# Feature abundance normalization/transformation. Feature abundance normalization/transformation for a microtable object or data.frame object. ## Examples ```r ## ------------------------------------------------ ## Method `trans_norm$new` ## ------------------------------------------------ library(microeco) data(dataset) t1 <- trans_norm$new(dataset = dataset) ## ------------------------------------------------ ## Method `trans_norm$norm` ## ------------------------------------------------ newdataset <- t1$norm(method = "clr") newdataset <- t1$norm(method = "log") ``` ## Methods ### Public methods * [list("trans_norm$new()")](#method-trans_norm-new) * [list("trans_norm$norm()")](#method-trans_norm-norm) * [list("trans_norm$clone()")](#method-trans_norm-clone) ### Method `new()` Get a transposed abundance table if the input is microtable object. In the table, rows are samples, and columns are features. This can make the further operations same with the traditional ecological methods. #### Usage ``` trans_norm$new(dataset = NULL) ``` #### Arguments - **`dataset`**: the `microtable` object or `data.frame` object. If it is `data.frame` object, please make sure that rows are samples, and columns are features. #### Returns data_table, stored in the object. #### Examples ``` library(microeco) data(dataset) t1 <- trans_norm$new(dataset = dataset) ``` ### Method `norm()` Normalization/transformation methods. #### Usage ``` trans_norm$norm( method = "rarefy", sample.size = NULL, rngseed = 123, replace = TRUE, pseudocount = 1, intersect.no = 10, ct.min = 1, condition = NULL, MARGIN = NULL, logbase = 2, ... ) ``` #### Arguments - **`method`**: default "rarefy"; See the following available options. Methods for normalization: * `"rarefy"`: classic rarefaction based on R sample function. * `"SRS"`: scaling with ranked subsampling method based on the SRS package provided by Lukas Beule and Petr Karlovsky (2020) \. * `"clr"`: Centered log-ratio normalization \ \. It is defined: $$ clr_{ki} = \log\frac{x_{ki}}{g(x_i)} $$ where $x_{ki}$ is the abundance of $k$th feature in sample $i$, $g(x_i)$ is the geometric mean of abundances for sample $i$. A pseudocount need to be added to deal with the zero. For more information, please see the 'clr' method in `decostand` function of vegan package. * `"rclr"`: Robust centered log-ratio normalization \. It is defined: $$ rclr_{ki} = \log\frac{x_{ki}}{g(x_i \> 0)} $$ where $x_{ki}$ is the abundance of $k$th feature in sample $i$, $g(x_i \> 0)$ is the geometric mean of abundances (\> 0) for sample $i$. In rclr, zero values are kept as zeroes, and not taken into account. * `"GMPR"`: Geometric mean of pairwise ratios \. For a given sample $i$, the size factor $s_i$ is defined: $$ s_i = \biggl( {\displaystyle\prod_{j=1}^{n} Median_{k|c_{ki}c_{kj} \ne 0} \lbrace \dfrac{c_{ki}}{c_{kj}} \rbrace} \biggr) ^{1/n} $$ where $k$ denotes all the features, and $n$ denotes all the samples. For sample $i$, $GMPR = \frac{x_{i}}{s_i}$, where $x_i$ is the feature abundances of sample $i$. * `"CSS"`: Cumulative sum scaling normalization based on the `metagenomeSeq` package \. For a given sample $j$, the scaling factor $s_{j}^{l}$ is defined: $$ s_{j}^{l} = {\displaystyle\sum_{i|c_{ij} \leqslant q_{j}^{l}} c_{ij}} $$ where $q_{j}^{l}$ is the $l$th quantile of sample $j$, that is, in sample $j$ there are $l$ features with counts smaller than $q_{j}^{l}$. $c_{ij}$ denotes the count (abundance) of feature i in sample $j$. For $l$ = 0.95$m$ (feature number), $q_{j}^{l}$ corresponds to the 95th percentile of the count distribution for sample $j$. Normalized counts $\tilde{c_{ij}} = (\frac{c_{ij}}{s_{j}^{l}})(N)$, where $N$ is an appropriately chosen normalization constant. * `"TSS"`: Total sum scaling. Abundance is divided by the sequencing depth. For a given sample $j$, normalized counts is defined: $$ \tilde{c_{ij}} = \frac{c_{ij}}{\sum_{i=1}^{N_{j}} c_{ij}} $$ where $c_{ij}$ is the counts of feature $i$ in sample $j$, and $N_{j}$ is the feature number of sample $j$. * `"eBay"`: Empirical Bayes