CRISPRsim: Simulate a CRISPR-Cas9 pooled screen
In tgac-vumc/CSSA: Analysis and Simulation Tools for CRISPR-Cas9 Pooled Screens
CRISPRsim R Documentation
Simulate a CRISPR-Cas9 pooled screen
Description
CRISPRsim simulates a CRISPR-Cas9 pooled screen with user-defined parameters.
These include drug treatment screens! Each "infected" cell expands over time
based on effect of gene knockout. Other parameters include the abundance of
each guide at the start of the experiment, the efficacy of the guide (chance
that it results in a successful gene knockout), and the frequency and depth
of sampling. In case of drug treatment, genes are assigned a
treatment-specific growth modifier as well. The result is a data frame that
contains the guide-relevant parameters and the sequencing coverage per guide
for the specified time intervals. Simulated screens will aid researchers with
their experimental setup. Furthermore, this offers a unique platform for the
evaluation of analysis methods for pooled gene knockout screens.
Usage
CRISPRsim(
genes,
guides,
a,
g,
f,
d,
e,
seededcells,
harvestedcells,
harvestall = TRUE,
cellreplace = FALSE,
treatmentdelay = 0,
seqdepth,
offtargets = FALSE,
allseed = NULL,
gseed,
fseed,
dseed,
eseed,
oseed,
t0seed,
repseed,
grm = 1,
em = 1,
perfectsampling = FALSE,
perfectseq = FALSE,
returnall = FALSE,
outputfile
)
Arguments
genes
Single integer or character vector. Specify how many or which
genes to include in the experiment respectively. Not required when a full
list of guides is given.
guides
Single integer, integer vector or character vector. In case of
single integer, specify by how many guides each gene is represented. In
case of an integer vector, specify per gene by how many guides it is
represented. In case of a character vector, guides are assumed to contain a
gene name, followed by an underscore, followed by an identifier within that
gene (e.g. a number or a nucleotide sequence)
a
Numeric. Specify the number of doublings between each "passaging".
For example, in case of an experiment that ends after 12 doublings and was
passaged 3 times, specify a = c(4,4,4)
g
Integer vector. Specify guide efficacies per guide. If omitted,
guide efficacies will be sampled from a representative distribution
f
Integer vector. Specify guide abundance at time of infection per
guide. If omitted, guide abundance will be sampled from a representative
distribution
d
Integer vector. Specify gene-specific growth effect. If omitted,
effect of gene knockout on growth will be sampled from a representative
distribution. If the length of the vector does not match the number of
genes, values will be randomly sampled from the specified distribution!
e
Integer vector. Specify treatment-specific growth effect per gene.
If omitted, effects will be sampled from a representative distribution. If
the length of the vector does not match the number of genes, values will be
randomly sampled from the specified distribution!
