thin_all | R Documentation |

Given a matrix of real RNA-seq counts, this function will apply a
thinning factor uniformly to every count in this matrix. This uniformly
lowers the read-depth for the entire dataset. The thinning factor should
be provided on the log2-scale. This is a specific application of the
binomial thinning approach in `thin_diff`

. Though this particular
form of thinning was used by Robinson and Storey (2014) in the context
of deriving read-depth suggestions. It is also
described in detail in Gerard (2020).

```
thin_all(mat, thinlog2, type = c("thin", "mult"))
```

`mat` |
A numeric matrix of RNA-seq counts. The rows index the genes and the columns index the samples. |

`thinlog2` |
A numeric scalar. This is the amount to shrink each count
in |

`type` |
Should we apply binomial thinning ( |

A list-like S3 object of class `ThinData`

.
Components include some or all of the following:

`mat`

The modified matrix of counts.

`designmat`

The design matrix of variables used to simulate signal. This is made by column-binding

`design_fixed`

and the permuted version of`design_perm`

.`coefmat`

A matrix of coefficients corresponding to

`designmat`

.`design_obs`

Additional variables that should be included in your design matrix in downstream fittings. This is made by column-binding the vector of 1's with

`design_obs`

.`sv`

A matrix of estimated surrogate variables. In simulation studies you would probably leave this out and estimate your own surrogate variables.

`cormat`

A matrix of target correlations between the surrogate variables and the permuted variables in the design matrix. This might be different from the

`target_cor`

you input because we pass it through`fix_cor`

to ensure positive semi-definiteness of the resulting covariance matrix.`matching_var`

A matrix of simulated variables used to permute

`design_perm`

if the`target_cor`

is not`NULL`

.

David Gerard

Gerard, D (2020). "Data-based RNA-seq simulations by binomial thinning."

*BMC Bioinformatics*. 21(1), 206. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1186/s12859-020-3450-9")}.Robinson, David G., and John D. Storey. "subSeq: determining appropriate sequencing depth through efficient read subsampling."

*Bioinformatics*30, no. 23 (2014): 3424-3426. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/bioinformatics/btu552")}.

`select_counts`

For subsampling the rows and columns of your real RNA-seq count matrix prior to applying binomial thinning.

`thin_diff`

For the more general thinning approach.

`thin_lib`

For thinning sample-wise.

`thin_gene`

For thinning gene-wise.

`ThinDataToSummarizedExperiment`

For converting a ThinData object to a SummarizedExperiment object.

`ThinDataToDESeqDataSet`

For converting a ThinData object to a DESeqDataSet object.

```
## Generate count data and set thinning factor
## In practice, you would obtain mat from a real dataset, not simulate it.
set.seed(1)
n <- 10
p <- 1000
lambda <- 1000
mat <- matrix(lambda, ncol = n, nrow = p)
thinlog2 <- 1
## Thin read-depths
thout <- thin_all(mat = mat, thinlog2 = thinlog2)
## Compare empirical and theoretical proportions
mean(thout$mat) / lambda
2 ^ -thinlog2
```

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