This study integrates sequencing and imaging-based single-cell transcriptomic profiling methods. The authors showed that incorportation of scRNAseq data improved cell type mapping over using seqFISH data analysis alone, while including the seqFISH data enabled the identification of spatial structure in the scRNAseq data (Figure from Zhu et al 2018). For our workshop, we focus on a subset of these data that contain matched seqFISH and scRNAseq data for the adult mouse visual cortex.

Zhu et al 2018, reference paper for seqFISH is available at: (https://www.nature.com/articles/nbt.4260)

Tasic et al 2016, reference paper for scRNAseq is available at: (https://www.nature.com/articles/nn.4216)

The dataset includes 1,597 single cells from adult mouse visual cortex and 125 genes mapped with seqFISH from Zhu et al 2018 and scRNA-seq data for ~1,600 cells from Tasic et al 2016 in the primary visual cortex in adult male mice. These combined data enable cell type mapping with spatial information in the adult mouse visual cortex.

The main goal of the multi-omics analysis for this data in the workshop is methods to enhance the molecular resolution of spatial molecular data by integrating seqFISH and scRNA-seq data. For example, types of questions or challenges that can be addressed computationally include:

• Can scRNA-seq data be overlaid onto seqFISH for resolution enhancement?
• What is the minimal number of genes needed for data integration?
• Are there signatures of cellular co-localization or spatial coordinates in non-spatial scRNA-seq data?

All original and processed data and scripts used to pre-process are available on Dropbox. The core processed data are described in the Easy data section below. Extra downloads options available on the spatial data bitbucket page The orginal scRNAseq count matrix can be downloaded from GEO.

Easy data link to find files for this project: (https://www.dropbox.com/sh/avj4nrd4la5i88u/AACafWwBbE-xsLvOGDwRZDpYa?dl=0)

The following files contain cross-platform noramlized expression for matched genes between both datasets used for spatial domain identification and SVM prediction in Zhu et al 2018.

txt file of normalized scRNAseq data for 113 genes x 1723 cells

        V2 V3 V4 V5 V6
abca15  11 42 17 42 35
abca9   22 46 22 46 39
acta2   15 47 15 42 34
adcy4   12 45 12 45 38
aldh3b2 27 49 27 49 42

txt file of normalized seqFISH data for 113 genes x 1597 cells

         V2 V3 V4 V5  V6
abca15   68 49 50 39  31
abca9    41 42 38 36  47
acta2    25 23 16 21  29
adcy4    39 54 37 18  37
aldh3b2 101 47 41 52 101

tsv file of spatial cluster labels and SVM learned cell types for seqFISH

Fields (tab-delimited):

1. Cell ID

2. Spatial cluster

3. Cell type class

4. ID of cell type class

5. (irrelevant)

6. Probability of the highest probability class

     V1 V2                   V3 V4 V5        V6
1     1  5 Glutamatergic Neuron  3  - 0.9827927
2     2  5 Glutamatergic Neuron  3  - 0.9445903
3     3  5 Glutamatergic Neuron  3  - 0.8299232
4     4  5 Glutamatergic Neuron  3  - 0.4659458
5     5  5 Glutamatergic Neuron  3  - 0.9863798

tsv file of cell type labels for scRNAseq

                      V1                             V2      V3
1              Astrocyte      Calb2_tdTpositive_cell_46   upper
2              Astrocyte      Calb2_tdTpositive_cell_48   upper
3              Astrocyte      Calb2_tdTpositive_cell_50   lower
4              Astrocyte      Calb2_tdTpositive_cell_53   lower
5              Astrocyte      Calb2_tdTpositive_cell_58   lower

Spatial cell coordinates

z-scored matrix incorporating the spatial gene expression of 69 genes