seededcells
Integer. Number of cells seeded at the start of each
experimental step. If the length of this argument is smaller than the
number of seedings, all unspecified steps will be assumed equal to the last
specified step! Defaults to 200 times the number of guides
harvestedcells
Integer. Number of cells from which to sample for
subsequent sequencing. This argument is especially useful to restrict the
expected DNA copies present in the PCR reaction. If you wish to do so, make
sure to set harvestall to FALSE. Defaults to be equal to seededcells
harvestall
Logical. If TRUE, all cells are collected and used for
sampling in subsequent sequencing step. Applies to all experimental time
points beyond t0. Default = TRUE
cellreplace
Logical. If FALSE, cells are sampled from the total pool
of cells without replacement. Note that this is the most realistic
simulation of a screen, but then you should also keep realistic passaging
times! It is recommended to keep the total number of cells in the
experiment below 200 million. Setting this to TRUE can dramatically speed
up simulations. Default = FALSE
treatmentdelay
Integer. In case of a treatment experiment, specify
when treatment starts. It is currently only possible to start treatment on
one of the experimental time points. Default = 0
seqdepth
Integer. Specify the amount of sequencing reads devoted to
each experimental arm. If omitted, depth will default to 500 times the
number of guides
offtargets
Logical or numeric. Specify the fraction of off-targets. If
TRUE, 1 in 1000 guides (0.001) will target a different gene. Default =
FALSE
allseed
Integer. All unspecified seeds default to this plus an
increment of 1 for each different seed. Defaults to NULL, in which case the
unspecified seeds are randomly generated. Default = NULL
gseed
Integer. Specify seed for guide effiency assignment
fseed
Integer. Specify seed for infectious units assignment, which
dictates a guide's abundance at the start of the experiment
dseed
Integer. Specify seed for straight lethality assignment of genes
eseed
Integer. Specify seed for sensitizer assignment of genes
oseed
Integer. Specify seed for off-target selection
t0seed
Integer. Specify seed for t0, which encompasses sampling of the
first seeding and the assignment of successful knockout cells versus no
knockout cells for each guide
repseed
Integer. Specify the seed after t0
grm
Numeric. Growth rate modifier. Specify adjusted growth rate under
treatment conditions. Default = 1
em
Numeric. Effect modifier. Specify how effective treatment is. This
can be used as a proxy for drug concentration. All individual e-values and
grm are modified by this multiplier. Default = 1
perfectsampling
Logical. If TRUE, all sampling steps are replaced by
simple equations to calculate representation of guides. Useful as null
control to isolate the effect of sampling. Default = FALSE
perfectseq
Logical. If TRUE, sequencing results are a perfect
representation (though still rounded) of guides in the harvested cells.
Applicable to speed up simulations, assuming sequencing is sufficiently
deep. Default = FALSE
returnall
Logical. If TRUE, function returns a list with the simulated
data in the guidesdf, summary per gene in the genesdf, and parameters.
Default = FALSE
outputfile
Character string. When used, returned data frame will be
saved as a tab-delimited text to the specified file path
Details
CRISPRsim performs a genome-wide (or subsetted) pooled CRISPR
knockout screen for you without having to go to the lab and spend
incredible amounts of time and money. This can be a tremendous help if you
want to design an experiment and answer questions such as: how many
replicates do I need, how much coverage, will I pick up genes with x
effect, et cetera. You can give it a spin, but I highly recommend checking
out the documentation for the available parameters! Especially seeds can be
relevant for a proper simulation. You can easily "practice" by simulating
some small experiments. The basis of the simulation is as follows. Between
time points cells with a certain knockout grow according to formula
cellsout = cellsin*2^((grm+d+e)*a)) Each guide has an efficacy,
which is the chance to create a successful knockout. The cellsin is
determined at t0 and depends on guide efficacy and guide abundance. If
there is no successful knockout, d and e are 0. Cells with and without
successful knockout are followed separately throughout the experiment, but
the pairs are pooled in terms of sequencing reads. grm is the growth rate
modifier and is generally 1, but it can be lowered to more properly
simulate resistance screens.
Value
Returns a data frame with every row representing a single guide.
Contains the pertinent parameters of each guide and the number of
sequencing reads on t0 and all other sampling time points. If the argument
returnall is set to TRUE, the function also returns a data frame with the
true values for the genes, and lists all parameters as well.
Note
Seeds are set using with_seed from the
withr package, thus leaving any
pre-existing seed intact. Avoid using the same seeds for different
arguments. If dseed and eseed are identical, the resulting values for d and
e will have a distinct pattern of correlation. In this case, CRISPRsim will
throw a warning. Given or generated seeds are returned when the option
returnall is set to TRUE.
Author(s)
Jos B. Poell
See Also
sortingsim, radjust, rrep,
jar, nestedradjust, doublejar
Examples
simdf <- CRISPRsim(18000, 4, a = c(3,3), e = TRUE, perfectsampling = TRUE)
hist(simdf$g, breaks = 100, main = "distribution of guide efficiencies")
d <- rle(simdf$d)$values
e <- rle(simdf$e)$values
plot(d, e, main = "straight lethality and sensitization")
tgac-vumc/CSSA documentation built on Oct. 10, 2022, 7:27 p.m.
