aggr_rep {spatialHeatmap} | R Documentation |
This function aggregates "sample__condition" (see data
argument) replicates by mean or median. The input data is either a data.frame
or SummarizedExperiment
.
aggr_rep(data, sam.factor, con.factor, aggr = "mean")
data |
An object of |
sam.factor |
The column name corresponding to samples in the |
con.factor |
The column name corresponding to conditions in the |
aggr |
Aggregate "sample__condition" replicates by "mean" or "median". The default is "mean". If the |
The returned value is the same class with the input data, a data.frame
or SummarizedExperiment
. In either case, the column names of the data matrix follows the "sample__condition" scheme.
Jianhai Zhang jzhan067@ucr.edu; zhang.jianhai@hotmail.com
Dr. Thomas Girke thomas.girke@ucr.edu
SummarizedExperiment: SummarizedExperiment container. R package version 1.10.1
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/
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
McCarthy, Davis J., Chen, Yunshun, Smyth, and Gordon K. 2012. "Differential Expression Analysis of Multifactor RNA-Seq Experiments with Respect to Biological Variation." Nucleic Acids Research 40 (10): 4288–97
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
## In the following examples, the 2 toy data come from an RNA-seq analysis on developments 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 with 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) # Aggregate "sample_condition" replicates in toy data1. df.aggr.chk <- aggr_rep(data=df.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.chk, sam.factor='organism_part', con.factor='age', aggr='mean') assay(se.aggr.chk)[1:3, 1:3]