CB2(CRISPRBetaBinomial) is a new algorithm for analyzing CRISPR data based on beta-binomial distribution. We provide CB2 as a R package, and the interal algorithms of CB2 are also implemented in CRISPRCloud.
logFC
parameter value of measure_gene_stats
to gene
will provide the logFC
calculate by gene-level CPMs.join_count_and_design
function. calc_mappability()
provide total_reads
and mapped_reads
columns.There are several updates.
measure_sgrna_stats
. The original name run_estimation
has been deprecated.data.frame
with character columns. In other words, you can use Currently CB2 is now on CRAN
, and you can install it using install.package
function.
install.package("CB2")
Installation Github version of CB2 can be done using the following lines of code in your R terminal.
install.packages("devtools")
devtools::install_github("LiuzLab/CB2")
Alternatively, here is a one-liner command line for the installation.
Rscript -e "install.packages('devtools'); devtools::install_github('LiuzLab/CB2')"
FASTA <- system.file("extdata", "toydata",
"small_sample.fasta",
package = "CB2")
df_design <- data.frame()
for(g in c("Low", "High", "Base")) {
for(i in 1:2) {
FASTQ <- system.file("extdata", "toydata",
sprintf("%s%d.fastq", g, i),
package = "CB2")
df_design <- rbind(df_design,
data.frame(
group = g,
sample_name = sprintf("%s%d", g, i),
fastq_path = FASTQ,
stringsAsFactors = F)
)
}
}
MAP_FILE <- system.file("extdata", "toydata", "sg2gene.csv", package="CB2")
sgrna_count <- run_sgrna_quant(FASTA, df_design, MAP_FILE)
sgrna_stat <- measure_sgrna_stats(sgrna_count$count, df_design,
"Base", "Low",
ge_id = "gene",
sg_id = "id")
gene_stat <- measure_gene_stats(sgrna_stat)
Or you could run the example with the following commented code.
sgrna_count <- run_sgrna_quant(FASTA, df_design)
sgrna_stat <- measure_sgrna_stats(sgrna_count$count, df_design, "Base", "Low")
gene_stat <- measure_gene_stats(sgrna_stat)
More detailed tutorial is available here!
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