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| CRISPRsim | R Documentation |
Simulate a CRISPR-Cas9 pooled screen
Description
CRISPRsim simulates a CRISPR-Cas9 pooled screen with user-defined parameters. These include drug treatment screens! Each "infected" cell expands over time based on effect of gene knockout. Other parameters include the abundance of each guide at the start of the experiment, the efficacy of the guide (chance that it results in a successful gene knockout), and the frequency and depth of sampling. In case of drug treatment, genes are assigned a treatment-specific growth modifier as well. The result is a data frame that contains the guide-relevant parameters and the sequencing coverage per guide for the specified time intervals. Simulated screens will aid researchers with their experimental setup. Furthermore, this offers a unique platform for the evaluation of analysis methods for pooled gene knockout screens.
Usage
CRISPRsim( genes, guides, a, g, f, d, e, seededcells, harvestedcells, harvestall = TRUE, cellreplace = FALSE, treatmentdelay = 0, seqdepth, offtargets = FALSE, allseed = NULL, gseed, fseed, dseed, eseed, oseed, t0seed, repseed, grm = 1, em = 1, perfectsampling = FALSE, perfectseq = FALSE, returnall = FALSE, outputfile )
Arguments
genes |
Single integer or character vector. Specify how many or which genes to include in the experiment respectively. Not required when a full list of guides is given. |
guides |
Single integer, integer vector or character vector. In case of single integer, specify by how many guides each gene is represented. In case of an integer vector, specify per gene by how many guides it is represented. In case of a character vector, guides are assumed to contain a gene name, followed by an underscore, followed by an identifier within that gene (e.g. a number or a nucleotide sequence) |
a |
Numeric. Specify the number of doublings between each "passaging". For example, in case of an experiment that ends after 12 doublings and was passaged 3 times, specify a = c(4,4,4) |
g |
Integer vector. Specify guide efficacies per guide. If omitted, guide efficacies will be sampled from a representative distribution |
f |
Integer vector. Specify guide abundance at time of infection per guide. If omitted, guide abundance will be sampled from a representative distribution |
d |
Integer vector. Specify gene-specific growth effect. If omitted, effect of gene knockout on growth will be sampled from a representative distribution. If the length of the vector does not match the number of genes, values will be randomly sampled from the specified distribution! |
e |
Integer vector. Specify treatment-specific growth effect per gene. If omitted, effects will be sampled from a representative distribution. If the length of the vector does not match the number of genes, values will be randomly sampled from the specified distribution! |
seededcells |
Integer. Number of cells seeded at the start of each experimental step. If the length of this argument is smaller than the number of seedings, all unspecified steps will be assumed equal to the last specified step! Defaults to 200 times the number of guides |
harvestedcells |
Integer. Number of cells from which to sample for subsequent sequencing. This argument is especially useful to restrict the expected DNA copies present in the PCR reaction. If you wish to do so, make sure to set harvestall to FALSE. Defaults to be equal to seededcells |
harvestall |
Logical. If TRUE, all cells are collected and used for sampling in subsequent sequencing step. Applies to all experimental time points beyond t0. Default = TRUE |
cellreplace |
Logical. If FALSE, cells are sampled from the total pool of cells without replacement. Note that this is the most realistic simulation of a screen, but then you should also keep realistic passaging times! It is recommended to keep the total number of cells in the experiment below 200 million. Setting this to TRUE can dramatically speed up simulations. Default = FALSE |
treatmentdelay |
Integer. In case of a treatment experiment, specify when treatment starts. It is currently only possible to start treatment on one of the experimental time points. Default = 0 |
seqdepth |
Integer. Specify the amount of sequencing reads devoted to each experimental arm. If omitted, depth will default to 500 times the number of guides |
