quant command takes a collated RAD file and performs feature (e.g. gene) quantification, outputting a sparse matrix of de-duplicated counts as well as a list of labels for the rows and columns. The
quant command takes an input directory containing the collated RAD file, a transcript-to-gene map, an output directory where the results will be written, and a “resolution strategy” (described below). Quantification is multi-threaded, so it also, optionally, takes as an arguments the number of threads to use concurrently.
The transcript-to-gene map (provided using the
--tg-map option) should be either:
A three-column (headerless) tab-separated file where the first column contains a target name, the second column contains the corresponding gene feature to which this target belongs and the third column contains either
Sdenoting the corresponding feature should be attributed to the spliced status of its parent gene and
Udenoting that it should be attributed to the unspliced status of its parent gene.
A two-column (headerless) tab-separated file where the first column contains a transcript name and the second column contains the corresponding gene name for this transcript.
quant command exposes a number of different resolution strategies. Note: If you are providing a three-column transcript-to-gene map, and hence quantifying in Unspliced/Spliced/Ambiguous (USA) mode, then only the
cr-like-em resolution modes are currently available. The different UMI resolution strategies are:
cr-like: This strategy is like the one adopted in cell-ranger, except that it does not first collapse 1-edit-distance UMIs. Within each cell barcode, a list of (gene, UMI, count) tuples is created. If a read maps to more than one gene, then it generates more than one such tuple. The tuples are then sorted lexicographically (first by gene id, then by UMI, and then by count). Any UMI that aligns to only a single gene is assigned to that gene. UMIs that align to more than one gene are assigned to the gene with the highest count for this UMI. If there is a tie for the highest count gene for this UMI, then the corresponding reads are simply discarded.
cr-like-em: This strategy is like
cr-like, except that when a UMI has genes to which it matches with equal frequency, rather than discard the UMIs, the genes are treated as an equivalence class, and the counts for each gene are determined via an expectation maximization algorithm.
full: This implements the algorithm described in the alevin paper. Briefly, it builds a graph among the set of reads that align to an overlapping set of transcripts and that have similar (within an edit distance of 1) UMIs. It then attempts to find a parsimonious cover for this graph using the fewest number of possible transcripts. If a unique parsimonious cover is found, then the (deduplicated) reads are assigned directly to the genes that yield the most parsimonious cover. If multiple equally-parsimonious covers exist, then the reads are considered multi-mapping at the gene level and they are probabilistically resolved using an expectation maximization (EM) algorithm.
parsimony: This strategy is the same as “full”, except that it does not probabilistically resolve reads that remain as gene-multimapping after applying the parsimony criterion. Instead, reads that do not have a unique most-parsimonious assignment are discarded.
parsimony-gene: This strategy is the same as
parsimonyabove, except that mappings of UMIs to transcripts are projected to their corresponding gene before the relevant graph (Parsimonious UMI Graph) is constructed. Thus, the vertices of the graph consist of sets of gene labels rather than sets of transcript labels. This method may be less precise than the
parsimonymethod (i.e. may wrongly group together UMIs that arise from different transcripts within the same gene), but it simultaneously likely to be more robust to misannotation or incomplete annotation (e.g. incomplete UTR annotation).
parsimony-gene-em: This strategy is the same as
parsimony-aboveabove, except that, like
parsimony-emany graphs that exhibit a multi-gene cover will have the multimapping resolved probabilistically with an EM algorithm..
trivial: This strategy does not search for 1 edit-distance neighbors of UMIs. Instead, it first discards any reads that multi-map at the gene level. The reads that remain then all map uniquely to a single gene. These reads are deduplicated by (exact) UMI, and the number of distinct UMIs mapping to each gene are taken as that gene’s count in the current cell. Note: This resolution strategy is not available in USA mode.
Additionally, this command can optionally take the following flags (note that not all resolution strategies are compatible with these flags):
-d, --dump-eqclasses: This flag will cause a gene-level, UMI-deduplicated, equivalence class counts file to be written to the output directory in addition to the gene-level count matrix. This can be used for subsequent analyses where gene-ambiguous reads have been neither resovled nor discarded.
-b, --num-bootstraps: This flag will cause bootstrap inferential replicate information to be written to the output directory. This provides a measure of the inferential uncertainty in the gene-level estimates provided by
alevin-frywhen run with a method using the EM algorithm for gene-level abundance estimation. This information can be used with downstream testing, like differential expression testing using swish. This flag is only meaningful with the
--summary-stat: This flag will write the summary statistics of the bootstrap replicates (i.e. the mean and variance of the inferential replicates). This provides the most important information for uncertainty-aware downstream analysis, while requiring much less storage space than the full bootstrap replicate information. This flag is only meaningful when
--quant-subset <SFILE>: This optional argument provides a file containing list of barcodes to quantify (one barcode per line, written as a string), those not in this list will be ignored during inference and will not appear in the output quantification matrix. If this argument is not provided, then all of the original barcodes will be quantified.
--use-mtx: This flag will cause the output to be written in matrix market coordinate format (which is the default).
--use-eds: This flag will cause the output to be written in EDS format rather than in matrix market format.
There are also a few flags that are not immediately exposed:
--umi-edit-dist <EDIST>: This option takes a parameter that sets the Hamming distance within which potentially colliding UMIs will be considered for correction. With resolution modes
parsimony-gene-emthe valid values are 0 and 1 (and the default is 1). With other resolution modes, the default (and currently the only supported value) is 0.
--large-graph-thresh <NVERT>: This option takes a parameter that sets the order (number of nodes) of a PUG above which an alternative (faster) resolution strategy will be applied. This option only has an effect for
parsimony-gene-emresolution modes. The default value is 1000.
The output of the
quant command consists of 5 files:
counts.eds.gz if run with the
quant.json file contains information about the quantification run, such as the method used for UMI resolution. The
featureDump.txt file contains cell-level information designed to be useful in post-quantification cell filtering (better determining “true” cells from background, noise, doublets etc.). The other three files all correspond to quantification information.
quant was executed in USA mode, then the resulting count matrix will be of dimension
C is the number of quantified cells (barcodes) and
G is the number of genes. This is because, in USA mode,
alevin-fry quantifies the UMI count attributable to each splicing state of each gene in each cell, where the splicing state is one of spliced (S), unspliced (U) or ambiguous (A). If
quant was run with a two-column transcript-to-gene map (not in USA-mode), then the resulting count matrix will be a
C``x``G matrix, as splicing status is not tracked. For more details on USA mode and its uses, please read the
alevin-fry paper or preprint, or the corresponding tutorial.
quants_mat.mtx is a matrix market coordinate format file (or if running with
counts.eds.gz is a gzipped file in EDS format) that stores the gene-by-cell expression matrix. The two other files provide the labels for the rows and columns of this matrix. The
quants_mat_cols.txt file is a text file that contains the names of the rows of the matrix, in the order in which it is written, with one gene name written per line. The
quants_mat_rows.txt file is a text file that contains the names of the columns of the matrix, in the order in which it is written, with one barcode name written per line.