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R version suggested is 4.3
install.packages("configr", version = '0.3.5')
install.packages("vroom", version = '1.6.3')
install.packages("data.table", version = '1.14.8')
install.packages("Ckmeans.1d.dp", version = '4.3.5')
install.packages("gplots", version = '3.1.3')
install.packages("ggplot2", version = '3.4.3')
install.packages("plotly", version = '4.10.4')
install.packages("ggcorrplot", version = '0.1.4.1')
install.packages("corrplot", version = '0.92')
install.packages("see", version = '0.8.0')
install.packages("ggbeeswarm", version = '0.7.2')
install.packages("reactable", version = '0.4.4')
install.packages("reshape2", version = '1.4.4')
install.packages("htmltools", version = '0.5.6')
install.packages("sparkline", version = '2.0')
install.packages("dendextend", version = '1.17.1')
install.packages("gtools", version = '3.9.4')
install.packages("BiocManager")
BiocManager::install(version = "3.18")
BiocManager::install("DESeq2")
BiocManager::install("DEGreport")
BiocManager::install("apeglm")
Load dependencies if required
library(configr)
library(vroom)
library(data.table)
library(Ckmeans.1d.dp)
library(gplots)
library(ggplot2)
library(plotly)
library(ggcorrplot)
library(corrplot)
library(see)
library(ggbeeswarm)
library(reactable)
library(htmltools)
library(sparkline)
library(dendextend)
library(reshape2)
library(gtools)
library(DESeq2)
library(DEGreport)
library(apeglm)
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Install from github
install.packages("devtools")
library(devtools)
install_github("wtsi-hgi/MAVEQC")
Or
Install from the compiled source file
install.packages("/path/of/MAVEQC.tar.gz", type = "source")
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sample_name | replicate | condition | ref_time_point | library_independent_count | library_dependent_count | valiant_meta | vep_anno | adapt5 | adapt3 | per_r1_adaptor | per_r2_adaptor | library_name | library_type |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
sample1 | R1 | D4 | D4 | s1.allcounts.tsv.gz | s1.libcounts.tsv.gz | meta.csv.gz | meta_consequences.tsv.gz | CTGACTGGCACCTCTTCCCCCAGGA | CCCCGACCCCTCCCCAGCGTGAATG | 0.21 | 0.10 | libA | screen |
sample2 | R2 | D4 | D4 | s2.allcounts.tsv.gz | s2.libcounts.tsv.gz | meta.csv.gz | meta_consequences.tsv.gz | CTGACTGGCACCTCTTCCCCCAGGA | CCCCGACCCCTCCCCAGCGTGAATG | 0.11 | 0.02 | libA | screen |
sample3 | R3 | D4 | D4 | s3.allcounts.tsv.gz | s3.libcounts.tsv.gz | meta.csv.gz | meta_consequences.tsv.gz | CTGACTGGCACCTCTTCCCCCAGGA | CCCCGACCCCTCCCCAGCGTGAATG | 0.01 | 0.18 | libA | screen |
sample4 | R1 | D7 | D4 | s4.allcounts.tsv.gz | s4.libcounts.tsv.gz | meta.csv.gz | meta_consequences.tsv.gz | CTGACTGGCACCTCTTCCCCCAGGA | CCCCGACCCCTCCCCAGCGTGAATG | 0.21 | 0.10 | libA | screen |
sample5 | R2 | D7 | D4 | s5.allcounts.tsv.gz | s5.libcounts.tsv.gz | meta.csv.gz | meta_consequences.tsv.gz | CTGACTGGCACCTCTTCCCCCAGGA | CCCCGACCCCTCCCCAGCGTGAATG | 0.11 | 0.02 | libA | screen |
sample6 | R3 | D7 | D4 | s6.allcounts.tsv.gz | s6.libcounts.tsv.gz | meta.csv.gz | meta_consequences.tsv.gz | CTGACTGGCACCTCTTCCCCCAGGA | CCCCGACCCCTCCCCAGCGTGAATG | 0.01 | 0.18 | libA | screen |
- please use the same headers in the example
- replicate, condition and ref_time_point are optional, but required for screen qc
- adapt5 and adapt3 are optional, please leave them blank if you don't have them, but required for reads with primers
- vep_anno, library_name and library_type are not necessary, leave them blank if not available
ID | NAME | SEQUENCE | LENGTH | COUNT | UNIQUE | SAMPLE |
---|---|---|---|---|---|---|
id1 | name1 | ACTTTTCT | 276 | 32 | 1 | sample1 |
id2 | name2 | ATCTTTCT | 275 | 132 | 0 | sample1 |
id3 | name3 | ATTCTTCT | 275 | 2 | 1 | sample1 |
- please use the same headers in the example
- please make sure library dependent sequences match with valiant meta file
- please refer to pyQUEST
SEQUENCE | LENGTH | COUNT |
---|---|---|
ACTTTTCT | 276 | 32 |
ATCTTTCT | 275 | 132 |
ATTCTTCT | 275 | 2 |
- please use the same headers in the example
- please refer to pyQUEST
Please use the VaLiAnT output file, refer to VaLiAnT
Please use one to one mapping file
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All the files are in the same directory including library dependent counts, library independent counts, valiant meta csv, vep annotation and the sample sheet.