offtargets |
Logical or numeric. Specify the fraction of off-targets. If TRUE, 1 in 1000 guides (0.001) will target a different gene. Default = FALSE |
allseed |
Integer. All unspecified seeds default to this plus an increment of 1 for each different seed. Defaults to NULL, in which case the unspecified seeds are randomly generated. Default = NULL |
gseed |
Integer. Specify seed for guide effiency assignment |
fseed |
Integer. Specify seed for infectious units assignment, which dictates a guide's abundance at the start of the experiment |
dseed |
Integer. Specify seed for straight lethality assignment of genes |
eseed |
Integer. Specify seed for sensitizer assignment of genes |
oseed |
Integer. Specify seed for off-target selection |
t0seed |
Integer. Specify seed for t0, which encompasses sampling of the first seeding and the assignment of successful knockout cells versus no knockout cells for each guide |
repseed |
Integer. Specify the seed after t0 |
grm |
Numeric. Growth rate modifier. Specify adjusted growth rate under treatment conditions. Default = 1 |
em |
Numeric. Effect modifier. Specify how effective treatment is. This can be used as a proxy for drug concentration. All individual e-values and grm are modified by this multiplier. Default = 1 |
perfectsampling |
Logical. If TRUE, all sampling steps are replaced by simple equations to calculate representation of guides. Useful as null control to isolate the effect of sampling. Default = FALSE |
perfectseq |
Logical. If TRUE, sequencing results are a perfect representation (though still rounded) of guides in the harvested cells. Applicable to speed up simulations, assuming sequencing is sufficiently deep. Default = FALSE |
returnall |
Logical. If TRUE, function returns a list with the simulated data in the guidesdf, summary per gene in the genesdf, and parameters. Default = FALSE |
outputfile |
Character string. When used, returned data frame will be saved as a tab-delimited text to the specified file path |
Details
CRISPRsim performs a genome-wide (or subsetted) pooled CRISPR
knockout screen for you without having to go to the lab and spend
incredible amounts of time and money. This can be a tremendous help if you
want to design an experiment and answer questions such as: how many
replicates do I need, how much coverage, will I pick up genes with x
effect, et cetera. You can give it a spin, but I highly recommend checking
out the documentation for the available parameters! Especially seeds can be
relevant for a proper simulation. You can easily "practice" by simulating
some small experiments. The basis of the simulation is as follows. Between
time points cells with a certain knockout grow according to formula
cellsout = cellsin*2^((grm+d+e)*a)) Each guide has an efficacy,
which is the chance to create a successful knockout. The cellsin is
determined at t0 and depends on guide efficacy and guide abundance. If
there is no successful knockout, d and e are 0. Cells with and without
successful knockout are followed separately throughout the experiment, but
the pairs are pooled in terms of sequencing reads. grm is the growth rate
modifier and is generally 1, but it can be lowered to more properly
simulate resistance screens.
Value
Returns a data frame with every row representing a single guide. Contains the pertinent parameters of each guide and the number of sequencing reads on t0 and all other sampling time points. If the argument returnall is set to TRUE, the function also returns a data frame with the true values for the genes, and lists all parameters as well.
Note
Seeds are set using with_seed from the
withr package, thus leaving any
pre-existing seed intact. Avoid using the same seeds for different
arguments. If dseed and eseed are identical, the resulting values for d and
e will have a distinct pattern of correlation. In this case, CRISPRsim will
throw a warning. Given or generated seeds are returned when the option
returnall is set to TRUE.
Author(s)
Jos B. Poell
See Also
sortingsim, radjust, rrep,
jar, nestedradjust, doublejar
Examples
simdf <- CRISPRsim(18000, 4, a = c(3,3), e = TRUE, perfectsampling = TRUE) hist(simdf$g, breaks = 100, main = "distribution of guide efficiencies") d <- rle(simdf$d)$values e <- rle(simdf$e)$values plot(d, e, main = "straight lethality and sensitization")
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