submatrix: Subset Target Assayed Items and Their Nearest Neighbors
In spatialHeatmap: spatialHeatmap
Description
Usage
Arguments
Value
Author(s)
References
Examples
Description
Given a vector of target assayed items (gene, protein, metabolite, etc), this function selects nearest neighbors for every target item independently, which share most similar abundance profiles with the targets. The selection is based on correlation or distance matrix computed by cor or dist from the "stats" package respectively. One of three alternative arguments p, n, v sets a cutoff for the selection.
Usage
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Arguments
data
A data.frame or SummarizedExperiment object returned by the function filter_data, where the columns and rows of the data matrix are samples/conditions and assayed items (e.g. genes, proteins) respectively. Since this function builds on coexpression analysis, variables of sample/condition should be at least 5. Otherwise, the results are not reliable.
ann
Applies to data argument of SummarizedExperiment. The column name corresponding to row item annotation in the rowData slot. Default is NULL.
ID
A vector of target item identifiers.
p
The proportion of top items with most similar expression profiles with the target items. Only items within this proportion are returned. Default is 0.3. It applies to each target item independently, and selected items of each target are returned together.
n
An integer of top items with most similar expression profiles with the target items. Only items within this number are returned. Default is NULL. It applies to each target independently, and selected items of each target are returned together.
v
A numeric of correlation (-1 to 1) or distance (>=0) threshold to select items sharing the most similar expression profiles with the target items. If fun='cor', only items with correlation coefficient larger than v are returned. If fun='dist', only items with distance less than v are returned. Default is NULL. It applies to each target independently, and selected items of each target are returned together.
fun
The function to calculate similarity/distance measure, 'cor' or 'dist', corresponding to cor or dist from the "stats" package respectively. Default is 'cor'.
cor.absolute
Logical, TRUE or FALSE. Use absolute values or not. Only applies to fun='cor'. Default is FALSE, meaning the correlation coefficient preserves the negative sign when selecting items.
arg.cor
A list of arguments passed to cor in the "stats" package.
Default is list(method="pearson").
arg.dist
A list of arguments passed to dist in the "stats" package.
Default is list(method="euclidean").
dir
The directory where the folder "customComputedData" is created automatically to save the subsetted matrix as a TSV-format file "sub_matrix.txt", which is ready to upload to the Shiny app launched by shiny_all. In the "sub_matrix.txt", the rows are assayed items and column names are in the syntax "sample__condition". This argument should be the same with the dir in adj_mod so that the files "adj.txt" and "mod.txt" generated by adj_mod are saved in the same folder "customComputedData". The default is NULL and no file is saved. This argument is used only when using the "customComputedData" in the Shiny app.
Value
The subsetted matrix of target items and their nearest neighbors.
Author(s)
Jianhai Zhang zhang.jianhai@hotmail.com; jzhan067@ucr.edu
Dr. Thomas Girke thomas.girke@ucr.edu
References
Langfelder P and Horvath S, WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559 doi:10.1186/1471-2105-9-559
Peter Langfelder, Steve Horvath (2012). Fast R Functions for Robust Correlations and Hierarchical Clustering. Journal of Statistical Software, 46(11), 1-17. URL http://www.jstatsoft.org/v46/i11/
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
Peter Langfelder, Bin Zhang and with contributions from Steve Horvath (2016). dynamicTreeCut: Methods for Detection of Clusters in Hierarchical Clustering Dendrograms. R package version 1.63-1. https://CRAN.R-project.org/package=dynamicTreeCut
Martin Morgan, Valerie Obenchain, Jim Hester and Hervé Pagès (2018). SummarizedExperiment: SummarizedExperiment container. R package version 1.10.1
Keays, Maria. 2019. ExpressionAtlas: Download Datasets from EMBL-EBI Expression Atlas
Love, Michael I., Wolfgang Huber, and Simon Anders. 2014. "Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2." Genome Biology 15 (12): 550. doi:10.1186/s13059-014-0550-8
Cardoso-Moreira, Margarida, Jean Halbert, Delphine Valloton, Britta Velten, Chunyan Chen, Yi Shao, Angélica Liechti, et al. 2019. “Gene Expression Across Mammalian Organ Development.” Nature 571 (7766): 505–9
Examples
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## In the following examples, the 2 toy data come from an RNA-seq analysis on development of 7
