filter_data: Filtering the Data Matrix

In jianhaizhang/spatialHeatmap: spatialHeatmap

Filtering the Data Matrix

Description

This function is designed to filter the numeric data in form of data.frame or SummarizedExperiment. The filtering builds on two functions pOverA and cv from the package genefilter (Gentleman et al. 2018).

Usage

filter_data(
  data,
  assay.na = NULL,
  pOA = c(0, 0),
  CV = c(-Inf, Inf),
  top.CV = 1,
  desc = NULL,
  sam.factor = NULL,
  con.factor = NULL,
  file = NULL,
  verbose = TRUE
)

Arguments

data	Terms spatial features: cells, tissues, organs, etc; variables: experimental variables such as drug dosage, temperature, time points, etc; biomolecules: genes, proteins, metabolites, etc; spatial heatmap: SHM. 'SummarizedExperiment' The assays slot stores the data matrix, where rows and columns are biomolecules and spatial featues respectively. Typically, at least two columns of spatial features and variables are stored in the colData slot respectively. When plotting SHMs, only identical spatial features between the data and aSVG will be colored according to the expression values of chosen biomolecules. Replicates of the same type in these two columns should be identical, e.g. "tissueA", "tissueA" rather than "tissueA1", "tissueA2". If column names in the assays slot follow the "spatialFeature__variable" scheme, i.e. spatial features and variables are concatenated by double underscore, then the colData slot is not required at all. If the data do not have experiment variables, the variable column in colData or the double underscore scheme is not required. 'data.frame' Rows and columns are biomolecules and spatial featues respectively. If there are experiment variables, the column names should follow the naming scheme "spatialFeature__variable". Otherwise, the column names should only include spatial features. The double underscore is a reserved string for specific purposes in spatialHeatmap, and thus should be avoided for naming spatial feature or variables. A column of biomolecule description can be included. This is only applicable in the interactive network graph (see network), where mousing over a node displays the corresponding description. vector In the function shm, the data can be provided in a numeric vector for testing with a single gene. If so, the naming schme of the vector is the same with the data.frame. Multiple variables For plotting SHMs, multiple variables contained in the data can be combined into a composite one, and the composite variable will be treated as a regular single variable. See the vignette for more details by running browseVignettes('spatialHeatmap') in R.
assay.na	The name of target assay to use when data is SummarizedExperiment.
pOA	Parameters of the filter function pOverA from the package genefilter (Gentleman et al. 2018). Genes with expression values >= "A" at the proportion >= "P" of all samples are retained. It is a vector of two numbers, where the first and second is "P" and "A" respectively. The default is c(0, 0), which means no filtering is applied.
CV	Parameters of the filter function cv from the package genefilter (Gentleman et al. 2018). Genes with coefficient of variation (CV) between CV1 and CV2 are retained. It is a vector of two numbers, where the first and second is CV1 and CV2 respectively. The default is c(-Inf, Inf), which means no filtering is applied.
top.CV	The proportion (0-1) of top coefficient of variations (CVs). Only genes with CVs in this proportion are kept. E.g. if top.CV=0.7, only genes with CVs ranked in the top 70% are retained. Default is 1, which means all genes are retained. Note this argument takes precedence over CV.
desc	A column name in the rowData slot of SummarizedExperiment. The default is NULL. In filter_data, this argument is only applicable if file is specified.
sam.factor	The column name corresponding to spatial features in colData of SummarizedExperiment. If the column names in the assay slot already follows the scheme "spatialFeature__variable", then the colData slot is not required and accordingly this argument could be NULL.
con.factor	The column name corresponding to experimental variables in colData of SummarizedExperiment. It can be NULL if column names of in the assay slot already follows the scheme "spatialFeature__variable", or no variable is associated with the data.
file	The output file name for saving the filtered data matrix in TSV format, which is ready to upload in the Shiny App (see shiny_shm). In this file, the rows are biomoleclues and column names are in the scheme "spatialFeature__variable". If row annotation is provided to desc, it will be appended to the output file. The default is NULL and no file is saved. This argument is applicable only when the data is in SummarizedExperiment and need to be uploaded to the the Shiny App.
verbose	Logical. If TRUE (default), the summary of statistics is printed.

