCarthy DJ, Campbell KR, Lun ATL, Willis QF (2017). “Scater: pre-processing, quality control, normalisation and visualisation of single-cell RNA-seq data in R.” Bioinformatics, 33, 1179-1186.|
|Log Transform||Simple log base 2 transformation|
|log1p||Natural log transformation and additionally adds 1 to remove zeros|
|Z-Score||Standard z-score scaling|
|trim||Trim values based on an upper and lower limits (can be applied with all of the above methods)|
Normalization tab can be opened up by clicking on the Normalization & Batch Correction from the top menu and further selecting the Normalization sub-tab in the subsequent window as shown below.
The Normalization user-interface is divided into three parts, a) Normalization Options, b) Assay Options, and c) Available Assays.
In the Normalization Options, users can select a method for normalization from
Scater - CPM or one of the methods from
CLR (Centered Log Ratio),
RC (Relative Counts) and
SCTransform. Users can also set a manual Scaling Factor (numeric value multiplied by the data values) and specify the Assay Name for the new
In addition to the normalization methods, manually implemented transformation methods can be used to perform different transformations on the data. These transformations include
log1p and standard
If trimming of an
assay is required, ‘Trim Assay’ option can be enabled and lower/upper bound values can be set. The default value for upper and lower trim values is