## chicken organs under 9 time points (Cardoso-Moreira et al. 2019). For conveninece, they are
## included in this package. The complete raw count data are downloaded using the R package
## ExpressionAtlas (Keays 2019) with the accession number "E-MTAB-6769". Toy data1 is used as
## a "data frame" input to exemplify data of simple samples/conditions, while toy data2 as
## "SummarizedExperiment" to illustrate data involving complex samples/conditions.
## Set up toy data.
# Access toy data1.
cnt.chk.simple <- system.file('extdata/shinyApp/example/count_chicken_simple.txt',
package='spatialHeatmap')
df.chk <- read.table(cnt.chk.simple, header=TRUE, row.names=1, sep='\t', check.names=FALSE)
# Columns follow the namig scheme "sample__condition", where "sample" and "condition" stands
# for organs and time points respectively.
df.chk[1:3, ]
# A column of gene annotation can be appended to the data frame, but is not required.
ann <- paste0('ann', seq_len(nrow(df.chk))); ann[1:3]
df.chk <- cbind(df.chk, ann=ann)
df.chk[1:3, ]
# Access toy data2.
cnt.chk <- system.file('extdata/shinyApp/example/count_chicken.txt', package='spatialHeatmap')
count.chk <- read.table(cnt.chk, header=TRUE, row.names=1, sep='\t')
count.chk[1:3, 1:5]
# A targets file describing samples and conditions is required for toy data2. It should be made
# based on the experiment design, which is accessible through the accession number
# "E-MTAB-6769" in the R package ExpressionAtlas. An example targets file is included in this
# package and accessed below.
# Access the example targets file.
tar.chk <- system.file('extdata/shinyApp/example/target_chicken.txt', package='spatialHeatmap')
target.chk <- read.table(tar.chk, header=TRUE, row.names=1, sep='\t')
# Every column in toy data2 corresponds with a row in targets file.
target.chk[1:5, ]
# Store toy data2 in "SummarizedExperiment".
library(SummarizedExperiment)
se.chk <- SummarizedExperiment(assay=count.chk, colData=target.chk)
# The "rowData" slot can store a data frame of gene annotation, but not required.
rowData(se.chk) <- DataFrame(ann=ann)
## As conventions, raw sequencing count data should be normalized, aggregated, and filtered to
## reduce noise.
# Normalize count data.
# The normalizing function "calcNormFactors" (McCarthy et al. 2012) with default settings
# is used.
df.nor.chk <- norm_data(data=df.chk, norm.fun='CNF', data.trans='log2')
se.nor.chk <- norm_data(data=se.chk, norm.fun='CNF', data.trans='log2')
# Aggregate count data.
# Aggregate "sample__condition" replicates in toy data1.
df.aggr.chk <- aggr_rep(data=df.nor.chk, aggr='mean')
df.aggr.chk[1:3, ]
# Aggregate "sample_condition" replicates in toy data2, where "sample" is "organism_part" and
# "condition" is "age".
se.aggr.chk <- aggr_rep(data=se.nor.chk, sam.factor='organism_part', con.factor='age',
aggr='mean')
assay(se.aggr.chk)[1:3, 1:3]
# Filter out genes with low counts and low variance. Genes with counts over 5 (log2 unit) in at
# least 1% samples (pOA), and coefficient of variance (CV) between 0.2 and 100 are retained.
# Filter toy data1.
df.fil.chk <- filter_data(data=df.aggr.chk, pOA=c(0.01, 5), CV=c(0.2, 100), dir=NULL)
# Filter toy data2.
se.fil.chk <- filter_data(data=se.aggr.chk, sam.factor='organism_part', con.factor='age',
pOA=c(0.01, 5), CV=c(0.2, 100), dir=NULL)