library(MAVEQC)
sge_objs <- import_sge_files("/path/to/input/directory", "sample_sheet.tsv")
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Test datasets are not available now, will add them soon
output_dir <- "/path/to/output/directory"
samqc <- create_sampleqc_object(sge_objs)
samqc <- run_sample_qc(samqc, "plasmid")
qcplot_samqc_all(samqc, qc_type = "plasmid", plot_dir = output_dir)
qcout_samqc_all(samqc, qc_type = "plasmid", out_dir = output_dir)
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This creates a html report concatenating all the results including figures and tables. Please make sure you have generated all the figures and tables, otherwise the report may be incomplete.
create_qc_reports("/path/to/sample/sheet", "plasmid", output_dir)
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Reference samples must be assigned. MAVEQC automatically creates reference samples (maveqc_ref_time_point
and maveqc_ref_time_point_samples
) from the sample sheet using ref_time_point
and sampe_name
.
output_dir <- "/path/to/output/directory"
samqc <- create_sampleqc_object(sge_objs)
samqc <- run_sample_qc(samqc, "screen")
qcplot_samqc_all(samqc, qc_type = "screen", plot_dir = output_dir)
qcout_samqc_all(samqc, qc_type = "screen", out_dir = output_dir)
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MAVEQC automatically creates the coldata (maveqc_deseq_coldata
) from sample sheet for Screen QC.
sample_name | replicate | condition |
---|---|---|
hgsm3_d4_r1 | R1 | D4 |
hgsm3_d7_r1 | R1 | D7 |
hgsm3_d15_r1 | R1 | D15 |
hgsm3_d4_r2 | R2 | D4 |
hgsm3_d7_r2 | R2 | D7 |
hgsm3_d15_r2 | R2 | D15 |
hgsm3_d4_r3 | R3 | D4 |
hgsm3_d7_r3 | R3 | D7 |
hgsm3_d15_r3 | R3 | D15 |
expqc <- create_experimentqc_object(samqc)
expqc <- run_experiment_qc(expqc)
qcplot_expqc_all(expqc, plot_dir = output_dir)
qcout_expqc_all(expqc, out_dir = output_dir)
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This creates a html report concatenating all the results including figures and tables. Please make sure you have generated all the figures and tables, otherwise the report may be incomplete.
create_qc_reports("/path/to/sample/sheet", "screen", output_dir)
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Pandoc is required to generate the R markdown report. Please download and install it from https://pandoc.org/installing.html
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the version of apeglm must be >= 1.22.1, optimHess problem in the lower version like below.
Error in optimHess(par = init, fn = nbinomFn, gr = nbinomGr, x = x, y = y, :
non-finite value supplied by optim
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When installing DESeq2, it may have error (Rlog1) on Mac M1 chip. Try cmd below to fix it.
export PKG_CPPFLAGS="-DHAVE_WORKING_LOG1P"
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