nida pervaiz authored on 02/03/2023 14:41:23
| ... | ... |
@@ -9,7 +9,7 @@ Normalizes the sce object according to the input parameters} |
| 9 | 9 |
runScanpyNormalizeData( |
| 10 | 10 |
inSCE, |
| 11 | 11 |
useAssay, |
| 12 |
- targetSum = 1, |
|
| 12 |
+ targetSum = 10000, |
|
| 13 | 13 |
maxFraction = 0.05, |
| 14 | 14 |
normAssayName = "scanpyNormData" |
| 15 | 15 |
) |
| ... | ... |
@@ -21,7 +21,7 @@ runScanpyNormalizeData( |
| 21 | 21 |
|
| 22 | 22 |
\item{targetSum}{If NULL, after normalization, each observation (cell) has a
|
| 23 | 23 |
total count equal to the median of total counts for observations (cells) |
| 24 |
-before normalization. Default \code{1}}
|
|
| 24 |
+before normalization. Default \code{1e4}}
|
|
| 25 | 25 |
|
| 26 | 26 |
\item{maxFraction}{Include cells that have more counts than max_fraction of
|
| 27 | 27 |
the original total counts in at least one cell. Default \code{0.05}}
|
npervaiz authored on 02/11/2022 12:13:26
| ... | ... |
@@ -39,6 +39,8 @@ Normalizes the sce object according to the input parameters |
| 39 | 39 |
} |
| 40 | 40 |
\examples{
|
| 41 | 41 |
data(scExample, package = "singleCellTK") |
| 42 |
+sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'") |
|
| 43 |
+rownames(sce) <- rowData(sce)$feature_name |
|
| 42 | 44 |
\dontrun{
|
| 43 | 45 |
sce <- runScanpyNormalizeData(sce, useAssay = "counts") |
| 44 | 46 |
} |
npervaiz authored on 05/10/2022 19:31:21
| ... | ... |
@@ -9,9 +9,8 @@ Normalizes the sce object according to the input parameters} |
| 9 | 9 |
runScanpyNormalizeData( |
| 10 | 10 |
inSCE, |
| 11 | 11 |
useAssay, |
| 12 |
- countsPerCellAfter = 10000, |
|
| 13 |
- countsPerCell = NULL, |
|
| 14 |
- minCount = 0, |
|
| 12 |
+ targetSum = 1, |
|
| 13 |
+ maxFraction = 0.05, |
|
| 15 | 14 |
normAssayName = "scanpyNormData" |
| 16 | 15 |
) |
| 17 | 16 |
} |
| ... | ... |
@@ -20,13 +19,12 @@ runScanpyNormalizeData( |
| 20 | 19 |
|
| 21 | 20 |
\item{useAssay}{Assay containing raw counts to use for normalization.}
|
| 22 | 21 |
|
| 23 |
-\item{countsPerCellAfter}{If None, after normalization, each cell has a total
|
|
| 24 |
-count equal to the median of the counts_per_cell before normalization.} |
|
| 22 |
+\item{targetSum}{If NULL, after normalization, each observation (cell) has a
|
|
| 23 |
+total count equal to the median of total counts for observations (cells) |
|
| 24 |
+before normalization. Default \code{1}}
|
|
| 25 | 25 |
|
| 26 |
-\item{countsPerCell}{Precomputed counts per cell.}
|
|
| 27 |
- |
|
| 28 |
-\item{minCount}{Cells with counts less than min_counts are filtered out
|
|
| 29 |
-during normalization.} |
|
| 26 |
+\item{maxFraction}{Include cells that have more counts than max_fraction of
|
|
| 27 |
+the original total counts in at least one cell. Default \code{0.05}}
|
|
| 30 | 28 |
|
| 31 | 29 |
\item{normAssayName}{Name of new assay containing normalized data. Default
|
| 32 | 30 |
\code{scanpyNormData}.}
|
npervaiz authored on 04/10/2022 16:33:00
| ... | ... |
@@ -9,8 +9,10 @@ Normalizes the sce object according to the input parameters} |
| 9 | 9 |
runScanpyNormalizeData( |
| 10 | 10 |
inSCE, |
| 11 | 11 |
useAssay, |
| 12 |
- normAssayName = "scanpyNormData", |
|
| 13 |
- normalizationMethod = "LogNormalize" |
|
| 12 |
+ countsPerCellAfter = 10000, |
|
| 13 |
+ countsPerCell = NULL, |
|
| 14 |
+ minCount = 0, |
|
| 15 |
+ normAssayName = "scanpyNormData" |
|
| 14 | 16 |
) |
| 15 | 17 |
} |
| 16 | 18 |
\arguments{
|
| ... | ... |
@@ -18,11 +20,16 @@ runScanpyNormalizeData( |
| 18 | 20 |
|
| 19 | 21 |
\item{useAssay}{Assay containing raw counts to use for normalization.}
|
| 20 | 22 |
|
| 23 |
+\item{countsPerCellAfter}{If None, after normalization, each cell has a total
|
|
| 24 |
+count equal to the median of the counts_per_cell before normalization.} |
|
| 25 |
+ |
|
| 26 |
+\item{countsPerCell}{Precomputed counts per cell.}
|
|
| 27 |
+ |
|
| 28 |
+\item{minCount}{Cells with counts less than min_counts are filtered out
|