## Select nearest neighbors for target genes 'ENSGALG00000019846' and 'ENSGALG00000000112',
## which are usually genes visualized in spatial heatmaps.
# Toy data1.
df.sub.mat <- submatrix(data=df.fil.chk, ID=c('ENSGALG00000019846', 'ENSGALG00000000112'),
p=0.1)
# Toy data2.
se.sub.mat <- submatrix(data=se.fil.chk, ann='ann', ID=c('ENSGALG00000019846',
'ENSGALG00000000112'), p=0.1)
# In the following, "df.sub.mat" and "se.sub.mat" is used in the same way, so only
# "se.sub.mat" illustrated.
# The subsetted matrix is partially shown below.
se.sub.mat[c('ENSGALG00000019846', 'ENSGALG00000000112'), c(1:2, 63)]
spatialHeatmap documentation built on Nov. 8, 2020, 5:46 p.m.
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Description Usage Arguments Value Author(s) References Examples
Description
Given a vector of target assayed items (gene, protein, metabolite, etc), this function selects nearest neighbors for every target item independently, which share most similar abundance profiles with the targets. The selection is based on correlation or distance matrix computed by cor or dist from the "stats" package respectively. One of three alternative arguments p, n, v sets a cutoff for the selection.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 |
Arguments
data |
A |
ann |
Applies to |
ID |
A vector of target item identifiers. |
p |
The proportion of top items with most similar expression profiles with the target items. Only items within this proportion are returned. Default is 0.3. It applies to each target item independently, and selected items of each target are returned together. |
n |
An integer of top items with most similar expression profiles with the target items. Only items within this number are returned. Default is NULL. It applies to each target independently, and selected items of each target are returned together. |
v |
A numeric of correlation (-1 to 1) or distance (>=0) threshold to select items sharing the most similar expression profiles with the target items. If |
fun |
The function to calculate similarity/distance measure, 'cor' or 'dist', corresponding to |
cor.absolute |
Logical, TRUE or FALSE. Use absolute values or not. Only applies to |
arg.cor |
A list of arguments passed to |
arg.dist |
A list of arguments passed to |
dir |
The directory where the folder "customComputedData" is created automatically to save the subsetted matrix as a TSV-format file "sub_matrix.txt", which is ready to upload to the Shiny app launched by |
Value
The subsetted matrix of target items and their nearest neighbors.
Author(s)
Jianhai Zhang zhang.jianhai@hotmail.com; jzhan067@ucr.edu
Dr. Thomas Girke thomas.girke@ucr.edu
References
Langfelder P and Horvath S, WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559 doi:10.1186/1471-2105-9-559
Peter Langfelder, Steve Horvath (2012). Fast R Functions for Robust Correlations and Hierarchical Clustering. Journal of Statistical Software, 46(11), 1-17. URL http://www.jstatsoft.org/v46/i11/
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
Peter Langfelder, Bin Zhang and with contributions from Steve Horvath (2016). dynamicTreeCut: Methods for Detection of Clusters in Hierarchical Clustering Dendrograms. R package version 1.63-1. https://CRAN.R-project.org/package=dynamicTreeCut
Martin Morgan, Valerie Obenchain, Jim Hester and Hervé Pagès (2018). SummarizedExperiment: SummarizedExperiment container. R package version 1.10.1
Keays, Maria. 2019. ExpressionAtlas: Download Datasets from EMBL-EBI Expression Atlas
Love, Michael I., Wolfgang Huber, and Simon Anders. 2014. "Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2." Genome Biology 15 (12): 550. doi:10.1186/s13059-014-0550-8
Cardoso-Moreira, Margarida, Jean Halbert, Delphine Valloton, Britta Velten, Chunyan Chen, Yi Shao, Angélica Liechti, et al. 2019. “Gene Expression Across Mammalian Organ Development.” Nature 571 (7766): 505–9
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | ## In the following examples, the 2 toy data come from an RNA-seq analysis on development of 7
