InputFromTable: Create Seurat object from Spatial Transcriptomics data

In jbergenstrahle/STUtility: Analysis and visualization of Spatial Transcriptomics data

Create Seurat object from Spatial Transcriptomics data

Description

This function is a wrapper to create a complete S4 Seurat object with all the samples and metadata. The input is a data.frame containing paths to all relevant files, s.a. gene count matrices, HE images and spot selection files. The function can also perform some basic filtering and gene conversion.

Usage

InputFromTable(
  infotable,
  transpose = TRUE,
  minUMICountsPerGene = 0,
  minSpotsPerGene = 0,
  minGenesPerSpot = 0,
  minUMICountsPerSpot = 0,
  topN = 0,
  annotation = NULL,
  id.column = NULL,
  replace.column = NULL,
  platform = "Visium",
  scaleVisium = NULL,
  disable.subset = FALSE,
  verbose = TRUE,
  ...
)

Arguments

infotable	table with paths to count files and metadata. See details below for more information.
transpose	transposes the gene expression matrices [default: TRUE]. Only active when reading data from platforms '1k' or '2k'ld
minUMICountsPerGene	filter away genes that has a total UMI count below this threshold (previously called "min.gene.count")
minSpotsPerGene	filter away genes that is not expressed below this number of capture spots (previously called "min.gene.spots)
minGenesPerSpot	ilter away capture spots that contains a total gene count below this threshold (previously called "min.spot.feature.count")
minUMICountsPerSpot	filter away capture spots that contains a total UMI count below this threshold (previously called "min.spot.count")
topN	OPTIONAL: Filter out the top most expressed genes
annotation	data.frame containing columns needed for gene id conversion. See the gene id conversion section for more information.
id.column	column name in annotation data.frame providing the gene ids of the input matrices
replace.column	column name in annotation data.frame providing the gene ids for the conversion
platform	name of the platform used to generate the data [options: 'Visium', '1k', '2k']
scaleVisium	10X visium scale factor for pixel coordinates matching the "tissue_hires_image.png" files [required]. If a numeric value is given, it is assumed that all samples have the same scaling factor. Alternatively, an additional column named "scaleVisium" can be provided with a scaling factor for each sample, or a column named "json" with paths to the "scalefactors_json.json" files.
disable.subset	Set to TRUE if you want to disable spot subsetting to include spots under tissue. Useful if you want to load the full expression matrix with spots outside of the tissue.
verbose	Print messages
...	additional parameters

Details

This wrapper function has been written to make it easier to various types of Spatial Transcriptomics data, both for the 10X Visium platform and the older array types, here called '1k' and '2k' (with 1000 and 2000 spots respectively). The infotable should at minimum contain paths to the gene count matrices in a column named 'samples', which can be provided as:

• '.tsv' or '.tsv.gz' if the platform is set to either '1k' or '2k'

• '.h5' or path to the output folder containing 'barcodes.tsv', 'features.tsv' and 'matrix.mtx' files if the platform is set to 'either 'Visium'

It is however recommended to include a column named 'imgs' in the 'infotable' with paths to the HE images, otherwise none of of the image related processing and visualization methods can be utilized. In addition to the images, a third column named 'spotfiles' should be provided with paths to the tables specifying what spatial coordinates are located under the tissue and which also specifies the pixel coorindates of the spots in the HE images. It is important that the 'spotfiles' pixel coordinates match the HE images provided in the 'imgs' column. If you have run SpaceRanger, the HE images would would correspond to the 'tissue_hires_image.png' files and the spotfiles to the 'tissue_positions_list.csv' files in the output folder. For '1k' and '2k' data, you have to make sure that whatever HE images ('imgs') you are using have a matched selection table in tsv format ('spotfiles'). See [spotdetector tool github](https://github.com/SpatialTranscriptomicsResearch/st_spot_detector/wiki/ST-Spot-Detector-Usage-Guide) for more information.

NOTE that for Visium samples, you also need to provide scaling factors which is described in the section below

samples

If you are analyzing 'Visium' samples, you should provide the '.h5' files in the 'samples' column of infotable. If you are analyzing '1k' or '2k' samples processed with the ST_Pipeline, you can provide the '.tsv' files as 'samples'. We recommend you to use the output '.tsv' file from the ST Pipeline without any modifications and make sure that the matrix is transposed correctly. The matrix will be transposed by defualt, but this can be deactivated by setting 'transpose = FALSE'.

imgs

If you are analyzing 'Visium' samples, you should provide the 'tissue_hires_image.png' files in the 'imgs' column of infotable. If you are analyzing '1k' or '2k' samples you should provide the HE images that were processed with the ST Spot Detector. It is important that these images match the spotfiles!

spotfiles

If you are analyzing 'Visium' samples, you should provide the 'tissue_positions_list.csv' files in the 'spotfiles' column of infotable. If you are analyzing '1k' or '2k' samples you should provide the HE images that were processed with the ST Spot Detector, typically called 'spot_data-selection...' or 'spot_data-all...'. It is important that these spotfiles are matched with the HE images!

scaleVisium

If you are analyzing 'Visium' samples, you need to provide a scaling factor to align the spots to the correct positions on the array. These scaling factors are provided in the 'scalefactors_json.json' file in the SpaceRanger output folder. The scaling factor needed for STutility is called "tissue_hires_scalef" and can be provided with the 'scaleVisium' argument in 'InputFromTable' if the scaling factor is the same for all of your samples. If they are not the same, you can either specify the scaling factors manually into a column named 'scaleVisium' or you can provide the paths to the json files into a column named 'json' in of the infotable data.frame.

gene id conversion

If you need to convert the gene ids of your expression matrices, you can provide a data.frame with an 'ìd.column' with gene symbols matching the symbols of your input matrices and a 'replace.column' with the gene symbols that you want to convert to. NOTE that any genes not found in the annotation data.frame will be discarded.

platform

If you are not analyzing 'Visium' data (default platform), you need to specify what other platform you have used, i.e. '1k' or '2k'. You can also provide a column named 'platform' in the infotable data.frame with a charcter vector specifying the platforms used for each sample.

meta data

You can also add additional meta data into the infotable data.frame which will be included in the '@meta.data' slot of the returned Seurat object.

notes

Make sure to check that the paths are correct and preferably absolute paths. If you change the working directory and want to reload the HE images into your Seurat object, you need to make sure that these files can be found on your system.

When creating the infotable data.frame, set the parameter 'stringsAsFactors = FALSE'.

jbergenstrahle/STUtility documentation built on March 14, 2023, 7:15 a.m.