aligned_tissue_image.jpg: This image is generated when both CytAssist and microscope image inputs are provided (Visium CytAssist v2 and Visium HD). It captures the results of the image registration by merging the registered images in a checkerboard pattern with adjacent squares alternating between the two images. Successful image registration can be confirmed by assessing the continuity of tissue structures (e.g. tissue edges and interior tissue morphology). Dark background fluorescence microscope images are inverted to have a white background. An interactive version of these images is available in the web_summary.html.

cytassist_image.tiff: Copy of the original CytAssist image input, which is a eosin-stained brightfield image in TIFF format that contains the fiducial frame. It has a resolution of 3,200 x 3,000 pixels (Visium HD) or 3,000 pixels in both dimensions (Visium v2).

tissue_hires_image.png and tissue_lowres_image.png: These files are downsampled versions of the original, full-resolution image inputs provided by the user. Downsampling is accomplished by box filtering, which averages RGB values in patches of pixels in the full-resolution image to obtain an RGB value of one pixel in the downsampled image. The downsampled images maintain the aspect ratio of the original image. The longest dimension of tissue_hires_image.png is as follows:

Irrespective of the slide type, the longest dimension of tissue_lowres_image.png is always 600 pixels.

aligned_fiducials.jpg: This file is useful to verify that fiducial alignment was successful. Fiducial spots detected by the fiducial alignment algorithm are highlighted in red. The aligned_fiducials.jpg contains information from the red channel since the fiducial frame is visible in it, and thus, the lack of tissue section contrast is expected.

• For Visium HD and Visium v2, the resolution is that of the CytAssist image (see above).
• For Visium v1, this image has the dimensions of tissue_hires_image.png.

detected_tissue_image.jpg: This image either has the dimensions of the CytAssist image or of the tissue_hires_image.png, and shows the following:

• Aligned fiducial spots as hollow circles which are red (Visium HD) or blue (Visium v1/v2)
• A blue bounding box. The interior designates the portion of the image where tissue is discriminated from background.
• Spots/squares found under tissue are blue (Visium HD) or red (Visium v1/v2).
• Spots/squares outside of tissue are gray.

scalefactors_json.json: The purpose of the file is to record the relative scales of the user-supplied image, the images in the spatial outs, and the Visium array. This file may contain the following fields:

• regist_target_img_scalef: A scaling factor that converts pixel positions in the original, full-resolution micrsocope image to the downsampled version used in image registration.
• tissue_hires_scalef: A scaling factor that converts pixel positions in the original, full-resolution image to pixel positions in tissue_hires_image.png.
• tissue_lowres_scalef: A scaling factor that converts pixel positions in the original, full-resolution image to pixel positions in tissue_lowres_image.png.
• fiducial_diameter_fullres: The number of pixels that span the diameter of a fiducial spot in the original, full-resolution image.
• spot_diameter_fullres: The number of pixels that span the diameter of a spot in the original, full-resolution image. This field is intended for visualization purposes, and can vary for different slide designs, ranging from 60-70 µm. Spot diameters and spot positions are estimates. It is better to use the known pixel dimensions of a calibrated microscope rather than trying to infer pixel dimensions from the spot diameter.

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$  cd /home/jdoe/runs/sample345/outs/spatial
$  cat scalefactors_json.json

{"tissue_hires_scalef": 0.17011142,
  "tissue_lowres_scalef": 0.051033426",
  "fiducial_diameter_fullres": 144.4773339857,
  "spot_diameter_fullres": 89.43834961021503}

The example scalefactors_json.json values above are derived from the adult mouse brain dataset which includes an image with dimensions of 11291 x 11757. The scalefactors are calculated as:

$scalefactor = \frac{\text{target size}}{max(\text{original image width, original image height})}$

Since the largest dimension for the tissue_hires_image.png is 2000 pixels, the tissue hires scalefactor will be: tissue_hires_scalef = 2000/11757 ≈ 0.17

Similarly for the tissue_lowres_image.png, the largest dimension is 600 pixels and the tissue lowres scalefactor is: tissue_lowres_scalef = 600/11757 ≈ 0.05

The spot diameter and fiducial diameter are estimated fractional pixels of the spot diameter in the original image. The estimation is based on the registration solution as well as the known size of the spots and fiducial frame. Prior knowledge of the image pixel sizes is not used.

In this example, the estimated pixel size was calculated from the spot diameter for the full resolution image: microns/pixels = 65/89.44 ≈ 0.73

tissue_positions.csv: This text file contains a table with rows that correspond to spots. From Space Ranger v2.0 onwards, this file, which was previously named tissue_positions_list.csv, is renamed and includes a header column. Excluding the header column, the file has 4,992 rows for Visium slides with a 6.5 mm Capture Area and 14,336 rows for Visium slides with an 11 mm Capture Area, which is the number of spots in the spatial array.