## chicken organs under 9 time points (Cardoso-Moreira et al. 2019). For conveninece, they are
## included in this package. The complete raw count data are downloaded using the R package
## ExpressionAtlas (Keays 2019) with the accession number "E-MTAB-6769". Toy data1 is used as
## a "data frame" input to exemplify data of simple samples/conditions, while toy data2 as
## "SummarizedExperiment" to illustrate data involving complex samples/conditions.
## Set up toy data.
# Access toy data1.
cnt.chk.simple <- system.file('extdata/shinyApp/example/count_chicken_simple.txt',
package='spatialHeatmap')
df.chk <- read.table(cnt.chk.simple, header=TRUE, row.names=1, sep='\t', check.names=FALSE)
# Columns follow the namig scheme "sample__condition", where "sample" and "condition" stands
# for organs and time points respectively.
df.chk[1:3, ]
# A column of gene annotation can be appended to the data frame, but is not required.
ann <- paste0('ann', seq_len(nrow(df.chk))); ann[1:3]
df.chk <- cbind(df.chk, ann=ann)
df.chk[1:3, ]
# Access toy data2.
cnt.chk <- system.file('extdata/shinyApp/example/count_chicken.txt', package='spatialHeatmap')
count.chk <- read.table(cnt.chk, header=TRUE, row.names=1, sep='\t')
count.chk[1:3, 1:5]
# A targets file describing samples and conditions is required for toy data2. It should be made
# based on the experiment design, which is accessible through the accession number
# "E-MTAB-6769" in the R package ExpressionAtlas. An example targets file is included in this
# package and accessed below.
# Access the example targets file.
tar.chk <- system.file('extdata/shinyApp/example/target_chicken.txt', package='spatialHeatmap')
target.chk <- read.table(tar.chk, header=TRUE, row.names=1, sep='\t')
# Every column in toy data2 corresponds with a row in targets file.
target.chk[1:5, ]
# Store toy data2 in "SummarizedExperiment".
library(SummarizedExperiment)
se.chk <- SummarizedExperiment(assay=count.chk, colData=target.chk)
# The "rowData" slot can store a data frame of gene annotation, but not required.
rowData(se.chk) <- DataFrame(ann=ann)
## As conventions, raw sequencing count data should be normalized, aggregated, and filtered to
## reduce noise.
# Normalize count data.
# The normalizing function "calcNormFactors" (McCarthy et al. 2012) with default settings
# is used.
df.nor.chk <- norm_data(data=df.chk, norm.fun='CNF', data.trans='log2')
se.nor.chk <- norm_data(data=se.chk, norm.fun='CNF', data.trans='log2')
# Aggregate count data.
# Aggregate "sample__condition" replicates in toy data1.
df.aggr.chk <- aggr_rep(data=df.nor.chk, aggr='mean')
df.aggr.chk[1:3, ]
# Aggregate "sample_condition" replicates in toy data2, where "sample" is "organism_part" and
# "condition" is "age".
se.aggr.chk <- aggr_rep(data=se.nor.chk, sam.factor='organism_part', con.factor='age',
aggr='mean')
assay(se.aggr.chk)[1:3, 1:3]
# Filter out genes with low counts and low variance. Genes with counts over 5 (log2 unit) in at
# least 1% samples (pOA), and coefficient of variance (CV) between 0.2 and 100 are retained.
# Filter toy data1.
df.fil.chk <- filter_data(data=df.aggr.chk, pOA=c(0.01, 5), CV=c(0.2, 100), dir=NULL)
# Filter toy data2.
se.fil.chk <- filter_data(data=se.aggr.chk, sam.factor='organism_part', con.factor='age',
pOA=c(0.01, 5), CV=c(0.2, 100), dir=NULL)
## Select nearest neighbors for target genes 'ENSGALG00000019846' and 'ENSGALG00000000112',
## which are usually genes visualized in spatial heatmaps.
# Toy data1.
df.sub.mat <- submatrix(data=df.fil.chk, ID=c('ENSGALG00000019846', 'ENSGALG00000000112'),
p=0.1)
# Toy data2.
se.sub.mat <- submatrix(data=se.fil.chk, ann='ann', ID=c('ENSGALG00000019846',
'ENSGALG00000000112'), p=0.1)
# In the following, "df.sub.mat" and "se.sub.mat" is used in the same way, so only
# "se.sub.mat" illustrated.
# The subsetted matrix is partially shown below.
se.sub.mat[c('ENSGALG00000019846', 'ENSGALG00000000112'), c(1:2, 63)]
|
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