In Sage-Bionetworks/sageseqr: Identify differentially expressed genes counts data

knitr::opts_chunk$set(
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A configuration file defines the inputs for the RNA-seq workflow. Update the default parameters to include the Synapse ID where your data is stored and to the factor and continuous variables you want to test in the covariate model selection. The full list of configurable options are:

counts:
synID: Required. Synapse ID to counts data frame with identifiers to
the metadata as column names and gene ids in a column.
version: Optional. Include Synapse file version number (e.g. 3).
gene id: Required. Column name that corresponds to the gene ids (e.g. feature).
metadata:
synID: Required. Synapse ID to cleaned metadata file with sample
identifiers in a column and variables of interest as column names.
version: Optional. Include Synapse file version number (e.g. 3).
sample id: Required. Column name that corresponds to the sample ids (e.g. donorid).
biomart:
synID: Optional. If left blank, Ensembl will be queried with the gene
ids provided in the counts. Otherwise, you may provide the
Synapse ID to gene metadata from Ensembl. This must include gene
length and GC content in order to implement Conditional Quantile
Normalization.
version: Optional. Include Synapse file version number (e.g. 3).
filters: Required. Column name that corresponds to the gene ids (e.g.
ensembl_gene_id).
host: Optional. A character vector specifying the BioMart database release
version. This specification is highly recommended for a reproducible
workflow. Defaults to ensembl.org.
organism: Required. A character vector of the organism name. This argument
takes partial strings. For example,"hsa" will match "hsapiens_gene_ensembl".
exon only: Optional. Set to TRUE if you want gene lengths and GC-content
to be calculated only for exons of gene features. Recomended
depending on you experimental design paradigm. Default is FALSE,
which considers the entire transcript start to stop (ie. includes
intronic regions).
custom build: Optional. If you want to bulid the biomart object from a user specified
or custom GTF and genome FASTA file specify this value as TRUE.
Default is FALSE. This would be reccomended for users analyzing
data from a model system with a trans-gene inserted into the
genome.
gtfID: Required IF custom build is set to TRUE. Synapse ID to the user
specified GTF file to build the biomart object from.
gtfVersion: Optional.Include Synapse file version number (e.g. 3).
fastaID: Required IF custom build is set to TRUE. Synapse ID to the user
specified genome FASTA file to build the biomart object from.
fastaVersion: Optional. Include Synapse file version number (e.g. 3).
factors: Required. List of factor variables in brackets. Variables must be
present in the metadata as column names (e.g. [ "donorid", "source"]).
random_effect: Optional. List of factor variables (must also be included in `factors`)
that are to be treated as random effects in the linear regression model.
(eg. ["donorid"])
continuous: Required. List of continuous variables in brackets. Variables must
be present in the metadata as column names (e.g. [ "rin", "rin2"]).
x_var: Required. This is your predictor or primary variable of interest.
Additionally, a boxplot will visualize the distribution of
continuous variables using the x_var as a dimension.
conditions: Optional. Filtering low-expression genes is a common practice to improve
sensitivity in detection of differentially expressed genes. Low
count genes that have less than a user-defined threshold of counts
per million (CPM) in a user-defined percentage of samples per the
conditions provided here will be removed. (e.g. ["diagnosis", "sex"]).
cpm threshold: Optional. The minium allowable CPM to keep a gene in the analysis.
percent threshold:Optional. The percentage of samples that should contain the minimum number
of CPM. If a condition is passed, the percentage will apply to
the samples in that sub-population.
sex check: Optional. The exact variable name that corresponds to reported gender or sex
to check the distribution of x and y marker expression across
samples.
dimensions: Required. Specify the PCA dimensions by variable name.
color:
shape:
size:
skip model: Optional. If TRUE, the exploratory data report is run. Model selection
is not computed.
force null model: Optional. Variables to add to the model aprori eg. sex that users want
to account for.
force model with: Optional. Force differential expression with this user defined model
instead of the output of stepwise regression.
cores: Optional. Specify an integer of cores to use with a BiocParallel
parallel backend. Null value results in the number of available
cores minus one being used. Parallel backend ccurrently only supports
BiocParallel::SnowParam(). BatchtoolsParam, MulticoreParam,
BiocParallelDoparParam, and SerialParam are not currently supported.
de FC: The fold-change (FC) of significant differentially expressed
(de) genes must exceed this value. This value will be transformed
into log2 FC.
de p-value threshold: The adjusted p-value of significant differentially expressed
(de) genes must exceed this value.
de contrasts: Required.
primary: Required. Variable(s) in the metadata to define comparisons between
groups. Currently must be either one numeric variable, or one or
more catagorical variables.
is_numeric_int: Optional. Specifies if there is a numeric interaction variable specified.
default (FALSE)
numeric: Optional. The numeric in variable which interacts with the
primary variable(s). default (NULL)
contrasts: Optional. A list specifying contrasts of the primary variable(s)
to consider for differential sequencing results if using factor(s)
as your primary variable. If not specified all combinations will
be tested. If specified this will speed up the pipeline. Specify
the contrast with the factor values involved in the contrast
seperated by a hyphen. (eg for diagnosis, `contrasts: ["AD-CT"]`
where AD is the value in diagnosis column for cases and CT is
the value for controls. For multi-level contrasts, eg. `primary:
["diagnosis", "Sex"] would have contrasts specified as; `contrasts:
["ZZ_F-CT_F", "ZZ_M-CT_M"]` to look at cases vs controls in
females and cases vs controls in males independently. While the
order before or after the hyphen doesn't matter, the order of values
before/after the underscore does matter. The value order must be
the same as the `primary:` specification.
eg. `primary: ["diagnosis","sex"]` must be CT_M
while `primary: ["sex","diagnosis"]` must be M_CT.
<any named list:> If there are multiple comparisons, set them up as nested lists.
visualization gene list: Label this list of genes in the volcano plot.
report: Required. The name of your project. This will become the name of your
output html file.
store output: Required. Folder Synapse Id to store output on Synapse.