|
| 29 |
+during normalization.} |
|
| 30 |
+ |
|
| 21 | 31 |
\item{normAssayName}{Name of new assay containing normalized data. Default
|
| 22 | 32 |
\code{scanpyNormData}.}
|
| 23 |
- |
|
| 24 |
-\item{normalizationMethod}{selected normalization method. Default
|
|
| 25 |
-\code{"LogNormalize"}.}
|
|
| 26 | 33 |
} |
| 27 | 34 |
\value{
|
| 28 | 35 |
Normalized \code{SingleCellExperiment} object
|
Irzam Sarfraz authored on 04/10/2022 14:16:00
| ... | ... |
@@ -4,7 +4,7 @@ |
| 4 | 4 |
\alias{runScanpyNormalizeData}
|
| 5 | 5 |
\title{runScanpyNormalizeData
|
| 6 | 6 |
Wrapper for NormalizeData() function from scanpy library |
| 7 |
-Normalizes the sce object according to the input parameters provided.} |
|
| 7 |
+Normalizes the sce object according to the input parameters} |
|
| 8 | 8 |
\usage{
|
| 9 | 9 |
runScanpyNormalizeData( |
| 10 | 10 |
inSCE, |
| ... | ... |
@@ -30,7 +30,7 @@ Normalized \code{SingleCellExperiment} object
|
| 30 | 30 |
\description{
|
| 31 | 31 |
runScanpyNormalizeData |
| 32 | 32 |
Wrapper for NormalizeData() function from scanpy library |
| 33 |
-Normalizes the sce object according to the input parameters provided. |
|
| 33 |
+Normalizes the sce object according to the input parameters |
|
| 34 | 34 |
} |
| 35 | 35 |
\examples{
|
| 36 | 36 |
data(scExample, package = "singleCellTK") |
nida pervaiz authored on 30/09/2022 14:12:22
| ... | ... |
@@ -4,7 +4,7 @@ |
| 4 | 4 |
\alias{runScanpyNormalizeData}
|
| 5 | 5 |
\title{runScanpyNormalizeData
|
| 6 | 6 |
Wrapper for NormalizeData() function from scanpy library |
| 7 |
-Normalizes the sce object according to the input parameters} |
|
| 7 |
+Normalizes the sce object according to the input parameters provided.} |
|
| 8 | 8 |
\usage{
|
| 9 | 9 |
runScanpyNormalizeData( |
| 10 | 10 |
inSCE, |
| ... | ... |
@@ -30,7 +30,7 @@ Normalized \code{SingleCellExperiment} object
|
| 30 | 30 |
\description{
|
| 31 | 31 |
runScanpyNormalizeData |
| 32 | 32 |
Wrapper for NormalizeData() function from scanpy library |
| 33 |
-Normalizes the sce object according to the input parameters |
|
| 33 |
+Normalizes the sce object according to the input parameters provided. |
|
| 34 | 34 |
} |
| 35 | 35 |
\examples{
|
| 36 | 36 |
data(scExample, package = "singleCellTK") |
nida pervaiz authored on 20/09/2022 12:56:20
| 1 | 1 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,40 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
|
| 2 |
+% Please edit documentation in R/scanpyFunctions.R |
|
| 3 |
+\name{runScanpyNormalizeData}
|
|
| 4 |
+\alias{runScanpyNormalizeData}
|
|
| 5 |
+\title{runScanpyNormalizeData
|
|
| 6 |
+Wrapper for NormalizeData() function from scanpy library |
|
| 7 |
+Normalizes the sce object according to the input parameters} |
|
| 8 |
+\usage{
|
|
| 9 |
+runScanpyNormalizeData( |
|
| 10 |
+ inSCE, |
|
| 11 |
+ useAssay, |
|
| 12 |
+ normAssayName = "scanpyNormData", |
|
| 13 |
+ normalizationMethod = "LogNormalize" |
|
| 14 |
+) |
|
| 15 |
+} |
|
| 16 |
+\arguments{
|
|
| 17 |
+\item{inSCE}{(sce) object to normalize}
|
|
| 18 |
+ |
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| 19 |
+\item{useAssay}{Assay containing raw counts to use for normalization.}
|
|
| 20 |
+ |
|
| 21 |
+\item{normAssayName}{Name of new assay containing normalized data. Default
|
|
| 22 |
+\code{scanpyNormData}.}
|
|
| 23 |
+ |
|
| 24 |
+\item{normalizationMethod}{selected normalization method. Default
|
|
| 25 |
+\code{"LogNormalize"}.}
|
|
| 26 |
+} |
|
| 27 |
+\value{
|
|
| 28 |
+Normalized \code{SingleCellExperiment} object
|
|
| 29 |
+} |
|
| 30 |
+\description{
|
|
| 31 |
+runScanpyNormalizeData |
|
| 32 |
+Wrapper for NormalizeData() function from scanpy library |
|
| 33 |
+Normalizes the sce object according to the input parameters |
|
| 34 |
+} |
|
| 35 |
+\examples{
|
|
| 36 |
+data(scExample, package = "singleCellTK") |
|
| 37 |
+\dontrun{
|
|
| 38 |
+sce <- runScanpyNormalizeData(sce, useAssay = "counts") |
|
| 39 |
+} |
|
| 40 |
+} |