This is a patch with no function updates. Startup message changed for linux build “Note”.
This is a patch with no function updates. Readme is slimmed down with links to the full vignettes on CRAN. Updated citation information. Updated license to CC0 per CRAN request.
this is a patch to fix a vignette rendering issue in the 1.0.0 release
Release notes from dsb 1.0.0:
This is the feature complete version of dsb being released with the publication of our preprint in Nature Communications.
ModelNegativeADTnorm.DSBNormalizeProtein to
implement additional error checking and messages / warnings during
function run.“Additional Topics - quantile.clipping - scale.factor - Python and
Bioc - multiplexing - multi batch - FAQ”
“Normalizing ADTs for datasets without empty droplets”
“Understanding how the dsb method works”
This is the feature complete version of dsb being released with the publication of our preprint in Nature Communications.
ModelNegativeADTnorm.DSBNormalizeProtein to
implement additional error checking and messages / warnings during
function run.“Additional Topics - quantile.clipping - scale.factor - Python and
Bioc - multiplexing - multi batch - FAQ”
“Normalizing ADTs for datasets without empty droplets”
“Understanding how the dsb method works”
Additional error checking on input cell and background
matrices:
- stop if input matrix rows are not equivalent length
https://github.com/niaid/dsb/issues/29 - stop if any names in input
matrices are not equivalent
- warn if the rows are not in the same order and reorder to match.
Improve warning and error messages for isotype control name matching issues.
Advanced users can now examine protein (mean sd) and cell level stats
to output if return.stats = TRUE. If
denoise.counts = FALSE. The output includes protein level
stats. If denoise.counts = TRUE, the output contains cell
level stats including the dsb technical component and derivative
variables used in step II.
dsb with full options specified:
# full options: defined pseudocount, isotype controls, outlier clip, stats
result = DSBNormalizeProtein(cell_protein_matrix = cells_citeseq_mtx[ ,1:100],
empty_drop_matrix = empty_drop_citeseq_mtx,
define.pseudocount = TRUE,
pseudocount.use = 5,
use.isotype.control = TRUE,
isotype.control.name.vec = rownames(cells_citeseq_mtx)[grepl(
rownames(cells_citeseq_mtx), pattern = 'otyp')],
quantile.clipping = TRUE,
return.stats = TRUE
)
# normalized data
result$dsb_normalized_matrix
# protein-level statistics
result$protein_stats
# cell-level statistics
result$technical_stats
If return.stats = FALSE, the output is a R matrix
equivalent to result$dsb_normalized_matrix above.
Add unit tests for changes.
Some advanced users may desire to look into the latent noise
variables estimated by dsb. Setting return.stats = TRUE now
returns a list, the first element is the dsb normalized ADT values, the
second element is a dataframe of noise variables for each cell,
including the derived “dsb technical component” along with its
constituent variables, the background mean and the isotype control
protein values for each cell.
Rarely, a small minority of cells can have outlier values for a
single protein, for example expression at level -25 after normalization.
These values can now be clipped to be the lowest / highest quantile by
default c(0.001, 0.9995). To use this feature set
quantile.clipping = TRUE and quantile.clip to be a vector
of lowest and highest quantile values to use; defaults to c(0.001,
0.9995).
Documentation has been overhauled, the main vignette now includes simpler estimation of cells vs empty drops by using the EmptyDrops method that is now implemented by Cell Ranger by default. A workflow for multimodal (CITE-seq) single cell analysis using the dsb method for normalization is provided including loading raw data, quality control, clustering and multiple versions of the Seurat Weighted Nearest Neighbors joint mRNA and protein clustering method. Added to FAQ section in vignette.
• dsb is now hosted on CRAN: go to dsb on CRAN
• Documentation is improved from the beta release based on user feedback with a clearer workflow for defining and performing QC on background droplets and cells defined from raw protein UMI data. Additional code is added for integrating dsb with Seurat, Scanpy in Python, and Bioconductor’s SingleCellExperiment class. Added an FAQ section based on user questions and a workflow for both multiplexing experiments or for non-multiplexing / single lane experiments. See the updated documentation on CRAN.
• The current dsb package defaults are:
denoise.counts = TRUE and
use.isotype.control = TRUE. Isotype controls are not
required for normalization. See package documentation.