Sigma: Calculate the variance covariance matrix used in the...
In YingMa0107/CARD: Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics
View source: R/CARD.imputation.R
Sigma R Documentation
Calculate the variance covariance matrix used in the imputation of the new grided locations
Description
Calculate the variance covariance matrix used in the imputation of the new grided locations
Usage
Sigma(locationOrig, trainInd, testInd, OptimalPhi, ineibor)
Arguments
locationOrig
Data frame, spatial location data frame of the original spatial resolved transcriptomics dataset, stored in the CARD_object@spatial_location
trainInd
Vector, index of the original spatial locations
testInd
Vector, index of the newly grided spatial locations
OptimalPhi
Numeric, the optimal phi value stored in CARD_object
ineibor
Numeric, number of neighbors used in the imputation on newly grided spatial locations, default is 10.
Value
Return a list with the imputed Cell type composition Vtest matrix on the newly grided spatial locations and predicted normalized gene expression
YingMa0107/CARD documentation built on Feb. 14, 2024, 12:23 p.m.
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View source: R/CARD.imputation.R
| Sigma | R Documentation |
Calculate the variance covariance matrix used in the imputation of the new grided locations
Description
Calculate the variance covariance matrix used in the imputation of the new grided locations
Usage
Sigma(locationOrig, trainInd, testInd, OptimalPhi, ineibor)
Arguments
locationOrig |
Data frame, spatial location data frame of the original spatial resolved transcriptomics dataset, stored in the CARD_object@spatial_location |
trainInd |
Vector, index of the original spatial locations |
testInd |
Vector, index of the newly grided spatial locations |
OptimalPhi |
Numeric, the optimal phi value stored in CARD_object |
ineibor |
Numeric, number of neighbors used in the imputation on newly grided spatial locations, default is 10. |
Value
Return a list with the imputed Cell type composition Vtest matrix on the newly grided spatial locations and predicted normalized gene expression
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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