Value

An object of a data.frame or SummarizedExperiment, depending on the input data.

Author(s)

Jianhai Zhang [e-mail]
Dr. Thomas Girke [e-mail]

References

Gentleman, R, V Carey, W Huber, and F Hahne. 2018. "Genefilter: Methods for Filtering Genes from High-Throughput Experiments." http://bioconductor.uib.no/2.7/bioc/html/genefilter.html
Matt Dowle and Arun Srinivasan (2017). data.table: Extension of 'data.frame'. R package version 1.10.4. https://CRAN.R-project.org/package=data.table
Martin Morgan, Valerie Obenchain, Jim Hester and Hervé Pagès (2018). 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 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 Amezquita R, Lun A, Becht E, Carey V, Carpp L, Geistlinger L, Marini F, Rue-Albrecht K, Risso D, Soneson C, Waldron L, Pages H, Smith M, Huber W, Morgan M, Gottardo R, Hicks S (2020). “Orchestrating single-cell analysis with Bioconductor.” Nature Methods, 17, 137–145. https://www.nature.com/articles/s41592-019-0654-x

Examples

## Two example data sets are showcased for the data formats of "data.frame" and 
## "SummarizedExperiment" respectively. Both come from an RNA-seq analysis on 
## 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". 

# Access example data 1.
df.chk <- read.table(system.file('extdata/shinyApp/data/count_chicken_simple.txt', 
package='spatialHeatmap'), header=TRUE, row.names=1, sep='\t', check.names=FALSE)

# Column names follow the naming scheme
# "spatialFeature__variable".  
df.chk[1:3, ]

# A column of gene description can be optionally appended.
ann <- paste0('ann', seq_len(nrow(df.chk))); ann[1:3]
df.chk <- cbind(df.chk, ann=ann)
df.chk[1:3, ]

# Access example data 2. 
count.chk <- read.table(system.file('extdata/shinyApp/data/count_chicken.txt', 
package='spatialHeatmap'), header=TRUE, row.names=1, sep='\t')
count.chk[1:3, 1:5]

# A targets file describing spatial features and variables is required for example  
# data 2, which should be made based on the experiment design. 

# Access the targets file. 
target.chk <- read.table(system.file('extdata/shinyApp/data/target_chicken.txt', 
package='spatialHeatmap'), header=TRUE, row.names=1, sep='\t')
# Every column in example data 2 corresponds with a row in the targets file. 
target.chk[1:5, ]
# Store example data 2 in "SummarizedExperiment".
library(SummarizedExperiment)
se.chk <- SummarizedExperiment(assay=count.chk, colData=target.chk)
# The "rowData" slot can optionally store a data frame of gene annotation.
rowData(se.chk) <- DataFrame(ann=ann)

# Normalize data.
df.chk.nor <- norm_data(data=df.chk, norm.fun='CNF', log2.trans=TRUE)
se.chk.nor <- norm_data(data=se.chk, norm.fun='CNF', log2.trans=TRUE)

# Aggregate replicates of "spatialFeature_variable", where spatial features are organs
# and variables are ages.
df.chk.aggr <- aggr_rep(data=df.chk.nor, aggr='mean')
df.chk.aggr[1:3, ]

se.chk.aggr <- aggr_rep(data=se.chk.nor, sam.factor='organism_part', con.factor='age',
aggr='mean')
assay(se.chk.aggr)[1:3, 1:3]

# Genes with experssion values >= 5 in at least 1% of all samples (pOA), and coefficient
# of variance (CV) between 0.2 and 100 are retained.
df.chk.fil <- filter_data(data=df.chk.aggr, pOA=c(0.01, 5), CV=c(0.2, 100))
se.chk.fil <- filter_data(data=se.chk.aggr, sam.factor='organism_part', con.factor='age', 
pOA=c(0.01, 5), CV=c(0.2, 100), file=NULL)

jianhaizhang/spatialHeatmap documentation built on April 21, 2024, 7:43 a.m.