Columns correspond to the following fields:

• barcode: The sequence of the barcode associated to the spot.
• in_tissue: Binary, indicating if the spot falls inside (1) or outside (0) of tissue.
• array_row: For Visium slide (6.5 mm Capture Area), the row coordinate of the spot in the array from 0 to 77. The array has 78 rows. For Visium slides (11 mm Capture Area), the row coordinate of the spot range from 0 to 127 as the array has 128 rows.
• array_col: The column coordinate of the spot in the array. In order to express the orange crate arrangement of the spots, for Visium slides (6.5 mm Capture Area) this column index uses even numbers from 0 to 126 for even rows, and odd numbers from 1 to 127 for odd rows with each row (even or odd) resulting in 64 spots. For Visium slides (11 mm Capture Area) this column index uses even numbers from 0 to 222 for even rows, and odd numbers from 1 to 223 for odd rows with each row (even or odd) resulting in 111 spots.
• pxl_row_in_fullres: The row pixel coordinate of the center of the spot in the full resolution image.
• pxl_col_in_fullres: The column pixel coordinate of the center of the spot in the full resolution image.
• pxl_row_in_fullres and pxl_col_in_fullres can be multiplied by the scale factors in scalefactors_json.json to get pixel coordinates in the lower resolution images.

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  $  cd /home/jdoe/runs/sample345/outs/spatial
  $  column -s, -t < tissue_positions.csv  | head -n 10

  barcode             in_tissue  array_row  array_col  pxl_row_in_fullres  pxl_col_in_fullres
  GTCACTTCCTTCTAGA-1  0          0          0          1832                11971
  CACGGTCTCCTTACGA-1  0          0          2          1832                11833
  ATAGCTGCGGATAAGA-1  0          0          4          1832                11695
  GTCAGTATGTCCGGCG-1  0          0          6          1832                11556
  ATGTACCAGTTACTCG-1  0          0          8          1831                11418
  ACGCTCAGTGCACCGT-1  0          0          10         1831                11280
  TCACTAACGTATAGTT-1  0          0          12         1831                11142
  CGGTTAGGCCTGGACG-1  0          0          14         1831                11004
  GATATCACCAGCATGG-1  0          0          16         1831                10866

The tissue_positions.csv contains the same information about the barcode whitelist and their slide coordinates as contained in the barcodes folder that comes bundled with Space Ranger (spaceranger-x.y.z/lib/python/cellranger/barcodes where x.y.z represent the version number).

spatial_enrichment.csv: File in CSV format that contains table of Moran's I values for each feature when specific conditions are met:

• The tissue must cover at least 37 spots
• The feature must be detected in at least 10 spots
• The feature must have a total UMI count of at least 20

The Moran's I value can be anywhere between -1 (perfectly dispersed) to 1 (perfectly enriched) but in biological samples values significantly below 0 are unexpected. A p-value is provided, as well as an adjusted p-value which is corrected using the Benjamini-Hochberg method for multiple comparisons.

From Space Ranger 2.0 onwards, the spatial_enrichment.csv file includes a Feature Type column the value for which is Gene Expression for single-library analysis.

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  $  cd /home/jdoe/runs/sample345/outs/spatial
  $  column -s, -t < spatial_enrichment.csv  | less -#2 -S

  Feature ID          Feature Name    Feature Type     I                        P value                 Adjusted p value        Feature Counts in Spots Under Tissue  Median Normalized Average Counts  Barcodes Detected per Feature
  ENSMUSG00000053310  Nrgn            Gene Expression  0.849230495400066        0.0                     0.0                     140702                                42.22927672729207                 2645
  ENSMUSG00000070570  Slc17a7         Gene Expression  0.8228828454463434       0.0                     0.0                     49919                                 15.927820652112793                2435
  ENSMUSG00000046447  Camk2n1         Gene Expression  0.8112121614961633       0.0                     0.0                     126518                                44.058800706638436                2698
  ENSMUSG00000021948  Prkcd           Gene Expression  0.8093848987347875       0.0                     0.0                     11675                                 4.552770117948635                 1430
  ENSMUSG00000061808  Ttr             Gene Expression  0.8088005899881938       0.0                     0.0                     348686                                76.73215387483816                 2674
  ENSMUSG00000041607  Mbp             Gene Expression  0.7965984861055619       0.0                     0.0                     258769                                112.84701279639039                2700
  ENSMUSG00000035383  Pmch            Gene Expression  0.7847484395395488       0.0                     0.0                     67814                                 17.005396313242215                1718
  ENSMUSG00000024985  Tcf7l2          Gene Expression  0.7769492292430982       0.0                     0.0                     6546                                  2.564212465278306                 1282
  ENSMUSG00000032532  Cck             Gene Expression  0.7605729556575779       0.0                     0.0                     67935                                 21.68537496001758                 2585

For multi-library runs containing both Gene and Protein Expression, changes to this file include the addition of Antibody IDs in the Feature ID column as well as inclusion of both Gene Expression and Antibody Capture values for Feature Type column. If the secondary_name column, which is optional, was included in the feature_reference.csv file, then an additional column Feature Secondary Name is included.