approach to normalization \<10.1186/s12859-020-03552-z\>. The implemented method is not tree-related. In the output, the sum of each sample is 1. * `"TMM"`: Trimmed mean of M-values method based on the `normLibSizes` function of `edgeR` package \. * `"DESeq2"`: Median ratio of gene counts relative to geometric mean per gene based on the DESeq function of `DESeq2` package \. This option can invoke the `trans_diff` class and extract the normalized data from the original result. Note that either `group` or `formula` should be provided. The scaling factor is defined: $$ s_{j} = Median_{i} \frac{c_{ij}}{\bigl( {\prod_{j=1}^{n} c_{ij}} \bigr) ^{1/n}} $$ where $c_{ij}$ is the counts of feature $i$ in sample $j$, and $n$ is the total sample number. * `"Wrench"`: Group-wise and sample-wise compositional bias factor \. Note that condition parameter is necesary to be passed to `condition` parameter in `wrench` function of Wrench package. As the input data must be microtable object, so the input condition parameter can be a column name of `sample_table`. The scaling factor is defined: $$ s_{j} = \frac{1}{p} \sum_{ij} W_{ij} \frac{X_{ij}}{\overline{X_{i}}} $$ where $X_{ij}$ represents the relative abundance (proportion) for feature $i$ in sample $j$, $\overline{X_{i}}$ is the average proportion of feature $i$ across the dataset, $W_{ij}$ represents a weight specific to each technique, and $p$ is the feature number in sample. * `"RLE"`: Relative log expression. Methods based on `decostand` function of vegan package: * `"total"`: divide by margin total (default MARGIN = 1, i.e. rows - samples). * `"max"`: divide by margin maximum (default MARGIN = 2, i.e. columns - features). * `"normalize"`: make margin sum of squares equal to one (default MARGIN = 1). * `"range"`: standardize values into range 0...1 (default MARGIN = 2). If all values are constant, they will be transformed to 0. * `"standardize"`: scale x to zero mean and unit variance (default MARGIN = 2). * `"pa"`: scale x to presence/absence scale (0/1). * `"log"`: logarithmic transformation. Other methods for transformation: * `"AST"`: Arc sine square root transformation. - **`sample.size`**: default NULL; libray size for rarefaction when method = "rarefy" or "SRS". If not provided, use the minimum number across all samples. For "SRS" method, this parameter is passed to `Cmin` parameter of `SRS` function of SRS package. - **`rngseed`**: default 123; random seed. Available when method = "rarefy" or "SRS". - **`replace`**: default TRUE; see `sample` for the random sampling; Available when `method = "rarefy"`. - **`pseudocount`**: default 1; add pseudocount for those features with 0 abundance when `method = "clr"`. - **`intersect.no`**: default 10; the intersecting taxa number between paired sample for `method = "GMPR"`. - **`ct.min`**: default 1; the minimum number of counts required to calculate ratios for `method = "GMPR"`. - **`condition`**: default NULL; Only available when `method = "Wrench"`. This parameter is passed to the `condition` parameter of `wrench` function in Wrench package It must be a column name of `sample_table` or a vector with same length of samples. - **`MARGIN`**: default NULL; 1 = samples, and 2 = features of abundance table; only available when method comes from `decostand` function of vegan package. If MARGIN is NULL, use the default value in decostand function. - **`logbase`**: default 2; The logarithm base. - **`...`**: parameters pass to `vegan::decostand`, or `metagenomeSeq::cumNorm` when method = "CSS", or `edgeR::normLibSizes` when method = "TMM" or "RLE", or `trans_diff` class when method = "DESeq2", or `wrench` function of Wrench package when method = "Wrench". #### Returns new microtable object or data.frame object. #### Examples ``` newdataset <- t1$norm(method = "clr") newdataset <- t1$norm(method = "log") ``` ### Method `clone()` The objects of this class are cloneable with this method. #### Usage ``` trans_norm$clone(deep = FALSE) ``` #### Arguments - **`deep`**: Whether to make a deep clone.

trans_norm function