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  # Gene Expression + Protein Expression output
  $  cd /home/jdoe/runs/sample345/outs/spatial
  $  column -s, -t < spatial_enrichment.csv  | less -#2 -S

  Feature ID       Feature Name  Feature Type      I                        P value                 Adjusted p value        Feature Counts in Spots Under Tissue  Median Normalized Average Counts  Barcodes Detected per Feature  Feature Secondary Name
  HLA-DRA          HLA-DRA       Antibody Capture  0.620710365248628        0.0                     0.0                     624501                                131.46681016369922                2963                           HLA-DR
  ENSG00000198938  MT-CO3        Gene Expression   0.5909662325215399       0.0                     0.0                     176905                                36.85280442442849                 2963
  ACTA2            ACTA2         Antibody Capture  0.5829500680472921       0.0                     0.0                     110008                                24.04130766646586                 2926                           alphaSMA
  ENSG00000090382  LYZ           Gene Expression   0.5731596519494577       0.0                     0.0                     35321                                 6.963638949483311                 2707
  CD8A             CD8A          Antibody Capture  0.5471755733893784       0.0                     0.0                     284717                                63.2355130893458                  2963                           CD8A
  CD3E             CD3E          Antibody Capture  0.5470800097794422       0.0                     0.0                     483642                                100.9584586030563                 2963                           CD3E
  ENSG00000168542  COL3A1        Gene Expression   0.5441240624975852       0.0                     0.0                     30832                                 7.117037655819745                 2710
  PDL1             PDL1          Antibody Capture  0.5423563651411019       0.0                     0.0                     203369                                35.819183769219975                2956                           CD274
  ENSG00000205542  TMSB4X        Gene Expression   0.5399878890015211       0.0                     0.0                     95380                                 19.488702934223657                2939
  CD68             CD68          Antibody Capture  0.5293575665804824       0.0                     0.0                     135790                                26.888763776634708                2950                           CD68
  ENSG00000198763  MT-ND2        Gene Expression   0.5201078732259159       0.0                     0.0                     124913                                25.089922503703956                2951

barcode_fluorescence_intensity.csv: File in CSV format that is generated for any input image specified by --darkimage. This file contains the statistics about the fluorescence intensity which is calculated for every spot and every fluorescence channel in the input image. If the channels were given custom names in the Loupe manual alignment session that produced the alignment JSON file, then the CSV header will use the given channel names. By default, channels are named channel1, channel2, etc. This file can be used for secondary analysis in R/Python and can also be combined with the tissue_positions.csv file. If the microscope image is smaller than the CytAssist image, and spots are detected outside of the microscope image, intensities will be reported as NA. If single-page TIFF images are provided in the command line as a comma separated list, only one intensity is reported.

Copy
  $  cd /home/jdoe/runs/sample345/outs/spatial
  $  column -s, -t < barcode_fluorescence_intensity.csv  | less -#2 -S

  barcode             in_tissue  channel1_mean       channel1_stdev      channel2_mean       channel2_stdev
  CGAGGATATTCAGAGC-1  0          4254.328631875882   59.72596599289198   2370.7574047954868  23.587614594004506
  TCTGGTACTAATGCGG-1  0          4242.12976022567    61.31391702047626   2348.9830747531737  22.960436841214605
  AGGTGACCATGACGCT-1  0          4237.420310296192   62.954555749425516  2330.277856135402   18.66240280182469
  CAGAGGTGTACGTAGC-1  0          4219.421720733428   60.38810661890725   2323.2045133991537  20.876943016829802
  TAGCTAGAAGATACAT-1  0          4202.056417489422   62.2343918192054    2309.320169252468   18.883665883691563
  AGCACTGACCGGCTTA-1  0          4185.466854724965   61.848950071485     2300.589562764457   17.98861741514344
  ATAACCTAAGGCGTCA-1  0          4149.37658674189    58.4482514520289    2292.1255289139635  19.824612785901802
  TAGAACCTCAGTTAGA-1  0          4156.802538787024   64.86614573399514   2298.7390691114247  19.838300058320733
  CCGACATAAGTATCAA-1  0          4194.483779971792   62.19636612986138   2313.277856135402   20.56714424032058
  GACTACCAACTGGTCC-1  0          4204.746121297602   62.91221526144468   2328.5232722143865  54.82377031240396